An examination of investor sentiment effect on G7 stock market returns

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our findings of equity put-call ratio (PCR) indicate that when PCR is low, value .... University of Michigan's Consumer Confidence Index Survey, the Conference.
An examination of investor sentiment effect on G7 stock market returns Deven Bathia

Don Bredin

University College Dublin



University College Dublin†

Abstract This paper examines the relationship between investor sentiment and G7 stock market returns. Using a range of investor sentiment proxies, viz. investors’ survey, equity fund flow, closed-end equity fund discount and equity put-call ratio, we examine if investor sentiment has significant influence on aggregate market returns, value stocks returns and growth stocks returns. Using monthly data for the period January 1995 to December 2007, our results indicate that investor sentiment has significant influence on stock market returns for different forecasting horizons. We find consistent results to previous studies in that when investor sentiment is high (low), future returns are low (high), thus showing negative relationship between investor sentiment and future returns. In addition, our results also show that the effect of survey sentiment is stronger for value stocks than for growth stocks whereas equity fund flow has significant effect on aggregate market returns and value stocks returns. Our results of closed-end equity fund discount show significant negative effect on value stocks returns and significant positive effect on growth stocks returns. And lastly, our findings of equity put-call ratio (PCR) indicate that when PCR is low, value stocks outperforms over next month by 6 basis points. However we did not find any evidence of the effect of PCR on aggregate market returns and growth stocks returns. Preliminary draft

Keywords: Investor sentiment, Consumer confidence, Mutual funds, Closed-end funds, Put-Call ratio JEL Classification: G12, G14, G23, G13 ∗

E-mail: [email protected]. E-mail: [email protected]. Corresponding author: Don Bredin, Graduate School of Business, University College Dublin, Blackrock, Co Dublin, Ireland. †

1

Introduction

Classical asset pricing theory (e.g. CAPM) assumes financial markets to be always efficient, though degree of efficiency may vary. It rules out the element of investor sentiment in asset pricing. It states that securities prices will be at par with its fundamental value due to the presence of rational investors. It further states that arbitrageurs play significant role in minimizing the volatility in security prices which may be due to the presence of irrational investors. Empirically there is evidence of prolonged price anomalies in the stock market. Previous literature attempted to relate these price anomalies to the presence of investors’ under-reaction and overreaction, De Bondt and Thaler (1985, 1987), Barberis et al (1998) and Daniel et al (1998). Researchers also attempted to link the price anomalies to noise trader theory and found that some investors do indeed trade on noise instead of fundamentals, De Long et al (1990) and Black (1986). Often lately, different terminologies viz. confidence, anxiousness, optimism, pessimism, nervousness or irrational exuberance1 , have been used by media to describe the behavior of stock market investors. Recent evidence [including Baker and Wurgler (2006) and Brown and Cliff (2005)] have highlighted the role of uninformed demand shocks and limits to arbitrage as potential explanations. Brown and Cliff (2005) highlight that investor sentiment is driven by a persistent uninformed demand shocks, while limits to arbitrage will deter informed traders from trading. Limits to arbitrage may be either due to high trading costs, financing costs or information costs that rational investors may have to incur in order to take an advantage of market mispricing. Shliefer and Vishny (1997) derive a model where they show that in extreme circumstances, professional arbitrageurs may not be successful in bringing security prices back to its fundamental values. Hence the recent finding of investor sentiment and stock returns relationship is not consistent with the classical framework. The behavioral explanation2 of the presence of investor sentiment causing securities mispricing still continue to remain widely controversial topic in finance literature. Many researchers therefore continue to explore in defense for different sentiment measures; some of these measures till date still remains highly debatable. For instance, closed-end fund dis1

Alan Greenspan first used the term ‘irrational exuberance’ at black tie meeting in Washington D.C.

in December 1996 to describe the behavior of stock market investors. The immediate follow up televised speech rattled the world stock market by an average of 3% 2 See Subrahmanyam (2007) for detailed literature of behavioral finance

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count, which is considered as a measure of small investor sentiment, still remains a puzzle3 . Previous literature have linked IPO first day returns to investor enthusiasm thus concluding that IPO are mostly underpriced4 . Other sentiment measures like investors’ survey, trading volume, mutual fund flow, retail investors trade, dividend premium and insider trading have all been considered as behavioral factors in explaining investors’ optimism and pessimism5 With the exception of survey data6 , this is the first international study examining the role of investor sentiment, using an extensive set of proxies, on the aggregate market returns, value stocks returns and growth stocks returns of G7 nations7 . A range of sentiment proxies included in our study are consumer confidence index, equity fund flow8 , closed-end equity fund discount and equity put-call ratio. We examine the role of sentiment for the individual countries and a panel of G7 countries cases. The cross sectional nature of both sentiment proxies and the cross country analysis will provide considerably greater power to our tests and a natural sensitivity test to the previously reported results predominantly on U.S. data. Previous studies mainly attempted to find the effect of investor sentiment on overall market returns. Only handful of studies account for investor sentiment effect on value stocks returns and growth stocks returns. For instance, Brown and Cliff (2004) find strongest relationship between institutional investor sentiment and large stocks and ruled out the conventional belief of individual investor sentiment affecting only small stocks returns. Our results show that consumer confidence and equity fund flow have significant effect on stock returns. We find that when the beginning of period survey sentiment is high (low), subsequent returns are low (high)9 . Further, the effect of survey sentiment decreases with the increase in forecast horizon. Our cross country results are particularly informative in relation to the role of limits to arbitrage. The effect of survey sentiment over next 3 month horizon is usually greater for all the countries if a particular country has significant 3

See Lee et al (1991), Chen et al (1993), Chopra et al (1993a, 1993b), Brauer (1993), Elton et al (1998),

and Russel (2005) for detailed study of closed-end fund discount 4 See Ritter (2003) and Ljungqvist (2006) for detailed discussion of IPO underpricing 5 See Baker and Wurgler (2007) for detailed explanation of each of these sentiment measures 6 See Schmeling (2009) and Verma and Soydemir (2006); their research is mentioned in brief later in literature review section 7 G7 nations include U.S., Canada, U.K., France, Germany, Italy and Japan 8 Equity fund flow and net sales are used interchangeably in rest of the paper 9 See Fisher and Statman (2000, 2003), Baker and Wurgler (2006) and Brown and Cliff (2005)

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coefficient in 1 month forecast horizon except France. For the U.S. we find consistent results in which only growth stocks are victim of survey sentiment, Brown and Cliff (2005). Our study of equity fund flow effect on stock returns show that the concurrent equity fund flow is positively related to stock returns and lagged equity fund flow is negatively related to stock returns. Our panel study findings indicate the presence of temporary price pressure on aggregate market and value stocks. We found France to be only country that displays an evidence of price pressure on value stocks due to an increase in equity fund flow at time t. The study of closed-end equity fund (CEEF) discount effect on stock returns indicate the presence of significant negative relationship between CEEF discount and value stocks returns and significant positive relationship between CEEF discount and growth stocks returns. Further, the CEEF discount effect minimizes but remains significant when we introduce industry-return indices in our calculation. This indicates that when CEEF discount decreases at time t, value stock increases in the same month whereas growth stocks decreases for the same period. Our CEEF discount study of individual countries show negative relationship between CEEF discount and the U.S. value stocks returns and positive relationship between growth stocks returns and CEEF discount for the U.S., Canada and U.K. When we examine the effect of equity put-call ratio (PCR) on future stock returns, we find that when PCR is low, value stocks yields positive returns over next month by 6 basis points. Our individual country study of PCR effect on stock returns show that low PCR yields positive returns in aggregate market and growth stocks in the U.S. and negative returns in aggregate market and growth stocks in Canada over next month.

