1

Myopic loss aversion and the momentum premium

Paul Dochertya and Gareth Hurstb

a The University of Newcastle, NSW, Australia.

b First State Super, Sydney, Australia.

Abstract

We examine how myopic loss aversion is related to the momentum premium by evaluating

momentum using a framework of cumulative prospect theory. We show that frequent portfolio

evaluations by momentum investors provide plausible arbitrage bounds that may explain the

long-run success of the strategy given momentum investors' short-termism. We explore the

impact of myopia over an international sample, finding that momentum is stronger in countries

that are predisposed to myopia and where the proportion of funds under delegated management

is high. As investor myopia may limit investors’ ability to arbitrage away momentum returns, our

results support behavioral models of momentum.

Acknowledgements: The comments from Peter Brooke, Stephen Brown, Steve Easton, Robert

Faff, Phil Gray, and Tom Smith have been greatly appreciated. This paper has also benefited

from the comments of seminar participants at the 27th Australasian Finance and Banking

Conference, Monash University and the University of Newcastle. Research funding provided by

Platypus Asset Management is greatly appreciated.

Key Words: Short-termism; Momentum; Cumulative prospect theory.

JEL classification G11, G12, G15

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I. Introduction

By combining two behavioral biases, loss aversion and mental accounting, Benartzi and Thaler

(1995) seek to explain the ‘equity premium puzzle’. Loss aversion suggests that the disutility of

accepting a loss is greater than the increased utility from an equal sized gain (Kahneman and

Tversky, 1979; Tversky and Kahneman, 1992). Mental accounting refers to the process

individuals use to evaluate transactions (Thaler, 1985). Mental accounting suggests that the

utility from disaggregated risky outcomes is assessed differently than when that risky outcome is

assessed in aggregate. The combination of these two behavioral biases is called myopic loss

aversion. A myopic loss averse investor would invest less in a risky investment the more

frequent the performance of the investment is evaluated. Using this framework, Benartzi and

Thaler (1995) show that the equity premium puzzle can be explained when the representative

investor evaluates their portfolio annually. This paper extends Benartzi and Thaler (1995) by

showing that myopic loss aversion may also provide a plausible arbitrage bound that explains the

long-run success of the momentum investment strategy. Further we report that international

differences in the momentum premium may be explained by cross-country variation in myopia.

There is substantial evidence supporting the notion that prospect theory better explains

investment decision making than traditional mean-variance utility. Experimental evidence shows

that individuals make investment decisions consistent with myopic loss aversion (Gneezy and

Potters, 1997; Thaler, Tversky, Kahneman and Schwartz, 1997). Moreover, experimental

evidence has shown that myopic loss aversion describes the decision making processes of

professional investors (Haigh and List, 2005) and investors managing other people’s money

(Eriksen and Kvaloy, 2010). Barberis, Mukherjee and Wang (2016) find that this behavior is

reflected in the cross-section of stock returns. They report that a stock whose past return

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distribution has a high (low) prospect theory value earns a low (high) subsequent return, which is

consistent with investors evaluating the stocks past distribution of returns in the manner

described by prospect theory.

A key implication from myopic loss aversion is that investors that have a propensity to

frequently evaluate their portfolios are likely to require higher returns from these investments.

Using the results from a survey of almost 700 fund managers, Menkhoff (2011) finds that

momentum investors are characterized by their short-investment horizon. Therefore, under the

framework of myopic loss aversion, the short-investment horizons of the momentum traders may

act as a limit to arbitrage explaining the persistence of the momentum strategy. The relationship

between short-investment horizons and momentum has previously been documented at the

individual stock level. Cremers and Pareek (2015) find that stocks that are held by short-term

institutional investors generate higher momentum returns and Dasgupta, Prat and Verardo (2011)

find that investor short-termism leads to herding and a stronger momentum effect. However, as

yet there is no paper that examines the relationship between country-level measures of myopia

and variation in the magnitude of the momentum premium.

In this paper we examine whether myopic loss aversion is related to the momentum

premium around the world. Given evidence that investors’ price stocks in a manner that accords

with prospect theory (Barberis et al., 2016), an investment strategy with a distribution of returns

that has low prospective utility should generate abnormally positive future returns if evaluated

through the lens of mean-variance utility. Barberis et al. (2016) use historical returns as a

parsimonious measure of an investors’ mental representation of a stock, however, there are both

empirical and theoretical reasons why it might be expected that investors would ex ante evaluate

momentum strategy returns to have low prospective utility. It has been recently documented that

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the returns to momentum strategies are negatively skewed and experience large, infrequent

drawdowns (Daniel and Moskowitz, 2016). An investor using prospective utility to evaluate an

investment strategy would overweight the probability of these drawdowns; decreasing the utility

they derive from the momentum strategy despite the long-run positive returns of the strategy.

Given this distribution of returns, coupled with the previously identified short investment

horizons of momentum traders (Menkhoff, 2011), it is therefore proposed that the persistence in

momentum returns may at least partly be explained by momentum investors’ myopic aversion

towards large negative drawdowns. If the marginal investor employing a momentum strategy has

frequent evaluation periods and preferences that accord with cumulative prospect theory, the

high Sharpe ratio of the strategy may not be enough to compensate for the impact of the

strategy’s higher moments. We therefore argue that investor myopia acts as a limit to arbitrage

that may explain the long-run success of the momentum strategy.

Our proposition is that the key channel by which investor myopia may be a limit to

arbitrage that explains the long-run success of the momentum strategy is myopic investment

management due to agency issues. Short-term investment management incentives may result in

short evaluation horizons and myopic investment decisions (Eriksen and Kvaloy, 2010). These

agency issues result in investors evaluating their portfolios frequently and demanding a higher

premium to invest in risky assets (Benartzi and Thaler, 1995). This proposition can also be

applied within asset classes; where the open-ending of mutual funds and the performance/fund

flow relationship encourages investment managers to avoid strategies with high tracking error,

such as momentum. The short-term incentives that drive frequent portfolio evaluation should be

more pronounced for momentum managers, given the short-investment horizons of momentum

investors. Further, given myopic loss aversion may provide an arbitrage bound that explains the

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persistence of momentum returns, this limit to arbitrage should be more likely to bind in

countries that are characterized by a higher level of investor myopia.

This paper contributes to the extant literature in two ways. Our first contribution is to

examine the performance of momentum under the framework of cumulative prospect theory

across 20 countries.1 We show that under a framework of myopic loss aversion, the traditional

momentum strategy, on average, provides lower prospective utility compared to the risk-free rate

for the representative investor. Building on recent evidence suggesting that momentum

drawdowns are predictable (Daniel and Moskowitz, 2016), we also account for this predictability

by examining the explanatory power of myopic loss aversion over a risk-managed momentum

strategy (Barroso and Santa-Clara, 2015). The median evaluation period that can explain the

risk-managed momentum premium across all countries is seven months. We argue that the

marginal investor for the risk-managed strategies are institutional investors who evaluate

portfolios frequently given the nature of incentives and performance/fund flows relationship

(Berk and Green, 2004; Stein, 2005).2 The myopic loss aversion of these professional traders acts

as a limit to arbitrage for the momentum premium.

Our second contribution is to investigate whether differences in country-level myopia

might explain observed cross-sectional differences in momentum premia across international

markets.3 Both the traditional and risk-management momentum returns across countries are

1 Menkhoff and Schmeling (2006), estimate the prospective utility for the traditional momentum strategy in just the

US market. However our study differs in two distinct ways. First, we examine an international sample of 20

countries over a longer sample period that includes the momentum drawdowns of 1932 or 2009. The skewness of

the momentum strategy form Menkhoff and Schmeling (2006) (-0.94) is less negative than the skewness (-2.46) that

characterize momentum returns over our longer sample period. Second, recent evidence suggests that the significant

drawdowns in momentum are predictable; therefore we estimate the prospective utility on a recently developed risk-

managed momentum strategy. 2 We provide an argument for this hypothesis in Section V. 3 See for example Chan, Hameed and Tong, 2000; Griffin, Ji and Martin, 2001; Chui, Titman and Wei, 2010.

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shown to be related to a composite index of country-level myopia that is constructed by

combining two measures of aggregate short-termism: Hofstede’s (2001) Long-Term Orientation

Index and Cuthbertson, Hayes and Nitzsche’s (1997) measure of short-termism. We extend this

analysis by testing our proposition that the relationship between country-level myopia and

momentum can be explained by myopic investment management decisions. Consistent with our

predictions, we show that the relationship between country-level myopia and momentum is

significantly larger for those countries that have a greater proportion of funds under

management.

This paper proceeds as follows. Section II provides a description of the data used in this

paper. Section III examines the utility generated by both the traditional and risk-managed

momentum strategies under a myopic loss aversion framework. Section IV provides an

examination of whether country-level myopia is related to the cross-section of international

momentum returns. An agency-based explanation for our results is provided in Section V.

