Capital Market Anomalies and Quantitative Research

Justin Birru, The Ohio State University*

Sinan Gokkaya, Ohio University

Xi Liu, Miami University

Abstract

Quantitative research analysts (Quants) produce in-depth quantitative and econometric modeling of market anomalies to assist sell-side analysts and institutional clients with stock selection strategies. We provide evidence that Quants add value to sell-side analyst recommendations and institutional client trades, leading to less mispricing for covered stocks. Quant-backed analysts exhibit more efficient forecasting behavior on anomaly predictors — stock recommendations issued on anomaly-longs (anomaly-shorts) are more (less) favorable. Investment value of such analysts’ buy (sell) recommendations is superior and their research reports are more likely to discuss implications of quantitative modeling and market anomalies for coverage stocks. Capital markets incorporate quantitative research into asset prices through thematic research reports published by Quants. Institutional broker clients receiving Quant research exhibit superior trading behavior on anomaly stocks. Most importantly, we provide evidence consistent with quantitative research increasing market efficiency by attenuating abnormal returns associated with anomaly based long-short strategies.

* Birru is an Assistant Professor of Finance at The Ohio State University (birru.2@osu.edu), Gokkaya is JPMorgan Chase Professor of Finance at Ohio University (gokkaya@ohio.edu), Liu is an Assistant Professor of Finance at Miami University (liux60@miamioh.edu). We thank seminar participants at Dartmouth College, the Helsinki Finance Seminar, The Ohio State University, and participants at the Second Israel Behavioral Finance Conference. The views expressed herein are those of the authors and do not necessarily represent those of the J.P. Morgan Chase Securities & Brokerage Services, Wealth Management, Financial Advisors, affiliates and/or subsidiaries.

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1. Introduction

A growing body of literature documents evidence of less than fully rational beliefs among financial

experts in roles such as FOMC members, CEOs, CFOs, entrepreneurs, and credit analysts.2 Recent

work focusing on sell-side analysts, one of the most prominent information producers in capital

markets, illustrates that these financial market professionals not only fail to exploit anomaly predictors,

but also get anomalies “wrong” (Jegadeesh, Kim, Krishe, and Lee, 2004; Engelberg, McLean, and

Pontiff, 2016; Engelberg, McLean, and Pontiff, 2017). This finding is more than puzzling, as it suggests

that following the advice of these skilled market experts would aggravate mispricing, a sharp contrast

to analyst’s role in mitigating information asymmetries as documented in prior research (Kelly and

Ljungqvist, 2012; Bradley, Gokkaya and Liu, 2017). Perhaps more surprisingly, a related body of

research documents that institutional investors fail to exploit well-known anomaly predictors (e.g.,

Lewellen, 2011; Edelen, Ince, and Kadlec, 2016). In this paper, we attempt to further our

understanding of the “big picture” of anomalies by focusing on a group of highly sophisticated

financial experts in Wall Street: Quantitative analysts (henceforth Quants). We assess the value of

Quants by focusing on channels through which Quants are most likely to influence financial markets

– sell-side analysts and institutional clients – and provide evidence that Quants propagate the

transmission of efficiency-enhancing information by increasing the accuracy and investment value of

analyst forecasts and by improving the trading decisions of institutions, leading to less mispricing for

covered stocks and contributing to market efficiency.

Capital market anomalies are a pervasive feature of financial markets, and there is a vast literature

that is focused on advancing and reconciling the extensive evidence of cross-sectional return

predictability.3 The existing evidence suggests that at least some of the cross-sectional return

predictability exhibited in equity markets is due to mispricing (e.g., Stambaugh, Yu, and Yuan, 2012)

that emanates from expectational errors of market participants (e.g., Engelberg, McLean, and Pontiff,

2016). The very existence and persistence of anomalies underlines the inherent difficulty in identifying

and eliminating mispricing, even in the presence of seemingly sophisticated market participants. Given

that Quants are sophisticated market participants specializing in identifying mispricing and generating

quantitative stock selection strategies, it is natural to ask whether they add value to markets by

2 See Malmendier, Nagel, and Yan (2017) for evidence related to FOMC members, Graham, Harvey, and Puri (2013) for CEOs, Ben-David, Graham, and Harvey (2013) and Greenwood and Shleifer (2015) for CFOs, Landier and Thesmar (2009) for entrepreneurs, and Fracassi, Petry, and Tate (2016) for evidence related to credit analysts. 3 See e.g., Stambaugh, Yu, and Yuan, 2012; Hanson and Sunderam, 2014; Fama and French, 2015; Hou, Xue, and Zhang, 2015; Jacobs, 2016.

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accurately identifying mispricing, or whether they fail to accurately identify and convey their

knowledge of mispricing. However, data on Quants is difficult to access and as a result Quants have

not been studied from an academic perspective. This paper seeks to fill this gap by providing the first

in-depth analysis of the value of Quants to analysts, institutional investors, and financial markets.

Quants entered the sell-side research industry more than three decades ago and are primarily

tasked with providing in-depth quantitative analyses on market anomalies for stock selection strategies.

Among other activities, a typical Quant uses equity valuation techniques, econometric and statistical

analyses, advanced programming, and back-testing techniques to find and exploit stock mispricing

and identify effective alpha generating long-short stock selection strategies. For instance, a job

description of a sample Quant states: “I provide in-depth analyses on market anomalies for stock selection research

using proprietary alpha models, equity valuation and accounting techniques, statistical and advanced econometric analyses

as well as efficient programming methodologies”. Quants frequently publish thematic research reports

containing lists of stocks in extreme portfolios of their proprietary alpha-generating strategies, and

make presentations about stock selection strategies to in-house sell-side analysts and institutional

clients.4 Further emphasizing the importance of Quants for securities research, Institutional Investor

Magazine (IIM) polls buy-side institutions and annually ranks All-American Quantitative Research

analysts. Despite their importance in Wall Street, to the best of our knowledge, there exists virtually

no academic research on Quants.

To fill this important gap, we construct a novel and comprehensive sample of Quants from Nelson’s

Information Directory of Investment Research (NIDIR), and investigate the relation between Quants and

capital market anomalies. Between 1993 and 2008, we identify 349 unique Quants employed at 142

unique brokers. Of the 211,727 recommendations in our sample (from 8,332 analysts covering 6,366

firms), about 41% of recommendations are issued by quant-backed analysts, and the average number

of Quants at brokerage houses increases from 1.54 in 1993 to 2.95 in 2008.5

As a first step in ultimately ascertaining whether quant research helps to alleviate mispricing, we

examine the impact of Quant research for sell-side analysts’ forecasting performance. We give

4 Take the content of a thematic report published by one of Quants as an example: “We conducted backtests within the S&P 1500 industrials industry groups from April 2000 through September 2005, the post-bubble period, on each of our 10 alpha generators. For each industry group, we segmented the universe into quintile buckets based on our alpha generators at the end of each month of our testing period. We then constructed an equal-weighted portfolio for each alpha generator by taking long positions in those stocks with the highest quintile rank (i.e., with the highest exposure to the factor) and taking short positions in those stocks with the lowest quintile rank (i.e., with the lowest exposure to the factor), and calculated the subsequent one-month return for the portfolio.” 5 The production and distribution of NIDIR ceased in 2008. Please refer to Section 2 and Appendix A for additional details on data collection and screening process.

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particular focus to Quant ability to mitigate return predictability associated with well-known

anomalies. If Quants provide in-depth anomaly research for stock selection, a critical component of

an analyst’s job (e.g., IIM annual polls), and analysts consider this anomaly research for coverage firms,

then analyst underreaction to capital market anomalies may be alleviated. On the other hand, if Quant

anomaly research adds no value, or if analysts rely solely on their own forecasting models and/or

fundamental analyses for firms in their coverage portfolio, then we would not expect to observe a

significant association between Quants and analyst forecasting behavior. To shed light on this open

empirical question, we compare an aggregate anomaly signal (Stambaugh, Yu, and Yuan, 2012; 2015)

to analyst expectations measured using the level of stock recommendations and confirm the findings

in prior research—analysts indeed get anomaly predictors “wrong”. However, when distinguishing

across analysts based on access to in-house quant research, our paper documents that quant-backed

analysts provide more efficient forecasting behavior on anomaly predictors relative to peers lacking

quant research. In economic terms, stock recommendations are 0.63% (0.875%) higher (lower) on

anomaly buys (sells) for a one standard deviation increase in the number of in-house Quants,

suggesting 46.8% (94%) higher forecast efficiency on anomaly longs (shorts)

In further investigation, we attempt to more clearly identify a causal effect of quant research for

the investment research market. First, we focus on a dynamic experiment arising from Quant

movements in and out of brokerage houses and compare the forecasting behavior of the same analyst

before and after she gains (loses) access to Quant research. Our results show that analysts become

more (less) sensitive to anomaly signals after a Quant joins (departs from) a brokerage house lacking

quantitative research. Second, we include analyst and broker fixed effects to alleviate concerns that

the results are influenced by unobserved analyst and/or broker specific characteristics. Finally, we

consider a rigorous propensity score matching methodology with replacement. Results remain highly

robust in all cases. In sum, our findings suggest that investment professionals’ errors in identifying

anomalies, as documented by prior studies, is largely mitigated by access to Quant research.

Next, we consider the implications of Quants for the investment value of stock recommendations.

Sell-side analysts’ recommendations provide valuable investment signals to capital market participants

such as individual and institutional investors (Boni and Womack, 2006; Bradley, Clarke, Lee and

Ornthanalai, 2014, among others). If quant research improves analyst forecasting accuracy of anomaly

predictors, stock recommendations should also generate superior returns. Our results are consistent

with this conjecture. In economic terms, a one standard deviation increase in the number of Quants

is associated with 0.38% higher abnormal monthly stock returns for buy recommendations, and 0.42%

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lower abnormal monthly stock returns for sell recommendations. To put this in economic perspective,

All-Star analysts issue buy (sell) recommendations that generate 0.22% (0.52%) more profitable

returns.6

Our next line of investigation seeks to provide a better understanding of Quants for the

investment research market. We first focus on the quality of quantitative research by identifying

Quants who obtain a spot in the All-American Quantitative Research team roster and find that All-

Star Quants add greater value to stock recommendations. Second, our empirical analyses document

that Quants have more pronounced effects on stock picking performance for less skilled sell-side

analysts. Finally, we consider the implications of market-wide sentiment. Proxying for market

sentiment with the Baker and Wurgler (2006) composite sentiment index, Stambaugh and Yuan (2012)

illustrate that anomaly returns are more prominent following high market sentiment levels. Consistent

with Quants’ benefits being greatest at times of greatest mispricing, we illustrate that analysts benefit

more from Quants during high sentiment periods.

To examine a plausible mechanism through which Quants add value to the investment research

market, we execute textual analyses of analyst research reports to shed light on the impact of Quants

on analysts’ incorporation and understanding of quantitative modeling and anomaly predictors while

conducting research. Specifically, we download the complete sample of full-text analyst research

reports from Thomson Reuters Investext and parse each report for the discussions of these topical content

and their variants. Our results document that analysts are 10.7% more likely to discuss the implications

of anomaly predictors and 6.7% more likely to incorporate implications of quantitative modeling for

coverage stocks for a one standard deviation increase in the number of in-house Quants.

Having established a robust association between Quants and investment research markets, our

paper examines whether Quant research is influential by measuring immediate market reactions to

thematic research reports authored by Quants. To do so, we manually collect the full sample of reports

published by Quants from Thomson Reuters Investext, and then identify the publication dates along with

the corresponding stocks mentioned in each thematic report. Investigating the abnormal trading

volume and price reactions, we find that one standard deviation increase in the number of such reports

is associated with 1.32% (0.02%) more pronounced abnormal volume (price) reactions for the

underlying stocks in the 3-day period following the publication date. This is economically important

6 These results are, again, robust to dynamic experiments designed based on Quant employment changes, analyst/broker fixed effects, and propensity score matching.

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given that sell-side analysts, the most prominent information agents in the capital markets, generate

4.58% (0.13%) higher volume (price) reactions with their research reports.