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Related Literature

Previous papers have identified different sentiment proxies and performed empirical studies to determine its influence on aggregate market returns and its ability to predict future returns. There are a number of methods to proxy market sentiment. Surveys are regularly being conducted in many countries to see how investors foresee the direction of both stock market and the overall economy. In the US, investors’ surveys are regularly conducted by many organization; e.g. American Association of Individual Investors (AAII), Investors Intelligence (II), University of Michigan’s Consumer Confidence Index Survey, the Conference Board, UBS Gallup Survey, to name a few. Fisher and Statman (2003) find that increase in the consumer confidence index is associated with an increase in the bullishness of indi3

vidual investors. Lemmon and Portniaguina (2006) find consumer confidence index to be useful in both forecasting small-cap stock returns and also the returns of stocks with low institutional ownership. Using II survey data as a measure of investor sentiment, Brown and Cliff (2004) find that investor optimism is associated with low subsequent returns as valuation levels return to intrinsic value. Similarly, Qiu and Welch (2006) find consumer confidence index to be useful predictor of excess returns on small deciles stocks. Some researchers have used closed end fund discount (CEFD) as sentiment proxy to determine its effect on stock market returns. The size of discount (difference between fund’s NAV and its market value) determines the level of investor sentiment. The smaller the size of discount, the greater is the bullishness observed in investors’ behavior; and larger the size of discount, the greater is the bearish behavior observed. Zweig (1973) finds CEFD to be a measure of an individual investors’ expectations. Using CEFD data, Lee et al (1991) find that when CEFD is high, investors are pessimistic about the future returns and when CEFD is low, investors are optimistic about the future returns, thus concluding CEFD as a measure of investor sentiment. By using three different sentiment proxies CEFD, the ratio of odd-lot sales to purchases and net mutual fund redemptions - Neal and Wheatley (1998) find closed-end fund discounts and mutual fund net redemptions to be positively related to small firm expected returns and very little evidence of oddlot ratio to be predictor of the small firm returns. Using individual investor survey data from American Association of Individual Investor (AAII), Brown (1999) finds unusual level of individual investor sentiment to be associated with greater volatility in closed end funds. Elton et al (1998) and Doukas and Milonas (2004) find no evidence of small investor sentiment, measured by closed-end funds discount, as a priced factor in return generating process. Thus there have been mixed opinions in past literature about whether closed end fund discount, as a measure of small investor sentiment, has any role in return generating process. Recently derivatives data, as sentiment proxy, have been widely studied to determine its importance in predicting future returns. Despite the constraints in derivatives data availability for international markets (excluding the U.S.), handful of research has been undertaken in this area. Put-Call ratio (PCR) which is constructed from equity options volume data is widely used sentiment measure besides VIX, open interest and options premium. A high PCR indicates pessimism in the market whereas low PCR indicates optimism in the market. Pan and Poteshman (2006) examined the information contained in option 4

volume in future stock price movement and find that stocks with low PCR outperforms stocks with high PCR over next day by 40 basis points and by 1% over 1 week. Bandopadhyaya and Jones (2008) finds PCR to be better measure than Volatility Index (VIX) for predicting future returns. Lee and Song (2003) find that value stocks outperforms growth stocks when PCR is low and growth stocks outperforms marginally or performs equally well to value stocks when PCR is high. Many practitioners also consider equity fund flow to be a measure of investor sentiment. Warther (1995) studies the relationship between US mutual fund flows and market returns. By using weekly data, he finds positive relationship between fund flows and subsequent returns and with monthly data, he finds negative relationship between returns and subsequent fund flows. By using daily equity fund flow data, Edelen and Warner (1998) find positive relationship between equity fund flow and concurrent market returns. Similarly, Brown et al (2002) find evidence of relationship between daily mutual fund flows and stock market returns of the U.S. and Japan and concludes that daily mutual fund flows can be used as a proxy for investor sentiment. Frazzini and Lamont (2006) use mutual fund flows as sentiment proxy and find high sentiment predicts low future returns and growth stocks tend to be usual victim of high sentiment. Indro (2004) examines the relationship between net aggregate equity fund flow and investors’ survey and finds when net aggregate equity fund flow is higher during any given week, individual investor becomes more bullish in that same week. As noted earlier, vast majority of literature focused mainly on study of investor sentiment effect on the U.S. stock market returns. Very few international studies have been conducted to measure sentiment effect on stocks returns; and these studies considered only investors’ survey sentiment measure. For instance, Schmeling (2009) examines the effect of consumer confidence index on stock market returns of 18 industrialized countries and finds negative relationship between consumer confidence and forecasted aggregate stock market returns. By investigating the degree of sentiment effect on stock market returns, Schmeling finds sentiment effects to be greater for countries where stock market is less institutionalized, have low market integrity and where investor are more prone to herd like behavior. Similarly, Verma and Soydemir (2006) study the US individual and institutional investor sentiment effects on international stock market returns by using survey data conducted by AAII and II. They find US investor sentiment had significant influence on international stock market returns; though the effect of sentiment on stock market returns were differ5

ent for different countries depending on trade ties with US and its institutional structure. This paper will be the first international study that attempts to find the effects of different sentiment proxies on aggregate market returns, value stocks returns and growth stocks returns.

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Data and Methodology

To study the effect of investor sentiment on aggregate market returns, value stocks returns and growth stocks returns10 of G7 nations, we employ monthly returns data for the period January 1995 to December 2007 from the Kenneth French’s data library11 . Please insert table 1 about here Table 1 provides a detail account of different sentiment proxies that are adopted. Consumer confidence index data for the U.S. market is taken from the University of Michigan Surveys of Consumers. The consumer confidence index data for Canada and Japan12 are obtained from the Conference Board of Canada and the Cabinet Office, Japan respectively. While data for the U.K., France, Germany and Italy are obtained from “Directorate Generale for Economic and Financial Affairs” (DG ECFIN). The details of consumer confidence index calculation is given in Appendix A. We obtain the equity fund flow data for the U.S. from the Investment Company Institute (ICI) and that of Canada from the Investment Funds Institute of Canada (IFIC). The fund flow data for the U.K market is sourced from the Investment Management Association, U.K (IMA). The equity fund flow data and total net equity fund assets of Canada and the U.K. are converted in U.S. Dollars by considering the month end foreign exchange rate. The data for France, Germany, Italy and Japan are all obtained from Lipper, Thomson 10

Stock market returns are from value weighted portfolio including dividends in US Dollar. We segregate

the aggregate stock market returns into value and growth stocks returns by considering the top 30% of stocks sorted by book-to-market ratio as value stocks and the bottom 30% of stocks sorted by book-tomarket ratio as growth stocks. 11 http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data library.html 12 The consumer confidence index data for Canada (available until December 2001) and Japan (available until March 2004) is only available on a quarterly frequency. Monthly observations were estimated via interpolation by adopting a piecewise cubic spine methodology

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Reuters and were made available in the U.S. Dollars. The equity fund flow data from Lipper is available from January 2002 onwards whereas that of U.S., Canada and U.K. is available from January 1995 onwards13 . We normalize the equity fund flow for each country by taking the percentage of current equity fund flow to its respective total net equity fund assets, Indro (2004). The closed-end equity fund (CEEF) data for G7 nations are obtained from the Morningstar

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. We specifically exclude bond funds within closed-end fund family. For each

country, we obtain CEEF’s monthly NAV and price data in U.S. dollars and then calculate each fund’s discount. Following Lee et al (1991) and Doukas and Milonas (2004), we construct value-weighted index of discount (VWD) for each country at monthly level: t weightt Discountit VWDt = Σni=1