Robustness tests are presented in Section VI and Section VII provides a conclusion.

II. Data description

The momentum return data for this paper is constructed by taking a long position in the top

decile of stocks sorted by past 12-month performance ‘winners’ and a short position in the

bottom decile of stocks sorted by past 12-month performance ‘losers’. Data for the international

momentum returns and market return for the US and nineteen other countries4 for the period

4 We include all countries with data from the start of our sample period. As such, we exclude Greece, Israel and

Portugal from our sample to reduce the bias from an overweighting of the probability of a momentum drawdown

that occurred in March-April 2009. For this reason we also exclude returns prior to the start of our sample for

Australia, Canada and the US. We also exclude Ireland due to missing data within the sample.

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from July 1987 to September 2014 are obtained from Asness, Frazzini and Pedersen (2014).5

Data used to construct the returns on the reversals and value portfolios and the risk-free rate of

return are from DataStream International. In Section IV we examine the determinants of country-

level differences in the momentum premia. A detailed description of each of the variables used in

that analysis and their sources is provided in the Internet Appendix, along with descriptive

statistics for each of the variables.

A. Country-level measures of myopia

Country-level myopia is an opaque concept that has not been defined in the literature. We

construct a myopia index by combining two proxies for country-level myopia that have

previously been identified in the literature. Given culture has been shown to influence the views

and behavior of asset managers (Beckmann, Menkhoff and Suto, 2008) and can influence

differences in the degree of loss aversion across countries (Wang, Rieger and Hens, 2016), we

argue that culture may influence the degree of investor myopia within a country. Therefore, we

use Hofstede’s (2001) Long-Term Orientation Index as our first input into our instrument for

country-specific myopia.6 Hofstede (2001, p. 210) defines long-term orientation as “fostering of

virtues orientated toward future rewards” and short-term orientation as “fostering of virtues

related to the past and present”. Hofstede (2001) also argues that in business, short-term

orientated countries are more likely to focus on short-term results, like quarterly profit

announcements, relative to long-term orientated countries. A natural extension of this argument

is that long-term orientation may proxy for the frequency in which a country evaluates their

wealth against the status quo. Previous studies have identified a relationship between Hofstede’s

5 We thank the authors for making this data available. 6 The Long-Term Orientation index is obtained from http://geert-hofstede.com/national-culture.html.

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cultural dimensions and financial markets. Eun, Wang and Xiao (2015) and Chui et al. (2010)

show a relationship between Hofstede’s (2001) Individualism Index and cross-country

differences in stock price co-movements and momentum returns respectively. To date, no study

has examined whether countries with short-term orientation (low scores on the Long-Term

Orientation Index) find the momentum strategy undesirable, requiring a higher expected return to

invest in the strategy.

Given the marginal investor in financial markets may be an international investor who is

largely unaffected by domestic culture, myopia may also be measured with reference to the

extent to which expected cash flows at different time horizons are embedded within market

valuations. We capture this dimension of country-level myopia by incorporating the Cuthbertson

et al. (1997) short-termism measure into our myopia index. If investors overweight near-term

forecasts this may result in a frequent evaluation of these forecasts and therefore a frequent

evaluation of the performance of the investor’s portfolio. Therefore periods of market-wide

short-termism should coincide with periods where the tolerance for risky strategies such as

momentum is low, increasing the returns to momentum strategies. This measure of short-termism

is defined as a propensity to underweight long-term expectations in a rational valuation formula

given by the equation:

where 𝛿𝑡 is the natural log of price to dividend ratio, 𝑉𝑡 is the instantaneous market variance

calculated as the squared ex-post real market return (Campbell and Shiller, 1989), ∆𝑑𝑡 is the

natural log of a change in real dividends and 𝜌 is a linearization constant where 𝜌 =

1 1 − 𝑒𝑥𝑝�̅�⁄ , 𝛼 is a coefficient of relative risk aversion measured as the excess ex-post market

𝛿𝑡 = ∑ 𝑥𝑡𝑗+1

𝜌𝑗𝐸𝑡(𝛼𝑉𝑡+1+𝑗 − ∆𝑑𝑡+1+𝑗)

4

𝑗=0

+ 𝑥𝑡6𝜌5𝐸𝑡𝛿𝑡+5

(1)

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return scaled by the market return variance and 𝑥 is the measure of short-termism at time t where

values less than 1 indicate market-wide short-termism.

To account for the expectation operators in Eq. (1), we use forecasts of a three-variable

VAR given by:

𝒁𝒕 = 𝑨𝒁𝒕−𝟏 + 휀𝑡 (2)

where 𝑍𝑡 is a vector of variables [𝛿𝑡, ∆𝑑𝑡, 𝑉𝑡]′ and 𝐴 is a 3x3 matrix of coefficients. Forecasts

from Eq. (2) are obtained using annual data.

Cuthbertson et al. (1997) estimate Eq. (1) using a terminal term of both the five-year-

ahead forecast of the log dividend-price ratio and for an infinite series of forecasts. We adopt the

former approach to obtain a value of 𝑥 for all countries in our sample across the period July 1987

to September 2014. By applying five-year rolling windows of log dividend-price forecasts, our

measure of short-termism is able to vary across time. While country-level measures of culture,

such as long-term orientation, are expected to be reasonably stable across short time frames, it is

possible that there may be some variation across the 27-year sample period examined in this

study. A lower value of 𝑥 in any period signifies that investors are overweighting near-term

forecasts relative to long-term forecasts, hence indicating greater country-level short-termism. If

investors overweight near-term forecasts this may result in a frequent evaluation of these

forecasts and therefore a frequent evaluation of the performance of the investor’s portfolio. We

therefore propose that a negative relationship exists between short-termism and momentum

returns.

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The Spearman’s rho correlation between our two measures of myopia indicates a weak

positive correlation between these two variables (r = 0.17). This weak positive correlation

implies that while the two variables are economically similar,7 they may be capturing different

components of country-level myopia. We therefore combine these two dimensions into a

synthetic index that comprises the aggregate level of myopia in each country.8 Given we make no

a priori assumptions about the relative importance of these two measures; we normalize the two

variables at each point in time and then equally-weight as follows:

𝑀𝑌𝑗,𝑡 =1

2(𝑧𝐿𝑂𝑗,𝑡

+ 𝑧𝑆𝑇𝑗,𝑡) (3)

where 𝑀𝑌𝑗,𝑡 is the aggregate level of myopia in country j in month m and 𝑧𝐿𝑂𝑗,𝑡 and 𝑧𝑆𝑇𝑗,𝑡

are the standardized values of Hofstede’s long-term orientation index and the Cuthbertson et al.

(1997) short-termism measure for country j in month m. A larger (smaller) value of 𝑀𝑌𝑗,𝑡 implies

that a country is less (more) myopic; therefore we expect to see a negative relationship between

our measure of myopia and cross-sectional differences in country-level momentum returns.

We test the validity of our instrument for country-level myopia by examining whether it

is related with country-level turnover. Cremers and Pareek (2015) use turnover as a proxy for

short-term trading and Yan and Zhang (2009) define short-term traders according to the level of

portfolio turnover. Therefore, countries that are characterized by more myopic investors should

7 While the correlation between these two measures is only weak, possibly due to the small sample of countries in the

cross-section, there is still evidence to suggest that the two measures are economically similar. Japan in the most long-

term oriented country according to Hofstede’s index and is the second least short-term oriented country when the

Cuthbertson et al. (1997) measure is applied. Similarly, Australia is the least long-term oriented country according to

Hofstede’s index and is the fourth most short-term oriented country when the Cuthbertson et al. (1997) measure is

applied. 8 All of our analysis is also undertaken using both the Hofstede Long-Term Orientation Index and the Cuthbertson et

al. (1997) measure of short-termism individually. The results, which are reported in the Appendix, are qualitatively

similar to those reported in this study.