Turning our investigation to a more direct channel through which Quants can potentially influence

mispricing and trading behavior, we examine implications of quantitative research for institutional

clients of brokers employing such analysts. In particular, we focus on mutual fund clients of

brokerages, as they represent the primary consumers of equity research. If these clients consider

quantitative research to be beneficial, then we expect that quant research will have identifiable effects

on the trading decisions of institutions. Using comprehensive equity commission allocation data from

N-SAR filings to identify mutual fund-brokerage relationships, we find that clients with access to

quant research exhibit more efficient trading behavior on market anomalies—clients are more likely

to purchase underpriced stocks and less likely to purchase overpriced stocks. The relationship holds

both unconditionally, and when conditioning on purchase responses to fund inflows. On the other

hand, mutual fund clients without access to quant support are less likely to exploit anomalies—they

are associated with a decreased tendency to purchase underpriced stocks and an increased tendency

to purchase overpriced stocks in response to fund inflows. The evidence is consistent with the

hypothesis that institutional buy-side clients benefit from Quants at brokerage firms, and reflects a

plausible channel through which quantitative research can be impounded in asset prices.

Finally, we investigate the asset pricing implications of Quant research. Consistent with

quantitative analyses enhancing market efficiency, we document that stocks receiving more intense

Quant coverage have more efficient prices. Specifically, stocks with more quant coverage exhibit lower

anomaly return predictability, consistent with Quant coverage mitigating mispricing and enhancing

market efficiency. The results are economically and statistically significant for each of 11 anomaly

variables we examine as well as an aggregate anomaly rank variable. In contrast, we find only limited

evidence that stocks with more non-quant coverage have less anomaly return predictability.

We also provide evidence of time-variation in the efficiency-enhancing benefits of Quant

coverage. In particular, Quant-associated decreases in anomaly return predictability are largest in high

sentiment periods, which is when mispricing is greatest. The evidence suggests that the benefits for

market efficiency that Quants contribute to the market are strongest when cross-sectional mispricing

in the market is greatest. Overall, the evidence shows that Quants provide benefits to sell-side analysts

and institutional investors and help to mitigate mispricing.

Our paper makes a number of primary contributions to the literature and should be of interest to

both academics and practitioners. First, we contribute to the literature by providing the first insight

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into the value of Quant research. Through textual analysis, we show direct evidence that Quants matter

to analysts, and enhance analysts’ focus on mispricing and anomalies.

Second, the paper contributes to a growing literature examining the relationship between

sophisticated market participants and anomalies. Recent research finds little evidence that institutional

investors in aggregate contribute to market efficiency, providing little evidence that institutions

purchase undervalued stocks (Lewellen, 2011; Akbas, Armstrong, Sorescu, and Subrahmanyam, 2015;

Edelen, Ince, and Kadlec, 2016).7 We show that quant coverage can help to mitigate inefficient trading

behavior of institutions by increasing (decreasing) their likelihood of buying underpriced (overpriced)

stocks.

Similarly, evidence suggests that analysts in aggregate provide advice that contributes to

mispricing, if acted upon (Jegadeesh, Kim, Krishe, and Lee, 2004; Engelberg, McLean, and Pontiff,

2016; Engelberg, McLean, and Pontiff, 2017). However, recent research provides evidence that hedge

funds reduce mispricing (Kokkonen and Suominen, 2015; Akbas, Armstrong, Sorescu, and

Subrahmanyam, 2015; Cao, Chen, Goetzmann, and Liang, 2016), and much evidence exists that

corporate insiders are able to correctly identify mispricing (e.g., Anginer, Hoberg, and Seyhun, 2017).

We also extend the related literature examining the relationship between sophisticated market

participants and sentiment by providing evidence that Quants are a group of sophisticated market

participants that do not appear prone to sentiment, in contrast to recent evidence suggesting that

sentiment affects institutional investors (DeVault, Sias, and Starks, 2014), analysts (Hribar and

McInnis, 2012), and option traders (Han, 2007).

Third, we advance our understanding of cross-sectional determinants of analyst performance

(Michaely and Womack, 1999; Malloy, 2005; Bae, Stulz and Hongping, 2008; Brown, Clement and

Sharp, 2016; Bradley, Gokkaya, Liu, 2017), influential recommendation changes (Loh and Stulz, 2011),

and analyst forecasting accuracy with respect to anomaly predictors (Jegadeesh, Kim, Krishe, and Lee,

2004; Engelberg, McLean, and Pontiff, 2016; Engelberg, McLean, and Pontiff, 2017). Investigation

into Quants also suggests that sell-side analysts’ research behavior and performance is related to an

important brokerage house source, furthering the notion that investment research is indeed an

outcome of many inputs obtained from research professionals employed at a brokerage firm.

Therefore, we extend the academic literature progressing on this front including the role of affiliated

asset management and lending units (Irvine, Simko and Nathan, 2004; Chen and Martin, 2011),

7 Boehmer and Kelley (2009) is an exception, as they document evidence that stocks with greater institutional ownership are priced more efficiently.

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strategy analysts (Kadan, Madureira, Wang and Zach, 2012); active macroeconomists (Hugon, Kumar

and Lin, 2014), fixed income analysts (Hugon, Lin and Markov, 2016), and Washington research

analysts (Bradley, Gokkaya, Liu and Michaely, 2017).

Our evidence also contributes to the literature documenting weaker return predictability for stocks

with greater analyst coverage (Hong, Lim, and Stein, 2000; Zhang, 2006; Ben-David, Birru, and Rossi,

2017). The results suggest that Quant coverage in particular, contributes to the attenuation of anomaly

return predictability. In contrast, increased coverage by analysts without Quant support has only small

and often insignificant effects on attenuating mispricing.

The paper proceeds as follows. Section 2 discusses the data and descriptive statistics. Section 3

examines whether quantitative research leads to more accurate stock-level analysis, especially with

respect to stocks in long or short legs of anomalies. Section 4 focuses on the investment value of

Quant-associated analysts relative to others and also uses textual analysis to explore the channel

through which Quants influence analysts. Section 5 explores whether the market understands the value

of quantitative research. Section 6 assesses the influence of Quants on institutional trading behavior.

Section 7 examines whether quantitative analysis mitigates anomalies and enhances market efficiency,

and Section 8 concludes.

2. Data and Descriptive Statistics

We construct our primary data from several sources. First, we manually collect the full names of

Quantitative equity analysts and their brokerage house information from Nelson’s Information Directory

of Investment Research (NIDIR) between 1989 and 2008 period. NIDIR provides comprehensive

information on the names of sell-side analysts and other research professionals employed by more

than 900 brokers over our sample period. Our sample period ends in 2008 as NIDIR ceased

production and distribution of information on research professionals at the end of 2008. Second, we

obtain full text research reports authored by Quants from Thomson Reuters Investext (Investext),

manually identify the first and last names of Quants authoring these reports and then supplement the

initial sample of Quants collected from NIDIR. In building our sample, we use a very conservative

approach and discard Quants for whom we are unable to uniquely match the brokerage names from

NIDIR and Investext with Institutional Broker Estimate System (IBES), where IBES brokerage house

information are constructed using broker translation file. This manual data collection results in 349

unique Quants employed by 142 unique brokerage houses over 1989 and 2008.

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Next, we merge this sample with CRSP/Compustat to obtain stock price and financial statement

information, and IBES to pull stock recommendations, analyst and broker specific characteristics. We

only retain sell-side equity analysts who provided at least one stock recommendation on domestic

stocks and were employed by brokerage houses listed in NIDIR directory. Appendix A provides a

detailed description of data collection and screening process.

****Insert Table I here****

Panel A of Table 1 documents the distribution of Quants over time and across brokers. Two time-

series patterns are clearly observed in Panel A. First, the number of Quants increases over time. For

instance, our sample contains less than 90 unique Quants prior to 1996 while this number gradually

increases over time, exceeding 200 unique Quants in 2006. Second, the average number of Quants

employed by brokers also increases (from 1.54 in 1993 to 2.95 in 2008). In light of heightened

importance of capital market anomalies for cross-section of stock returns and institutional investors’

demand for stock picking from sell-side analysts (e.g., IIM polls), these patterns may not be completely

surprising. Focusing on the distribution of stock recommendations, columns 4 and 5 of Panel A

indicate that our sample comprises 70% of the universe of sell-side analysts and 48% of the total

number of recommendations in IBES sample, where 36% of recommendations are issued by sell-side

analysts with access to in-house quantitative research. Our sample of coverage firms averages 2,185

per year, representing 43% of firms in IBES.

In Panel B, we report summary statistics by firm-year level. The mean analyst coverage per firm is

4.74 while each firm receives research from 2 unique Quants, on average. The average size percentile

is 0.68 and book-to-market percentile averages 0.42. Consistent with related work, these statistics

suggest that analysts have a tendency to follow larger stocks with growth characteristics.

Finally, we report summary statistics of key variables in Panel C. Appendix B provides detailed

explanations on variable definitions. For instance, the average analyst in our sample possesses 3.89

years of general forecasting experience, firm-specific forecasting experience of 1.46 years while the

coverage portfolio size averages 15.78 firms. 8% of analysts obtain rankings in All-American Research

Team roster and 56% work for top ranked brokerage houses. Other analyst and broker characteristics

are also roughly consistent with prior related work.

3. Quantitative Research and Analyst Forecasting Efficiency on Capital Market Anomalies

This section examines analyst efficiency with respect to capital market anomalies and its

association with access to Quant research. Prior research documents that sell-side analysts fail to

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incorporate the implications of anomaly longs and shorts for coverage firms, resulting in inaccurate

research outputs (e.g. Jegadeesh, Kim, Krishe, and Lee, 2004; Engelberg, McLean, and Pontiff, 2016,

2017). For instance, analysts tend to issue less (more) favorable recommendations to stocks for which

anomaly signals predict higher (lower) returns. If Quants provide in-depth quantitative analyses on

market anomalies for the design and improvement of stock selection strategies and sell-side analysts

benefit from such in-house research for coverage stocks, then sell-side analysts’ underreaction to

capital market anomalies is expected to be alleviated. Alternatively, analysts may simply rely on their

own forecasting models and/or fundamental analyses and may not devote enough time to

understanding the implications of Quant research for coverage stocks. If so, analyst forecasting

efficiency on anomaly predictors should be unrelated to access to quantitative research.

To test this open research question, we measure analyst opinions with the level of stock

recommendations constructed at the analyst-level. Consistent with prior work, the recommendation

takes the value of 5 (4) for “Strong Buys (Buys)”, 3 for “Holds” and 2 (1) for “Sells (Strong Sells).

Next, we measure stock over/undervaluation using the commonly adopted aggregate anomaly score

from Stambaugh, Yu, and Yuan (2012; 2015). Stambaugh, Yu, and Yuan (2012) identify 11 anomalies

that purportedly reflect mispricing, in order to examine the extent to which overpricing, and therefore

anomaly return predictability, varies with investor sentiment. Below we list the 11 anomalies along

with the relevant studies discussing the anomalies. The list is the same as in Stambaugh, Yu, and Yuan

(2012; 2015).8

(1) Failure probability (Campbell, Hilscher, and Szilagyi, 2008)

(2) O-Score bankruptcy probability (Ohlson, 1980)

(3) Net stock issuance (Ritter, 1991; Loughran and Ritter, 1995)

(4) Composite equity issuance (Daniel and Titman, 2006)

(5) Total accruals (Sloan, 1996)

(6) Net operating assets (Hirshleifer, Hou, Teoh, and Zhang, 2004)

(7) Momentum (Jegadeesh and Titman, 1993)

(8) Gross profitability (Novy-Marx, 2013)

(9) Asset growth (Cooper, Gulen, and Schill, 2008)

(10) Return on assets (Fama and French, 2006)

8 Many recent studies of mispricing have focused on the list of anomalies from Stambaugh, Yu, and Yuan (2012). See e.g., Akbas, Armstrong, Sorescu, and Subrahmanyam (2015), Chu, Hirshleifer, and Ma (2016), Avramov, Cheng, and Hameed (2016), and Anginer, Hoberg, and Seyhun (2017).