(1)

where, NAVit NAVit

(2)

( NAVit − Priceit ) X 100 NAVit

(3)

weighti =

Discountit =

t Σni=1

NAVit = Net asset value of each individual CEEF i at time t Priceit = Market price of each individual CEEF i at time t nt = the number of funds with the available NAVit and Priceit We also construct change in value weighted index of discount (∆VWD) and used that as a proxy to measure CEEF effect on value stocks returns and growth stocks returns: ∆ VWDt = VWDt − VWDt−1 13

(4)

We use net equity fund flow data including distributions for the reasons given in Appendix B. This

Appendix also details the procedure of collecting and maintaining equity fund flow data by different sources 14 Refer Appendix C for detailed data description of closed end equity funds considered in our research

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To calculate equity put-call ratio, we obtain equity options volume data from various sources. The equity derivatives data for the U.S., Italy and Japan15 is obtained from Chicago Board of Options Exchange (CBOE), Borsa Italiana and Tokyo Derivatives Exchange respectively; while those of Canada and Germany16 is obtained from Montreal Derivatives Exchange and Deutsche Borse Group respectively. The equity derivatives data for both the U.K. and France is sourced from NYSE Euronext17 . Following Pan and Poteshman (2006), we calculate equity put-call ratio (PCR) as follows:

Put-Call Ratioit =

Put Volumeit Put Volumeit + Call Volumeit

(5)

where, put volume for country i is total number of put contracts traded in a month t and call volume in country i is total number of call contracts traded in a month t. In our computation of equity PCR, we consider total equity options volume data (including European and American options). Hence it takes into account all the four different equity options trades, viz. ‘open-buys’ and ‘close-buys’ initiated by the buyer to open new long position and close existing short position respectively and ‘open-sells’ and ‘close-sells’ initiated by a seller to open new position and close existing long position respectively. Our constructed equity PCR differs from that of Pan and Poteshman (2006) where they study the effect of open-buy PCR in predicting t day ahead stock returns. Please insert table 2 and 3 about here The descriptive statistics of stock market returns, consumer confidence index and equity fund flow is reported in table 2. We calculate the presence of autocorrelation in the consumer confidence index series at different lags. Due to the presence of high autocorrelation in the series, we tested for a unit root in the series. The results of the panel unit root test in table 3 confirms our belief that the consumer confidence index series is stationary. In order to study the effect of consumer confidence index on aggregate market returns, value 15

End of the month total equity call and put options traded were made available End of the month total call and put options traded for each individual security was made available 17 End of the day equity put and call options traded for each individual security was made available for 16

the U.K. and France. To arrive at the total equity call and put options traded in any given month, we summed end of the day each individual security call and put contracts traded

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stocks returns and growth stocks returns, we estimate panel fixed effect regression of the following form: i,(k)

i rt+k = δ0

(k)

(k) i,(k)

+ δ1 CCit + δ2 γt

i,(k)

+ ξt+k

(6)

where, k is the number of months from time t for each country i. The right hand side of the equation includes consumer confidence (CC) index for each country i and several other macro economic variables, e.g. annual CPI inflation, annual percentage change in industrial production, de-trended 6-month CD rate, term spread and dividend yield which are incorporated in γ. These macro-economic variables are considered in our regression so as to remove the effect of common risk factors on returns18 . We estimate the above equation using panel fixed-effects approach in which all countries enter the regression jointly. This approach will allow us to have different intercepts for each countries and constant slope coefficients. The panel fixed effect is studied over forecast horizon from 1 month to 24 months. We employ moving-block-bootstrap simulation procedure to overcome predictive nature of regression and so as to account for biased coefficient estimates and standard errors. The moving block bootstrap is performed by employing block length of 6 observations19 . The influence of survey sentiment measure of individual G7 countries’ on its respective stocks returns is also examined over the forecast horizon of 1 month to 12 months. We estimate its coefficient by employing GMM (exactly identified) and employ a similar bootstrapping procedure to that used by Schmeling (2009). The coefficient of individual countries for forecast horizon from 12 month to 24 months is insignificant and therefore not reported here to conserve space. Please insert table 4 about here Due to the presence of high autocorrelation in fund flow series (refer table 2), we tested for a unit root in the series. The result of panel unit root test is given in table 4. The null hypothesis of a unit root is strongly rejected, a result which is consistent with the consumer confidence survey measure. The presence of high autocorrelation in the series indicate that fund flow is highly predictable. As a result, we employ traditional Box-Jenkins method to identify time-series properties of the equity fund flow and decompose the net equity fund 18 19

See Baker and Wurgler (2006), Lemmon and Portniaguina (2006) and Schmeling (2009) We also adopted different block lengths and found consistent results

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flow into expected and unexpected equity fund flow, Warther (1995). By performing the Breusch and Gofrey test of autocorrelation, we determine different AR model (refer table 2) for each country and use that model to find the expected net sales. The unexpected net sales is then calculated by taking the difference between the actual net sales and expected net sales. To study the effect of equity fund flow onto aggregate market returns, value stocks returns and growth stocks returns, we again run the panel fixed effect regression of the following form: i,(k)

rti = δ0

(k)

i,(k)

+ δ1 FFit−k + ξt

(7)

where, k is the number of lags employed from time t for each country i. We study the effect of equity fund flow (FF) at different lags on stock returns. We also study the individual country equity fund flow effect on stock returns in which we run the similar regression for each individual country at different lags. Please insert table 5 about here The descriptive statistics of value-weighted index of (CEEF) discounts and equity PCR is given in table 5. From the table, it is evident that Canadian market has the highest mean VWD of 22.84% with high standard deviation of 4.70% followed after Japan. Despite Japan having highest standard deviation of 5.56%, it is the only country with the lowest mean VWD of 5.15%. In order to study the effect of CEEF discount on stocks returns, we run two different models. In model 1, we run panel fixed effect regression where we study the relation between value stocks returns and growth stocks returns against ∆V W D and aggregate market returns, Lee et al (1991) and Doukas and Milonas (2004). rti = δ0i + δ1 ∆VWDit + δ2 Aggregate returnsit + ξti

(8)

where, r is either value stocks returns or growth stocks returns at time t for each country i, ∆V W D is change is value-weighted index of discounts for each country i at time t. Consistent with Elton et al (1998) and Doukas and Milonas (2004), in model 2 we adopt industry-return indices, an unsystematic component, along with ∆V W D and aggregate market returns. This test will allow us to determine the sensitivity of value stocks returns and growth stocks returns against ∆V W D, industry-return indices and aggregate market 10

returns. We choose FTSE industry-return indices20 (viz. Banking, Basic materials, Industrials and Consumer goods) for two main reasons, viz. a) they represent almost more than three-quarters of the the stocks in which CEEF invests in for each of the respective markets and b) the ease in data availability for the same time horizon. Similarly, we also run model 1 and model 2 for each individual country so as to determine the significance of investor sentiment, measured by ∆V W D, in return generating process. Looking at the statistics of equity PCR given in table 5, we see that Japan is the only market that has highest mean PCR of 0.49 and high standard deviation of 20.32% whereas Canadian derivatives market has both lowest mean PCR of 0.28 and standard deviation of 4.44%. To study the effect of equity PCR on future returns, we again run panel fixed effect regression of the following form: i,(k)

i rt+k = δ0

(k)

i,(k)

+ δ1 PCRit + ξt+k

(9)

where, k is the number of months from time t for each country i. We decided to run panel fixed effect regression starting from the year January 2002 as for almost all the countries, equity derivatives data is available from 2002 onwards21 except for the countries like the U.S. and Japan for which data is available from Jan 1995 and July 1997 onwards respectively. We also run similar regression for each individual countries starting from the date from which data is first available.

