Forecasting the CNH-CNY pricing differential: the role of

investor attention

Liyan Han1, Yang Xu

1, Libo Yin

2,*

(1School of Economics and Management, Beihang University, Beijing, China)

(2School of Finance, Central University of Finance and Economics, Beijing, China)

First Author: Liyan Han

Position: Professor

Affiliation: School of Economics and Management, Beihang University, Beijing, China

Second Author: Yang Xu

Position: Ph.D. candidate

Affiliation: School of Economics and Management, Beihang University, Beijing, China

Corresponding Author: Libo Yin

Position: Associate Professor

Affiliation: School of Finance, Central University of Finance and Economics, Beijing, China

Tel: (86) 18801061962

Email: yinlibowsxbb@126.com

Address: No. 39 South College RD., HaiDian DIST., Beijing, 100081, China.

Acknowledgements

This research is financially supported by the National Natural Science Foundation of China

under projects No. 71401193 and No. 71371022, the Program for Innovation Research in the

Central University of Finance and Economics, and the Innovation Foundation of BUAA for PhD

Graduates.

Assessing the CNH-CNY pricing differential: Role of

investor attention

Abstract: As the exponential expansion in the international use of RMB, the issues concerning

“one currency, two markets” have attracted increasing attentions from both policymakers and

academics. We investigate the forecast ability of investor attention on the CNH-CNY pricing

differential for the period of March 2011 to November 2015. Our results show that investor

attention displays economically and statistically significant in-sample and out-of-sample

predictabilities of the CNH-CNY pricing gap at both monthly and weekly frequencies. In addition,

investor attention could generate substantial economic values in asset allocation at both monthly

and weekly frequencies. Furthermore, we find that investor attention provides economically and

statistically significant out-of-sample forecast for the CNY carry trade on weekly basis.

Keywords: CNH-CNY pricing differential, investor attention, macroeconomic factors,

out-of-sample forecast, carry trade

1. Introduction

On the heels of China’s strong economic performance in the past decades, there is a rapid

growth in the international use of RMB1, which is mainly attributed to the development of

offshore RMB (CNH) exchange rate market. Since the officially sanction of offshore renminbi

trading in July 2009, issues concerning “one currency, two markets” have attracted increasing

attentions from both policymakers and academics.

Besides the exponential expansion of trading volumes2, one important feature for offshore

renminbi market is the persistent deviations existing between the CNH and CNY spot exchange

rates. As shown in Figure 1, the CNH spot rate displayed greater volatility in daily movements,

even though they both have followed the same broad trend, depreciating by around 10% during

the period of March 2011 to November 2013 then constantly appreciating by around 5% until the

end of 2015. Take one episode for example, while on October 2012 the offshore rate of 6.2640

renminbi to the US dollar was nearly 100 pips below the onshore rate of 6.2735, by December the

gap widened to roughly 400 pips (6.2148 offshore to 6.2566 onshore). As the offshore renminbi

market is the very key component of RMB’s internationalization (Ma and McCauley, 2010),

which could provide fundamental influences on both the country of issuance as well as the global

economy (Maziad and Kang, 2012; Shu et al., 2014)3, the understanding of pricing differential

between onshore (CNY) and offshore (CNH) markets will have significant implications for

promoting the process of RMB’s internationalization.

To explain this issue, existing studies extensively concentrate on two sets of factors that can

1 According to the Bank of International Settlement’s 2013 Triennial Central Bank Survey that renminbi now ranks the ninth most traded

currency in the world and the most traded in Asia (Funke et al., 2015). In October 2013, the RMB surpassed euro and Japanese Yen and

became the second most used currency in traditional trade finance covering letters of credit and collections, and was ranked the 12th

currency for international payments in the world (Shu et al., 2014).

2 According to Shu et al. (2014), the daily average volume of inter-dealer transactions in offshore market increased from 0.398 billion in

2007 to 3.903 billion in 2013.

3 Particularly for the Chinese, it would reduce liquidity and exchange rate risks facing domestic economic agents, and allow both the

public and private sectors to finance in domestic currency from the global market, as well as improve the cross-border transactions. Form

a global perspective, the internationalizing of RMB could effectively reflect the structure of the global economy based on real economic

activities and the growth drivers (Maziad and Kang, 2012), thereby improving global risk sharing and managing systemic vulnerability.

potentially influence the pricing spread: those related to capital market liberalization policies (Shu

et al., 2014; Funke et al., 2015); and those related to fundamentals or economic conditions (Shu et

al., 2007; Fratzscher and Mehl, 2011; Subramanian and Kessler, 2012; Ding, et al., 2012; Funke et

al., 2015). As for the first, the CNY market remains constrained by the central bank’s intervention

and the stipulation of a daily trade band. By contrast, in the CNH market, central bank shows no

presence in the price formation or in setting trading limits. The distinct feature of the onshore

(CNY) and offshore (CNH) markets have frequently caused the two exchange rates to diverge

from each other. Second, the two markets are likely to have different investor bases and liquidity

circumstances (Funke et al., 2015). For example, Maziad and Kang (2012) find that onshore spot

rates have an influence on both spot and forward rates in the offshore market under normal market

conditions; while under conditions of financial stress offshore exchange rate movements impact

onshore spot rate, and volatility spillovers exist in both directions. Ding et al. (2014) find that

price discovery differences in the offshore markets stem from the offshore spot tracking onshore

interest rates while the offshore forward contracts tracking onshore spot rates.

In this paper, we seek to investigate the pricing differentials between offshore (CNH) and

onshore (CNY) exchange rates from a new perspective— investor attention—which is recently

popular used in asset pricing and market efficiency (Kim et al., 2014; Yuan, 2015). Our line of

thoughts stem from the following two aspects. First, existing literatures provide empirical

evidence supporting the dynamic relationship between fundamental economic factors and the

onshore and offshore renminbi exchange rate (Shu et al., 2007; Fratzscher and Mehl, 2011;

Subramanian and Kessler, 2012; Ding et al., 2012; Funke et al., 2015), while little research has

been taken from the perspective of investor attention. Only considering information from

fundamentals is not sufficient as some literatures, however, note that a puzzling feature of

currency is that dramatic exchange rate movements occasionally happen without fundamental

news announcements, indicating that many abrupt asset price movements cannot be attributed to

fundamental news events (Cutler et al., 1989; Fair, 2002; Balke et al., 2013). Besides that,

macroeconomic factors are frequently claimed to have weak out-of-sample predictability of

exchange rates since Messe and Rogoff (1983), and by many others (Cheung et al., 2002; Killian

and Taylor, 2003). One notable exception in investigating the predictability of exchange rate is Yu

(2013) who shows that investor sentiment helps account for the forward premium puzzle. Another

study closest to ours is by Craig et al. (2013) who attribute the CNH-CNY pricing differential to

onshore investor risk sentiment and capital account liberalization. To our best knowledge, investor

attention has been widely proved to be statistically and economically significant for security

markets (Merton, 1987; Sims, 2003; Hirshleifer and Teoh, 2003; Peng and Xiong, 2006; Barber

and Odean 2008; Da et al. 2015), inspiring us to take an exploration of investor attention in

currency market. Therefore, we would expect similar predictive ability of investor attention for the

pricing differential between CNH and CNY.

Second, the existence of CNH-CNY pricing differential provides a naturally testing pool for

investigating the relationship between investor attention and currency market. For one thing, the

CNH market is exposed to more complex global factors and appears to be more informationally

integrated than the CNY market. Specifically, compared to the CNY market, the CNH market is a

free market, with a more diversified range of products, including spot, forward, swap and options,

and participant base, including exporters, importers offshore financial institutions, hedge funds

and Hong Kong residents (Kim et al., 2014; Yuan, 2015). Thus from the global perspective, the

study of predictive ability of investor attention for the CNH-CNY pricing differential could

provide implications for other international currencies, especially currencies of emerging markets.