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(11) Investment to assets (Titman, Wei, and Xie, 2004; Xing, 2008)

To define the anomaly portfolios, we sort stocks each month based on the values of the anomaly

characteristics. Following Stambaugh, Yu, and Yuan (2015), we aggregate the information in the 11

anomaly variables by ranking stocks into decile portfolios for each anomaly variable, with higher ranks

reflecting higher relative degrees of undervaluation. We then calculate the aggregate anomaly score as

the average of the rankings across all of the anomalies. Stocks with higher scores are those for which

anomalies predict higher future returns. This is a relative mispricing variable that is defined relative to

the cross-section of stocks in a given month. Similar methodology has been followed by Engelberg,

McLean and Pontiff (2016, 2017), Stambaugh, Yu, and Yuan (2015), Jacobs (2016).

We define coverage stocks as Anomaly Buy (Anomaly Sell) if a corresponding stock is associated

with the top (bottom) quintile aggregate anomaly score. We also control for an exhaustive set of

analyst, brokerage house and firm specific characteristics that may be also correlated with

recommendation levels. Specifically, our econometric models control for analyst general and firm

specific forecasting experience (Gexp, Fexp), analyst reputation (All-Star), analyst portfolio size and

complexity (PortSize, Portgind), analyst effort (No of Forecasts and Drop Coverage), and investment

banking affiliation (Affiliated). We further control for other broker characteristics including broker size

(Top 10), brokerage industry specialization (Broker Ind. Spec, No of Analysts within Ind.), and coverage

firm characteristics (Size, BM and past 6m returns). To mitigate concerns on potential matching between

brokerage houses and Quants, we also control for broker fixed effects throughout our analyses.

Identification therefore comes from within-broker variation in quant coverage. T-statistics are

adjusted for heteroskedasticity and within analyst correlation by using heteroskedasticity-consistent

standard errors clustered at the forecasting analyst level. In addition to broker fixed effects, our model

includes industry and year fixed effects, and is as follows:

Rec Leveli,j,t = β1(Anomaly Buy/Sell Signal) + β2 (Anomaly Buy/Sell Signal * No of Quants)+ β3 (No of Quants) +β4 (Size) + β5(BM)+ β6 (Gexp) + β7(Fexp) + β8(Port size) + β9 (Top 10) + β10(Portgind) + β11 (All-Star) + β12 (Affiliated) + β13 (Past 6m ret) + β14 (Ind. spec) + β15 (No of Analyst within Ind) + β16 (No of Forecasts) + β17 (Drop Cov) + Year Fixed Effects + Industry Fixed Effects +Broker Fixed Effects + ε (1)

****Insert Table II here****

Table II provides regression results. All coefficients are multiplied by 100. Model 1 documents

that analyst recommendations are inaccurate with respect to long and short side of anomaly predictors.

The mean recommendation level for the sample in Table II is 3.92 with a standard deviation of 0.98.

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In particular, the coefficient of -3.9 in model 1 indicates that stock recommendations are about 1%

lower relative to the mean recommendation level for anomaly longs and 0.56% (0.0217/3.92) higher

relative to the mean recommendation level for anomaly shorts, confirming the view in Engelberg,

McLean, and Pontiff (2016; 2017) that analysts get anomalies “wrong”. In model 2 of Table 2, we

include the number of Quants a sell-side analyst has access to at her brokerage house and interact

Anomaly Buy/Sell signals with the number of Quants. Consistent with our main conjecture, model 2

shows that sell-side analysts exhibit more efficient forecasting behavior on market anomalies when

they have access to in-house quant research. The standard deviation of the number of quants is 2.9,

indicating that a one-standard-deviation increase in Quants is associated with stock recommendations

for anomaly longs that are 0.63% (2.9*0.0085/3.92) higher relative to the mean recommendation level,

suggesting that such analysts’ forecasting efficiency with respect to anomaly longs is 46.8%

(0.63/(0.053/3.92)) higher relative to peers lacking quant research. Likewise, stock recommendations

on anomaly sells are 0.875% (3.43/3.92) lower relative to the mean recommendation level when the

number of Quants is increased by one standard deviation, translating into 94% (3.43%/ 3.65%)

superior forecasting efficiency on anomaly shorts. In model 3, we also include interactions between

analyst and broker specific variables and anomaly buy and anomaly sell. The result on our coefficients

of interest, Anomaly Buy*No of Quants and Anomaly Sell*No of Quants, continue to remain economically

and statistically significant. Analyst experience covering a stock is one of the few newly included

interaction terms that attains significance and allows for an alternative interpretation of the economic

magnitude of the results. The unreported results indicate that a one-standard deviation increase in

analyst experience with the stock increases recommendation efficiency (for both buys and sells) by

less than half as much as a one-standard deviation increase in the number of quants.

In Panel B of Table 2, we estimate a number of alternative specifications in an attempt to more

cleanly identify the effect of Quants on analyst forecasting behavior. First, we isolate a source of

identification in Table 2 and focus on a dynamic setting arising from Quants moving in and out of

brokerage houses. We identify 2,046 cases where a sell-side analyst losses complete access to in-house

quantitative research and 2,361 cases where an analyst without quant research gains access to such

research. Next, we compare forecasting accuracy for the same analyst on Anomaly Buy/Sell stocks

before and after she gains (loses) access to a Quant. In particular, Post Quant gain equals one if the

stock recommendation is issued in the years following a Quant joining a brokerage house that

previously lacked a Quant analyst, and equals zero for recommendations issued in the years before.

Similarly, Post Quant loss takes the value of one for recommendations made after an analyst loses

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complete access to quant research due to quant departures, and zero for recommendations issued

prior to such loss. Next, we interact Anomaly Buy/Sell signals with these indicators to assess the

impact of Quant job changes on analyst forecasting efficiency with respect to market anomalies. The

econometric specification includes the interactions as well as binary indicators for Quant movements

along with other controls in Table 2, however, for parsimony, presents results only on interactions.

Model 1 presents empirical evidence that suggests that analyst responsiveness to market anomalies

improves for anomaly longs and shorts after a Quant joins a brokerage house lacking such research.

Likewise, model 2 finds that analysts are less sensitive to anomaly buy and sell signals during periods

of no access to Quant research compared to years during which they had in house quantitative research

at their brokerage firms.

Next, to address potential concerns of unobservable analyst characteristics biasing our estimates,

we repeat eq (1) with analyst fixed effects to exploit within analyst variation in Quant coverage. This

analysis compares the same analyst’s response to market anomalies in periods when she has Quant

research compared to periods when she does not have access to Quants. Model 3 continues to

document an economically and statistically significant association between Quant research and analyst

forecasting behavior on both the long and short side of anomalies.

Finally, we attempt to alleviate concerns regarding unobservable and/or uncontrolled brokerage

characteristics potentially biasing our estimates by using a rigorous propensity score matching with

replacement (Xuan, 2009; Huang and Kisgen, 2013; Custódio and Metzger, 2014). Brokers with

Quants tend to be larger and employ more analysts. By including brokerage fixed effects in all

regression specifications, our analysis exploits time variation in Quant coverage within the same

brokerage, helping to ensure that the results aren’t driven by brokerage characteristics. In this section,

we take the further step of propensity-score matching to ensure that the analysis only includes

brokerages that are relatively similar along observable dimensions. As a first step, we estimate a probit

model of Quant employment by analyst i’s brokerage house j on a class of broker characteristics (i.e.

Top 10, Ind. Spec, No of Analysts within Ind.). Next, we employ a nearest-neighbor matching with

propensity scores obtained from probit regressions and match the stock recommendations issued by

this analyst on firm k in time t to recommendations issued by a propensity score matched peer analysts

employed at a similar broker with the exception of their access to Quant research. This process helps

us compare the responsiveness to market anomalies by analysts at brokers with Quant research to that

of analysts at matched brokers that are similar to control brokerage houses with the exception of

employment of in-house Quants. Model 4 re-estimates eq (1) using the control and matched sample

13

of brokers and continues to find that analysts employed at brokerage houses with quant research issue

more optimistic (pessimistic) stock recommendations for anomaly buys (shorts) compared to their

peers employed at similar brokers lacking such Quant research. Finally, in model 5, we include stock-

year fixed effects to more fully control for stock-specific characteristics. The coefficient estimates are

similar to those from Panel A and again indicate that Quant research is associated with more efficient

recommendations for both anomaly buys and anomaly sells.

Taken as a whole, this section provides robust empirical evidence consistent with the notion that

analyst forecasting inefficiency with respect to market anomalies can be partly alleviated by Quants

who provide in-depth analyses to exploit stock mispricing for generating stock selection strategies.

4. Quantitative Research and Investment Value of Stock Recommendations

The previous section presented evidence that analysts with Quant support tend to provide more

(less) favorable recommendations to stocks for which anomaly signals predict higher (lower) returns.

A natural question is whether the advice of Quant-backed analysts more successfully predicts future

returns than the advice of analysts without Quant support. To examine this question, and as a second

method of examining the implications of Quant support for analyst forecasting behavior, we next

examine the association between Quants and the investment value of stock recommendations. Sell-

side analysts are among the most prominent information agents in securities markets and their

recommendations provide valuable information to individual and institutional investors (Boni and

Womack, 2006; Bradley, Clarke, Lee and Ornthanalai, 2014; among others). If Quants indeed improve

analyst ability to identify return predictability associated with anomalies on coverage stocks, then stock

recommendations issued by analysts should generate superior investment returns.

To assess the relationship between the investment value of stock recommendations and Quant

support, we first employ a calendar time portfolio approach (e.g., Barber, Lehavy, McNichols, and

Trueman, 2006; Cohen, Frazzini and Malloy, 2010). In particular, we follow Cohen et al. (2010) and

define buy stocks as those that are upgraded relative to the previous recommendation and also stocks

with Strong Buy and Buy recommendation reiterations, initiations or resumes. Similarly, sells are

defined as stocks downgraded compared to the prior outstanding recommendation as well as stocks

for which a Strong Sell and Sell is reiterated, initiated or resumed. Following Cohen et al. (2010), we

rebalance these portfolios daily when analysts revise outstanding recommendations and skip a day

between recommendation and investment (i.e., buy (sell) the recommended stock at the close of day

t+1). If a stock recommendation is not reiterated, revised, or dropped from coverage within the next

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12 months, it is considered expired. Buy (sell) stocks remain in the analysis until they are either

dropped from coverage, downgraded (upgraded), or expired. Abnormal portfolio returns are

measured using Daniel, Grinblatt, Titman and Wermers (1997) (henceforth DGTW) characteristic-

adjusted returns.9

Panel A of Table 3 presents calendar time portfolio returns for analyst recommendations. Our

results illustrate that quant-backed analysts generate better stock recommendations. In economic

terms, buy recommendations issued by analysts with access to in-house quant research generate 0.91%

monthly DGTW returns, while those of peers without quant research generates 0.32% monthly

DGTW returns. Therefore, a long-short portfolio that purchases stocks after buy recommendations

of Quant-backed analysts and shorts stocks after buy recommendations of non-Quant-backed analysts

earns 59 basis points per month. Similarly, sell recommendations of Quant-backed analysts are

associated with 0.26% lower DGTW characteristic adjusted returns compared to analysts without

Quant access. The calendar time portfolio strategy results indicate that the recommendations of quant-

backed analysts generate superior investment returns than the recommendations of non-quant backed

analysts.

****Insert Table III here****

In Panel B, we employ panel regressions and regress DGTW-adjusted returns on the

comprehensive class of analyst, brokerage house and firm specific covariates as described in Section

3. Following prior work, regressions are estimated on a daily basis, however, coefficients are converted

to monthly returns for ease of interpretation (e.g., Cohen, Frazzini and Malloy, 2010). All standard

errors are adjusted for heteroskedasticity and clustered at the analyst level. As in the earlier analysis,

regressions include broker fixed effects, and therefore exploit within broker variation in Quant

support. In addition to broker fixed effects, the regressions also include year and industry fixed

effects.10 Our model is formally specified as follows:

Buy/Sell Recommendations (DGTW)i,j,t = β1(No of Quants) + β2 (Size) + β3(BM)+ β4 (Gexp) + β5(Fexp) + β6(Port size) + β7 (Top 10) + β8(Portgind) + β9 (All-Star) + β10 (Affiliated) + β11 (Past 6m ret) + β12 (Ind. spec) + β13 (No of Analyst within Ind) + β14(Rec Level)+ β15 (Optimism) + β16 (No of Forecasts) + β17 (Drop Cov) + Year Fixed Effects + Industry Fixed Effects +Broker Fixed Effects + ε (2)

Panel B of Table III presents the results. Model 1 shows a positive and significant coefficient

estimate on Quants. In economic terms, one standard deviation increase in the number of Quants is

9 Results are similar when excess returns are measured with risk adjusted returns from Fama and French (1993)’s three-factor model, 2) Carhart (1997)’s four-factor model and 3) Pastor and Stambaugh (2003)’s five-factor model 10 The results are also robust to the inclusion of year-month fixed effects.