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Empirical Results

4.1

Consumer confidence Index:

The results of the panel fixed effects regression of stock returns on consumer confidence index is given in table 6. Please insert table 6 about here It can be seen that there is negative survey sentiment-return relationship for aggregate market returns, value stock returns and growth stock returns over different forecast horizon 20 21

FTSE industry indices are taken from Datastream Refer table 1 for detailed data source and time horizon for which equity derivatives data is available

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from 1 month to 24 months. Panel A reports the effect of sentiment on aggregate market returns. The results indicate that one standard deviation shock in survey sentiment leads to aggregate market returns (refer Panel A) decrease by 48 basis points over next month and by 60 basis points over the next 3 months at 1% significance level. However, the decrease in returns is gradually reduced over 6 month, 12 month and 24 month forecast horizon. The effect of survey sentiment on value stock returns and growth stock returns are similar. For instance, we find one standard deviation shock in survey sentiment decreases value stock returns (refer Panel B) by 59 basis points over next month. Also the decrease in returns over the next 3 month is greater i.e. 79 basis points, than that in the first month. The reduction in persistence of sentiment on returns for aggregate market, value stocks and growth stocks are similar across all the forecast horizons. We also find that consumer confidence has consistently greater impact on value stock returns than the aggregate market and growth stock returns for all horizons. For instance at 1% significance level, aggregate market and growth stock returns decreases by an average of 60 basis points over the next 3 months whereas value stock returns decreases by 79 basis points for the same time horizon. Our results are consistent with the previous studies [see Lemmon and Portniaguina (2006) and Schmeling (2009)] where they all find both a negative survey sentiment-return relationship and value stocks being more influenced by survey sentiment than the growth stocks. However, our results differ from that of Brown and Cliff (2005) and Baker and Wurgler (2006) where they find survey sentiment effect to be stronger for growth stocks than on the value stocks. Please insert table 7 about here The table 7 shows the results of predictive power of survey sentiment for individual countries on stock returns for different forecast horizons from 1 month to 12 months22 . It can be seen that survey sentiment has a significant negative effect on stock returns over 3 month forecast horizon for all the countries except U.K. and Japan. The impact of sentiment disappears for all the countries beyond the 6 month forecast horizon except for value stock returns in Italy and growth stock returns in U.K. Although the consumer confidence survey for Japan represents the most comprehensive survey (accounting for 6,000 households), there is no significant effect of survey sentiment on aggregate market returns, 22

The coefficient of survey sentiment is statistically insignificant for the forecast horizon greater than 12

months. Hence they are not reported here to conserve space

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value stocks returns and growth stocks returns. Overall, the results show that the effect of survey sentiment on stock returns gradually decreases with the increase in forecast horizon as well as the existence of a negative relationship between survey sentiment and future stocks returns.

4.2

Equity Fund Flow:

We run panel fixed effect regression for three different model to study the effect of equity fund flow on aggregate stock market returns, value and growth stock returns. The coefficient and p-value of all the three models are reported in table 8. Please insert table 8 about here In model 1, we regress stock returns at t on equity fund flow at t to t-3. The results show that the stock returns are significant and positively related to concurrent net equity fund flow and significant and negatively related to lagged equity fund flow23 . It can be seen that one standard deviation shock, 0.95%, to equity fund flow, is associated with an increase in aggregate market returns by 59 basis points in the same month. The increase in returns is greater for value stocks than that for the growth socks. For instance, value stocks increases by 84 basis points at time t, whereas growth stocks increases by only 56 basis points. However the aggregate market returns, value stock returns and growth stock returns decreases at subsequent months indicating the presence of negative relation between returns and lagged equity fund flows. Warther (1995) pointed out that the negative coefficient of lagged sales could be due to the possibility of stock prices overreacting to net sales at time t and then reverting in subsequent months or the lagged sales could be responsible for expected concurrent equity fund flows thus affecting returns in subsequent months. Therefore in model 2, we regress returns at t on equity fund flow at t-1 to t-3. The insignificant negative coefficients at all lags of model 2 indicate the presence of lagged sales effect on expected concurrent net equity fund flow. We therefore estimate model 3 in which we regress returns at time t on expected and unexpected concurrent equity fund flow and unexpected net equity fund flow at t-1 and t-2. The results of model 3 shows that the coefficient of unexpected concurrent equity fund 23

We do not report the insignificant coefficient at lags greater than 3 to conserve space

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flow is highly significant and has positive effect on aggregate market returns, value stocks returns and growth stocks returns. However, the coefficients of expected concurrent net equity fund flow and unexpected lagged equity fund flow are insignificant. For instance, one standard deviation shock, 0.95%, to the unexpected concurrent equity fund flow will power up the aggregate stock market returns by 0.82% and value stock returns and growth stock returns by 1.02% and 0.81% respectively. The results indicate the possibility of presence of temporary price pressure due to an increase in concurrent returns. We therefore perform reversal test in the last column of table 8, where we regress unexpected net sales on aggregate market returns, value stock returns and growth stock returns at t-1, t and t+1. If the increase in unexpected equity fund flow causes temporary price pressure on concurrent returns, than we should get significant negative coefficient of subsequent returns. From the results, it can be seen that there is no evidence for the support of feedback trader hypothesis which states that fund flow should lag returns, as mutual fund investors are generally considered to be feedback traders. If mutual fund investor indeed chases past returns, than we would have gotten significant positive coefficient of lagged returns, which is not the case here. The results further shows the evidence of price reversals for aggregate market returns and value stock returns as we get significant negative coefficient of subsequent returns. Therefore in our panel study, we find an evidence that increase in equity fund flow has significant effect on security prices; in other words, an increase in unexpected inflow of equity funds leads to an increase in prices in concurrent month and price reversals in subsequent month for aggregate market and value stocks. Our findings are not consistent with the findings by the previous authors, [Warther (1995) and Edelen and Warner (2001)], where they do not find any support for price pressure hypothesis. We run the above regression of each model for individual countries. The results of model 1 for individual countries are reported in table 9. Please insert table 9 and 10 about here From Panel A of table 9, it is evident that coefficient for all the countries except France is positive and significant at time t for aggregate market returns (refer Panel A). France displays zero effect of concurrent and lagged equity fund flow on aggregate market returns and growth stock returns. Japan is the only country that shows a negative effect of equity fund flow on aggregate market returns and growth stock returns at t and t-1. This means 14

one standard deviation shock, 2.41%, to equity fund flow will lead to decrease in aggregate market returns and growth stocks returns by an average of 1.03% at t and 0.84% at t-1. Table 10 reports the results of model 2 for individual countries. Overall, the table shows that lagged net equity fund flow has insignificant effect on stock returns except for the U.K., France and Japan where coefficient of equity fund flow is statistically significant for value stocks returns at t-1 and t-2. The model 3 for individual countries are reported in table 11. Please insert table 11 and 12 about here The coefficients of concurrent expected fund flow of all the countries are insignificant. Overall, the results show that unexpected concurrent net equity fund flow has a significant positive effect on aggregate market returns, value stock returns and growth stock returns. The lagged component of unexpected equity fund flow for all the countries is statistically insignificant except for the value stock returns of U.K. The negative coefficient of unexpected equity fund flow at t-2 indicates the possibility of price pressure effect on value stock returns of U.K. Both France and Germany display no effect of concurrent unexpected equity fund flow on growth stock returns; and Japan displays no evidence of unexpected concurrent equity fund flow effect on value stock returns. Overall the result suggest that unexpected equity fund flow has significant effect on aggregate market returns, value stock returns and growth stocks returns; and this may be due to the presence of price pressure effect or an information effect. To confirm if the effect of unexpected equity fund flow is due to price pressure or new information, we perform reversal tests in which we regress unexpected equity fund flow on stock returns at t-1, t and t+1. The results are reported in table 12. We find evidence that inflow of unexpected equity fund flow has significant effect on value stocks returns in France as we get significant positive coefficients at t followed by significant negative coefficient at t+1. Thus there is evidence of a price pressure hypothesis that exists in France as previous returns are reversed in the following month. Further, we find Japan is the only country where inflow of unexpected equity funds has a negative effect on aggregate market returns and growth stocks returns at t and t+1. Overall, our individual country study findings (except France and Japan) show positive effect of unexpected equity fund flow on stock returns; this effect may be either due to price pressure or the arrival of new positive information causing security prices to rise.