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exhibit higher turnover due to more frequent portfolio evaluation and associated trading. We

measure country-level turnover (𝑇𝑂𝑗,𝑡) as the market-weighted average turnover of all the

constituent stocks in country j in month t. When testing for the relationship between turnover and

myopia, we also include control variables for other factors that have been shown to be related

with turnover and liquidity costs including market size, volatility, transaction costs and

asymmetric information. The model that is estimated is specified as follows:

𝑇𝑂𝑗𝑡 = 𝛼0 + 𝛽1𝑀𝑌𝑗𝑡 + 𝛽2𝐼𝑁𝐷𝑗 + 𝛽3𝑆𝑖𝑧𝑒𝑗𝑡 + 𝛽4𝐿𝑛(𝑉)𝑗𝑡 + 𝛽5𝐿𝑛(𝑇𝑟𝑎𝑛)𝑗𝑡 +

𝛽6𝐼𝑛𝑠𝑖𝑑𝑒𝑟𝑗𝑡 + 𝛽7𝑃𝑜𝑙𝑖𝑡𝑖𝑐𝑎𝑙𝑗𝑡 + 𝛽8𝐶𝑟𝑒𝑑𝑖𝑡𝑗𝑡 + 휀𝑗𝑡 (4)

where 𝛽2𝐼𝑁𝐷𝑗 is value of Hofstede’s individualism index in country j in month t, 𝑆𝑖𝑧𝑒𝑗𝑡 is the

ratio of the total equity market capitalization to GDP, 𝐿𝑛(𝑉)𝑗𝑡 is the natural log of equity market

volatility, 𝐿𝑛(𝑇𝑟𝑎𝑛)𝑗𝑡 is the natural log of a transaction cost index, 𝐼𝑛𝑠𝑖𝑑𝑒𝑟𝑗𝑡 is an insider index,

𝑃𝑜𝑙𝑖𝑡𝑖𝑐𝑎𝑙𝑗𝑡 is a political risk index and 𝐶𝑟𝑒𝑑𝑖𝑡𝑗𝑡 is the ratio of total private credit to GDP. Each

of these variables are explained in further detail in the Internet Appendix.

The results from the estimation of Eq. (4) are reported in Table 1. As expected, there is a

positive and significant relationship between volatility and turnover, while both political risk and

the insider trading index and also related to country-level turnover, albeit at the 10% confidence

level. Of most importance, the significant coefficient on the coefficient for country level myopia

(𝑀𝑌𝑗,𝑡) provides support to the validity of our instrument as, consistent with expectations, higher

turnover is observed in more myopic countries.

[INSERT TABLE ONE HERE]

III. Momentum and myopic loss aversion

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Momentum strategies earn positive returns of about 1% per month which are unable to be

explained in a mean-variance framework (Fama and French, 1996). However, experimental

evidence suggests that prospect theory better represents investor preferences than the mean-

variance framework (Thaler et al., 1997). Further, as the expected returns to momentum

strategies are non-normal (Daniel and Moskowitz, 2016), an alternative method to measuring

investor utility that captures the higher moments of the return distribution is more appropriate.9

We follow Benartzi and Thaler (1995) and Barberis et al. (2016) who use cumulative prospect

theory to explain the equity risk premium puzzle and the cross-section of equity returns

respectively. They undertake this analysis by using Tversky and Kahneman’s (1992) nonlinear

value function:

where 𝑥 is a change in wealth relative to the status quo and 𝜆 is the coefficient of loss aversion.

Tversky and Kahneman (1992) estimate 𝜆, α and β to be 2.25, 0.88 and 0.88 respectively. Thus, a

loss has a negative impact that is 2.25 times greater in absolute terms than a gain of the same

magnitude.

The prospective utility of a game G, which pays off xi with a probability pi is given by:

𝑉(𝐺) = ∑ 𝜋𝑖𝑣(𝑥𝑖) (6)

9 Alternative utility functions, such as power utility functions, could also be used to take into account non-normality.

𝑣(𝑥) = {

𝑥∝

−𝜆(−𝑥)𝛽 𝑖𝑓 𝑥 ≥ 0𝑖𝑓 𝑥 < 0

(5)

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where 𝜋𝑖 is the decision weight associated with outcome i. Under cumulative prospect theory,

these decision weights depend on the entire cumulative distribution of G, not just the probability

of outcome i. The decision weight attached to 𝑥𝑖 is calculated as:

where Pi is the probability of obtaining an outcome at least as good as 𝑥𝑖 and P𝑖∗ is the weighted

probability of obtaining an outcome that is better than 𝑥𝑖. The probability weighting function

from Eq. (7) (ω) is also calculated using the Tversky and Kahneman (1992) approach, as

follows:

where γ is estimated to be 0.61 for increases in wealth and 0.69 for decreases in wealth. The

weighting function need not be a behavioral bias, rather an investor preference for positively

skewed assets.

Under cumulative prospect theory, large negative returns significantly reduce the level of

utility at any time horizon. The more frequently an investor evaluates these losses, the more

sensitive they are to taking risks.10 However, by evaluating the returns of a strategy less often the

negative tail events are eventually averaged out and a strategy can therefore increase utility.

Recent evidence suggests that momentum drawdowns are predictable. If investors can

reasonably predict periods where the probability of negative momentum returns are high, then

rational investors are likely to reduce their exposure to the momentum strategy, thus reducing the

impact of the drawdowns on the momentum strategy. Therefore it is only the unavoidable impact

of the momentum drawdowns that provides a limit to arbitrage for the momentum strategy. In

10 See for example Gneezy and Potters (1997); Thaler, Tversky, Kahneman and Schwartz (1997) and Haigh and List

(2005).

𝜋𝑖 = ω(P𝑖) − ω(P𝑖∗) (7)

ω(p) = 𝑝𝛾 (𝑝𝛾 + (1 − 𝑝)𝛾)1

𝛾⁄⁄ (8)

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this section we test the robustness of the explanatory power of myopic loss aversion as a limit to

arbitrage using an optimized momentum strategy that has been recently established within the

literature (Barroso and Santa-Clara, 2015). We make no claim that the risk-managed strategy

examined in this paper is the strategy used by the representative institutional investor but rather

use these results as a robustness test for the traditional momentum strategy.

We construct the risk-managed momentum strategy from Barroso and Santa-Clara (2015)

that scales the momentum strategy to have a constant annualized volatility of twelve percent

given by the following equation:

�̂�𝑊𝑀𝐿,𝑡𝐶𝑉 =

𝜎𝑡𝑎𝑟𝑔𝑒𝑡

�̂�𝑡𝑟𝑊𝑀𝐿,𝑡 (9)

where 𝑟𝑊𝑀𝐿,𝑡 is the return to the traditional unscaled momentum, 𝜎𝑡𝑎𝑟𝑔𝑒𝑡 is consistent with

Barroso and Santa-Clara (2015) at an annualized twelve percent, �̂�𝑊𝑀𝐿,𝑡𝐶𝑉 is the return to the scaled

momentum strategy and �̂�𝑡 is given by the equation:

where �̂�𝑊𝑀𝐿,𝑡2 is the one period ahead volatility estimate and 𝑟𝑊𝑀𝐿,𝑑𝑡−1−𝑗

2 is the squared daily

momentum return.

Fig. 1 reports the descriptive statistics for the returns generated by both the traditional

and risk-managed strategies. Panel A (Panel B) report the mean returns (standard deviation of

returns) for both the traditional momentum and the risk-managed strategy. For eighteen (sixteen)

countries, the risk-managed strategy increased (decreased) the mean returns (standard deviation)

of the traditional momentum strategy. In unreported results, the risk-managed strategy was

shown to only increase the maximum return in three countries (Spain, Japan and US), whereas

�̂�𝑊𝑀𝐿,𝑡2 = 21 ∑ 𝑟𝑊𝑀𝐿,𝑑𝑡−1−𝑗

2

125

𝑗=0

/126 (10)

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the minimum return was improved for all countries. This result suggests that extreme returns

occur during periods when ex-ante volatility is high where the weighting on momentum is less

than 1. Consistent with this result, Panel C shows that the returns on the traditional momentum

strategy are negatively skewed in nineteen of the countries and the risk-managed strategy

reduces this negative skew in all countries except Japan. Similarly, Panel D shows that the

returns on the risk-managed strategy are less leptokurtic compared with the traditional

momentum strategy. Given the improved returns and reduced standard deviations, the Sharpe

ratios of all countries are improved by adopting the risk-managed momentum strategy, however

in unreported results the Jarque-Bera statistic shows that the distribution of returns are not

normally distributed for all countries except Austria and New Zealand, limiting the efficacy of

the Sharpe ratio as an evaluation tool unless we assume quadratic utility.11

[INSERT FIGURE 1 HERE]

Table 2 reports the prospective utility of both the traditional and risk-managed

momentum strategies over different evaluation periods across 20 countries. Excess prospective

utility is reported in bold for the minimum evaluation periods over which the strategy yields

higher utility than an investment at the risk-free rate. Panel A shows that in only eight countries

(Australia, Canada, Germany, Denmark, Finland, Great Britain, Norway and New Zealand) does

the prospective utility of the traditional momentum strategy exceed the risk-free rate for

evaluation periods of up to twelve-months, while the median evaluation period across all

countries at which the prospective utility of the momentum strategy exceeds the risk-free rate is

fourteen-months. As Benartzi and Thaler (1995) argue that the representative investor evaluates

11 Barberis and Huang (2008) demonstrate that cumulative prospect theory yields different results to the CAPM with

non-normal data. The Spearman rho correlation between one-month excess prospective utility and the alpha

estimated from the CAPM is 0.05 (p-value = 0.84).