Furthermore, since the information about economic conditions for both exchange rate markets are

from the same source of underlying economic fundamentals, experiments on the CNH-CNY

pricing differential is free of issues resulting from identifying different control variables.

To fulfill the above objectives, we first construct our own attention indices by applying the

partial least squares (PLS) approach following Wold (1966, 1975) and Kelly and Pruitt (2013,

2014); then investigate the in-sample and out-of-sample predictive ability of investor attention on

the CNH-CNY pricing differential; then for interest of comparison, we compare the forecast

performance of investor attention with that of macroeconomic variables; we also explore the

economic implications of investor attention through asset allocations; and finally we investigate

the predict power of investor attention on CNY carry trade.

This paper contributes to existing literature in the following four aspects. First, unlike

existing literatures about exchange rate which extensively focus on macroeconomic factors, such

as inflation, interest rate, output, balance of payment, etc. (Cheung et al., 2002; Killian and Taylor,

2003), and microeconomic factors, such as order flow (Evans and Lyons, 2005), we first provide

evidence concerning on the relationship between investor attention and exchange rate. To our best

knowledge, it is the first time that investor attention is introduced to currency market.

Second, existing literatures mostly investigate the relationship between securities and

investor attention by simply applying the number of attention terms (except for Da et al. 2015,

who use a weighted average attention index), while we construct an aligned attention index

through PLS method in order to capture the information that most related to target exchange rate.

Additionally, current studies frequently use the attention to index to investigate its relationship

with the corresponding index return (Vozlyublennaia, 2014; Kim et al., 2014), while we consider a

much wider set of potential attention terms in order to reflect information from three aspects. In

particular, we first consider the attention terms “CNH” and “CNY” to represent information

contained in currency name and generate the first aligned attention index, “self attention:. Then we

compile a series of attention terms that are directly linked to real economy to generate the aligned

“macro attention” index. And the last group of attention terms are derived from the terms used in

“FEARS” index (Da et al., 2015), to possibly reflect investor attention in financial markets.

Additionally, by comparing the forecast performance, we also testify that macro attention captures

information different from macro factors.

Third, we provide evidence that investor attention has both in-sample and out-of-sample

predictability of the CNH-CNY pricing differential, while current studies mostly concentrate on

the relationship between the pricing spread and fundamental factors and barely investigate

out-of-sample forecast (Shu et al., 2007; Fratzscher and Mehl, 2011; Subramanian and Kessler,

2012; Ding et al., 2012). Moreover, we prove economic significance of investor attention by

investigating its predictability of carry trade, filling the gap in existing literatures.

Lastly, a common problem with existing papers is their reliance on ex-post revised economic

data for forecasting analysis. The revisions to macroeconomic data may be substantial and are not

available to either policy makers or market participants at the time forecasts made. Instead, we use

the actual realized values of investor attention to possibly prevent the revised data from yielding

misleading inference of exchange rate forecast. Moreover, the macroeconomic data are usually

disclosed at a lower frequency (monthly, quarterly, or annually), while we apply the actual realized

values of investor attention whose data can be obtained at a relatively higher frequency (weekly,

daily)4. In that sense, with good feasibility at a higher frequency, the actual realized investor

attention may play a significant role in risk management area.

The rest of this paper is structured as following: In section 2, we explain the econometric

method to construct aligned attention index. Section 3 describes data. Section 4 analyzes the

in-sample and out-of-sample empirical results and Section 5 concludes.

2. Construction of aligned attention index

In this section, we provide the econometric method for constructing our aligned attention

indices. We assume that the one-period ahead expected CNH-CNY pricing differential explained

by investor attention follows the standard linear equation,

ttt ADE 1 , (1)

where tA is the investor attention that matters for forecasting CNH-CNY pricing differential. The

realized differential then equals to its conditional expectation plus an unpredictable shock,

1111 )( tttttt ADED , (2)

where 1t is unpredictable and unrelated to

tA .

Let tNtt xxx ,,1 ,..., denote an 1N vector of individual investor attention proxies at

period Ttt ,...,1 . We assume that Nix ti ,...,1, has a factor structure,

tititiiti eEAx ,2,1,0,, , Ni ,...,1 . (3)

wheretA is the investor attention that matters for forecasting CNH-CNY pricing differential, 1,i

is the factor loading that summarizes the sensitivity of sentiment proxy tix , to movements in tA ,

tE is the common approximation error component of all the proxies that is irrelevant to the pricing

spread, and tie , is the idiosyncratic noise associated with i only.

The objective is to impose the above factor structure on the proxies to efficiently estimatetA ,

4 We have free access to weekly data from Google Trend since Jan 2004 till the current, and daily data can be

obtained up to the recent 90 days.

the collective contribution to the true investor attention, and at the same time, to eliminatetE ,

their common approximation error, and tie , from the estimation process. Following Wold (1966,

1975) and Kelly and Pruitt (2013, 2014), we apply the partial least squares (PLS) approach to

extracttA and filter out the irrelevant component

tE , while the commonly used principal

component (PC) method cannot by guaranteed to do so. The key idea is that PLS extracts the

investor sentiment,tA from the cross-section according to its covariance with future CNH-CNY

pricing differentials and forms a linear combination of attention proxies which can provide

optimal forecast. In doing so, PLS can be implemented by the following two steps of OLS

regressions. In the first-step, for each individual investor attention proxyix , we run a time-series

regression of 1, tix on a constant and realized CNH-CNY pricing differentialtD ,

1,0,1, titiiti Dx , Tt ,...,1 . (4)

Instrumented by future pricing differentialtD , the loading

i captures the sensitivity of each

attention proxy 1, tix to the attention index1tA . Since the expected component of

tD is driven

by 1tA , attention proxies are related to the expected CNH-CNY pricing differentials and are

uncorrelated with the unpredictable spreads. Therefore, the coefficient i in the first-stage

time-series regression in Eq. (4) approximately describes how each attention proxy depends on the

true investor attention.

In the second-step, for each time period t , we run a cross-sectional regression of tix , on the

corresponding loading i estimated in Eq. (4),

tiittti Acx ,,ˆ , Ni ,...,1 , (5)

wheretA is the estimated investor attention. That is, in Eq. (5), the first-stage loadings become the

independent variables, and the aligned investor attention tA is the regression slope to be

estimated.

In practice, if the true factor loading i was known, we could consistently estimate tA by

simply running cross-sectional regressions of tix , on i period-by-period. Since

i is unknown,

however, the first-stage regression slopes prove a preliminary estimation of how tix , depends on

tA . In other words, PLS uses time t+1 differential to discipline the dimension reduction to extract

tA relevant for forecasting and discards common and idiosyncratic components such as tE and

tie , that are irrelevant for forecasting.

Mathematically, the 1T vector of aligned investor attention index, Tt AAA ,...,1 , can

be expressed as a one-step linear combination of tix , ,

DJDDJXXJJDDJXXJA TTNTTN

1 , (6)

where X denotes the NT matrix of individual investor attention proxies, TxxX ,...,1 ,

and D denotes the 1T vector of CNH-CNY pricing differentials as 12 ,..., TDDD . The

matrices TTTTT

IJ 1

and NNNNN

IJ 1

is entered in the formula because each

regression is run with a constant. TI is a -T dimensional identity matrix and T is a -T vector

of ones. The weight on each individual measure tix , in tA is based on its covariance with the

CNH-CNY pricing differential to capture the inter-temporal relationship between the aligned

investor attention and the expected pricing differentials.

3. Data

3.1 Search terms

Following Da et al. (2011, 2015), we use the public Search Volume Index (SVI) from Google

Trends ( http://www.google.com/trend/) as our investor attention proxies. The numbers present

search probabilities of a given keyword at a given time. To build a list of attention indices that

have explanatory ability toward the CNH-CNY pricing differential, we work on search terms from

three sources. The first group of attention terms is based on the name of exchange rate itself.