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4.3

Closed end equity funds:

The results of panel fixed effect regression of both, model 1 and model 2, are given in table 13. As mentioned earlier, in model 1, we regress value stocks returns and growth stocks returns at time t on ∆V W D and aggregate market returns. Please insert table 13 about here From model 1, it is evident that 1% decrease in ∆V W D is associated with an increase in value stocks returns by 19 basis points. However with the drop of 1% in ∆V W D, growth stocks returns decrease by 11 basis points. The significant coefficient of ∆V W D suggest that investor sentiment as measured by ∆V W D has significant influence on both value stocks returns and growth stocks returns. The decrease in discount has positive effect on value stocks returns and negative effect on growth stocks returns. Our findings indicate that investors’ optimism is associated with increase in returns along with decrease in CEEF discount of value stocks returns. These findings contradicts with the findings of Lee et al (1991) where they find smaller stocks, which usually tends to be growth stocks, to be usual victims of investor sentiment. Besides, we found growth stocks to be mainly affected with narrowing CEEF discounts. In order to study the relativeness of ∆V W D to aggregate market returns and industry-return indices, we also run panel fixed effect regression in model 2. Contrary to the previous findings by Elton et al (1998) and Doukas and Milonas (2004), we find significant coefficient of both value stocks returns and growth stocks returns at 5% and 1% significance level respectively. Our results show that 1% decrease in ∆V W D leads to increase in value stocks returns by 10 basis points and decrease in growth stocks returns by 7 basis points. Except for consumer goods returns, we further find that industryreturn indices do enter in return generating process as we get significant coefficients. Also at the same time we see, that there is an increase in R2 for both value stocks returns and growth stocks returns. Thus our findings indicate that CEEF discount does enter into return generating process, similar to the results obtained by Lee et al (1991). Please insert table 14 and 15 about here The study of CEFF discount effect on value and growth stocks returns for individual countries is given in table 14 and 15. Model 1 of table 14 shows that for value stocks returns, we get significant negative coefficient of ∆V W D for the U.S., Canada and Germany 16

indicating that the decrease in ∆V W D is associated with an increase in value stocks returns. Similarly for growth stock returns, we get significant positive coefficient of ∆V W D for the U.S., Canada, U.K. and Italy indicating that increase in ∆V W D leads to increase in growth stocks returns and vice-versa. Model 2 of table 15 shows the effect of ∆V W D on value and growth stocks returns after considering industry-return indices and aggregate market returns. For value stocks returns, we find U.S. as only country that is affected by ∆V W D. For the U.S. market, except for industrials, all other industry-return indices are significant at 1% level indicating that they also enter into return generating process. Growth stocks returns are also significantly affected by ∆V W D for the U.S., Canada and U.K. We get significant positive coefficient for these countries indicating positive relationship between ∆V W D and growth stocks returns. The effect of industry-return indices in return generating process is mixed for different countries. Model 2 also shows significant improvement in R2 over model 1 for both value stocks returns and growth stocks returns for each individual countries.

4.4

Equity Put-Call ratio:

Table 16 presents the results of panel fixed effect regression of current t returns and forecasted t+k returns on equity PCR. The significant and negative coefficient of equity PCR in month t indicates the presence of influence of equity PCR in the same month t returns. Further, our results indicate that only value stocks are significantly affected over next month t+1, a finding similar to the one obtained by Lee and Song (2003). The coefficient of equity PCR for value stocks is significant and negative for t+1 month, which indicates that if one buys value stocks when equity PCR is low or sells value stocks when equity PCR is high, can yield 6 basis points over next month. The fact that investor can yield very low returns in t+1 is because information content in options volume is almost incorporated in the stock price in the month t. Our findings are similar to previous literature where authors have found the fading effect of equity PCR on stock returns, Pan and Poteshman (2006). For t+1 month, we get insignificant coefficient for growth stocks and aggregate market. The effect of equity PCR in t+2 is nil for aggregate market returns, value stocks returns and growth stocks returns. Please insert table 16 and 17 about here

17

Table 17 shows the effect of equity PCR on returns for each individual countries. It is interesting to note that for the U.S. and Canada market, information content in equity options volume gets slowly incorporated in stock price as investors have an opportunity to gain in month t+1. For instance in the U.S., when equity PCR is low an investor can gain aggregate market returns of 7 and 6 basis points in the month t+1 and t+2 respectively. Also if an investor invests in growth stocks with low equity PCR in t, then it would yield him 8 and 7 basis points in the month t+1 and t+2 respectively. However, we get contradicting results for Canadian aggregate market returns and growth stocks returns. For instance, an investment in growth stocks with high equity PCR can help an investor to fetch 46 and 37 basis points in the month t+1 and t+2 respectively. In the case of value stocks, we did not find significant coefficient for all countries in the month t+1. Similarly in the month t+2, all countries except for the U.S. and Canada, we did not find any effect of equity PCR on aggregate market returns, value stocks returns and growth stocks returns.

5

Conclusion

In this paper, we study the effect of consumer confidence index, equity fund flow, closedend fund equity (CEEF) discount and equity put-call ratio, widely considered as sentiment proxies amongst many practitioners, on aggregate market returns, value stocks returns and growth stocks returns. We find that there is a significant negative relationship between survey sentiment and stocks returns. When investor sentiment is high (low) in the given month, subsequent stocks returns are low (high). Our findings indicate that the effect of survey sentiment is stronger on value stocks than that on the growth stocks which is consistent with the previous empirical evidence [e.g. Schmeling (2009) and Lemmon and Portniaguina (2006)]. The results are similar for net equity fund flow where we find negative relationship between returns and subsequent equity fund flow. We find that the increase in an unexpected concurrent equity fund flow has a positive effect on stock returns. In our equity fund flow panel study, we find an evidence of temporary price pressure on aggregate market returns and value stocks returns as we get highly significant estimates of return reversal test. The increase in concurrent price of growth stocks and its subsequent decrease may be either due to price pressure effect or an information effect. Further, we find that value stocks returns and growth stocks returns are significantly affected by the change in the CEEF discount. When CEEF discount narrows, value stocks 18

outperforms and growth stocks under performs; therefore indicating negative relationship between CEEF discount and value stocks returns and positive relationship between CEEF discount and growth stocks returns. Our results remain the same when we add industryreturn indices as natural candidates so as to measure the relativity of CEEF discount with industry-return indices. Our findings are similar to that of Lee et al (1991) but contradicts the findings of Elton et al (1998) and Doukas and Milonas (2004); therefore we conclude that CEEF discount plays significant role in return generating process. Our results of equity PCR indicate that only value stocks yields positive returns in t+1 when PCR is low in the month t. However, we did not find any evidence of equity PCR influencing aggregate market returns and growth stocks returns.