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their portfolio every twelve-months, Table 2 provides evidence that suggests the traditional

momentum strategy is not desirable to the average investor. This result is particularly noteworthy

in the context of evidence that suggests momentum investors are short-term orientated and hence

may be expected to evaluate their portfolios with greater frequency (Menkoff, 2011). The results

reported in Table 2 do not necessarily explain the momentum premium in equilibrium for a

rational investor, as it is possible that momentum may be used in conjunction with other trading

strategies (see for example Asness, Moskowitz and Pedersen; 2013) or that momentum

drawdowns, such as those evidenced in July and August 1932 and March to May 2009, driving

the negative skew of the momentum strategy returns can actually be avoided. In this context the

traditional momentum strategy is inefficient as rational investors would avoid exposure to the

momentum strategy when the probability of a momentum drawdown is high, thus increasing the

expected returns of the strategy.

Panel B shows that the Barroso and Santa-Clara (2015) risk-managed strategy improves

the prospective utility of all countries; however the excess prospective utility is negative in all

but four countries if investors evaluate their portfolios at least quarterly and the median

evaluation period at which the prospective utility of the risk-managed strategy exceeds the risk-

free rate is seven-months. Barroso and Santa-Clara (2015) argue that the marginal investor for

risk-managed strategies is an institutional investor. As there is significant evidence to suggest

that institutional investors are likely to evaluate their portfolios more frequently than the average

investor, it may be contended that the evaluation period that these investors would be indifferent

between investing in the risk-free rate and the risky momentum strategy should be at a shorter-

term horizon. Where information is provided at frequent intervals investors are likely to focus on

recent past performance while neglecting the long-run performance of the strategy. Our results

17

demonstrate that if institutional investors are rewarded at quarterly intervals or if individual

investors are free to withdraw from a fund at frequent intervals, the strategy becomes

undesirable; requiring a higher rate of return to induce investment. Under this framework,

preferences of the myopic loss averse investor provides limits to arbitrage for the momentum

premium. An alternative explanation for the cumulative prospect utility of the risk-managed

shown in Panel B of Table 2 is in line with the view that investors learn about mispricings from

academic studies (McLean and Pontiff, 2016). Given that Barroso and Santa-Clara (2015) was

published at the end of our sample period, the mispricing associated with this investment strategy

may become lower in the post-publication period, and hence the evaluation period required to

explain risk-managed momentum returns may become longer in future samples.

[INSERT TABLE 2 HERE]

IV. Country-level myopia and momentum

The results reported in Section III demonstrate substantive variability in momentum profitability

across our international sample. These differences are consistent with previous studies that have

found that the drivers for US based momentum strategies do not necessarily hold in an

international sample (see for example Antoniou, Lam, and Paudyal, 2007). We examine whether

a possible explanation for this variation is cross-country differences in the level of country-wide

myopia.

18

Consistent with Chui et al. (2010), we estimate the explanatory power of country-level

myopic loss aversion by using cross-sectional regressions that test the relationship between

momentum returns and country-level myopia after controlling for variables that have been shown

to explain momentum returns:

where 𝑀𝑜𝑚𝑗𝑡 is the country specific momentum return at time t, 𝑀𝑌𝑗𝑡 is our proxy for

myopia in country j, 𝐹𝑗 is a vector of static control variables, 𝐴𝑗𝑦 is a vector of control variables

that update annually and 𝑀𝑗𝑡 is a vectors of control variables that update monthly. These control

variables are described in the Internet Appendix and relate to four different models of comprising

competing explanations for momentum from previous studies; a behavioral model, a market

development model, an institutional quality model and a macro economic model. The results

from the estimation of Eq. (11) for each of these models are reported in Table 3.

A. Behavioral model

Given a number of studies suggest that behavioral factors may explain the momentum premium,

we include a model that captures differences in the asymmetry and diffusion of information

across markets. Our behavioral model consists of the following variables: the natural log of

market turnover (Ln TN) (Lee and Swaminathan, 2000), natural log of market capitalization of

the market (Ln SZ) (Zhang, 2006), return volatility (Ln V) (Zhang, 2006), past three-years market

return (PastRet) (Cooper, Gutierrez and Hameed, 2004), cash flow volatility (Cfvol) (Chui et al.

2010), analyst forecast error (Afe) (Dhaliwal, Radhakrishnan, Tsang and Yang, 2012) and

individualism (Ind) (Chui et al. 2010).

𝑀𝑜𝑚𝑗𝑡 = 𝛼0 + 𝛽1𝑀𝑌𝑗𝑡 + 𝛾1𝐹𝑗 + 𝛾2𝐴𝑗𝑦 + 𝛾3𝑀𝑗𝑡 + 휀𝑗𝑡 (11)

19

The results of the behavioral model cross-sectional regression reported in Panel A of

Table 3 show that the coefficient on myopia is negative and significant at the 1% level. The other

variables that are significant are Ind, Ln TN and Afe. The coefficient on Ind is positive, consistent

with Chui et al. (2010) who argues that the higher individualism may reflect a culture of more

overconfident and self-attribution-biased investors, thereby increasing the impact of these

behavioral biases that generate momentum returns. The significance of both the MY and Ind

coefficients indicates that country-level myopia is a determinant of momentum returns that is

separate from individualism. This result supports our proposition that the degree of country-level

short-termism provides a measure related to investor myopia as a limit to arbitraging away

momentum, and is not simply an empirical test of existing behavioral theories. The coefficient on

the natural log of turnover is negative suggesting that countries and periods where turnover is

higher are associated with lower momentum returns and the coefficient on the average analyst

forecast error is positive, suggesting that countries and periods where there are larger errors to

analyst forecasts are associated with higher momentum returns. The significance of both of these

coefficients may be viewed as further evidence that limits to arbitrage prevent rational investors

from exploiting the momentum premia; as these results suggest that momentum returns are

stronger in markets and periods where the level of liquidity is lower and information

asymmetries are higher.

Panel B reports the results of the behavioral model with risk-managed momentum as the

dependent variable. We note that myopia is negative and significant at the 1% level. Other than

Ind, Ln TN, and Ln SZ both measures of volatility are negative and significant which is

consistent with Chui et al. (2010), however Afe is no longer significant.

B. Market development model

20

Our market development model is designed to capture the informational efficiency of a country’s

financial markets (Chui et al. 2010). Our control variables include the ratio of total private credit

to GDP (Credit), an index on capital flow restrictions (Control) suggested by Stulz and

Williamson (2003), the average common language variable dummy (Lang) used by Chan, Covrig

and Ng (2005) and the ratio of total equity market capitalization to GDP (EquityGDP) suggested

by Dou, Truong and Veeraraghvan (2015).

Panel C and Panel D reports the results of the market development model for the

traditional and risk-managed momentum strategies respectively. For both the traditional and risk-

managed momentum strategies, the coefficient on the myopia variable is negative and

significantly different from zero. The coefficient on Control is positive and significant at the 1%

level suggesting that greater levels of capital controls are associated with higher momentum

returns. This result is consistent with the informational efficiency gains from a reduction in

capital controls (Bae, Ozoguz, Tan and Wirjanto, 2012). An increase in informational efficiency

(decrease in Control) should enable investors to make more informed stock valuations and

thereby arbitrage away momentum profits if they occurred as a result of behavioral mispricing.

C. Institutional quality model

Higher institutional quality improves market integrity and informational efficiency. We follow

Chui et al. (2010) and construct an institutional quality model that includes the indices of insider

trading prevalence (Insider) and investor protection (Protection) used by La Porta, Lopez-De-

Silanes and Shleifer (2006), a corruption (Crp) and political risk index (Political) used by Chui

et al. (2010) and the natural log of transaction costs (Ln Tran) used by Chan et al. (2005).

21

The results of the institutional quality model for the traditional and risk-managed

momentum strategies are reported in Panel E and F respectively. The coefficient on myopia is

negative and significant at the 1% level in both panels, whilst the insider index Insider is

significant at the 5% level in both models, while the investor protection Protection measures are

significantly related to only the traditional momentum strategy returns. These results suggest that

momentum returns are higher in countries that have a greater prevalence of insider investors and

lower standards of investor protection; the existence of which might be expected to present

frictions that prevent rational investors from arbitraging away momentum profits.

D. Macroeconomic model

There is evidence that suggests exposure to macroeconomic risk may explain momentum returns

(Chordia and Shivakumar, 2002). Our macroeconomic model includes the yield on 3-month

short-term securities (Yld), the term spread (Term) and the dividend yield (Dy).

We examine the relationship between myopia and macroeconomic variables in Panel G

and Panel H for the traditional and risk-managed momentum respectively. For both the

traditional and risk-managed momentum the coefficient on short-termism is negative and

significant at the 1% confidence level. However, none of the control variables are significantly

related to either the traditional or risk-managed momentum strategies.