15

associated with 0.38% higher monthly abnormal returns for buy recommendations. Other controls

have consistent directions. For instance, the investment value of buy recommendations from All-Star

and longer experienced analysts (All-Star, Fexp, Gexp) is higher. Also, analysts exerting more

forecasting effort (Drop Coverage) and those with less complex coverage portfolios (Port Gind) issue

more profitable buy recommendations.

Re-estimating eq (2) for sell recommendations, Model 2 of Panel B documents that the investment

value of sell recommendation is also related to quantitative research. Economically, one standard

deviation increase in the number of Quants is associated with 0.42% more profitable (lower) abnormal

stock returns for sell recommendations.

****Insert Table IV here****

To better isolate and identify the Quant channel, Table IV explores further empirical specifications

that are analogous to those in Panel B of Table 2. First, we investigate the impact of Quant research

for analyst performance through a dynamic setting of Quant gains/loss emanating from their

movements into and out of brokerage houses. Repeating eq (2), models 1 and 2 of Panel A in Table

IV illustrate that buy recommendations generate 0.81% (0.85%) incrementally higher (lower)

characteristic adjusted returns after an analyst gains (losses) access to Quant research. Models 1 and 2

of Panel B re-estimate analogous regressions for sell recommendations and again find results

consistent with the calendar time portfolios and panel regressions in Table 3. In model 3 of Panels A

and B, we find that the results are also robust to exploiting within analyst variation in access Quant

support by including analyst fixed effects. As a final robustness test, we also use the same propensity

score matching methodology discussed in Section 3 and continue to find qualitatively and

quantitatively similar results (model 4).

4.1. Quantitative Research, Forecasting Expertise and Market Sentiment

Next, we seek to better understand the value of Quants for sell-side analyst performance by

investigating whether the marginal effect of Quants exhibits cross-sectional variation across the quality

of Quants and sell-side analysts. We also consider the implications of time-series variation in market-

wide sentiment..

We first ask whether all Quants are equally effective in providing value added research. Our main

conjecture is that more reputable Quants should be associated with superior application of quantitative

research on market anomalies, resulting in superior stock selection research. To proxy for Quant

reputation, we focus on the All-American Quantitative Research rankings awarded by institutional

16

investors participating in the IIM annual poll and distinguish between All-Star and non-All-Star

Quants. We include separate variables to distinguish the effects of All-Star Quants and non-All-Star

Quants on investment value of stock recommendations and re-estimate eq (2). Model 1 in Panels A

and B of Table V show that the association between buy (sell) recommendations and Quants is larger

for quantitative research obtained from All-Star Quants relative to that from unranked Quants (t-stats

for difference=4.29/5.46 for buy/sell recommendations).

****Insert Table V here****

Next, we consider cross-sectional variation in the characteristics of sell-side analysts. If Quants

assist sell-side analysts in identifying and exploiting anomalies for their stock selection function, then

such benefits are expected to be even higher for analysts who has more to benefit from such stock

selection research. All-Star analysts represent a select group of skilled analysts for their ability to

produce superior stock recommendations compared to their unranked peers, a finding we also confirm

in Section 4. We focus on the All-Star status of sell-side analysts, defined as those analysts who made

it to IIM’s roster in their coverage industry. We then re-estimate the marginal impact of Quant research

on All-Star ranked analysts and remaining analysts separately and present the results in model 2 of

Panels A and B. While the results presented in model 2 of Panels A and B show that Quants benefit

both ranked and unranked analysts, the incremental effect of Quants on the investment value of BUYs

is higher when the forecasting analysts is not ranked as an All-Star.

We also explore whether the benefits of Quant analysis exhibit time-series variation related to

market-wide sentiment. Stambaugh, Yu, and Yuan (2012) show that anomaly return predictability is

strongest following periods of high market sentiment. If Quants help analysts better understand the

implications of capital market anomalies on stock selection, then quantitative research should be more

valuable during high market sentiment periods when mispricing is greater. We proxy for market

sentiment with Baker and Wurgler (2006) (sentiment) composite sentiment index and classify stocks

into high vs low sentiment months based on the median value of sentiment for the overall sample

period. Partitioning our coefficient of interest into No of Quant- High Sentiment and No of Quant- Low

Sentiment, model 3 illustrates that sell-side analysts benefit more from anomaly research during high

market sentiment periods. For instance, a one standard deviation increase in Quant research is

associated with 0.35% (0.51%) more profitable abnormal stock returns for buy (sell) recommendations

during high market sentiment, compared to 0.22% (0.11%) for low market sentiment era. The

difference in coefficients across high and low sentiment periods is also statistically significant at 1%

17

level. The evidence is consistent with sell-side analysts benefitting more from quantitative research

during times when mispricing is larger.

4.2. Quantitative Research and Sell-Side Analyst Reports: Textual Analyses

Our results thus far suggest that Quants provide a statistically and economically important benefit

to analysts. Next, we examine a potential channel through which Quants may benefit analyst research.

Specifically, we focus on full-text analyst reports to directly investigate whether sell-side analysts are

more likely to incorporate the implications of quantitative modeling as well as capital market anomalies

for their coverage stocks when they have in-house Quant support.. An added benefit of this analysis

is that we can directly pin down a more direct link between Quants and sell-side analyst research

behavior.

We start our textual analyses by downloading complete analyst reports from Thomson Reuters

Investext. We then parse each analyst report for discussions on variants of “Quantitative modeling”

and “Anomaly”. To identify our list of keywords, we randomly select 100 research reports (from both

quant-backed and non-quant backed analysts) and identify the ways sell-side analysts discuss these

topics and then compile a keyword list by examining every bigram word combination. Our keyword

list for quantitative modeling include variants of “modeling” whereas reports with anomaly discussions

include variants of “mispricing”, “cross-sectional”, “overvalued”, “undervalued” and “depart from

fundamental”.11

We define analyst-year observations as “Quantitative Modeling” and “Anomaly” for a given

coverage firm if at least one of the research reports issued by the covering analyst in a given calendar

year contains at least one of the above corresponding keywords. Next, we estimate a logistic regression

and explore the association between Quants, financial modeling as well as anomaly reports after

controlling for the analyst, broker and firm characteristics. Our key independent variable of interest is

No of Quants, which measures the number of Quants employed in an analyst’s brokerage firm.

****Insert Table VI here****

Model 1 of in Table 6 presents the regression results for quantitative modeling. We find that the

coefficient on Quants is positive and significant, suggesting that Quant-backed analysts are more likely

to discuss and incorporate implications of quantitative modeling for coverage stocks compared to

11 The full list for quantitative modeling is: financial model, financial modeling, quant model, quant modeling, quantitative model, quantitative modeling and the complete keywords for anomalies include mispricing, mispriced, cross-section, cross-sectional, overvaluation, overpricing, overvalued, overpriced, undervaluation, underpricing, undervalued, underpriced, depart from fundamental, unfundamental, nonfundamental, and puzzle.

18

peers lacking such research. In model 2, we investigate the association between discussions of

mispricing and access to Quant research. Consistent with the job description of Quant research in

investment research market, our results illustrate a positive association between access to in-house

Quant research and mispricing related discussion in analyst reports. In economic terms, one standard

deviation increase in the number of in-house quants is associated with 10.7% higher likelihood of

anomaly related research in full-text reports. Finally, we identify reports in which analysts incorporate

both quantitative modeling and anomaly research and repeat our logistic regressions. Results remain

similar.

In sum, the textual analyses in this section provide direct empirical support for the notion that

sell-side analysts are more likely to directly incorporate quantitative modeling based research as well

as anomaly characteristics for their coverage stocks when they have access to Quants and highlight an

important potential channel through which Quants benefit sell-side analyst research behavior.

5. Capital Market Recognition of Quantitative Research

Given the strong evidence on Quants and analyst research, in this section, we examine whether

the capital market participants also recognize the value of Quants through direct examination of

thematic research reports published by Quants. There are several reasons to believe market

participants may value quantitative research. First, Quants often make presentations to institutional

and individual clients about the design of stock selection strategies and forecasting models based

primarily on exploitation of mispriced stocks. Second, Quants frequently author thematic research

reports and distribute them externally to current and prospective brokerage house clientele through

blast emails, broker research platforms, as well as third party data providers (e.g. Thomson Reuters,

Bloomberg and Capital IQ). Third, given that stock selection has consistently been ranked as one of

the most important traits by institutional investors participating in the IIM annual poll, it is very likely

that sell-side analysts also make the availability of in-house quantitative research known to buy-side

clients in private communications. Nevertheless, prior work documents that capital market

participants often underreact to performance related cues and fail to incorporate this information into

stock prices in a timely fashion (e.g., Gleason and Lee, 2003). Therefore, the association between

Quant research reports and market reactions is an open empirical question.

As a first step, we download topical research reports directly published by Quants from

Thomson Reuters Investext and identify publication dates, names of quantitative analysts authoring the

reports, and their brokerage firms. Next, we parse the textual content of each report’s heading, identify

19

the index and/or industry each report is published on, and then compare the short term trading

volume reactions three days [0,+2] around Quant report publication dates with the trading volume

over the past one year [-1, -250] for each report’s focus stocks. To make sure we are capturing the

incremental market reaction to Quant reports, we include controls for the number of sell-side analyst

reports, earnings and non-earnings news for focus stocks released during Quant report event window.

Our model also controls for firm size, BM, and no of coverage analysts, along with firm and year fixed

effects (model 1) and firm and year-month paired fixed effects (model 2).

****Insert Table VII here****

Panel A of Table 7 documents a positive and significant association between Quant reports and

abnormal market reactions to focus stocks. In economic terms, model 1 suggests that a one standard

deviation increase in the number of Quant reports results in 1.32% more prominent abnormal volume

reactions for the underlying stocks over the 3-day window. To put this result in relative economic

terms, trading volumes are 4.58% higher following a one standard deviation increase in sell-side analyst

reports published on the corresponding stock. Other controls are also signed as expected. For

instance, trading volumes are more pronounced following firm specific earnings and non-earnings

news. Next, we use abnormal price reactions over the same window as an alternative way to measure

the incremental importance of Quant reports for capital markets. Panel B of Table 7 presents the

results from this analysis and indicates that abnormal price reactions of focus stocks are 0.02% higher

when number of Quant reports is increased by one standard deviation, compared to 0.13% for sell-

side analyst reports. In sum, the results from this section are consistent with the interpretation that

capital markets participant also recognize Quants and incorporate their research into stock prices.

6. Does Quantitative Research Help Client Buy-side Institutions?

This section examines whether quantitative research is related to the trading behavior of

institutional clients. Recent evidence suggests that mutual fund managers fail to exploit well-known

sources of predictability in returns (e.g., Lewellen, 2011; Akbas, Armstrong, Sorescu, and

Subrahmanyam, 2015; Edelen, Ince, and Kadlec, 2016). More surprisingly, Edelen, Ince, and Kadlec

(2016) and Avramov, Chen, and Hameed (2016) show that mutual funds have a strong tendency to

buy stocks classified as overpriced. In particular, we ask whether quant coverage influences the

propensity of mutual fund clients to buy overpriced or underpriced stocks, both unconditionally and

in response to fund inflows. We hypothesize that client funds with access to quant research will be

20

less (more) likely to buy overpriced (underpriced) stocks in response to inflows compared to funds

lacking such research.

To examine this question, we follow prior work and first obtain broker equity trading commission

allocation information of mutual funds from semi-annual N-SAR filings over 1999 and 2008 (e.g.

Edelen, Evans and Kadlec, 2012; Christoffersen, Evans, and Musto, 2013; Gokkaya et al., 2015).