Vozlyublennaia (2014) find that the attention to an index has a significant short-term effect on the

index return. So we consider the attention terms “CNH” and “CNY” to generate the first attention

index, and name as “self attention”. The second group, named “macro attention”, consists of

economic terms that are linked directly to economic fundamentals. The majority of economic

terms are well-known factors claimed to have predictabilities on exchange rate by studies, such as

money supply, inflation, interest rate, etc., although terms are subjectively chosen. For the last

group, considering abundant evidence of dynamic relationship between stock prices and exchange

rates (Korajczyk and Viallet, 1992; Phylaktis and Ravazzolo, 2005; Hau and Rey, 2006;

Cumperayot et al., 2006), we are interested to find out if attention terms that reflect information

from financial market can also explain the exchange rate movements. Thus, in line with Da et al.

(2015), we consider 30 terms which are suggested to be useful for forecasting stock prices in the

last group to represent information from financial market.

The data covers a weekly period from March 2011 until November 20155. The empirical

analysis is carried out at both monthly and weekly frequency, although we start from weekly

search terms. The monthly data is derived by averaging four weeks search amount and the

construction of weekly and monthly proxies for investor attention follows the same procedure as

discussed above. Following the extant literature of Fama (1988) and especially Da et al. (2011,

2015), we work in logarithms of search terms for ease of exposition and notation. Table 2 displays

some summary statistics of the attention terms over full sample, and the statistics are generally

consistent with literature.

3.2 Other data

The CNH-CNY pricing differential is computed as the log difference between the spot

exchange rate of offshore and onshore Renminbi. The data of CNY and CNH is obtained from

Reuters (via DataStream). The data spans March 2011 until November 2015 and summary

statistics are reported in Pane A, Table 1. The weekly CNH has a mean of 6.2605 with a standard

deviation of 0.1266 and the weekly CNY has a mean of 6.2584 with a standard deviation of

5 According to Reuters (via DataStream) the spot exchange rate data for CNH at Hong Kong SAR starts from

February 28, 2013. To possibly include large sample we employ weekly attention data from the first week of

March 2013.

0.1208. The monthly CNH-CNY pricing differential is derived by averaging four weeks pricing

data and follows the same mathematic process.

To investigate the economic value of investor attention, we also examine the forecast ability

of our attention indices on currency carry trade. The data for 1 month forward exchange rate

versus the USD is obtained from Reuters (via DataStream) and covers the sample period from

March 2011 to November 2015. As presented in Table 1, the weekly carry trade has a mean of

1.8355 and a standard deviation of 0.0208, with high autocorrelation. The monthly carry trade has

a mean of 1.8339 and a standard deviation of 0.0189 with relatively lower autocorrelation than

weekly data.

For interest of comparison, we also consider four monthly economic variables that are widely

acknowledged as useful predictors of exchange rate in a number of studies such as Molodtsova

and Papell (2009), Wu and Hu (2009), Zwart et al. (2009), Balke et al. (2013), Ince (2014),

Bekiros (2014), and many others, which are interest rate (IR), the amount of narrow money supply

(M1), the amount of broad money supply (M3), and consumer price index (CPI). The data spans

March 2011 to November 2015 and summary statistics, reported in Table 1, are generally

consistent with literatures.

4. Empirical Results

In this section, we present a number of empirical results. Section 4.1 reports the in-sample

estimates of the spread of onshore and offshore Renminbi exchange rates by various attention

indices. Section 4.2 examines the out-of-sample forecast ability of attention indices. Section 4.3

assesses the economic value of predictability via asset allocation and section 4.4 investigates the

predictability of the CNY carry trade.

4.1 In-sample analysis

To investigate the predictive power of individual attention indices, we employ a simple

univariate prediction model. As evident from previous literatures, it is presumed that changes in

investor attention should cause changes in security prices and returns (Kim et al., 2014; Yuan,

2015). While this proposition has been examined in the literature primarily for individual

securities in stock markets, here we test it in currency market, specifically for the pricing

differentials between offshore (CNH) and onshore (CNY) Renminbi. The simple univariate

predictive regression is specified as following:

1,,1 titiiit AD , (7)

where 1tD denotes the pricing differentials between CNH and CNY at period 1t , tiA , denotes

the investor attention that is available at period t , and 1, ti is a zero-mean disturbance term. In line

with Inoue and Kilian (2004), who recommend a one-sided alternative hypothesis to increase the

power of in-sample predictability tests, we test 0 : 0iH against : 0A iH using a

heteroscedasticity-consistent t-statistic corresponding to ˆi in Eq. (7).

In addition, to directly compare the predict power of attention indices to that of

macroeconomic variables employed in traditional structural models, we generate a monthly index

for macroeconomic variables following the same PLS procedure as discussed above and evaluate

its performance by replacing tiA , in Eq.(7) with tX , which takes the formulation:

11 ttt XD , (8)

where tX refers to the macroeconomic index at period t , and the denotations of

1tD and

1, ti are defined the same as those in Eq. (7). Similarly, we test 0:0 H against

0: AH using a heteroscedasticity-consistent t-statistic that corresponds to , the OLS

estimate of in Eq. (8).

Statistically, there are issues that may have adverse impact on the statistical inference about

the attention indices. First, there is potentially a spurious regression concern when a predictor is

highly persistent (Ferson et al., 2003). Second, the first-step regression for the in-sample PLS

estimation, Eq. (4), introduces a look-forward bias as it uses future information. Although Kelly

and Pruitt (2013, 2014) show that this bias will vanish as the sample size becomes large, it is still a

concern with finite sample here.

We employ two strategies to alleviate the above issues. First, we base the inference on

empirical p values using a wild bootstrap procedure that accounts for the persistence in

predictors, correlations between the differentials and predictor innovations, and general forms of

distribution. Second, we construct a look-ahead bias-free PLS forecast. To calculate i at time

1t , we run the first-step time-series regression of Eq. (4) with information up to time t only.

Then, the regression slopes are used as independent variables for the second-step regression of Eq.

(5), whose slope is therefore the attention indices tA at time t . Repeating this procedure

recursively, we obtain a look-ahead bias-free attention index. In this paper, we use the first three

year data as the initial training sample when computing recursively the look-ahead bias-free

attention indices.

[Insert Table 3 Here]

Table 3 reports the results of the in-sample predictive regression. Panel A provides monthly

estimates ofi for the attention indices, over the sample period of March 2011 through

December 2013. Overall, attention indicesiA generate small and positive regression slopes

i of

0.0116, 0.0123, and 0.0064 for macro attention, stock attention, and self attention, respectively.

The -t statistics are large in absolute values, with marginally significance at the 1% level. After

elimination of look-ahead information, the look-head bias-free indices yield large 2R of 51.66%,

57.35%, and 16.2% for macro, stock, and self attention respectively. The estimated slope

coefficients of the three attention indices appear to be in similar patterns in terms of signs and

significance. The macroeconomic index, in comparison, provides a relatively good estimate result

as well, indicating that macroeconomic variables which are claimed to have poor predictive

performance for exchange rate in traditional structural models (Messe and Rogoff, 1983; Cheung

et al., 2002; Killian and Taylor, 2003) substantially improve predict power by eliminating the

common noise component of the proxies, which is made possible with the PLS method developed

by Kelly and Pruitt (2013, 2014). The regression slope is equal to 0.0104, slightly lower than

stock and macro attention index while higher than self attention index, with a significant -t

statistic at 1% significance level. Also, the2R is slightly lower than those of stock and macro

attention indices but greater than that of self attention index. Although macro index has significant

in-sample estimates, its influence is still no stronger than macro and stock attention indices, in

terms of the magnitude of and 2R statistics. The results are consistent with existing studies

on renminbi that investors based on the onshore and offshore markets may react differently to the

same fundamental movements or same macroeconomic news which can trigger the immediate

adjustment in exchange rate and thus lead to the gap between CNH and CNY (Funke et al., 2015).