Taken as a whole, the results reported in Table 3 provide two key implications. First,

given the coefficient on the myopia variable is negative and significant across all four models,

supporting the contention that country-level myopia is related to momentum profitability. This

result is a natural extension of the implications from myopic loss aversion, as shorter-term

orientated countries would comprise investors who evaluate their portfolios more frequently and

22

hence market participants would be less inclined to arbitrage away momentum profits in those

countries. Second, the coefficients on individualism, turnover, capital controls and an insider

index are all related to country-level variation in both traditional and risk-managed momentum

returns. Each of these variables are representative of either behavioral biases or limits to

arbitrage that prevent biases from being eliminated by rational investors. Therefore, the evidence

reported in this table can be taken to support behavioral explanations for the momentum

premium. Of note, in the models that include macroeconomic controls none of the coefficients

are significant; which may be viewed as supporting the argument that momentum can be

explained by behavioral, as opposed to rational forces (Griffin, Ji and Martin, 2003).

[INSERT TABLE 3 HERE]

There may be implications for the returns on other investment strategies from our

proposition that myopic loss aversion could provide plausible arbitrage bounds that explain the

long-run performance of the momentum investment strategy. Behavioral models of momentum

tend to be based on a combination of investor underreaction to news and overreaction to prices; a

notion that supports the empirical observation that momentum returns mean revert over the long-

run. If our proposition is true that investor myopia acts as an arbitrage bound that limits the

exploitation of momentum returns by rational investors, then it might be expected that long-run

reversals are also stronger in countries that exhibit higher levels of myopia. This argument is

consistent with Chui et al., (2010), who suggest that if individualism is related to behavioral

explanations of momentum returns, then cross-country differences in individualism should also

explain variation in the returns of long-run reversals. While our aim is not to distinguish between

competing behavioral explanations for why stock returns exhibit momentum and subsequent

long-run reversals, the observed profitability of these strategies over a long time-series is only

23

plausible where limits to arbitrage can be identified that prevent rational investors from

exploiting behavioral mispricing.

To construct the portfolio of reversals we form portfolios based on their returns over the

period from sixty months to thirteen months prior to the portfolio formation period. The reversal

returns comprise the zero investment portfolio that takes a long position in the lowest quartile of

past returns and a long position in the highest quartile of past returns. We also collect

international returns for the value strategy and the market risk premium from Asness, Frazzini

and Pedersen (2014) to examine whether country-level myopia can pervasively explain all risky

investment strategies. Eq. (11) is then re-estimated with returns on each of these premia as the

dependent variable.

The results reported in Table 4 show the relationship between long-term orientation and

variation in international returns on the reversals strategy. Results for the relationship between

value and the market risk premium and country-level myopia are reported in the Internet

Appendix. Consistent with the notion that country-level myopia provides an international

measure of how frequently investors evaluate their portfolio across countries and hence a

measure of the magnitude of the limits to arbitrage under myopic loss aversion, there is evidence

of a negative relationship between country-level myopia and reversals across all models. In

contrast, myopia is not related to cross-country variation in the value premium or the market risk

premium across any of the models.12 This result can be interpreted to mean that investor myopia

is a limit to arbitrage that may explain the momentum premium, supporting behavioral

12 The different results generated by the reversals and value strategies can be attributed to cross-country differences

in their unconditional means. While the time-series correlation between the US value and reversals strategies across

our sample is 0.29, the cross-country correlation between means is only 0.09.

24

explanations for the momentum anomaly, however cannot be used to explain returns across all

risky investment strategies.

[INSERT TABLE 4 HERE]

V. Agency issues and myopia

Recent evidence suggests that agency-induced preferences of institutional investors play a causal

role with equity market mispricing (Edelen, Ince and Kadlec, 2016). An agency interpretation for

our results is consistent with evidence regarding known agency conflicts associated with

institutional investing, including excessive turnover (Chalmers, Edelen and Kadlec, 1999) and

risk shifting (Huang, Sialm and Zhang, 2011). Extending on this evidence, it is reasonable to

propose that momentum investors exhibit a high degree of myopic loss aversion due to agency

conflicts. Fellner and Sutter (2009) find that two forces drive the extent of myopic loss aversion.

The first is the flexibility of investment horizon. An investor with flexibility to change

investment resources will focus less on the long-term performance and be more influenced by

past returns. The second force is the frequency of information feedback of performance.

Institutional investors have been shown to be momentum investors in aggregate (Edelen

et al., 2016). Experimental evidence shows that institutional investors tend to behave like myopic

loss averse traders (Haigh and List, 2005). This evidence is supported by Stein (2005) who

proposes a model where the open-ending of mutual funds, hence the flexibility of investors to

change their investment resources, encourages fund managers to invest more cautiously;

reducing the desirability of risky arbitrage strategies, such as momentum. Evidence of window

dressing supports the argument that mandatory reporting, hence increased frequency of

25

information feedback, causes frequent portfolio evaluation and resultant trading activity.13 Given

momentum investors are characterized by their short-term investment horizon (Menkoff, 2011),

the frequency of evaluation of momentum funds and hence flows of funds should be shorter than

for those that adopt other investment strategies.

Incentives for frequent evaluation by professional investors may exist if investors are

sensitive to fund performance. If the compensation schemes of mutual fund managers is a

function of assets under management and mutual fund investors are sensitive to past

performance, then mutual fund managers may consider a strategy that is successful in the long-

run but volatile in the short-run to be unappealing.14 These agency issues will result in investors

being averse to investment strategies that have a high tracking error.

A testable implication of our proposition that agency issues within the delegated funds

management industry are a key channel by which myopic loss aversion limits arbitrageurs from

eliminating momentum is that the relationship between the level of myopia and momentum

returns should be stronger in those countries that have a more prominent funds management

industry. Therefore, we augment Eq. (11) with two additional variables: the ratio of funds under

management to equity market capitalization and an interaction term between this ratio and the

short-termism measure. Given the funds under management variable is positive definite, the

coefficient on the interaction term in each of the regressions would be negative and significant if

the relationship between myopia and momentum was stronger in countries with a greater

proportion of funds under delegated management. These regressions are specified as follows:

13 See for example Lakonishok, Shleifer, Thaler and Vishney (1991); Musto (1999); He, Ng and Wang (2004). 14 High water marks may also reduce the desirability of momentum strategies. It took over 30-years for the high water

mark set prior to the momentum drawdown in 1932 to be reached again by the traditional momentum strategy (13-

years for the risk-managed strategy).

26

where 𝐹𝑈𝑀𝑗𝑡 is the ratio of funds under management to the total market capitalization in country

j at time t.

The results from the estimation of Eq. (12) are reported in Table 5. Of note, the funds

under management variable is positive and significant across all four models. This result

provides country-level evidence that is consistent with Cremers and Pareek (2015), who report

that the presence of short-term institutional investors increases momentum profitability. The

interaction term between funds under management and myopia is negative and significant at the

5% level across three models and is negative and significant at the 10% level for the behavioral

model. Therefore, the relationship between country-level short-termism and momentum is

amplified by the proportion of funds under management in that country; this relationship

becomes more negative in countries with a high proportion of funds under management. This

result supports an agency argument as being the channel by which myopic loss aversion creates a

limit to arbitraging away momentum.

[INSERT TABLE 5 HERE]

VI. Additional robustness checks

A. Comprehensive model

It may be of interest to consider whether the measures of country-level short-termism remain

significant after controlling for all of the variables instead of estimating separate models for each

group of controls. However, incorporating all of the variables together may result in limited

degrees of freedom given our panel only comprises 20 countries. Further, it may be argued that

the control variables in each of the models capture common variation associated with the various

𝑀𝑜𝑚𝑗𝑡 = 𝛼0 + 𝛽1𝑀𝑌𝑗𝑡 + 𝛽2𝑀𝑌𝑗𝑡. 𝐹𝑈𝑀𝑗𝑡 + 𝛽3𝐹𝑈𝑀𝑗𝑡 + 𝛾1𝐹𝑗 + 𝛾2𝐴𝑗𝑦 + 𝛾3𝑀𝑗𝑡 + 휀𝑗𝑡 (12)

27

determinants of the momentum premium, and hence the estimation of the standard errors in the

regression results may be affected by multicollinearity. To estimate a comprehensive model that

is not affected by limited degrees of freedom or multicollinearity, we use principal components

analysis to extract common factors from the eighteen control variables. This analysis, which is

available from the authors on request, shows that there are seven principal components with an

Eigenvalue greater than one. We use the component weightings of these seven principal

components to extract orthogonal factors and use them as a comprehensive model that

simultaneously includes all of the control variables by estimating the following regression:

where 𝑃𝑗𝑡 is a vector of the extracted principal components that update monthly.