Registered investment companies are required to list the names of 10 brokerage houses to which they

paid the most equity trading commissions. Next, we hand-match the names of funds in N-SAR filings

to those in Thomson Financial’s CDA Mutual Fund Holdings database and use I/B/E/S to identify

sell-side analysts providing coverage on stocks in a given mutual fund’s portfolio.12 For each fund-

stock-quarter observation, we identify the number of analysts covering the stock and employed at a

brokerage house that receives commissions from a given fund. To test the association between Quant

research and client funds’ trading behavior on anomaly stocks, we then create two analyst variables,

one that counts the number of analysts with quant support that cover the stock (No of Analysts with

Quant), and one that counts the number of analysts without quant support that cover the stock (No of

Analysts without Quant).We follow the approach of Avramov, Chen, and Hameed (2016) and estimate

the following regression,

Mispricing,f, j,t = β1(Inflow) + β2 (Inflow x No of Analysts with Quant)+ β3 (Inflow x No of Analysts without Quant) +β4 (No of Analysts with Quant) + β5(No of Analysts without Quant)+ β6 (Mret) + β7(Log Fund TNA) + β8(Turnover Ratio) + β9 (Expense Ratio) + β10(Log Fund Age) + β11 (Log Manager Tenure) + β12 (Size) + β13 (Return) + β14 (Turnover) + β15 (Log Illiquidity) + Year Fixed Effects + Industry Fixed Effects + ε (3)

****Insert Table VIII here****

Mispricing takes a value of one if mutual fund f increases its holding in stock j at time t, and zero

otherwise. Model 1 estimates the relationship for the sample of stocks that are overpriced (anomaly

short-leg stocks), while Model 2 estimates the relationship for stocks that are underpriced (anomaly

long-leg stocks). Inflow is a dummy variable equal to one if fund f has received net inflows in quarter t-

1, and zero otherwise. Finally, we follow Avramov, Chen, and Hameed (2016) and include a number

of stock-specific and fund-specific control variables. A detailed description of these variables is

provided in Appendix B

12 Consistent with related work, we exclude bond, balanced, international, index and sector funds from our sample (e.g., Chen et al, 2004; Kacperzyk et al., 2005; Brown, Wei, and Wermers, 2013). Funds with with fewer than 10 stocks in stock portfolios are also excluded (e.g. Kacperzyk and Seru, 2007).

21

The positive value for β1 in Model 1 shows that funds increase their purchases of overpriced stocks

in response to fund flows. This relation is consistent with the findings of Avramov, Chen, and Hameed

(2016). The negative value of β2 in Model 1 confirms that quant support mitigates this relation, as no

of quant backed analysts is associated with a decreased propensity of client mutual funds to purchase

overpriced stocks in response to fund inflows. Conversely, coverage from analysts without quant

support actually increases the likelihood of fund purchase of overpriced stocks in response to inflows.

Finally, the negative and statistically significant value for β4 indicates that client funds are

unconditionally less likely to purchase anomaly short-leg stocks receiving more intense coverage by

quant-backed analysts, while no such relation is observed for analysts lacking quant research at their

brokerage firms.

Model 2 repeats analogous logistic regressions for anomaly buys. We find that client funds also

increase their purchases of underpriced stocks in response to fund flows, again consistent with the

relation documented in Avramov, Chen, and Hameed (2016). The interaction of inflows with the

number of analysts with quant support is positive and statistically significant, indicating that funds are

more likely to purchase underpriced stocks when they have access to quant coverage for that stock.

In contrast, access to analysts without quant support decreases the likelihood that a client fund

purchases underpriced stocks. Finally, coefficients β4 and β5 indicate that the unconditional likelihood

of a client fund purchasing an underpriced stock is increasing in the number of analysts with quant

support, and decreasing in the number of analysts without quant support.

In Panel B, we repeat the analysis, now only focusing on the propensity-matched sample of

analysts and brokerages. Models 1 and 2 confirm that our results still hold when restricting the sample

of analysts without quant support to only those from brokerages that are comparable to those of

analysts with quant support.

The combined evidence suggests that access to quantitative research increases (decreases) client

funds’ propensity to buy underpriced (overpriced) stocks, both unconditionally, and also in response

to inflows. On the other hand, greater access to analysts without quant support is associated with a

relatively higher likelihood of purchasing overpriced stocks in response to inflows and a lower

likelihood of purchasing underpriced stocks in response to inflows. The evidence is consistent with

institutional clients benefiting from quant research, consistent with the job description of Quants on

Wall Street.

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7. Does Quantitative Research Improve Market Efficiency?

The association between Quant research and analyst forecast performance, coupled with the

evidence in Sections 5 and 6, showing that capital markets recognize the value of Quant research and

client institutions demonstrate superior investment behavior with respect to anomalies, suggests that

stocks receiving more intense quant coverage should have more efficient prices. Therefore, in this

section, we turn our attention to the association between Quants and the predictability of anomaly

return strategies.

In particular, we examine the extent to which mispricing varies with Quant-backed analysts by

using the 11 anomalies and the comprehensive aggregate anomaly variable discussed above in Section

3. To the extent that Quants increase market efficiency, mispricing should be smaller for stocks

covered by more Quant-backed analysts. This will take the form of weaker anomaly returns for such

stocks.

We start by running separate monthly Fama-MacBeth cross-sectional regressions for each

anomaly. No of Analysts with Quant captures the number of analysts with Quant support, and No of

Analysts without Quant captures the number of analysts covering a stock who do not have Quant

support. We interact each of these variables separately with the anomaly variable. The interaction term

captures the extent to which quantitative research mitigates anomaly return predictability. If more

quant coverage is associated with more efficient pricing, then stocks with high quant coverage should

have anomaly strategy returns that are smaller in magnitude.13 The full regression model, specified

below, also includes a number of stock-specific controls. Appendix B provides definitions of the

variables.

Return,j,t = β1(AnomalyVar) + β2 (AnomalyVar x No of Analysts with Quant)+ β3 (AnomalyVar x No of Analysts without Quant) +β4 (No of Analysts with Quant) + β5(No of Analysts without Quant)+ β6 (EAM) + β7(mom) + β8(F_Score) + β9 (Log BM) + β10(Ret (t-1)) + β11 (SUE) + β12 (Vol) + β13 (Log Size) + β14 (Turnover) + ε (4)

****Insert Table IX here****

The results are reported in Panel A of Table 9. The coefficient on the interaction of No of Analysts

with Quant and anomaly is statistically significant and of the opposite direction of the anomaly variable

in all regressions, indicating that increased Quant coverage is indeed associated with a decrease in

anomaly return predictability. The evidence suggests that greater quant coverage is associated with

13 In unreported analysis we have also interacted the anomaly variable with variables that capture limits to arbitrage (e.g., size, volatility) to control for the possibility that the analyst variables are proxying for limits to arbitrage. The results are quantitatively similar.

23

anomaly returns being attenuated toward zero. In contrast, the number of non-quant backed analysts

is only significant for four of the 11 anomalies, and the coefficients are smaller in relative magnitude.

In relative terms, the largest significant coefficient on the non-quant interaction variable is less than

25% of the magnitude of the coefficient on the quant interaction variable. The findings provide

evidence that Quants help to enhance market efficiency, while providing economically and statistically

weak evidence that analysts without Quant support contribute to attenuating anomaly predictability.

The evidence also contributes to the previous literature documenting weaker return predictability for

stocks with greater analyst coverage (Hong, Lim, and Stein, 2000; Zhang, 2006; Ben-David, Birru, and

Rossi, 2017). In particular, the findings suggest that the attenuation of return predictability for higher

analyst-coverage stocks is primarily confined to analysts with Quant support. In contrast, non-quant-

backed analyst coverage has only small effects on attenuating mispricing.

Panel B repeats the analysis using the rank variable rather than continuous variable. This also

allows us to examine the aggregate anomaly rank variable. The results are consistent with those in

Panel A. As before, the rank variable takes larger values for stocks with higher predicted anomaly

returns. This is reflected in the large and significant coefficients for the An Rank variable in each of

the twelve regressions. Importantly, the interaction of the rank variable with the Quant variable has a

negative and statistically significant coefficient in all regressions, again indicating that increased Quant

coverage attenuates anomaly return predictability. The results again strongly support the conclusion

that Quants help to enhance market efficiency.

7.1. Quantitative Research and Market-Wide Sentiment

If the decrease in cross-sectional return predictability for anomaly stocks is due to a decrease in

mispricing, then clear implications emerge when conditioning on the state of market sentiment. In

particular, mispricing is expected to be largest during times of high sentiment due to the presence of

short-sales impediments (Stambaugh, Yu, and Yuan, 2012). If the decrease in magnitude of cross-

sectional return predictability reflects a decrease in mispricing, we should expect that the benefits of

Quant coverage will be strongest in high sentiment periods.

****Insert Table X here****

In Table 10 we examine whether Quant effects on return predictability are symmetric across

sentiment periods. Consistent with mispricing being strongest in high sentiment periods and quant

coverage mitigating mispricing, more Quant coverage is associated with a larger decrease in anomaly

return predictability following high sentiment periods. Focusing only on anomaly variables for which

24

sentiment has predictive power, Panel A shows this is true for the continuous anomaly variable and

Panel B shows it is also true when instead using the ranked anomaly variable.14 The evidence suggests

that the benefits for market efficiency that Quants contribute to the market are strongest when cross-

sectional mispricing in the market is greatest. In sum, the results provide evidence in support of

Quants’ ability to mitigate mispricing in financial markets.

8. Conclusion

We provide insight into the implications of quant research in financial markets. Quantitative

research has long been thought to add value, and as such, many brokerages employ Quants. To our

knowledge, we are the first to examine the importance of quantitative research for sell-side analysts,

institutional investors, as well as market efficiency.

We document economically important benefits to analysts from quant support. Analysts with

access to in-house Quants issue superior buy (sell) recommendations relative to analysts lacking such

research. In particular, stock recommendations are higher (lower) recommendations on underpriced

(overpriced) stocks relative to those issued by peers without in-house quants. Recommendations from

analysts with access to in-house quants also have higher investment value. Further analyses illustrate

that Quants benefits analysts through increased understanding of anomaly-specific mispricing, as

analysts with quant support are more likely to discuss the implications of quantitative modeling and

anomalies in their research reports.

Capital markets appear to understand the benefits of quant research and exhibit price and volume

reactions to the releases of research reports published directly by Quants. Offering a plausible channel

through which quant support affects market prices, we show that the likelihood of buy-side

institutional clients purchasing underpriced (overpriced) stocks is increasing (decreasing) in the

number of related analysts with quant support. Importantly, the effects of quant analysis are reflected

in more efficient asset prices, as greater quant coverage is associated with reduced anomaly return

predictability. In short, the evidence supports the conclusion that quantitative research mitigates

mispricing and improves market efficiency.

14 Specifically, we identify the anomalies in Table 2 of Stambaugh, Yu, and Yuan (2012) that exhibit statistically significant sensitivity to sentiment (at the 5% level). See Birru (2017) for a discussion and analysis of the sensitivity of specific anomalies to sentiment.

25

Appendix A. Data screening and collection process

Steps Recommendations Firms Analysts

All analyst recommendations between 1993 and 2008 from I/B/E/S.

487,754 15,950 13,020

Merge with CRSP/COMPUSTAT for stock price data and firm characteristics. Remove all the observations with firm market value less than $1 million, and penny stocks with stock price below $5.

329,312 8,983 11,502

Merge above sample with Nelson Information’s Director of Investment Research (NDIR) by hand-matching broker names.

311,906 8,884 10,676

Remove observations with missing values for any of the control variables

211,727 6,366 8,332

26

Appendix B. Variable definitions

Variable Definition Recommendation level Recommendation level takes a value of 5 (4) for “Strong Buys (Buys)”,

3 for “Holds”, and 2 (1) for “Sells (Strong Sells).

Anomaly Buy/Sell Anomaly Buy (Anomaly Sell) is an indicator variable that equals 1 if the stock is in the top (bottom) quintile aggregate anomaly score.

No of Quants Number of quants employed at brokerage firm of the analyst.

No of Analysts with Quants Number of analysts covering the stock that have quant support. In the institutional analysis, only analysts from client brokers are considered.

Size The market capitalization of the covered firm (in $millions) at the end of June.

BM Book value of equity in the fiscal year prior to the earnings forecast, divided by the current market value of equity.

Rec_Level Recommendation level. It takes a value of 5(4) for “Strong Buys (Buys)”, 3 for “Holds” and 2(1) for “Sells (Strong Sells).