Panel B presents weekly predictive results for the attention indices. Overall, attention indices

iA generate small and positive regression slopesi of 0.0138, 0.0179, and 0.0137 for macro

attention, stock attention, and self attention, respectively. The -t statistics are large in absolute

value, with marginally statistical significance at the 1% level. Also, the weekly attention indices

iA yield large 2R of 11.93%, 18.62%, and 11.48% for macro, stock, and self attention

respectively. The estimates of the slope i are positive and remain statistically significant at 1%

level, in line with the results reported in Panel A. all of the 2R s in Panel B are substantially

smaller than those in Panel A but are still greater than 10%. Economically, if this level of

predictability can be sustained out-of-sample, it will be of substantial economic significance

(Kandel and Stambaugh, 1996). This point will be analyzed further in Section 4.2.

Summarizing Table 1, the aligned investor attention indices iA exhibit statistically and

economically significant in-sample predictability of the monthly and weekly CNH-CNY pricing

differentials. In addition, two of the attention indices provide better estimations than the

macroeconomic index in terms of2R , suggesting that investor attention may contain sizable

forecasting information beyond what is contained in the macroeconomic predictors.

4.2 Out-of-sample analysis

Although the in-sample analysis provides efficient parameter estimates and thus more precise

pricing differentials forecast, Goyal and Welch (2008), among others, argue that out-of-sample

tests seem more relevant for assessing genuine predictability in real time and avoid the in-sample

over-fitting issue. In addition, out-of-sample tests are much less affected by the small-sample size

distortions such as the Stambaugh bias (Busetti and Marcucci, 2002) and the look-ahead bias

concern of the PLS approach (Kelly and Pruitt, 2013, 2014). Hence, it is of interest to investigate

the out-of-sample predictive performance of investor attention.

The key requirement for out-of-sample forecasts at time t is that we can only use

information available up to t to forecast the pricing differentials at 1t . Following Goyal and

Welch (2008), Kelly and Pruitt (2013), we run the out-of-sample analysis by estimating the

predictive regression model recursively based on individual investor attention index,

tttt

m

t AD ;11ˆˆˆ

： , (9)

where t and t are the OLS estimates from regressing 1

11

t

s

m

sD on a constant and an

attention index 1

1;1

t

s

k

stA： . Like our in-sample analogues in Table 3, we consider macro, stock,

and self attention in both monthly and weekly basis, as well as a monthly macroeconomic index.

For interest of comparison, we consider the combination forecast that is widely used in

econometric forecasting applications and that often beats sophisticated optimally estimated

forecasting weighs (Timmermann, 2006). In finance, Rapach et al. (2010) show that a simply

equal-weighted average of univariate regression forecasts can consistently predict the market risk

premium. It is hence of interest to see how well it performs in the context of using the attention

proxies.

Let p be a fixed number chosen for the initial sample training, so that the future expected

pricing differentials can be estimated at time Tppt ,...,2,1 . Hence, there are pTq

out-of-sample evaluation periods. That is, we have q out-of-sample forecasts: 1

1ˆ

T

pt

m

tD . More

specifically, we use the data covers March 2011 through December 2013 as the initial estimation

period so that the forecast evaluation period spans over January 2014 through November 2015.

To evaluate out-of-sample forecast performance we compute three statistics as follows. First,

we evaluate the out-of-sample forecast based on the widely used Campbell and Thompson (2008)

1 211

21

112

)(

)ˆ(-1

T

pt

m

tm

t

T

pt

m

t

m

t

OS

DD

DDR , (10)

where m

tD 1 denotes the historical average benchmark corresponding to the constant expected

pricing differentials model ( 11 t

m

tD ),

t

s

m

s

m

t Dt

D1

1

1. (11)

Goyal and Welch (2008) show that the historical average is a very stringent out-of-sample

benchmark, and individual economic variables typically fail to outperform the historical average.

The 2

OSR statistic lies in the range 1- ， . If 02 OSR , it means that the forecast m

tD 1ˆ outperforms

the historical average m

tD 1 in terms of MSFE.

The second statistic we report is Diebold and Mariano (1995) statistic modified by

McCracken (2007), which tests for the equality of the mean squared forecast errors (MSFE) of one

forecast relative to another. Our null hypothesis is that the historical average has a MSFE that is

less than, or equal to, that of the predictive regression model. Comparing a predictive regression

forecast to the historical average entails comparing nested models, as the predictive regression

reduces to the historical average under the null hypothesis. McCracken (2007) shows that the

modified DM-test statistic follows a nonstandard normal distribution when testing nested models,

and provides bootstrapped critical values for the nonstandard distribution.

The third statistic is the MSFE-adjusted statistic of Clark and West (2007). It tests the null

hypothesis that the historical average MSFE is less than or equal to the predictive regression

forecast MSFE against the one-sided (upper-tail) alternative hypothesis that the historical average

MSFE is greater than the predictive regression forecast MSFE, corresponding to 0: 2

0 OSRH

against 0: 2 OSA RH . Clark and West (2007) show that the test has an asymptotically standard

normal distribution when comparing forecasts form the nested models. Intuitively, under the null

hypothesis that the constant expected return model generates the data, the predictive regression

model produces a noisier forecast than the historical average benchmark because it estimates slope

parameters with zero population values. We thus expect the benchmark model’s MSFE to be

smaller than the predictive regression model’s MSFE under the null. The MSFE-adjusted statistic

accounts for the negative expected difference between the historical average MSFE and predictive

regression MSFE under the null, so that it can reject the null even if the 2

OSR statistic is negative.

[Insert Table 4(a) Here]

Table 4(a) presents monthly results for the out-of-sample period of January 2014 through

November 2015. The first and second columns report the MSFE and MSFE-adjusted statistics; the

forth column presents -p values for the Clark and West (2007) MSFE-adjusted statistic; the third

column presents 2

OSR values; and the last two columns report the Theil (1966) MSFE

decomposition into the squared forecast bias and a remainder term. The first row of Table 4(a)

provides monthly out-of-sample forecast results of historical average as the evaluation benchmark.

Panel A of Table 4(a) shows that the macro, stock, and self attention indices generate positive

2

OSR statistics (42.0116%, 16.3614%, and 44.5094%, respectively), and thus deliver lower

MSFEs than the historical average. Moreover, all three attention indices provide significant

MSFE-adjusted statistics at 1% significant level according to their bootstrapped p values. The

last two columns report the MSFE decompositions into a squared forecast bias and a forecast error

variance. The remainder term depends, among other things, on the forecast volatility, and limiting

forecast volatility helps to reduce the remainder term (Rapach et al., 2010). The squared bias

(remainder term) is 0.0203‰ (0.0042‰) for the historical average forecast. All investor attention

indices have squared bias well below that of the historical average while the forecast error

variances exceed the benchmark. The results indicate strong out-of-sample predictive ability of

investor attention for CNH-CNY pricing differentials. The macroeconomic index, on the contrary,

is not statistically significant according to its DM- and CW- test statistics. It also yields a negative

2

OSR statistic (-0.2706%). Thus, the macroeconomic index exhibits weak out-of-sample forecast

ability of the CNH-CNY pricing gap, confirming the widely acknowledged argument by Meese

and Rogoff (1983) Cheung et al. (2002), Killian and Taylor (2003), and many others that

macroeconomic variables have little out-of-sample predictability of exchange rate. Also, this result

suggests that, while multiple predictors tend to improve in-sample performance through

implementing PLS, but the out-of-sample performance may not be necessarily improved (Huang

et al., 2014).