The results from the estimation of the comprehensive model that includes the extracted

factors described above as control variables are reported in Table 6. The coefficient on the

myopia variable is negative and statistically significant, indicating that its relationship with

momentum returns is robust to the inclusion of all of the determinants of international

momentum profitability that have been discussed in the previous literature. The third principal

component is also a significant determinant of cross-country momentum returns. This factor

loads most heavily on measures of market development. Similarly, the myopia measure is also

negative and significant for the returns on the risk-management momentum strategy. After

adjusting for multicollinearity, none of the control variables used to explain momentum returns

in the previous literature are related to variation in risk-managed momentum returns. A plausible

explanation for this result is that the returns to this strategy are anomalous and have not been

𝑀𝑜𝑚𝑗𝑡 = 𝛼0 + 𝛽1𝑀𝑌𝑗𝑡 + 𝛾1𝑃𝑗𝑡 + 휀𝑗𝑡 (13)

28

eliminated in the past given the efficacy of this investment strategy was only recently published,

consistent with the argument put forward by McLean and Pontiff (2016).

Taken as a whole, the results reported in Table 6 show that our main result of a

relationship between short-term orientation and momentum returns is robust to alternative

specifications of the control variables and potential issues with multicollinearity.

[INSERT TABLE 6 HERE]

B. Excluding East-Asian countries

Given Asian countries tend to be more long-term orientated and have weak momentum returns,

we examine whether our results hold outside of this region. Eq. (11) is re-estimated after

excluding East-Asian countries from the sample. The results are shown to be robust to the

exclusion of East-Asian countries, as the coefficient on the myopia index variable remains

negative and significant across all four regression specifications and the direction and

significance of the control variables are qualitatively similar to the results reported in Table 3.

The results of these regressions are reported in the Internet Appendix.

C. Alternative specifications of country-level myopia

As discussed in Section II.A, our measure of myopia combines two proxies for country-level

short-termism that have been identified in the literature. In this section, we examine whether our

results are robust to the use of either Hofstede’s Long-Term Orientation Index or the

Cutherbertson et al. (1997) short-termism measure as a stand-alone proxy for myopia. As shown

in the Internet Appendix, our results are qualitatively to those reported in Table 3 when either of

these proxies are employed, as the coefficient on the myopia measure is negative and significant

29

in all regressions except the market development model for the Long-Term Orientation measure

and across all four models for the Cutherbertson et al. (1997) short-termism measure.

VII. Conclusion

Momentum strategies appear anomalous when evaluated from the perspective of long-horizon

mean-variance investors; however there is considerable experimental and field evidence to

suggest that investor preferences accord more with prospect theory. Under the framework of

myopic loss aversion, short evaluation periods may act as a limit to arbitrage and explain the

long-run persistence to the returns of momentum strategies. Menkhoff (2011) has recently shown

that momentum traders are characterized by their short-investment horizons. These short-

investment horizons may result in frequent evaluation of the performance of momentum

strategies. Extending the methodology of Benartzi and Thaler (1995) we find that a myopic loss

averse investor is indifferent between the traditional momentum strategy and an investment in

the risk-free rate for the median country (United States) with evaluation periods of fourteen

(seventeen) months. As Benartzi and Thaler (1995) argue that the average investor evaluates

their portfolios every twelve-months our results provide a plausible arbitrage bound for the

premium of the traditional momentum strategy.

Given observed cross-country differences in momentum profitability, we examine

whether international differences in the degree of myopia can explain momentum returns. Using

a proxy for country-level myopia, we show that more myopic countries have a larger momentum

premium. We argue that the key channel by which investor myopia may be a limit to arbitrage

that explains the long-run success of the momentum strategy is myopic investment management

due to agency issues that are unique to momentum investors. Consistent with this argument we

30

show that the relationship between country-level myopia and momentum is significantly larger

for those countries that have a greater proportion of funds under management.

Our results shed light on the growing literature that examines the impact of country-

specific factors on asset returns. These results suggest that future research into international

momentum returns take myopia into account. Given we have identified a plausible limit to

arbitrage that may explain why seemingly anomalous momentum returns differ across countries

and have not been eliminated across time, our results can also be interpreted as providing support

for behavioral models of momentum profitability.

31

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Figure 1: International Momentum Moments

This figure reports the percentage mean (Panel A), standard deviation (Panel B), skewness (Panel C) and kurtosis

(Panel D) for our international sample. Descriptive statistics for the traditional momentum strategy are shown with

solid fill and the results for the risk-managed strategy are reported as striped bars. The sample period is 1987:07-

2014:09.

Panel A: Mean traditional momentum and risk-managed momentum returns

Panel B: Standard deviation of returns for traditional and risk-managed momentum

0

5

10

15

20

25

30

AUS AUT BEL CAN CHE DEU DNK ESP FIN FRA GBR HKG ITA JPN NLD NOR NZL SGP SWE USA

Traditional Momentum Risk-Managed

0

5

10

15

20

25

AUS AUT BEL CAN CHE DEU DNK ESP FIN FRA GBR HKG ITA JPN NLD NOR NZL SGP SWE USA

Traditional Momentum Risk-Managed

35

Panel C: Skewness of returns for traditional and risk-managed momentum

Panel D: Kurtosis of returns for traditional and risk-managed momentum

-4

-3

-2

-1

0

1

2

3

AUS AUT BEL CAN CHE DEU DNK ESP FIN FRA GBR HKG ITA JPN NLD NOR NZL SGP SWE USA

Traditional Momentum Risk-Managed

0

5

10

15

20

25

AUS AUT BEL CAN CHE DEU DNK ESP FIN FRA GBR HKG ITA JPN NLD NOR NZL SGP SWE USA

Traditional Momentum Risk-Managed

36

Intercept -3.67 (-1.25)

MY -0.12 (-2.49)*

Ind -0.00 (-0.20)

Size 0.36 (2.58)**

Ln(V) 19.35 (6.89)**

Ln(Tran) 0.39 (1.15)

Insider -0.67 (-0.92)

Political 0.02 (1.26)

Credit -0.00 (-0.25)

Adj. R2 0.251

Table 1: Turn-over and country-level myopia

Monthly values for value-weighted country-level turnover are regressed on the myopia index and a series of

controls. The control variables are the ratio of market capitalization to GDP (Size), natural log of stock

market volatility (LnV ), natural log of transaction cost index (Ln Tran), an insider index (Insider), the ICRG

political risk index (Political) and total private credit expressed as a ratio of GDP (Credit). White period

standard errors are used to compute the t-statistics which are reported in parenthesis. * Denotes significant at

the 5% level, ** denotes significant at the 1% level. The sample period is 1987:07-2014:09.

37

Panel B: Risk-managed Momentum

Table 2: International Prospective Utility

In this table we report the excess prospective utility (in utils) for our international sample for each evaluation

period. Positive excess prospective utility (in bold) represents evaluation periods where the momentum strategy

is preferable to an investment at the risk-free rate. Panel A reports the results for the traditional momentum

strategy. Panel B reports the results for the risk-managed strategy. The sample period is 1987:07-2014:09.