19

Appendix A: Consumer Confidence Index Data Calculation: Consumer surveys have been carried out in almost all the developed countries for more than 20 years. These surveys are conducted to study consumers’ present and future financial situation and their outlook on the economy over next year. The questions in the surveys are usually standardized across all the countries. For instance, the University of Michigan surveys at least 500 consumers households about their present and future financial situation, their business and economic outlook over next 1 year and 5 years respectively and also about their willingness to spend on durable goods in the near future. Similarly, the Conference Board of Canada surveys Canadian households about their present and future financial situation and their level of optimism about the current economic conditions and over the next 6 months. Besides studying the consumers’ current and future financial situation and their outlook on the economy, DG ECFIN also seeks to find the likelihood of consumers ability to save money over the next 12 months. DG ECFIN conduct consumer survey on at least 2,000 households of U.K., Germany and Italy each and 3,300 households of France. Of all, Japan surveys maximum household, i.e. 6,000 households. Their survey questionnaire consists questions similar to that of U.S. and Canada, viz. consumers’ perception of the economy, price expectation and their present and future financial situation. Overall, the consumer survey questions are similar across the G7 countries and hence the outcome of the survey can be made easily comparable. However, the calculation of index differs across countries. For instance, DG ECFIN employs ‘Dainties’ method and Japan employs X-11 of the Census Bureau USA. For ease of comparison, we standardize consumer confidence survey measure across all the countries.

Appendix B: Equity Fund Flow Data Calculation: This appendix details the reason for using the net equity fund flow data including distributions. Equity Fund Flow data has been widely used as sentiment proxy by many practitioners. The net sales of new equity funds is considered as an increase in confidence in the economy by an investor as they are willing to display bullish behavior in the stock market. The Investment Company Institute of U.S. is the national trade association for the investment company industry. Almost all the mutual funds, closed-end equity funds, exchange-traded funds (ETF) and unit investment trust (UIT). domiciled in the U.S. are the members of ICI. For each fund, ICI collects the following information, viz. net new sales, reinvested dividends, net redemptions and net switches between the funds. In order 20

to study the effect of net equity fund flow, previous authors, Warther (1995) and Indro (2004), have used net sales excluding distributions. The similar breakdown of net sales including and excluding distributions is maintained by The Investment Fund Institute of Canada (IFIC). The Investment Management Association, U.K. (IMA), maintains the net sales of equity funds of all the mutual fund managers domiciled in United Kingdom. The net sales data maintained by IMA includes distributions and is net of switches made between the funds and net redemptions. Lipper, subsidiary of Thomson Reuters maintains the database of net equity fund flow including distributions for France, Germany, Italy and Japan with effect from January 2002. They calculate net sales as a difference between total equity fund assets between two months after stripping the performance of all the assets within the funds. The data maintained by them includes reinvested distributions and also takes into account both net redemptions and net switches between the funds. In order to consider consistent data across all the countries, we decide to use net equity fund flow data including distributions valued in U.S. Dollars. For this reason, we employ value weighted stocks returns including re-invested distributions provided by Kenneth French24 .

Appendix C: Details of closed-end equity funds data considered: This appendix discuss different type of closed-end funds collected by Morningstar that are considered in our research. For each fund, Morningstar gathers net asset value (NAV) and price data. NAV data are sourced directly from the fund manager whereas price data are obtained from trading exchanges. In our research, we have considered only conventional closed end equity funds. Bond funds are specifically excluded. Also within closed end equity funds, we do not consider fund warrants, C-shares and split funds in our analysis. Fund Warrants are not considered as they don’t have NAV; C-shares are a way of existing funds raising money and hence are not considered as conventional closed end funds. Split funds are investment trusts with a fixed life, where the shares are divided into more than one category. The simplest form of split funds is a split between capital shares and income shares. The other variation of closed-end funds, which are not in their conventional form, includes zero dividend preference shares, highly geared shares, participating income shares and stepped preference shares. These funds have been specifically excluded in our discount calculation. Lastly, CEEF for which either NAV data or price data is not available are excluded in our VWD calculation. 24

http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data library.html

21

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[14] Daniel, K., Hirshleifer, D., and Subrahmanyam, A., 1998. Investor psychology and security market under and overreactions. Journal of Finance 53. 1839 1886. [15] De Bondt, W., and Thaler, R., 1985. Does the stock market overreact?. Journal of Finance 40. 793-805. [16] DeBondt, W., and Thaler, R. H., 1987. Further evidence on investor overreaction and stock market seasonality. Journal of Finance 42. 557-581 [17] De Long, J.B., Shleifer, A., Summers, L. and Waldmann, R., 1990. Noise Trader Risk in Financial Markets. Journal of Political Economy 98. 703-738. [18] Doukas, J., and Milonas, N., 2004. Investor Sentiment and the closed end fund puzzle: Out of sample evidence. European Financial Management 10. 235-266. [19] Edelen, R., and Warner, J., 2001. Aggregate price effects of institutional trading: A study of mutual fund flow and market returns. Journal of Financial Economics 59. 195-220. [20] Elton, E., Gruber, M., and Busse, J., 1998. Do investors care about sentiment?. Journal of Business 71. 477-500. [21] Fisher, K., and Statman, M., 2000. Investor Sentiment and Stock Returns. Financial Analysts Journal 56. 16-23. [22] Fisher, K., and Statman, M., 2003. Consumer Confidence and Stock Returns. Journal of Portfolio Management 30. 115-127. [23] Frazzini, A., and Lamont, O., 2006. ‘Dumb Money: Mutual Fund Flows and the Cross Section of Stock Returns. Journal of Financial Economics 88. 299-322. [24] Indro, D., 2004. Does Mutual Fund Flow Reflect Investor Sentiment?. Journal of Behavioral Finance 5. 105-115. [25] Lee, C., Shleifer, A., and Thaler, R., 1991. Investor Sentiment and the Closed End Puzzle. Journal of Finance 46(1). 75-109. [26] Lee, Y. and Song, Z., 2003. When do value stocks outperform growth stocks? Investor Sentiment and Equity Style Rotation Strategies. Working Paper. University of Rhode Island. 23

[27] Lemmon, M., and Portniaguina, E., 2006. Consumer confidence and Asset Prices: Some Empirical Evidence. Review of Financial Studies 19. 1499-1529. [28] Ljungqvist, A., 2006. IPO Underpricing. Handbook of Corporate Finance: Empirical Corporate Finance Chapter 7. Elsevier/North-Holland Publication. [29] Neal, R., and Wheatley, S., 1998. Do Measures of Investor Sentiment Predict Returns?. Journal of Financial and Quantitative Analysis 33. 523-547. [30] Pan, J., and Poteshman, A., 2006. The Information in Option Volume for Future Stock Prices. Review of Financial Studies 19. 871-908. [31] Qiu, L., and Welch, I., 2006. Investor Sentiment Measures. Working Paper. Brown University. [32] Ritter, J., 2003. Investment Banking and Securities Issuance. Handbook of the Economics of Finance Chapter 5. Elsevier Science Publication. [33] Russel, P., 2005. Closed-End Fund Pricing: The Puzzle, The Explanations, and Some New Evidence. Journal of Business and Economic Studies 11. 34-49. [34] Schmeling, M., 2009. Investor Sentiment and Stock Returns: Some International Evidence. Journal of Empirical Finance 16. 394-408. [35] Shiller, R., 2005. Irrational Exuberance. Princeton, NJ: Princeton University Press. [36] Shleifer, A., and Vishny, R., 1997. The limits of arbitrage. Journal of Finance 52. 35-55. [37] Subrahmanyam, A., 2007. Behavioral Finance: A Review and Synthesis. European Financial Management 14. 12-29. [38] Verma, R., and Soydemir, G., 2006. The Impact of US Individual and Institutional Investor Sentiment on Foreign Stock Market. Journal of Behavioral Finance 7. 128-144. [39] Warther, V., 1995. Aggregate mutual fund flows and security returns. Journal of Financial Economics 39. 209-235. [40] Zweig, M., 1973. An investor expectations stock price predictive model using closedend premiums. Journal of Finance 28. 67-78.