Gexp The total number of years that analyst i appears in I/B/E/S

Fexp The total number of years since analyst i started to cover firm j

Portsize The number of firms followed by analyst i at time t

Top10 Indicator variable equal to one if the analyst works at a top decile brokerage house

Portgind The number of GICS industries followed by analyst i at time t

All-Star Indicator variable equal to one if the analyst is named to Institutional Investor’s all-star team in current year, and zero otherwise.

Affiliated

Indicator variable equal to one if analyst’s brokerage house was the underwriter/advisor of the covered firm’s IPO/SEO/MA deal during the past 3 years, and zero otherwise.

Broker Ind. spec Percentage of total analysts in a broker that cover firm i’s GICS industry.

No of Analyst within Ind Number of analysts in a broker that cover firm i’s GICS industry.

27

Optimism Indicator variable equal to one if the most recent recommendation issued by the analyst within the last 12 months is above the median consensus recommendation, and zero otherwise.

No of Forecasts The number of forecasts issued by analyst i at time t

Drop Cov Indicator variable equal to one if the analyst drops coverage in the following year

Undervalued/Overvalued stocks

An indicator variable equal to one if the stock is in the top (bottom) quintile aggregate anomaly score.

Broker gain (loses) Quant Analyst

An indicator variable equal to one if the forecast is issued in the years after the corresponding quant analyst moves into (out of) the broker, and zero if issued in the years before.

All Star / No All Star Quant Analyst

An indicator variable equal to one if the broker employs at least one all-star quant analyst/quant analyst who isn’t ranked as an all-star analyst, and zero otherwise.

High/Low Sentiment An indicator variable equal to one if the month is associated with above/below median Baker and Wurgler (2006) composite sentiment index.

CAR (0,2) CRSP-VW index-adjusted abnormal returns over the 3 day window (0, +2) around the announcement date of recommendation revision.

Revision magnitude The absolute percentage change of the EPS forecast from the most recent forecast/the magnitude of the recommendation revision from the previous level.

Anomaly variable Anomaly variables are (in the order of the model number): O score, failure probability, composite equity issues, net stock issues, total accruals, net operating assets, momentum, gross profitability, asset growth, return on assets and investment to assets.

EAM An indicator variable equal to one if the month is an earnings announcement month, and zero otherwise.

F_score

FSCORE is a summary of nine binary signals that award higher values for improvements in firms’ profitability, financial leverage, and operating efficiency relative to the prior year (see Piotroski (2000) and Piotroski for further details on its construction).

Log(BM) Log(BM) is the log of one plus a firm’s book-to-market ratio.

28

Mom Mom is defined as the CRSP-VW index-adjusted abnormal returns over the previous twelve months.

Ret Ret is the firm’s raw return in month t.

SUE SUE is a firm’s standardized unexplained earnings, defined as the realized EPS minus EPS from four quarters prior, divided by the standard deviation of this difference over the prior eight quarters.

Vol Vol is the standard deviation of monthly returns over the twelve months ending in month t.

Log(size) Market capitalization of the covered firm (log-transformed) at the end of the month.

Turnover Turnover is lagged twelve-month share turnover

29

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33

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34

Table I. Descriptive Statistics

This table reports summary statistics of the sample. Panel A presents summary statistics for distribution of quant analysts over time and across brokers. Panel B presents summary statistics by firm-year level. Panel C presents summary statistics of key regression variables. Refer to Appendix B for a detailed description of variables. Information on quant analysts are obtained from Nelson Information’s Directory of Investment Research and supplemented with Thomson Reuters Investext. and All-star information is retrieved from Institutional Investor Magazine. Refer to Appendix B for a detailed description of variables. Analyst data are from I/B/E/S and stock price/ financial accounting data is from CRSP/Compustat.

Panel A. Sample Statistics

# Unique Quants % Brokers w Quant Avg Quants/ Broker # Recs % Recs w Quants % IBES analysts % IBES firms Overall

349 17.47 2.15 211,727 40.88 67.33 40.24

1993 86 22.11 1.54 7,061 39.97 82.86 46.93 1994 92 19.83 1.59 12,170 41.82 76.85 40.15 1995 89 18.11 1.65 12,248 41.28 75.30 41.78 1996 111 19.59 1.66 12,110 40.24 73.87 41.68 1997 122 18.93 1.67 11,991 40.59 73.79 42.46 1998 150 16.30 1.92 13,527 37.12 72.27 44.40 1999 158 18.72 1.95 13,855 46.10 75.22 41.75 2000 156 17.65 2.05 12,046 50.73 75.79 43.10 2001 164 15.25 2.22 12,590 49.78 77.43 45.88 2002 169 14.53 2.28 19,589 44.37 72.77 47.22 2003 165 16.11 2.20 16,416 37.22 70.45 45.30 2004 189 18.44 2.39 15,480 36.54 70.64 48.63 2005 189 18.56 2.45 13,389 36.12 69.08 47.20 2006 207 18.18 2.52 13,734 38.52 68.32 46.63 2007 210 16.44 2.92 12,396 37.54 64.90 43.77 2008 195 13.29 2.95 13,125 36.69 64.40 41.75

Panel B. Coverage Firm Characteristics

Mean Median Minimum Maximum Std Dev Analyst Coverage 4.74 3.00 1.00 36.00 4.06 Analyst Coverage (Quant) 2.00 1.00 0.00 18.00 2.25 Size percentile 0.68 0.70 0.03 1.00 0.22 BM percentile 0.42 0.40 0.01 0.98 0.25

35

Table I (continued)

Panel C. Analyst/Broker Characteristics

Mean Median Minimum Maximum Std Dev gexp 3.89 3.00 0.00 14.00 3.38 fexp 1.46 1.00 0.00 11.00 2.14 Portsize 15.78 14.00 1.00 93.00 10.88 top10 0.56 1.00 0.00 1.00 0.50 portgind 3.13 2.00 1.00 18.00 2.45 All_Star 0.08 0.00 0.00 1.00 0.26 affiliated 0.10 0.00 0.00 1.00 0.31 Ind_spec 0.20 0.00 0.00 1.00 0.40 No_of_Analyst_Ind 5.69 4.00 1.00 24.00 4.45 Rec_Level 3.72 4.00 1.00 5.00 0.98 Optimism 0.24 0.00 0.00 1.00 0.43 No of Forecasts 4.80 4.00 1.00 15.00 2.76 drop_cov 0.22 0.00 0.00 1.00 0.42

36

Table II: Quantitative Research and Analyst Efficiency on Capital Market Anomalies

This table presents OLS regression results for analysts’ recommendation level between 1993 and 2008. The dependent variable is the recommendation level. It takes a value of 5(4) for “Strong Buys (Buys)”, 3 for “Holds” and 2(1) for “Sells (Strong Sells). Anomaly Buy (Sell) signal equals one if the stock is in the bottom (top) quintile aggregate anomaly score based on 11 anomaly variables in Stambaugh, Yu, and Yuan (2012), and zero otherwise. Information on quant analysts are obtained from Nelson Information’s Directory of Investment Research and supplemented with Thomson Reuters Investext. All-star information is retrieved from Institutional Investor Magazine. Refer to Appendix B for a detailed description of variables. Analyst data are from I/B/E/S and price and financial accounting data are from CRSP/Compustat. Coefficients are multiplied by 100. T-statistics are in parentheses with heteroskedastic-consistent standard errors clustered at analyst level. Industry, year and broker fixed effects are included. *, **, and *** indicate statistical significance at the 10%, 5%, and 1%, respectively.

Panel A. Panel Regressions on Recommendation Level

Model 1 Model 2 Model 3 Anomaly Buy -3.90*** -5.30*** -9.05*** (-6.48) (-7.80) (-5.93) Anomaly Sell 2.17** 3.65*** 7.81*** (2.57) (4.86) (4.08) No of Quants -1.18 -1.16 (-0.98) (-0.97) Anomaly Buy * No of Quants 0.85*** 0.92*** (8.33) (7.34) Anomaly Sell * No of Quants -1.18*** -1.42*** (-5.64) (-5.98) Size 0.12*** 0.12*** 0.12*** (5.56) (5.57) (5.48) BM -20.90*** -20.78*** -20.65*** (-20.23) (-20.22) (-20.12) Gexp 0.67*** 0.67*** 0.49*** (5.66) (5.71) (3.52) Fexp -1.16*** -1.17*** -1.14*** (-6.15) (-6.23) (-5.82) Portsize -0.12* -0.12* -0.13* (-1.87) (-1.92) (-1.88) Top10 7.39* 6.57 6.56 (1.68) (1.56) (1.56) Portgind -0.21 -0.17 -0.03 (-0.42) (-0.35) (-0.06) All-star 1.45 1.43 1.56 (0.92) (0.91) (0.89) Affiliated 13.47*** 13.50*** 13.48*** (12.44) (12.51) (12.56) Past 6m return 20.65*** 20.70*** 20.60*** (16.52) (16.46) (16.36) Ind Spec -2.26** -2.38** -2.42** (-2.35) (-2.47) (-2.51) No of Analyst within Ind 0.12 0.15 0.15 (0.59) (0.80) (0.80) No of Forecasts -0.79*** -0.79*** -0.76*** (-4.97) (-4.91) (-4.67) Drop Cov -12.28*** -12.29*** -11.84*** (-12.26) (-12.32) (-11.72) Year Fixed Effects Yes Yes Yes Industry Fixed Effects Yes Yes Yes Broker Fixed Effects Yes Yes Yes Anomaly Buy/Sell* Control variables No No Yes R2 10.98% 11.04% 11.07% N 211,727 211,727 211,727

37

Table II: (continued)

Panel B. Quant Job Changes, Analyst Fixed Effects, Propensity Score Matching and Recommendation Levels

Model 1 Model 2 Model 3 Model 4 Model 5 Anomaly Buy * Post Quant gain 6.32** (2.25) Anomaly Sell * Post Quant gain -6.21** (-2.24) Anomaly Buy * Post Quant Loss -6.32** (-2.20) Anomaly Sell * Post Quant Loss 5.77** (2.03) Anomaly Buy * No of Quants 0.93*** 0.59*** (5.46) (3.42) Anomaly Sell * No of Quants -1.32*** -1.27*** (-6.77) (-6.34) Anomaly Buy * No of Quants (PS matched) 0.53*** (2.92) Anomaly Sell * No of Quants (PS matched) -1.17*** (-6.00) Year Fixed Effects Yes Yes Yes Yes No Control variables Yes Yes Yes Yes Yes Firm-Year Fixed Effects No No No No Yes Industry Fixed Effects Yes Yes Yes Yes No Broker Fixed Effects Yes Yes Yes Yes Yes Analyst Fixed Effects No No Yes No No R2 11.73% 13.60% 17.55% 13.24% 30.27% N 25,367 22,656 211,727 164,120 211,727

38

Table III Quantitative Research and Returns to Stock Recommendations

This table presents calendar time monthly portfolio returns (Panel A) and panel regressions (Panel B) of investment value of stock recommendations over 1993 and 2008. Buy (sell) recommendation include stocks upgraded (downgraded) relative to the prior outstanding recommendation as well as stocks with Strong Buy (Strong Sell) and Buy (Sell) recommendation reiterations/initiations/resumes in the Buy (Sell) recommendation portfolio. These portfolios are then rebalanced daily when analysts revise their recommendations, drop coverage or when the recommendation becomes stale (i.e. no change for 1 year). Panel A reports Daniel, Grinblatt, Titman and Wermers (1997) (DGTW) characteristic-adjusted stock monthly returns from calendar time portfolios (%). In Panel B the dependent variable is future DGTW characteristic-adjusted returns (%). Following Cohen, Frazzini, and Malloy (2010), regressions are run daily and coefficients are adjusted to represent monthly returns in percent. Information on quant analysts is obtained from Nelson Information’s Directory of Investment Research and supplemented with Thomson Reuters Investext. All-star information is retrieved from Institutional Investor Magazine. Refer to Appendix B for a detailed description of variables. Analyst data are from I/B/E/S and stock price financial accounting data is from CRSP/Compustat. T-statistics are in parentheses with heteroskedastic-consistent standard errors clustered at analyst level. Industry, year and broker fixed effects are included. *, **, and *** indicate statistical significance at the 10%, 5%, and 1%, respectively.