Panel A: Traditional Momentum

1 2 3 4 5 6 7 8 9 10 11 12

AUS -0.0118 -0.0109 0.0005 0.0136 0.0261 0.0394 0.0537 0.0673 0.0816 0.0958 0.1093 0.1236

AUT -0.0474 -0.0648 -0.0761 -0.0838 -0.0868 -0.0906 -0.0927 -0.0929 -0.0932 -0.0923 -0.0897 -0.0887

BEL -0.0350 -0.0420 -0.0447 -0.0477 -0.0442 -0.0417 -0.0406 -0.0373 -0.0342 -0.0336 -0.0291 -0.0244

CAN -0.0273 -0.0273 -0.0206 -0.0089 0.0032 0.0145 0.0266 0.0410 0.0495 0.0536 0.0622 0.0750

CHE -0.0305 -0.0364 -0.0321 -0.0276 -0.0293 -0.0286 -0.0271 -0.0220 -0.0129 -0.0074 -0.0046 -0.0016

DEU -0.0275 -0.0266 -0.0232 -0.0127 -0.0062 -0.0019 0.0037 0.0162 0.0239 0.0345 0.0373 0.0464

DNK -0.0293 -0.0316 -0.0274 -0.0239 -0.0188 -0.0124 -0.0049 0.0038 0.0144 0.0244 0.0355 0.0470

ESP -0.0444 -0.0542 -0.0593 -0.0631 -0.0663 -0.0674 -0.0655 -0.0636 -0.0610 -0.0587 -0.0560 -0.0514

FIN -0.0385 -0.0438 -0.0363 -0.0244 -0.0191 -0.0120 -0.0031 0.0052 0.0098 0.0158 0.0233 0.0343

FRA -0.0351 -0.0438 -0.0432 -0.0399 -0.0374 -0.0380 -0.0431 -0.0414 -0.0392 -0.0354 -0.0375 -0.0359

GBR -0.0317 -0.0394 -0.0391 -0.0337 -0.0283 -0.0267 -0.0234 -0.0150 -0.0097 -0.0040 -0.0029 0.0011

HKG -0.0504 -0.0700 -0.0797 -0.0852 -0.0915 -0.1020 -0.1086 -0.1098 -0.1093 -0.1116 -0.1098 -0.1058

ITA -0.0354 -0.0443 -0.0442 -0.0421 -0.0429 -0.0479 -0.0517 -0.0507 -0.0488 -0.0464 -0.0469 -0.0491

JPN -0.0451 -0.0609 -0.0703 -0.0759 -0.0804 -0.0829 -0.0853 -0.0904 -0.0905 -0.0941 -0.0982 -0.1030

NLD -0.0446 -0.0627 -0.0732 -0.0784 -0.0841 -0.0886 -0.0931 -0.0937 -0.0968 -0.0970 -0.0981 -0.0990

NOR -0.0326 -0.0371 -0.0355 -0.0343 -0.0306 -0.0222 -0.0159 -0.0075 0.0043 0.0166 0.0200 0.0316

NZL -0.0241 -0.0228 -0.0213 -0.0148 -0.0072 -0.0005 0.0115 0.0209 0.0328 0.0431 0.0507 0.0603

SGP -0.0602 -0.0836 -0.0971 -0.1077 -0.1151 -0.1229 -0.1286 -0.1358 -0.1407 -0.1459 -0.1510 -0.1528

SWE -0.0467 -0.0586 -0.0595 -0.0297 -0.0612 -0.0597 -0.0575 -0.0600 -0.0576 -0.0591 -0.0625 -0.0737

USA -0.0386 -0.0520 -0.0554 -0.0562 -0.0571 -0.0574 -0.0573 -0.0562 -0.0563 -0.0590 -0.0641 -0.0656

AUS -0.0054 0.0064 0.0266 0.0480 0.0700 0.0919 0.1122 0.1336 0.1552 0.1751 0.1943 0.2142

AUT -0.0223 -0.0255 -0.0260 -0.0258 -0.0247 -0.0228 -0.0175 -0.0103 -0.0048 0.0024 0.0093 0.0148

BEL -0.0185 -0.0169 -0.0107 -0.0053 -0.0009 0.0034 0.0103 0.0180 0.0260 0.0316 0.0400 0.0483

CAN -0.0132 -0.0016 0.0165 0.0340 0.0515 0.0713 0.0905 0.1116 0.1320 0.1493 0.1706 0.1945

CHE -0.0187 -0.0175 -0.0055 0.0037 0.0094 0.0151 0.0218 0.0302 0.0383 0.0453 0.0511 0.0561

DEU -0.0168 -0.0075 0.0016 0.0139 0.0238 0.0317 0.0410 0.0531 0.0642 0.0763 0.0885 0.1021

DNK -0.0173 -0.0151 -0.0082 0.0007 0.0095 0.0190 0.0281 0.0384 0.0485 0.0592 0.0713 0.0831

ESP -0.0272 -0.0290 -0.0282 -0.0262 -0.0261 -0.0235 -0.0187 -0.0153 -0.0117 -0.0070 -0.0028 0.0037

FIN -0.0203 -0.0205 -0.0177 -0.0114 -0.0075 -0.0032 0.0014 0.0082 0.0130 0.0175 0.0234 0.0312

FRA -0.0224 -0.0224 -0.0159 -0.0055 0.0025 0.0098 0.0134 0.0188 0.0288 0.0419 0.0527 0.0646

GBR -0.0072 0.0066 0.0263 0.0506 0.0726 0.0921 0.1094 0.1290 0.1497 0.1743 0.1975 0.2218

HKG -0.0272 -0.0296 -0.0247 -0.0182 -0.0140 -0.0133 -0.0078 0.0011 0.0097 0.0195 0.0316 0.0443

ITA -0.0262 -0.0253 -0.0193 -0.0121 -0.0076 -0.0077 -0.0076 -0.0027 0.0021 0.0076 0.0122 0.0170

JPN -0.0472 -0.0614 -0.0685 -0.0708 -0.0743 -0.0754 -0.0778 -0.0849 -0.0879 -0.0909 -0.0912 -0.0884

NLD -0.0230 -0.0313 -0.0333 -0.0319 -0.0308 -0.0307 -0.0300 -0.0277 -0.0247 -0.0210 -0.0195 -0.0138

NOR -0.0146 -0.0152 -0.0138 -0.0139 -0.0119 -0.0085 -0.0040 0.0016 0.0083 0.0158 0.0212 0.0295

NZL -0.0154 -0.0099 -0.0041 0.0032 0.0108 0.0178 0.0271 0.0353 0.0449 0.0553 0.0628 0.0718

SGP -0.0366 -0.0463 -0.0478 -0.0481 -0.0470 -0.0455 -0.0432 -0.0413 -0.0395 -0.0367 -0.0328 -0.0283

SWE -0.0233 -0.0274 -0.0239 -0.0183 -0.0127 -0.0108 -0.0087 -0.0055 0.0007 0.0062 0.0100 0.0159

USA -0.0214 -0.0159 -0.0035 0.0107 0.0250 0.0407 0.0551 0.0710 0.0854 0.1004 0.1111 0.1276

38

Table 3: Momentum and Synthetic Index of Myopia

Monthly returns on country specific momentum and risk-managed momentum portfolios are regressed on a synthetic index used to measure myopia and a set of explanatory

variables. Panel A and Panel B reports the results of the regressions of momentum and risk-managed momentum respectively on variables including Hofstede’s Individualism

Index (Ind), natural log of market trading volume (Ln TN), natural log of stock market volatility (Ln V), natural log of market capitalization (Ln SZ), past three-year market

returns (PastRet) and the average analysts forecast error (Afe). Panel C and Panel D reports the results of the regressions of momentum and risk-managed momentum

respectively on variables including total private credit expressed as a ratio of GDP (Credit), the average common language dummy variable (Lang), the ratio of market

capitalization to GDP (Open) and an index of control of capital flows (Control). Panel E and Panel F reports the results of the regressions of momentum and risk-managed

momentum respectively on variables including insider index (Insider), ICRG corruption index (Crp), ICRG political risk index (Political), natural log of transaction cost index

(Ln Tran) an investor protection index (Protection). Panel G and Panel H reports the results of the regressions of momentum and risk-managed momentum respectively on

variables including yield on 3-month Treasury Bills (Yld), the term spread (Term) and the dividend yield (Dy). White period standard errors are used to compute the t-statistics

which are reported in parenthesis. The sample period is 1987:07-2014:09.

Panel A:

Traditional

Momentum

Behavioral Model

Panel B:

Risk-Managed

Behavioral Model

Panel C:

Traditional

Momentum

Mkt Development

Panel D:

Risk-Managed

Mkt

Development

Panel E:

Traditional

Momentum

Inst. Quality

Panel F:

Risk-Managed

Inst. Quality

Panel G:

Traditional

Momentum

Macro

Panel H:

Risk-Managed

Macro

Intercept -0.70 (-0.78) -0.79 (-0.74) -4.23 (-2.48)* -1.29 (-1.52) -1.04 (-0.68) 0.70 (0.40) 1.31 (5.04)** 1.14 (4.57)**

MY -0.31 (-5.20)** -0.23 (-5.82)** -0.41 (-3.52)** -0.15 (-2.18)* -0.26 (-3.41)** -0.24 (-3.91)** -0.23 (-3.34)** -0.16 (-3.14)**

Ind 0.01 (2.51)* 0.01 (2.62)**

Ln TN -0.28 (-3.69)** -0.26 (-3.06)**

Ln V -0.18 (-1.13) -0.25 (-2.55)*

Cfvol -0.26 (-0.37) -1.20 (-2.28)*

Ln SZ 0.14 (2.16)* 0.18 (2.47)*

PastRet 0.31 (1.76) 0.27 (1.57)

Afe 0.11 (2.14)* 0.04 (1.03)

Credit -0.00 (-0.47) 0.00 (0.61)

Lang -2.35 (-1.85) -1.35 (-1.15)

EquityGDP -0.05 (-0.35) 0.05 (0.45)

Control 0.71 (3.65)** 0.30 (3.04)**

Insider 1.56 (2.32)* 1.94 (2.32)*

Crp 0.07 (0.84) 0.02 (0.16)

Political 0.01 (1.48) 0.01 (0.56)

Ln Tran -0.00 (-0.01) -0.30 (-0.82)

Protection -0.12 (-2.58)* -0.08 (-1.07)

Yld 0.03 (0.08) 0.11 (0.21)

Term -0.10 (-1.62) -0.01 (-0.22)

Dy -0.15 (-1.74) -0.37 (-0.06)

39

Table 4: Reversals and synthetic index of myopia

Monthly returns on country specific returns on the long-run reversals strategy are regressed on a synthetic

index used to measure myopia and a set of explanatory variables. Panel A reports the results of the regressions

of long-run reversals strategy returns on variables including Hofstede’s Individualism Index (Ind), natural

log of market trading volume (Ln TN), natural log of stock market volatility (Ln V), natural log of market

capitalization (Ln SZ), past three-year market returns (PastRet) and the average analysts forecast error (Afe).