Panel B of Table 4(a) reports the combination forecast results of all attention indices.

Intuitively, the economic sources of predictability of investor attention are supposed to be the

same which suggests that they are likely to capture very much similar information towards the

same proxies. However, we are still interested in finding their differences in forecasting power and

thus implement the forecast combination, which is widely known as viable method for

improving forecast performance with multiple predictors, following Bates and Granger (1969),

Stock and Watson (2004) , Aiolfi and Timmermann (2006), Rapach et al. (2010), and many others.

The results in Panel B of Table 4(a) show that combination forecasts based on three attention

indices generally perform very well over the out-of-sample period. The third column of Panel B

shows that all of the 2

OSR values are sizable and all significant at 1% level of significance using

the MSFE-adjusted p values. In addition, all MSFE statistics in the first column are well below

that of the historical average. Similar to the results of individual predictive regression, all

combination forecasts have squared bias well below that of the historical average while the

forecast error variances exceed the benchmark. Surprisingly, we find that the kitchen sink model

provides the highest 2

OSR value with significance, 48.5608%, among five combination approaches.

The kitchen sink model usually suffers from a serious over-fitting issue and its

out-of-sample-performance is very poor (Goyal and Welch, 2008). However, it seems not the case

in our study, which maybe mainly attributed to the fact that the number of regressors is as few as

three in our combination.

To further understand the predict power of investor attention and their economic sources, we

also examine the combination forecast of all indices including the macroeconomic index. Results

in Panel C of Table 4(a) show that the forecast performances are dragged down, except for the

diffusion index model, when macroeconomic index is included in combination. Combination

forecasts in Panel B provide an average 2

OSR value of 42.8586%, while in Panel C the average

2

OSR value drops to 38.4941%, even though the statistics are sizable and all significant at 1% level

of significance using the MSFE-adjusted p values. Additionally, all MSFE statistics in the first

column are well below that of the historical average, and all forecasts have squared bias well

below benchmark while the forecast error variances exceed that of the historical average.

Diffusion index model provides the best performance in Panel C of Table 4(a), with 2

OSR reaching

51.3478%. Nonetheless, it is still not sufficient to suggest that macroeconomic index has

out-of-sample predictability and improve the combination performance since the overall

performances are pulled down compared to the results in Panel B. Thus, according to results in

Table 4(a), we may conclude that investor attention indices have economically and statistically

out-of-sample forecast ability of the CNH-CNY pricing differential with monthly data, while

macroeconomic variables barely have predict power of exchange rate over the same sample period

which is in line with many literatures (Meese and Rogoff, 1983; Engle and West, 2007).

[Insert Table 4(b) Here]

As suggested by existing literatures that investor attention tends to have short term effect on

stock market, while due to the limits of arbitrage, its predictability will be substantially impaired

over a long horizon Shu et al. (2014). Here we investigate its predictability of the CNH-CNY

pricing gap with weekly data. Table 4(b) reports the out-of-sample forecasting results of

CNH-CNY pricing differentials over the sample period of January 2014 through November 2015.

Results in Panel A of Table 4(b) demonstrate that macro, stock, and self attention indices

individually performs well out-of-sample forecasts, with positive 2

OSR statistics reaching

38.3716%, 25.9971%, and 10.6620% respectively, and thus generates lower MSFE than that of the

historical average. Also, all three attention indices deliver significant MSFE-adjusted statistics at 1%

significant level according to their bootstrapped p values. As for the decomposition of MSFE

presented in the last two columns, all three attention indices have squared forecast biases well

below that of historical average and forecast error variance equal to or below the benchmark.

Panel B of Table 4(b) presents the weekly results of combination forecast of all attention

indices for the CNH-CNY pricing spread. All combination forecasts deliver significant positive

2

OSR statistics with an average over 33% and thus have MSFE values well below that of the

historical average. More specifically, the simple average model provides the highest 2

OSR value

reaching 37.2768%, which is in accord with the literature that simple average scheme usually

exhibits the best forecast performance than other combination models (Rapach et al., 2010). Also,

all combination forecasts present squared forecast biases well below that of the historical average

and forecast error variance equal to or below the benchmark.

In summary, this section shows that aligned investor attention indices iA display strong

marginal out-of-sample forecasting power for the CNH-CNY pricing differentials at both monthly

and weekly frequencies, consistent with our previous in-sample results (Table 3). In addition,

combination forecasts effectively improve the predictability of individual attention indices. The

inclusion of macroeconomic index in combination forecast does not strengthen the performance

since it has poor out-of-sample predictability of the CNH-CNY spread.

4.3 Asset allocation implications

In this part, we examine the economic value of CNH-CNY pricing differential forecasts

based on the aligned investor attention indices tiA , . Following Kandel and Stambaugh (1996),

Campbell and Thompson (2008) and Ferreira and Santa-Clara (2001), we compute the certainty

equivalent return (CER) gain and Sharpe Ratio for a mean-variance investor who optimally

allocates across assets and the risk-free asset using the out-of-sample predictive regression

forecasts. This exercise also contributes to many existing studies of investor attention that fail to

incorporate risk aversion into the asset allocation decision.

At the end of period t , the investor optimally allocates

2

1

1

ˆ

ˆ1

t

tt

Dw

, (12)

of the portfolio CNH-CNY pricing differential during period 1t ,where is the risk aversion

coefficient, 1ˆtD is the out-of-sample forecast of the CNH-CNY pricing differential, and

2

1t is

the variance forecast. The investor then allocates tw-1 of the portfolio to risk-free bills, and the

1t realized portfolio return is

f

ttt

p

t RDwR 111ˆ

, (13)

where f

tR 1 is the gross risk-free return. Following Campbell and Thompson (2008), we assume

that the investor uses a six-month moving window of past monthly returns to estimate the variance

of the CNH-CNY pricing differential and constrains tw to lie between 0 and 1.5 to exclude short

sales and to allow for at most 50% leverage.

The CER of the portfolio is

2ˆ5.0ˆpppCER , (14)

where p and 2ˆp are the ample mean and variance, respectively, for the investor’s portfolio over

the q forecasting evaluation periods. The CER gain is the difference between the CER for the

investor who uses a predictive regression forecast of market return generated by Eq. (9) and the

CER for an investor who uses the historical average forecast generated by Eq. (11). We multiply

this difference by 4 so that it can be interpreted as the monthly portfolio management fee that an

investor would be willing to pay to have access to the predictive regression forecast instead of the

historical average forecast. To examine the effect of risk aversion, we consider portfolio rules

based on risk aversion coefficients of 5. In addition, we also consider the case of 50bps transaction

cost which is generally considered as a relatively high number.

For assessing the statistical significance, we test whether the CER gain is indistinguishable

from zero by applying the standard asymptotic theory (DeMiguel, Garlappi, and Uppal, 2009). In

addition, we also calculate the weekly (monthly) Sharpe ratio of the portfolio which is the mean

portfolio return in excess of the risk-free rate divided by the standard deviation of the excess

portfolio return. Following again DeMiguel, Garlappi, and Uppal (2009), we use the approach of

Jobson and Korkie (1981) corrected by Memmel (2003) to test whether the Sharpe ratio of the

portfolio strategy based on predictive regression is statically indifferent from that of the portfolio

strategy based on historical average.

The fifth through eighth columns of Table 4(a) report the monthly results of the average

utility gains for each of the individual predictive regression models, Sharpe ratio, turnover ratio,

and utility gains net of transaction cost, respectively. As shown in the first row, the CER for the

portfolio based on the historical average forecast is 4.7452% for January 2014 through November

2015. The CER gains are positive for individual attention indices in Panel A, while their gains are

lower than the historical average. Specifically, the macro, stock, and self attention yield CER gains

of 2.7252%, 0.5786%, and 2.5652%, respectively. As for the macroeconomic index, while it also

generates a positive CER gain of 1.6094%, the result is lower than that of macro and self attention.