Panel A. Calendar-Time Portfolios: DGTW (1997) Characteristic Adjusted Stock Returns

Full Sample Analysts with Quant Research

Analysts without Quant Research Difference

Buy Recommendations 0.56*** 0.91*** 0.32*** 0.59*** (10.32) (16.42) (5.30) (13.98) Sell Recommendations -0.09 -0.18*** 0.07 -0.26*** (-1.37) (-2.61) (1.12) (-5.77)

39

Table III (continued)

Panel B. Panel Regressions: DGTW (1997) Characteristic Adjusted Stock Returns

Buy

Sell No of Quants 0.12*** -0.12***

(12.15) (-11.67) Size -0.00*** -0.00***

(-7.69) (-9.39) BM 1.24*** 1.71***

(19.89) (29.05) Gexp 0.01* -0.06***

(1.87) (-9.46) Fexp 0.05*** -0.01

(5.69) (-1.08) Portsize -0.01** -0.01***

(-2.52) (-5.68) Top10 0.06 -0.25***

(0.90) (-3.59) Portgind 0.02** 0.04***

(2.32) (3.77) All-star 0.23*** -0.52***

(3.31) (-8.10) Affiliated -0.14*** -0.36***

(-2.61) (-5.95) Past 6m return 1.43*** 0.57***

(31.57) (10.60) Ind Spec -0.06 -0.51***

(-1.25) (-10.03) No of Analyst within

-0.03*** -0.07***

(-4.41) (-11.68) Rec Level 0.15*** 0.14***

(3.70) (3.98) Optimism -0.45*** -0.14***

(-10.83) (-3.43) No of Forecasts 0.00 -0.03***

(-0.39) (-5.09) Drop Cov -0.40*** 0.00

(-9.64) (-0.07) Year-Month Fixed

Yes Yes

Industry Fixed

Yes Yes Broker Fixed

Yes Yes

N 11,517,732

10,177,284

40

Table IV Quantitative Research and Returns to Stock Recommendations

This table presents panel regressions of investment value of stock recommendations over 1993 and 2008. Buy (sell) recommendation include stocks upgraded (downgraded) relative to the prior outstanding recommendation as well as stocks with Strong Buy (Strong Sell) and Buy (Sell) recommendation reiterations/initiations/resumes in the Buy (Sell) recommendation portfolio. These portfolios are then rebalanced daily when analysts revise their recommendations, drop coverage or when the recommendation becomes stale (i.e. no change for 1 year). The dependent variable is Daniel, Grinblatt, Titman and Wermers (1997) (DGTW) characteristic-adjusted stock monthly returns (%). Information on quant analysts are obtained from Nelson Information’s Directory of Investment Research and supplemented with Thomson Reuters Investext. All-star information is retrieved from Institutional Investor Magazine. Refer to Appendix B for a detailed description of variables. Analyst data are from I/B/E/S and stock price financial accounting data is from CRSP/Compustat. T-statistics are in parentheses with heteroskedastic-consistent standard errors clustered at analyst level. Industry, year and broker fixed effects are included. *, **, and *** indicate statistical significance at the 10%, 5%, and 1%, respectively.

Panel A. Buy Recommendations: Quant Job Changes, Analyst Fixed Effects, Propensity Score Matching

Model 1 Model 2 Model 3 Model 4 Post Gain Quant 0.72*** (3.425) Post Lose Quant -0.83** (-2.12) No of Quants 0.18*** (16.97) No of Quants (PS matched) 0.12*** (13.46) Analyst-specific controls Yes Yes Yes Yes Broker-specific controls Yes Yes Yes Yes Firm-specific controls Yes Yes Yes Yes Year-Month Fixed Effects Yes Yes Yes Yes Industry Fixed Effects Yes Yes Yes Yes Broker Fixed Effects Yes Yes Yes Yes Analyst Fixed Effects No No Yes No N 1,428,670 1,272,298 11,517,732 9,297,397

41

Table IV (continued)

Panel B. Sell Recommendations: Quant Job Changes, Analyst Fixed Effects, Propensity Score Matching

Model 1 Model 2 Model 3 Model 4 Post Gain Quant -0.97*** (-4.17) Post Lose Quant 1.00** (2.09) No of Quants -0.21*** (-20.74) No of Quants (PS matched) -0.15*** (-17.24) Analyst-specific controls Yes Yes Yes Yes Broker-specific controls Yes Yes Yes Yes Firm-specific controls Yes Yes Yes Yes Year-Month Fixed Effects Yes Yes Yes Yes Industry Fixed Effects Yes Yes Yes Yes Broker Fixed Effects Yes Yes Yes Yes Analyst Fixed Effects No No Yes No N 1,153,795 1,087,073 10,177,284 9,570,753

42

Table V. Quantitative Research and Returns to Stock Recommendations: All-Star Quants and Sell-Side Analysts, and Market Sentiment

This table presents panel regressions of investment value of stock recommendations over 1993 and 2008. The dependent variable is Daniel, Grinblatt, Titman and Wermers (1997) (DGTW) characteristic-adjusted stock monthly returns (%). Panel B presents the result of logistic regressions where the dependent variable is a binary indicator taking the value of one if the analyst report contains discussion of anomaly predictors, and zero otherwise. Information on quant analysts are obtained from Nelson Information’s Directory of Investment Research and supplemented with Thomson Reuters Investext. All-star information is retrieved from Institutional Investor Magazine. Refer to Appendix B for a detailed description of variables. Analyst data are from I/B/E/S and stock price financial accounting data is from CRSP/Compustat. T-statistics are in parentheses with heteroskedastic-consistent standard errors clustered at analyst level. Industry, year and broker fixed effects are included. *, **, and *** indicate statistical significance at the 10%, 5%, and 1%, respectively.

Panel A. Buy recommendations Panel B. Sell recommendations No of Quants- All Star Quant Analyst 0.14*** -0.13*** (12.74) (-12.44) No of Quant- Unranked Quant Analyst 0.09*** -0.08*** (7.46) (-5.74) No of Quants- All Star Sell-Side Analyst 0.09*** -0.05*** (5.65) (-3.62) No of Quant- Unranked Sell-Side Analyst 0.14*** -0.14*** (12.62) (-13.19) No of Quants-High Sentiment 0.14*** -0.20*** (12.86) (-17.31) No of Quant- Low Sentiment 0.10*** -0.05*** (8.01) (-4.07) Difference -0.05*** 0.05*** -0.05*** 0.05*** -0.09*** 0.15*** (-4.01) (3.53) (-4.34) (4.46) (-6.82) (15.74) Analyst-specific controls Yes Yes Yes Yes Yes Yes Broker-specific controls Yes Yes Yes Yes Yes Yes Firm-specific controls Yes Yes Yes Yes Yes Yes Year-Month Fixed Effects Yes Yes Yes Yes Yes Yes Industry Fixed Effects Yes Yes Yes Yes Yes Yes Broker Fixed Effects Yes Yes Yes Yes Yes Yes N 11,517,73

11,517,73

11,517,73

10,177,28

10,177,284

10,177,28

43

Table VI. Quantitative Research and Analyst Reports: Textual Analyses

This table present logistic regression results for the effect of Quants on thematic content of analyst research reports. The dependent variable equals one if one of the analyst report contains at least one of quantitative modeling discussion (model 1) anomaly/mispricing discussions (model 2) or both (model 3) for firm j’s reports at year t in Thomson Reuters Investext, and zero otherwise. Information on quant analysts are obtained from Nelson Information’s Directory of Investment Research and supplemented with Thomson Reuters Investext. All-star information is retrieved from Institutional Investor Magazine. Refer to Appendix B for a detailed description of variables. Analyst data are from I/B/E/S and stock price financial accounting data is from CRSP/Compustat. T-statistics are in parentheses with heteroskedastic-consistent standard errors clustered at analyst level. Industry, year and broker fixed effects are included. *, **, and *** indicate statistical significance at the 10%, 5%, and 1%, respectively.

Quantitative

modeling Anomaly

Quantitative modeling

& Anomaly No of Quants 2.68** 3.98*** 5.18***

(2.37) (3.24) (3.30) Size -2.97* -1.67 -0.49

(-1.65) (-0.96) (-0.20) BM -43.63*** -21.48** -24.10*

(-4.44) (-2.19) (-1.75) Gexp 2.22** 2.69*** 4.16***

(2.55) (3.08) (3.30) Fexp 3.63*** 2.21* 3.35**

(3.27) (1.94) (2.28) Portsize -0.50 -0.35 0.60

(-1.15) (-0.94) (1.07) Top10 45.78*** 8.66 40.65

(3.03) (0.57) (1.64) Portgind 6.68*** -0.33 5.55**

(3.80) (-0.18) (2.21) All-star 32.29*** 20.63** 34.12***

(4.18) (2.53) (3.24) Affiliated -8.98 -11.30 -0.24

(-1.19) (-1.44) (-0.02) Past 6m return 19.86*** 8.63 13.77

(2.72) (1.20) (1.31) Ind Spec 7.41 5.62 16.45

(0.85) (0.64) (1.37) No of Analyst within Ind 0.19 -2.76*** -2.47*

(0.20) (-2.90) (-1.83) Rec Level 18.65*** 18.49*** 13.99***

(6.16) (5.91) (3.22) Optimism -7.13 2.57 -2.74

(-1.04) (0.38) (-0.29) No of Forecasts 8.11*** 7.46*** 9.92***

(8.45) (7.55) (7.57) Drop Cov -40.88*** -43.30*** -45.28***

(-5.24) (-5.47) (-3.90) Year-Month Fixed Effects Yes Yes Yes Industry Fixed Effects Yes Yes Yes Broker Fixed Effects Yes Yes Yes N 149,142 149,142 149,142

44

Table VII. Quantitative Research Reports and Abnormal Volume and Price Reactions

Panel A (B) reports abnormal volume (price) reactions to thematic quant research reports for the underlying stocks three days [0,+2] around publication dates. Information on quant analysts are obtained from Nelson Information’s Directory of Investment Research and supplemented with Thomson Reuters Investext. All-star information is retrieved from Institutional Investor Magazine. Refer to Appendix B for a detailed description of variables. Analyst data are from I/B/E/S and stock price financial accounting data is from CRSP/Compustat. T-statistics are in parentheses with heteroskedastic-consistent standard errors. Year and firm fixed effects are included. *, **, and *** indicate statistical significance at the 10%, 5%, and 1%, respectively.

Panel A: Abnormal Volume Reactions Panel B: Absolute Price Reactions Quant analyst report count 0.61*** 0.57*** 0.01*** 0.01*** (33.16) (28.39) (25.73) (18.07) Sell side analyst report count 2.56*** 2.55*** 0.07*** 0.07*** (95.41) (95.26) (92.96) (92.02) Earnings announcement day 8.79*** 8.63*** 0.11*** 0.09*** (110.15) (108.69) (48.06) (38.65) 8k filing count 3.05*** 3.09*** 0.05*** 0.05*** (60.26) (61.40) (31.80) (34.01) Firm size -0.00*** 0.00*** 0.00*** 0.00*** (-42.73) (-42.45) (14.13) (13.63) BM 0.01*** 0.01*** 0.00*** 0.00*** (12.49) (12.52) (8.71) (7.84) No of analysts -0.08*** -0.08*** -0.01*** -0.01*** (-10.84) (-11.41) (-56.33) (-55.12) Year Fixed Effects Yes No Yes No Year-Month Fixed Effects No Yes No Yes Firm Fixed Effects Yes Yes Yes Yes R2 7.65% 8.87%

12.22% 14.84%

N 5,602,599 5,602,599 5,602,599 5,602,599

45

Table VIII. Quantitative Research, Client Mutual Funds and Inflows to Anomaly Stocks

This table present results of quarterly logistic regression regressions of the effect of Quants on the likelihood of institutional clients’ stock purchases. The dependent variable equals one if the institution increases its holding in overpriced (model 1) or underpriced (model 2) stock i in quarter t, and zero otherwise. Model 1 (model 2) contains only overpriced (underpriced) stocks. Inflow takes a value of one if the quarterly flow is positive in that quarter and zero otherwise. Information on quant analysts are obtained from Nelson Information’s Directory of Investment Research and supplemented with Thomson Reuters Investext. Information on broker commission allocations are from NSAR filings. Refer to Appendix B for a detailed description of variables. T-statistics are in parentheses with heteroskedastic-consistent standard errors clustered at analyst level. Industry, and year-quarter fixed effects are included. *, **, and *** indicate statistical significance at the 10%, 5%, and 1%, respectively.