Panel B reports the results of the regressions of long-run reversals strategy returns on variables including

total private credit expressed as a ratio of GDP (Credit), the average common language dummy variable

(Lang), the ratio of market capitalization to GDP (Open) and an index of control of capital flows (Control).

Panel C reports the results of the regressions of long-run reversals strategy returns on variables including

insider index (Insider), ICRG corruption index (Crp), ICRG political risk index (Political), natural log of

transaction cost index (Ln Tran) an investor protection index (Protection). Panel D reports the results of the

regressions of long-run reversals strategy returns on variables including yield on 3-month Treasury Bills

(Yld), the term spread (Term) and the dividend yield (Dy). White period standard errors are used to compute

the t-statistics which are reported in parenthesis. * Denotes significant at the 5% level, ** denotes significant

at the 1% level. The sample period is 1987:07-2014:09.

Panel A: Long-

run reversals

Behavioral Model

Panel B: Long-run

reversals

Mkt Development

Panel C: Long-

run reversals

Inst. Quality

Panel D:

Long-run

reversals

Macro

Intercept -4.83 (-1.86) -1.54 (-0.88) 1.23 (0.33) 0.64 (2.37)*

MY -0.16 (-2.00)* -0.23 (-2.09)* -0.23 (-2.06)* -0.15 (-2.60)**

Ind 0.02 (2.43)*

Ln TN 0.04 (0.45)

Ln V -0.15 (-0.98)

Cfvol 1.20 (1.04)

Ln SZ 0.34 (1.79)

PastRet -0.06 (-0.53)

Afe -0.10 (-0.86)

Credit 0.00 (1.16)

Lang 4.81 (1.32)

EquityGDP -0.03 (-0.27)

Control 0.09 (0.55)

Insider -0.42 (-0.30)

Crp -0.60 (-2.74)**

Political 0.06 (3.25)**

Ln Tran -0.86 (-0.81)

Protection 0.09 (0.83)

Yld 0.08 (0.22)

Term 0.08 (1.16)

Dy -0.06 (-0.91)

40

Table 5: Funds under management and the relationship between myopia and momentum

Monthly returns on country specific value and momentum combined portfolios are regressed on a synthetic

index used to measure myopia and a set of explanatory variables. Panel A reports the results of the regressions

of momentum returns on variables including Hofstede’s Individualism Index (Ind), natural log of market

trading volume (Ln TN), natural log of stock market volatility (Ln V), natural log of market capitalization (Ln

SZ), past three-year market returns (PastRet) and the average analysts forecast error (Afe). Panel B reports the

results of the regressions of momentum returns on variables including total private credit expressed as a ratio

of GDP (Credit), the average common language dummy variable (Lang), the ratio of market capitalization to

GDP (Open) and an index of control of capital flows (Control). Panel C reports the results of the regressions

of momentum returns on variables including insider index (Insider), ICRG corruption index (Crp), ICRG

political risk index (Political), natural log of transaction cost index (Ln Tran) an investor protection index

(Protection). Panel D reports the results of the regressions of momentum returns on variables including yield

on 3-month Treasury Bills (Yld), the term spread (Term) and the dividend yield (Dy). White period standard

errors are used to compute the t-statistics which are reported in parenthesis. * Denotes significant at the 5%

level, ** denotes significant at the 1% level. The sample period is 1987:07-2014:09.

Panel A:

Traditional

Momentum

Behavioral Model

Panel B:

Traditional

Momentum

Mkt Development

Panel C:

Traditional

Momentum

Inst. Quality

Panel D:

Traditional

Momentum

Macro

Intercept 1.17 (0.47) -5.29 (-3.83)** -1.32 (-1.00) 1.29 (2.94)**

MY -0.35 (-2.23)* -0.33 (-2.51)* -0.35 (-2.71)** -0.09 (-0.79)

FUM*MY -0.53 (-1.88) -1.53 (-2.47)* -0.59 (-2.69)** -0.78 (-2.40)*

FUM 1.41 (3.01)** 3.63 (3.49)** 1.93 (7.98)** 1.34 (2.09)*

Ind 0.01 (1.11)

Ln TN -0.31 (-2.15)*

Ln V 0.41 (0.02)

Cfvol 1.86 (0.81)

Ln SZ -0.01 (-0.06)

PastRet 0.27 (1.55)

Afe 0.15 (1.61)

Credit 0.00 (-0.45)

Lang -5.83 (-2.69)**

EquityGDP 0.73 (2.05)*

Control 0.85 (5.12)**

Insider 3.19 (3.66)**

Crp 0.47 (5.42)**

Political -0.01 (-1.24)

Ln Tran 0.19 (0.55)

Protection -0.29 (-5.28)**

Yld 3.83 (3.42)**

Term -0.08 (-0.58)

Dy -0.41 (-4.30)**

41

Table 6: Momentum and myopia in a composite model

To eliminate potential issues with multicollinearity we use principal components analysis to decompose 18 control

variables into their principal components and included these variables along with the synthetic myopia index as

explanatory variables for momentum. Panel A reports the factor loadings on all principal components with an

Eigenvalue greater than one, along with the percentage of common variable explained by each component. Panel B

reports the results of the regression analysis where monthly returns on country-specific traditional momentum and

risk-managed momentum portfolios are regressed on the myopia index (MY) and a set of explanatory variables.

White period standard errors are used to compute the t-statistics which are reported in parenthesis. * Denotes

significant at the 5% level, ** denotes significant at the 1% level. The sample period is 1987:07-2014:09.

Panel A: Principal components analysis

Variable PC 1 PC 2 PC 3 PC 4 PC 5 PC 6 PC 7

IND -0.001 0.328 -0.438 -0.163 0.127 -0.005 0.150

LN_TN 0.228 -0.016 -0.240 0.181 0.385 0.002 -0.271

LNV -0.034 0.023 -0.146 0.230 -0.143 0.607 -0.344

CFVOL -0.188 0.019 0.130 0.024 0.581 0.180 -0.030

LN_SZ 0.342 -0.131 -0.167 -0.297 -0.140 0.007 -0.141

PASTRET 0.051 0.028 -0.086 0.149 -0.479 0.318 0.104

AFE -0.361 -0.065 0.177 -0.256 -0.266 0.016 -0.141

CREDIT 0.342 -0.021 0.023 -0.302 -0.145 0.204 -0.062

LANG 0.188 0.393 -0.150 -0.099 -0.112 0.003 0.406

OPEN 0.227 -0.114 0.424 0.143 -0.034 0.115 -0.152

CONTROL 0.046 0.238 0.443 0.154 0.036 0.040 -0.044

INSIDER 0.462 -0.023 0.097 0.079 0.071 -0.044 0.025

CRP -0.078 0.387 0.220 -0.217 0.102 0.260 -0.001

LN_TRAN 0.013 0.385 0.246 -0.181 0.090 0.260 0.175

POLITICAL 0.480 0.043 0.105 0.089 0.098 0.002 0.029

PROTECTION 0.000 0.053 0.195 0.464 -0.213 -0.232 0.329

TERM -0.022 0.438 -0.099 0.246 -0.069 -0.307 -0.275

YLD -0.010 -0.387 0.016 -0.021 0.189 0.210 0.551

DY -0.076 0.031 -0.263 0.443 0.043 0.334 0.144

Eigenvalue 3.794 2.894 2.186 2.008 1.241 1.183 1.119

% Explained 0.200 0.152 0.115 0.106 0.065 0.062 0.059

Cum. % Explained 0.200 0.352 0.467 0.573 0.638 0.700 0.759

Panel B: Momentum and Synthetic Short-termism Index with principal components as controls

Traditional Momentum Risk-Managed

Intercept -0.960 (-0.72) 0.792 (0.46)

MY -0.477 (-3.61)** -0.398 (-2.73)**

PC1 -0.010 (-0.09) 0.178 (1.07)

PC2 0.013 (0.09) 0.088 (0.43)

PC3 0.111 (2.07)* 0.028 (0.35)

PC4 -0.147 (-0.45) -0.578 (-1.24)

PC5 -0.296 (-1.17) -0.241 (-0.70)

PC6 -0.107 (-0.62) 0.196 (0.86)

PC7 0.436 (1.40) 0.530 (1.32)