The three attention indices produce higher monthly Sharpe ratios than that of the historical

average, with macro attention generating the highest ratio of 1.1152. The macroeconomic index

also delivers a Sharpe ratio of 0.7746 that is higher than that of historical average. The average

turnover is 11.6397% for the historical average. Portfolio based on attention indices turn over

approximately 1/10 to 1/5 times less often than the historical average portfolio and the

macroeconomic index portfolio turns over roughly 1/4 times as much. After accounting for

transaction cost, the relatively low turnovers for attention indices reduce the CER gains but still

remain positive, with self attention index provides the highest CER gains of 2.5508%. The

macroeconomic index yields a net-of-transaction-cost CER of 0.2051% which is lower than the

counterparts of all three attention indices.

The Panel B of Table 4(a) reveals that portfolios based on attention indices combinations

generally outperform those based on individual index. All attention indices combinations deliver

sizable CER gains in the fifth column, reaching a maximum of 376 basis points. Portfolios based

on the combinations turn over approximately 1/5 times less often than the historical average

portfolio. Due to this turnover, the net-of-transactions-costs CER gains are positive—and as high

as 295 basis points—for all the attention indices combinations. Panel C of Table 4(a) reports

performance measures for combination forecasts based on three attention indices and

macroeconomic index together. The CER gains and net-of-transaction-costs CER gains are

positive for all combinations. Specifically, the CER gains for simple average model (1.7711%)

and discount MSFE model (1.7710%) are relatively less than that of a Kitchen sink model

(3.4601%), Bayesian model (3.4602%) or diffusion index model (3.0674%). While after

accounting for transaction costs, the simple average model and discount MSFE model generate

higher gains of 1.7612% and1.7418% than other three models, which are 0.6144%, 0.6146, and

1.6311%, respectively. The monthly Sharpe ratios are approximately around 1 compared to that of

historical average, while the turn-over ratios are considerably lower than that of historical average.

This may be due to the short-lived nature of investor attention (Yuan, 2015). Generally, the CER

gains and net-of-transaction-cost CER gains are slightly decreased after considering

macroeconomic index in combinations compared to those of purely attention indices combinations.

The asset allocation exercise in Table 4(a) demonstrates substantial economic value of combining

information from macro, stock, and self attention indices.

Table 4(b) reports the results of portfolio analysis based on weekly data. As shown in the first

row, the CER of the portfolio based on historical average forecast is 11.3281% for December 2013

to November 2015. The CER gains are positive for individual attention index in Panel A, with

macro attention, stock attention, and self attention providing gains of more than 700 basis points,

which are in accord with the sizable 2

OSR statistics in Table 3. The three attention indices produce

hither weekly Sharpe ratios than that of the historical average, with macro attention generating the

highest ratio of 0.7419. The average turnover is 2.3084% for the historical average. Although the

macro attention portfolio turns over roughly 2.5 times more often than the historical average

portfolio, it still improves the net-of-transactions-costs by 922 basis points. Portfolios based on

stock attention self attention terms also provide sizable CER gains of 8.0948% and 7.1096%, with

the Sharpe ratios of 0.6556 and 0.6065, higher than that of the historical average portfolio. Due to

their moderate turnover ratios, which are equal to or less than that of the historical average, their

net-of-transaction-costs CER remain considerably high as 8.0599% and 6.2023%, respectively.

The overall level of weekly CER and net-of-transaction-costs CER are positive and considerably

higher than those of portfolios based on monthly attention data. The performance of individual

investor attention indices not only demonstrates the substantial economic value of weekly

attention indices but also indicates that investor attentions provide information more powerful at

high frequency.

4.4 Carry trade predictability

In this section, we examine the relations between investor attention indices and currency

carry trades, constructed by selecting onshore RMB to be bought or sold against the US dollar,

based on forward discounts. The currency carry trade, consisting of borrowing in low interest rate

currencies and investing in high interest rate currencies, has been well documented for at least 30

years (Hansen and Hodrick, 1980, 1983; Fama, 1984; Lusting and Verdelhan, 2007; Lusting, et al.,

2011). As a popular trading strategy, carry trade forms a profitable investment portfolio, violates

UIP and gives rise to the “forward premium puzzle” (Fama, 1984; Yu, 2013). Moreover, one

puzzling feature of currencies is that dramatic exchange rate movements occasionally happen

without fundamental news announcements, indicating that many abrupt asset price movements

cannot be attributed to fundamental news events (Cutler et al., 1989; Fair, 2002). As demonstrated

by many studies, carry trade is part of the explanation of foreign exchange rate puzzles

(Brunnermeier et al., 2009). Therefore, by investigating the predictability of CNY carry trade, we

seek to identify a driving force that can possibly explain the failure of UIP and the sudden

exchange rate movements of CNH and CNY unrelated to news announcements.

[Insert Table 5 Here]

Table 5 reports the results of out-of-sample predictive performance of investor attention

indices on CNY carry trades. Similar to section 4.2, we first run the out-of-sample predictive

regression model recursively based on individual investor attention indices, and then employ the

combination forecast models. This exercise is taken twice, based on monthly and weekly data.

Again, we compute three statistics for evaluation, namely2

OSR , MSFE, and MSFE-adjusted

statistics in respective. The monthly results are presented in the second through the fifth columns.

Panel A of Table 5 reports out-of-sample results for bivariate predictive regression forecasts based

on individual attention indices. Only macro attention index provides positive2

OSR statistics

(1.8524%); the other two indices thus fail to outperform the historical average benchmark in terms

of 2

OSR (-17.5652% and -1.5741%) . In accord with the2

OSR statistics, macro attention index

delivers MSFE less than the historical average MSFE while stock and self attention indices

generate MSFEs greater than the historical average MSFE. Nevertheless, the MSFEs for these

three predictors are insignificant at a conventional level according to the MSFE-adjusted statistics

and corresponding P -values. Reminiscent of Goyal and Welch (2008), individual investor

attention indices displays poor monthly out-of-sample predictive ability in Table 5, Panel A. Panel

B, reports the monthly results of combination forecasts. Not surprisingly, all combination models

deliver negative 2

OSR statistics, with the lowest approaching -23.7839% by the Kitchen sink

model, and thus fail to outperform the historical average benchmark in terms of MSFE. Also, their

MSFEs are not significant at a conventional level according to MSFE-adjusted statistics. Overall,

investor attention indices appear to have limited predictive power over monthly CNY carry trades.

The sixth through ninth columns, in Table 5, report the weekly results of out-of-sample

forecast of CNY carry trades. All2

OSR statistics are positive in the eighth column of Panel A; each

of the attention indices thus delivers a lower MSFE than the historical average benchmark. Three

2

OSR statistics exceed 10%, with the highest reaching 67.7609%, and MSFEs for all individual

attention indices are significantly less than the historical average MSFE based on the

MSFE-adjusted statistics. Matching the individual weekly results, combination models also

generate out-of-sample forecasts better than the historical average forecasts. As presented in Panel

B, all combination models provide sizable 2

OSR statistics, with four of them exceeding 50%, and

consequently each delivers a significantly lower MSFE than the benchmark at conventional level.

Overall, investor attention indices appear to have economically and statistically significant

predictability of CNY carry trade at weekly frequency.

5. Conclusion

We investigate the predict power of investor attention on the CNH-CNY pricing differential

in the period of March 2011 through November 2015, by utilizing our aligned attention indices

constructed by applying the partial least squares (PLS) approach following Wold (1966, 1975) and

Kelly and Pruitt (2013, 2014). For interest of comparison, we also compare the forecast

performance of investor attention indices to that of macroeconomic index. Additionally, we

compute the economic value of investor attention through asset allocation. At last, we explore the

forecast ability of investor attention on CNY carry trade.