Panel A: Full Sample

Anomaly Sells Anomaly Buys

Model 1 Model 2 Inflow 12.19***

12.38***

(11.39)

(10.07) Inflow*No of Analysts with Quant -0.88**

2.46***

(-2.31)

(7.11) Inflow* No of Analysts without Quant 4.14***

-3.35***

(4.34)

(-3.77)

No of Analysts with Quant -0.94***

1.22*** (-3.36)

(4.88)

No of Analysts without Quant 0.32

-3.29*** (0.54)

(-5.97)

Mret 71.19***

104.41*** (9.56)

(12.83)

Log Fund TNA 2.90***

3.26*** (16.96)

(19.64)

Turnover Ratio 0.03

0.33*** (0.71)

(7.18)

Expense Ratio 0.73**

0.36 (2.20)

(0.91)

Log Fund age -3.82***

-2.54*** (-5.94)

(-4.06)

Log Manager Tenure 2.85***

3.19*** (7.44)

(8.64)

Size 1.40***

-2.36*** (2.73)

(-4.80)

Return 37.32***

45.04*** (27.64)

(21.45)

Turnover -0.51***

-0.41*** (-6.95)

(-3.48)

Log illiquidity -0.35

0.12

(-0.75)

(0.20)

Industry FE Yes Yes Year-Quarter FE Yes

Yes

R2 3.08%

3.00% N 431,324

442,819

46

Table VIII (continued)

Panel B. Propensity-Score Matched Sample

Anomaly Sells Anomaly Buys

Model 1

Model 2 Inflow 12.24***

12.31***

(11.41)

(10.04) Inflow*No of Analysts with Quant -0.86**

2.44***

(-2.27)

(7.06) Inflow* No of Analysts without Quant 4.03***

-3.22***

(4.20)

(-3.60)

No of Analysts with Quant -0.95***

1.23*** (-3.39)

(4.92)

No of Analysts without Quant 0.41

-3.30*** (0.68)

(-5.96)

Mret 71.19***

104.46*** (9.56)

(12.84)

Log Fund TNA 2.90***

3.26*** (17.00)

(19.62)

Turnover Ratio 0.03

0.33*** (0.71)

(7.18)

Expense Ratio 0.73**

0.36 (2.21)

(0.91)

Log Fund age -3.82***

-2.55*** (-5.95)

(-4.08)

Log Manager Tenure 2.86***

3.19*** (7.45)

(8.66)

Size 1.40***

-2.37*** (2.72)

(-4.81)

Return 37.32***

45.07*** (27.55)

(21.51)

Turnover -0.51***

-0.41*** (-6.95)

(-3.49)

Log illiquidity -0.34

0.13

(-0.74)

(0.21)

Industry FE Yes Yes Year-Quarter FE Yes

Yes

R2 3.08%

3.00% N 431,324

442,819

47

Table IX. Quantitative Research and Asset Pricing Implications of Anomalies

This table reports time-series averages of monthly Fama-MacBeth coefficient estimates. The dependent variable is monthly return (%). Information on quant analysts are obtained from Nelson Information’s Directory of Investment Research and supplemented with Thomson Reuters Investext, stock price data are obtained from CRSP and All-star information is retrieved from Institutional Investor Magazine. Refer to Appendix B for a detailed description of variables. Analyst data are from I/B/E/S. T-Statistics are Newey-West adjusted with 10 lags, and are shown in parentheses. *, **, and *** indicate statistical significance at the 10%, 5%, and 1%, respectively.

Panel A: Anomaly Variables

Oscore FP CEI NSI Accrual

NOA Mom Profit AG ROA I/A AnomalyVar -0.10*** -0.38*** -0.66*** -2.40*** -2.43*** -0.72*** 0.41*** 0.69*** -0.78*** 6.22*** -2.40***

(-4.36) (-4.15) (-9.22) (-5.51) (-8.24) (-6.64) (8.77) (6.66) (-5.86) (3.38) (-8.54) AnomalyVar*No of Analysts w Quant 0.05*** 0.20*** 0.58** 0.73* 1.51** 0.64*** -0.09*** -0.25*** 0.98** -6.75** 1.72**

(3.35) (3.29) (2.05) (1.71) (2.46) (2.64) (-4.00) (-3.41) (2.13) (-2.02) (2.23) AnomalyVar*No of Analysts w/out Quant 0.01 0.03** 0.03 0.18** -0.01 -0.01 -0.01 -0.01 0.07*** -0.96*** 0.01

(1.15) (2.35) (1.57) (2.10) (-0.16) (-0.40) (-1.11) (-0.64) (2.74) (-3.11) (0.24)

Diff (No of Analysts w and w/out Quant) 0.05** 0.17*** 0.55* 0.55 1.52** 0.64*** -0.08*** -0.24*** 0.91** -5.79* 1.70** (2.57) (2.66) (1.96) (1.12) (2.44) (2.75) (-3.34) (-2.89) (2.04) (-1.76) (2.23)

Firm-specific controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Panel B: Ranked Anomaly Variables Oscore FP CEI NSI Accrual

NOA Mom Profit AG ROA I/A Aggregate

AnomalyRank 1.06*** 0.85*** 0.40**

0.60*** 0.64*** 1.08*** 1.61*** 0.73*** 0.79*** 0.87*** 1.15*** 3.78*** (4.91) (3.31) (3.20) (3.70) (4.46) (6.57) (8.76) (4.89) (4.42) (3.42) (6.85) (12.32)

AnomalyRank*No of Analysts w Quant -0.35*** -0.49*** -0.49** -0.62*** -0.42*** -0.64*** -0.36*** -0.27*** -0.83*** -0.43*** -0.67*** -1.44*** (-3.67) (-3.35) (-2.35) (-7.30) (-2.82) (-3.96) (-3.79) (-3.37) (-4.43) (-2.94) (-2.62) (-7.12)

AnomalyRank*No of Analysts w/out

-0.08* -0.09** -0.05** -0.05* 0.01 0.03 -0.03 -0.07** -0.09** -0.18*** -0.07** -0.22*** (-1.68) (-2.45) (-2.04) (-1.89) (0.16) (0.87) (-1.21) (-2.07) (-2.04) (-3.64) (-2.08) (-3.07)

Diff (No of Analysts w and w/out Quant) -0.27** -0.40*** -0.44** -0.56*** -0.43*** -0.67*** -0.33*** -0.21** -0.74*** -0.25* -0.60** -1.22*** (-2.26) (-2.70) (-2.15) (-7.05) (-2.70) (-3.91) (-3.23) (-2.22) (-3.90) (-1.73) (-2.27) (-5.18)

Firm-specific controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

48

Table X: Quantitative Research and Asset Pricing Implications of Anomalies: Market Sentiment

This table reports time-series averages of monthly Fama-MacBeth coefficient estimates. The dependent variable is monthly return (%). All regressions include firm-specific controls. In panel B, the explanatory variables are transformed into decile ranks (before forming the interaction terms), which are then standardized to take values between zero and one. Information on quant analysts are obtained from Nelson Information’s Directory of Investment Research and supplemented with Thomson Reuters Investext. Returns are from CRSP and All-star information is from Institutional Investor Magazine. Appendix B provides variable definitions. Analyst data are from I/B/E/S. T-Statistics are Newey-West adjusted with 10 lags, and are shown in parentheses. *, **, and *** indicate statistical significance at the 10%, 5%, and 1%, respectively.

Panel A: Raw Anomalies High Sentiment Oscore FP CEI NSI NOA AG ROA

AnomalyVar -0.16*** -0.51*** -0.75*** -4.10*** -0.69*** -1.03*** 9.88*** (-5.45) (-4.26) (-6.46) (-7.30) (-4.79) (-4.35) (3.94)

AnomalyVar*No of Analysts w Quant 0.07** 0.26** 1.01* 1.42* 0.93** 1.82** -11.31* (2.29) (2.25) (1.85) (1.71) (1.98) (2.06) (-1.74)

AnomalyVar*No of Analysts w/out Quant 0.01 0.04* 0.03 0.29* 0.03 0.11** -1.07** (1.10) (1.94) (1.34) (1.83) (0.73) (2.23) (-2.14)

Diff (No of Analysts w and w/out Quant) 0.06* 0.22* 0.97* 1.13 0.90* 1.71** -10.23 (1.78) (1.90) (1.81) (1.17) (1.97) (1.99) (-1.61)

Firm-specific controls Yes Yes Yes Yes Yes Yes Yes Low Sentiment Oscore FP CEI NSI NOA AG ROA

AnomalyVar -0.05 -0.25** -0.58*** -1.05*** -0.74*** -0.53*** 2.45 (-1.65) (-2.07) (-6.32) (-3.36) (-5.06) (-6.87) (1.17)

AnomalyVar*No of Analysts w Quant 0.04*** 0.14*** 0.14*** 0.64*** 0.33*** 0.12*** -2.07*** (4.82) (6.58) (3.03) (4.39) (7.40) (2.80) (-4.67)

AnomalyVar*No of Analysts w/out Quant 0.00 0.03 0.03 -0.13** -0.05** 0.04* -0.85** (0.59) (1.43) (1.07) (-2.03) (-2.12) (1.87) (-2.40)

Diff (No of Analysts w and w/out Quant) 0.04*** 0.11*** 0.11* -0.05 0.38*** 0.09 -1.23* (2.97) (3.00) (1.68) (-0.31) (6.31) (1.53) (-1.68)

Firm-specific controls Yes Yes Yes Yes Yes Yes Yes

49

Table X (continued)

Panel B: Ranked Anomalies High Sentiment Oscore FP CEI NSI NOA AG ROA Aggregate

AnomalyRank 1.60*** 1.09*** 0.42** 1.00*** 1.04*** 0.90*** 1.30*** 2.28*** (6.04) (3.15) (2.02) (3.97) (4.68) (3.05) (4.12) (4.74)

AnomalyRank*No Analysts w Quant -0.45** -0.63** -0.83** -1.01*** -0.88*** -1.41*** -0.68** -2.07*** (-2.56) (-2.23) (-2.11) (-9.06) (-2.90) (-4.33) (-2.45) (-6.52)

AnomalyRank*No Analysts w/out Quant

-0.09 -0.11** -0.07** -0.16*** 0.01 -0.15** -0.23*** -0.13 (-1.28) (-2.02) (-2.12) (-3.75) (0.13) (-1.98) (-2.88) (-1.23)

Diff (No of Analysts w and w/out Quant) -0.36* -0.52* -0.76** -0.85*** -0.89*** -1.27*** -0.45 -1.94*** (-1.71) (-1.85) (-1.99) (-6.66) (-2.79) (-3.74) (-1.64) (-5.42)

Firm-specific controls Yes Yes Yes Yes Yes Yes Yes Yes Low Sentiment Oscore FP CEI NSI NOA AG ROA Aggregate

AnomalyRank 0.51** 0.60* 0.39*** 0.19 1.11*** 0.68*** 0.43 1.92*** (2.14) (1.84) (3.24) (1.56) (4.99) (4.25) (1.29) (5.03)

AnomalyRank*No Analysts w Quant -0.25*** -0.35*** -0.13** -0.21*** -0.39*** -0.23*** -0.17*** -0.85*** (-3.90) (-7.72) (-2.35) (-5.40) (-5.91) (-3.68) (-3.96) (-10.93)

AnomalyRank*No Analysts w/out Qaunt -0.07 -0.06 -0.03 0.05*** 0.05 -0.02 -0.12*** -0.03 (-1.44) (-1.50) (-0.82) (2.83) (1.42) (-0.63) (-3.33) (-0.48)

Diff (No of Analysts w and w/out Quant) -0.18* -0.28*** -0.11 -0.27*** -0.44*** -0.20** -0.05 -0.82*** (-1.88) (-3.56) (-1.29) (-5.07) (-4.50) (-2.13) (-0.71) (-6.98)

Firm-specific controls Yes Yes Yes Yes Yes Yes Yes Yes