24

Table 1: Data Sources: The below table details data sources of different sentiment proxies, viz. consumer confidence index, equity fund flow, closed-end equity fund discount and equity put-call options volume. This table also list the time horizon for each data source and for each individual country. Country

Time Horizon

Source

Panel A: Consumer Confidence Index U.S. Jan 1995-Dec 2007 Canada Jan 1995-Dec 2007 U.K. Jan 1995-Dec 2007 France Jan 1995-Dec 2007 Germany Jan 1995-Dec 2007 Italy Jan 1995-Dec 2007 Japan Jan 1995-Dec 2007

The University of Michigan Consumer Surveys The Conference Board, Canada Directorate Generale for Economic & Financial Directorate Generale for Economic & Financial Directorate Generale for Economic & Financial Directorate Generale for Economic & Financial The Cabinet Office, Japan

Panel B: Equity Fund Flow U.S. Jan 1995-Dec Canada Jan 1995-Dec U.K. Jan 1995-Dec France Jan 2002-Dec Germany Jan 2002-Dec Italy Jan 2002-Dec Japan Jan 2002-Dec

The Investment Company Institute The Investment Fund Institute of Canada The Investment Management Association, U.K. Lipper, Thomson Reuters Lipper, Thomson Reuters Lipper, Thomson Reuters Lipper, Thomson Reuters

2007 2007 2007 2007 2007 2007 2007

Panel C: Closed End Equity Fund U.S. Jan 1995-Dec 2007 Canada Jan 1995-Dec 2007 U.K. Jan 1995-Dec 2007 France Jan 1995-May 2004 Germany Jan 1995-Dec 2007 Italy Jan 1995-Jan 2003 Japan Jan 1995-Dec 2007

Morningstar, Morningstar, Morningstar, Morningstar, Morningstar, Morningstar, Morningstar,

Panel D: Individual Equity Options Volume U.S. Jan 1995-Dec 2007 Canada Dec 2000-Dec 2007 U.K. Jan 2000-Dec 2007 France Jan 2002-Dec 2007 Germany Jan 2001-Dec 2007 Italy Jan 2001-Dec 2007 Japan Jul 1997-Dec 2007

The Chicago Board of Options Exchange Bourse De Montreal Inc NYSE Euronext NYSE Euronext Deutsche Borse Group Borsa Italiana Tokyo Stock Exchange

25

Inc Inc Inc Inc Inc Inc Inc

Affairs Affairs Affairs Affairs

(DG (DG (DG (DG

ECFIN) ECFIN) ECFIN) ECFIN)

Table 2: Descriptive Statistics: Stocks Returns, Consumer Confidence Index and Equity Fund Flow The table below reports the descriptive statistics of aggregate market returns, value stocks returns, growth stocks returns, consumer confidence index and equity fund flow of G7 countries. Autocorrelation at Lag 1 is reported for consumer confidence index and equity fund flow for each country. The last column reports the AR model used for each country to predict expected fund flow. Country U.S. Canada U.K. France Germany Italy Japan

Market µ σ 1.00 4.24 1.41 5.17 1.00 3.70 1.25 5.07 1.12 5.78 1.23 6.17 0.20 5.55

Value µ σ 1.19 3.86 1.31 6.13 1.12 4.84 1.40 6.45 1.78 6.82 1.29 7.71 0.99 7.51

Growth µ σ 0.95 4.49 1.28 6.73 0.97 3.61 1.24 5.32 1.08 6.60 1.11 6.33 -0.14 5.67

CC Index µ σ 94.63 8.71 89.07 13.19 -3.74 4.02 -14.35 8.26 -8.61 8.51 -11.69 5.66 41.85 4.54

ρ(1) 0.88 0.93 0.81 0.93 0.95 0.89 0.95

µ 0.53 0.80 0.23 0.47 -0.21 -1.13 0.65

Equity σ 0.53 1.11 0.22 0.58 0.66 1.10 2.41

Fund Flow ρ(1) AR Model 0.71 3 0.60 4 0.62 15 0.29 2 0.21 9 0.35 3 0.08 12

Table 3: Panel Unit Root Test: Consumer Confidence Index The below table reports the panel unit root test of consumer confidence indices. The tests by lm, Pesaran, and Shin, Augmented Dickey Fuller and Phillips-Perron test the null of individual unit root processes. The lag length selection is based on Schwarz Information Criteria (SIC) and the test equation contains intercept. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels respectively.

lm, Pesaran and Shin Augmented Dickey Fuller Phillips Perron

Test Statistic -2.72 30.85 28.91

p-value (0.00)*** (0.00)*** (0.00)***

Obsv 1079 1079 1085

Table 4: Panel Unit Root Test: Equity Fund Flow The below table reports the panel unit root test of equity fund flow data. The tests by lm, Pesaran, and Shin, Augmented Dickey Fuller and Phillips-Perron test the null of individual unit root processes. The lag length selection is based on Schwarz Information Criteria (SIC) and the test equation contains intercept. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels respectively.

lm, Pesaran and Shin Augmented Dickey Fuller Phillips Perron

Test Statistic -9.88 125.62 185.43

26

p-value (0.00)*** (0.00)*** (0.00)***

Obsv 722 722 749

Table 5: Descriptive Statistics: VWD and Equity PCR The below table reports the descriptive statistics of value-weighted index of discount (VWD) and equity put-call ratio (PCR). The VWD is calculated by taking the sum of individual CEEF’s weight multiplied by its discount. The equity PCR is equity put volume divided by equity call volume and equity put volume Country U.S. Canada U.K. France Germany Italy Japan

Value Weighted Discount (%) Mean Min Max Std Dev Obsv 7.82 0.47 15.01 3.84 156 22.84 14.22 34.84 4.70 156 9.72 4.59 14.94 2.37 156 15.60 1.98 26.01 3.95 113 14.71 5.74 25.00 3.93 156 14.19 3.14 25.69 4.27 97 5.15 -8.38 19.90 5.56 156

Mean 0.36 0.28 0.45 0.45 0.45 0.43 0.49

Equity Put Call Ratio Min Max Std Dev (%) 0.24 0.68 9.20 0.18 0.39 4.44 0.37 0.59 4.62 0.33 0.58 5.74 0.23 0.60 5.83 0.27 0.53 4.85 0.10 0.88 20.32

Obsv 156 85 96 72 84 84 126

Table 6: Panel fixed effects regression: Consumer confidence index The below table reports the results of panel fixed effect regression of aggregate market returns, value stocks returns and growth stocks returns on consumer confidence index and different macro-economic variables. The coefficient of macro-economic variables are not reported here for the sake of brevity. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels respectively. Forecast Horizon

1 month

3 months

6 months

12 months

24 months

Panel A: Aggregate market returns Survey Sentiment -0.48 p-value (0.00)*** R2 0.02 Obs 1085

-0.60 (0.00)*** 0.03 1071

-0.44 (0.05)** 0.02 1050

-0.33 (0.22) 0.02 1008

-0.26 (0.27) 0.04 924

Panel B: Value stocks returns Survey Sentiment -0.59 p-value (0.00)*** R2 0.02 Obs 1085

-0.79 (0.00)*** 0.02 1071

-0.66 (0.03)** 0.02 1050

-0.49 (0.09)* 0.02 1008

-0.16 (0.63) 0.03 924

Panel C: Growth stocks returns Survey Sentiment -0.49 p-value (0.00)*** R2 0.02 Obs 1085