Our results show that the aligned investor attention indices display strong marginal in-sample

forecast power for the CNH-CNY pricing differentials at both monthly and weekly frequencies,

clearly on par with that of macroeconomic index. Consistent with in-sample results, individual

investor attention indices also exhibits statistically and economically significant out-of-sample

predictive power on both monthly and weekly CNH-CNY pricing spread. In addition, the

employment of combination forecasts effectively improves the out-of-sample forecast of

individual attention indices. In comparison, macroeconomic index has poor out-of-sample

predictability for the monthly pricing spread, and the inclusion of macroeconomic index in

combination forecast does not strengthen the performance. Furthermore, the investor attention

could generate substantial economic values in asset allocation at both monthly and weekly

frequencies. Finally, we find that investor attention indices provide economically and statistically

significant out-of-sample forecast of the CNY carry trade on weekly basis. In economic sense,

these empirical findings directly and indirectly indicate that investor attention contains

information that drives the persistent pricing differential between CNH and CNY, and investor

attention has much stronger influence, especially out-of-sample forecast power, than

macroeconomic variables on the CNH and CNY exchange rates.

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Figure 1 CNH and CNY spot exchange rate differential

Note: The spot exchange rate data for CNH (at Hong Kong SAR) and CNY span March 2011 until November 2015 (via Datastream). Rhs: in basis points. Lhs:

vis-à-vis USD.

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Lhs:CNY

Table 1 Summary statistics of macro variables

Mean Std. dev. Min Max Skewness Kurtosis Jarque-Bera Auto-correlation

Panel A: weekly data

CNY 6.2584 0.1208 6.0589 6.5767 0.5969 2.6900 12.47*** 0.9723

CNH 6.2605 0.1266 6.0188 6.5650 0.4085 2.4682 10.14*** 0.9677

CT_weekly 1.8355 0.0208 1.8000 1.8820 0.3449 2.5719 6.99*** 0.9729

Panel B: monthly data

IR 1.5167 0.2182 1.1314 1.9459 0.0202 2.2508 2.21 -0.3732

M1 4.8465 0.0946 4.6839 4.7158 -0.4244 1.7543 13.74*** 0.1376

M3 5.0159 0.1849 4.6895 5.3171 0.3419 -0.2009 9.74*** 0.0696

CPI 4.7040 0.0312 4.6405 4.7501 -0.2611 1.8224 11.37*** 0.1112

CT_monthly 1.8339 0.0189 1.8088 1.8796 0.5590 2.3107 4.80*** 0.8577

Note: CNY (CNH) refers to the spot exchange rate of onshore (offshore) renminbi, IR refers to 3-month interest rate, M1 (M3) refers to the index of narrow (broad)

money supply, CT_weekly (CT_monthly) refers to weekly (monthly) carry trade payoffs of Chinese yuan. The spread is calculated as the log difference of CNY and

CNH. Eighth column computes the chi-squared value for Jarque-Bera normality test and *** indicates the 1% level of significance. All macro variables are calculated

in log form. The sample period is from March, 2011 to November, 2015.

Table 2 Attention terms from the full sample

Mean Std. dev. Min Max Skewness Kurtosis Jarque-Bera Auto-correlation

Panel A: self attention

CNH 3.8138 0.1543 3.3673 4.2341 0.2745 2.9627 3.20 0.6642

CNY 3.3135 0.2906 2.7726 4.6042 1.0277 5.0571 37.16*** 0.8849

Panel B: macro attention

Exchange rate 2.7398 0.1524 2.3026 3.2958 0.5432 3.7542 13.52*** 0.8890

CPI 1.1471 0.2211 0.6931 2.0794 0.0855 4.5209 10.01*** 0.5980

PPI 2.5020 0.2657 1.7918 3.1780 0.1527 3.4476 3.16 0.8829

M1 3.5629 0.0825 3.4012 3.8712 0.6259 3.9238 16.98*** 0.5450

M2 3.8885 0.3743 3.4012 4.6052 0.6816 1.6704 15.45*** 0.9863

M3 4.0303 0.0743 3.8712 4.3041 0.6299 2.9358 12.74*** 0.8668

Interest Rate 3.6275 0.1185 3.1781 4.0253 -0.4355 5.6813 22.40*** 0.7008

PPP 3.5926 0.0936 3.2188 3.9703 0.3857 5.9868 22.90*** 0.6626

Financial stress 3.0249 0.2681 2.3029 4.6109 -0.1730 2.6435 2.75 0.3295

GLI 4.4098 0.7846 4.0943 4.6051 -1.0368 4.9751 37.00*** 0.7869

VIX 1.9128 0.1901 1.6094 3.0445 0.0361 1.5862 24.65*** 0.6430

ASX 3.7749 0.1461 3.2958 4.4659 0.2133 0.7073 29.72*** 0.8006

SPASX 3.1522 0.3257 2.1972 4.6052 0.6212 5.1009 23.84*** 0.7001

Panel C: stock attention

Gold prices 1.0869 0.3759 0.6931 2.5649 0.9491 4.1356 28.88*** 0.8314

Gold price 2.9575 0.2676 2.5649 4.6052 1.9527 9.6193 26.64*** 0.7690

Depression 1.4216 0.1461 1.0986 1.7918 -0.4108 3.2800 7.43*** 0.6385

Gold 4.0439 0.0958 3.8918 4.6052 2.1794 11.2283 21.16*** 0.6596

Economy 1.6593 0.1455 1.0986 1.9459 -0.9544 3.7523 26.74*** 0.7251

Frugal 0.5185 0.3016 0.0000 0.6931 -1.1422 2.3047 37.89*** 0.5512

GDP 2.2014 0.1525 1.6094 2.5649 -0.9276 4.3605 29.53*** 0.7464

Bankruptcy 2.0778 0.2832 1.3863 2.6391 0.1426 1.7609 55.11*** 0.8902

Unemployment 0.7220 0.1368 0.3266 0.9704 -0.3544 1.9928 35.81*** 0.8806

Bankrupt 3.0113 0.2512 2.4849 4.6052 1.3217 9.2517 65.77*** 0.7198

Car donate 0.6495 0.1984 0.0000 1.0986 -2.4311 9.5862 64.20*** 0.1984

Expense 0.7070 0.1450 0.0000 1.0986 -1.0037 15.3015 69.58*** 0.4352

Donation 1.5229 0.1397 1.3863 2.3979 1.4985 9.8726 73.47*** 0.5508

Default 3.6530 0.0794 3.4657 3.9703 0.8429 4.4669 27.32*** 0.8048

Benefits 4.3988 0.1013 4.0604 4.6052 -0.4450 3.0432 7.53*** 0.8239

Unemployed 0.4029 0.3427 0.0000 0.6931 -0.3296 1.1086 24.60*** 0.7201

Note: Table 2 presents the summary statistics of attention terms, derived from Goolgle Search Volume Index and computed in log form. Eighth column computes the

chi-squared value for Jarque-Bera normality test and *** indicates the 1% level of significance. The sample period is from March, 2011 to November, 2015.

Table 3 In-sample Predictive Regression Estimation Results

Predictor Slope coefficient t statistic %2R

Panel A: monthly estimates

Macro attention 0.0116*** 5.7556 51.66

Stock attention 0.0123*** 6.4562 57.35

Self attention 0.0064*** 2.4481 16.20

Macro variables 0.0104*** 4.8937 43.58

Panel B: weekly estimates

Macro attention 0.0138*** 4.4022 11.93

Stock attention 0.0179*** 5.7207 18.62

Self attention 0.0137*** 4.3064 11.48

Note: Pale A reports estimates from the OLS regressions of monthly differentials on four indices

generated from macro attention terms, stock attention terms, self attention terms, and macro

variables, respectively. Pale B reports estimates from the OLS regressions of weekly spread on

three attention indices. In-sample period is from March, 2011 to December, 2013. *** indicates the

1% level of significance.

Table 4(a) Evaluations for out-of-sample forecast of CNH-CNY differentials (monthly)

MSFE (‰) adjusted

MSFE

-

(%)2

osR

P

-value

)(ann

(%)

Sharpe

ratio

Average

turnover

（%）

)(ann

cost=50bps(%)

2)(e

(‰)

2)(eVar

(‰)

HA 0.0245 4.7452 0.5580 11.6397 4.0251 0.0203 0.0042

Panel A: bivariate predictive regressions

Macro attention 0.0142 2.4030 42.0116 0.0081 2.7252 1.1152 2.9911 1.2708 0.0047 0.0095

Stock attention 0.0205 1.8622 16.3614 0.0313 0.5786 0.6645 1.0021 0.5723 0.0152 0.0053

Self attention 0.0136 3.1767 44.5094 0.0007 2.5652 1.0508 1.0091 2.5508 0.0083 0.0054

Macro variables 0.0246 0.1855 -0.2706 0.4264 1.6094 0.7746 2.9810 0.2051 0.0194 0.0052

Panel B: attention indices combinations

Kitchen sink 0.0126 3.1206 48.5608 0.0009 3.7611 1.2780 2.9842 2.2951 0.0007 0.0119

SIC 0.0141 2.7180 42.3235 0.0033 3.7661 1.2783 2.9948 2.2994 0.0013 0.0128

POOL-AVG 0.0147 2.6304 40.1420 0.0043 2.5652 1.0508 1.0091 2.5508 0.0089 0.0058

POOL-DMSFE 0.0142 2.5948 41.9447 0.0047 2.5050 1.0511 1.0062 2.4885 0.0079 0.0063

Diffusion index 0.0144 2.8094 41.3218 0.0025 2.0298 0.9126 2.9857 0.6058 0.0081 0.0063

Panel C: all indices combinations

Kitchen sink 0.0156 2.7276 36.1917 0.0032 3.4601 1.2507 4.9665 0.6144 0.0004 0.0152

SIC 0.0163 2.4676 33.6179 0.0068 3.4602 1.2507 4.9666 0.6146 0.0019 0.0144

POOL-AVG 0.0163 2.6255 33.7271 0.0043 1.7711 0.9016 1.0063 1.7612 0.0111 0.0051

POOL-DMSFE 0.0153 2.5158 37.5859 0.0059 1.7710 0.9022 1.0034 1.7418 0.0094 0.0059

Diffusion index 0.0119 2.4569 51.3478 0.0070 3.0674 1.0478 2.9894 1.6311 0.0048 0.0071

Note: Panel A reports monthly forecast results of four indices generated from macro attention terms, stock attention terms, self attention terms, and macro variables,

respectively. Panel B reports monthly combination forecast results of three attention indices and Panel C reports monthly combination forecast results of all four

indices. Kitchen sink, SIC, POOL-AVG, POOL-DMSFE, and Diffusion index refer to five combination methods, namely, kitchen sink forecast, combination forecast

based on Bayesian model (Cremers, 2002), simple combination forecast, discount MSFE combination forecast (Rapach et al., 2010), and diffusion indices forecast

(Ludvigson and Ng, 2007; Neely et al., 2012). First row reports the results of historical average as benchmark. Out-of-sample period is from January, 2014 to

November, 2015.

Table 4(b) Evaluations for out-of-sample forecast of CNH-CNY differentials (weekly)

MSFE

(‰) adjusted

MSFE

-

(%)2

osR

P

-value

)(ann

(%)

Sharpe

Ratio

Average

Turnover

（%）

)(ann

cost=50bps(%)

2)(e

(‰)

2)(eVar (‰)

HA 0.0302 11.3281 0.2830 2.3084 10.8880 0.0166 0.0135

Panel A: bivariate predictive regressions

Macro attention 0.0186 3.2686 38.3716 0.0005 11.1133 0.7419 5.1290 9.2266 0.0057 0.0129

Stock attention 0.0223 4.4670 25.9971 0.0000 8.0948 0.6556 1.0490 8.0599 0.0088 0.0135

Self attention 0.0270 2.8400 10.6620 0.0023 7.1096 0.6065 2.9810 6.2023 0.0126 0.0144

Panel B: attention indices combinations

Kitchen sink 0.0206 3.0739 31.6410 0.0011 10.1803 0.7000 4.9367 8.3804 0.0062 0.0145

SIC 0.0189 3.1970 37.2768 0.0007 11.1135 0.7419 5.1288 9.2446 0.0059 0.0131

POOL-AVG 0.0216 3.6250 28.5332 0.0001 9.3167 0.6616 1.0561 9.2782 0.0088 0.0128

POOL-DMSFE 0.0202 3.3068 32.9828 0.0005 9.3415 0.6632 1.0558 9.3045 0.0080 0.0123

Diffusion index 0.0192 3.4904 36.4236 0.0002 10.8231 0.7243 1.0646 10.7799 0.0059 0.0133

Note: Panel A reports weekly forecast results of three indices generated from macro attention terms, stock attention terms, self attention terms, respectively. Panel B

reports weekly combination forecast results of three attention indices. Kitchen sink, SIC, POOL-AVG, POOL-DMSFE, and Diffusion index refer to five combination

methods, namely, kitchen sink forecast, combination forecast based on Bayesian model (Cremers, 2002), simple combination forecast, discount MSFE combination

forecast (Rapach et al., 2010), and diffusion indices forecast (Ludvigson and Ng, 2007; Neely et al., 2012). First row reports the results of historical average as

benchmark. Out-of-sample period is from January, 2014 to November, 2015.

Table 5 Evaluations for out-of-sample forecast of carry trade

Monthly results Weekly results

MSFE

(‰) adjustedMSFE - (%)2

osR P -value MSFE

(‰) adjustedMSFE - (%)2

osR P -value

Historical Average 0.1927 0.0964

Panel A: bivariate predictive regressions

Macro attention 0.1892 0.7955 1.8524 0.2131 0.0854 5.5084 11.3300 0.0000

Stock attention 0.2266 -0.5816 -17.5652 0.7196 0.0714 4.6885 25.8536 0.0000

Self attention 0.1958 -1.1694 -1.5741 0.8789 0.0311 3.9161 67.7069 0.0000

Panel B: attention indices combinations

Kitchen sink 0.2386 -0.4767 -23.7839 0.6832 0.0310 4.0582 67.8270 0.0000

SIC 0.2296 -0.4661 -19.0993 0.6794 0.0311 4.0524 67.7240 0.0000

POOL-AVG 0.2010 -0.5896 -4.2709 0.7223 0.0491 4.4316 49.0868 0.0000

POOL-DMSFE 0.2019 -0.6626 -4.7319 0.7462 0.0396 3.9451 58.8782 0.0000

Diffusion index 0.2054 -0.9309 -6.5895 0.8241 0.0315 4.0993 67.2752 0.0000

Note: Panel A reports monthly (weekly) forecast results of three indices generated from macro attention terms, stock attention terms, self attention terms, respectively.

Panel B reports monthly (weekly) combination forecast results of three attention indices. Kitchen sink, SIC, POOL-AVG, POOL-DMSFE, and Diffusion index refer

to five combination methods, namely, kitchen sink forecast, combination forecast based on Bayesian model (Cremers, 2002), simple combination forecast, discount

MSFE combination forecast (Rapach et al., 2010), and diffusion indices forecast (Ludvigson and Ng, 2007; Neely et al., 2012). First row reports the results of

historical average as benchmark. Out-of-sample period is from January, 2014 to November, 2015.