-0.60 (0.00)*** 0.02 1071

-0.47 (0.06)*** 0.02 1050

-0.32 (0.28) 0.02 1008

-0.30 (0.27) 0.04 924

27

Table 7: Consumer confidence index: Individual country effect The below table reports the returns predictability of consumer confidence index, as sentiment measure, across different forecast horizon. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels respectively. 1 month 3 months Survey p-value Survey p-value Panel A: Aggregate market returns U.S. -0.10 (0.02)** -0.12 (0.04)** Canada -0.07 (0.08)* -0.13 (0.00)*** U.K. 0.01 (0.82) -0.11 (0.18) France -0.16 (0.01)*** -0.06 (0.30) Germany -0.12 (0.03)** -0.31 (0.00)*** Italy -0.22 (0.00)*** -0.26 (0.00)*** Japan -0.07 (0.60) -0.18 (0.17)

6 months Survey p-value

12 months Survey p-value

-0.09 -0.03 0.12 -0.07 -0.11 -0.14 0.01

(0.06)* (0.25) (0.04)** (0.29) (0.26) (0.11) (0.94)

-0.18 -0.02 0.10 -0.12 -0.17 -0.09 0.00

(0.00)*** (0.47) (0.31) (0.00)*** (0.08)* (0.30) (0.97)

Panel B: Value stocks returns U.S. -0.02 (0.64) Canada -0.13 (0.02)** U.K. 0.02 (0.76) France -0.18 (0.04)** Germany -0.20 (0.12) Italy -0.27 (0.00)** Japan 0.01 (0.90)

-0.04 -0.12 0.00 -0.11 -0.36 -0.40 -0.22

(0.24) (0.05)** (0.95) (0.17) (0.00)*** (0.00)*** (0.23)

-0.01 -0.00 0.04 -0.06 -0.19 -0.29 -0.08

(0.72) (0.97) (0.18) (0.43) (0.00)*** (0.00)*** (0.63)

-0.09 0.02 0.11 -0.07 -0.08 -0.26 -0.25

(0.00)*** (0.61) (0.28) (0.37) (0.32) (0.02)** (0.17)

Panel C: Growth stocks returns U.S. -0.12 (0.01)*** Canada -0.09 (0.07)* U.K. 0.01 (0.86) France -0.18 (0.01)*** Germany -0.04 (0.52) Italy -0.20 (0.00)*** Japan -0.11 (0.40)

-0.12 -0.17 -0.10 -0.08 -0.30 -0.23 -0.19

(0.05)** (0.00)*** (0.16) (0.26) (0.00)*** (0.00)*** (0.15)

-0.10 -0.04 0.19 -0.09 -0.13 -0.12 -0.01

(0.07)* (0.24) (0.00)*** (0.23) (0.30) (0.22) (0.91)

-0.20 -0.04 0.10 -0.14 -0.20 -0.04 0.07

(0.00)*** (0.44) (0.34) (0.00)*** (0.11) (0.64) (0.43)

28

Table 8: Panel fixed effect regression: Equity fund flow The below table reports the panel fixed effect regression of G7 countries’ aggregate market returns, value stocks returns and growth stocks returns on equity fund flow at different lags. Model 1 of the table reports the regression of stocks returns on equity fund flow at t, t-1, t-2 and t-3. Model 2 of the table reports the regression of stocks returns on equity fund flow at t-1, t-2 and t-3. Model 3 of the table reports the regression of stocks returns on expected fund flow at t and unexpected fund flow at t, t-1 and t-2. In the last column, we regress unexpected net sales on returns at t-1, t and t+1. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels respectively. Independent Variables

Model 1 Coeff p-value Panel A: Aggregate market returns Fund Flowt 0.62 (0.00)*** Fund Flowt−1 -0.32 (0.07)* Fund Flowt−2 -0.30 (0.10)* Fund Flowt−3 -0.40 (0.02)** Expected Fund Flowt Unexpected Fund Flowt Unexpected Fund Flowt−1 Unexpected Fund Flowt−2 Aggregate Returnst−1 Aggregate Returnst Aggregate Returnst+1 R2 0.03 Panel B: Value stocks returns Fund Flowt 0.88 Fund Flowt−1 -0.40 Fund Flowt−2 -0.46 Fund Flowt−3 -0.49 Expected Fund Flowt Unexpected Fund Flowt Unexpected Fund Flowt−1 Unexpected Fund Flowt−2 Value stocks Returnst−1 Value stocks Returnst Value stocks Returnst+1 R2 0.04 Panel C: Growth stocks returns Fund Flowt 0.59 Fund Flowt−1 -0.30 Fund Flowt−2 -0.34 Fund Flowt−3 -0.38 Expected Fund Flowt Unexpected Fund Flowt Unexpected Fund Flowt−1 Unexpected Fund Flowt−2 Growth stocks Returnst−1 Growth stocks Returnst Growth stocks Returnst+1 R2 0.02

(0.00)*** (0.06)* (0.03)** (0.01)***

Model 2 Coeff p-value

-0.18 -0.21 -0.31

Model 3 Coeff p-value

(0.29) (0.24) (0.08)* 0.12 0.86 -0.17 -0.30

0.01

-0.21 -0.34 -0.37

29

(0.22) (0.00)*** (0.06)*

-0.05 2.27 -1.42 0.03

(0.92) (0.00)*** (0.01)***

1.01 1.61 -0.88 0.02

(0.12) (0.01)*** (0.17)

(0.68) (0.00)*** (0.22) (0.25)

0.03

(0.37) (0.18) (0.13) 0.10 0.85 -0.09 -0.39

0.01

0.87 2.14 -1.30 0.02

(0.32) (0.11) (0.08)*

0.02

-0.17 -0.26 -0.29

(0.73) (0.00)*** (0.41) (0.17)

0.03

0.17 1.07 -0.31 -0.30

(0.00)*** (0.13) (0.08)* (0.05)**

Unexpected net sales Coeff p-value

0.03

(0.78) (0.00)*** (0.71) (0.10)*

30

0.08

1.27

(0.00)*** (0.00)***

-0.05

-1.75

0.44

-0.46

-1.47

0.40

Fund Flowt−3

R2

Panel B: Value stocks returns Fund Flowt 6.63

-3.00

Fund Flowt−2

Fund Flowt−1

Fund Flowt−2

Fund Flowt−3

R2

0.24

-1.76

0.38

Fund Flowt−2

Fund Flowt−3

R2

(0.03)**

(0.79)

0.09

-0.07

-1.93

-0.09

-4.41

Fund Flowt−1

(0.00)***

2.11

0.04

-0.49

-0.37

-0.67

-0.12

Panel C: Growth stocks returns Fund Flowt 7.57 (0.00)***

(0.03)**

(0.55)

(0.01)***

(0.94)

-1.14

-0.38

-4.29

Fund Flowt−1

(0.00)***

1.63

(0.91)

(0.00)***

(0.89)

(0.00)***

(0.43)

(0.58)

(0.32)

(0.03)**

(0.81)

(0.04)**

(0.49)

(0.00)***

Canada Coeff p-value

US Coeff p-value Panel A: Aggregate market returns Fund Flowt 7.66 (0.00)***

Independent Variables

0.09

-1.20

-2.79

-2.58

4.58

0.08

0.52

-6.59

0.02

4.93

0.11

-0.61

-4.19

-1.89

4.83

Coeff

(0.49)

(0.19)

(0.23)

(0.01)***

(0.82)

(0.02)**

(0.99)

(0.03)**

(0.73)

(0.05)**

(0.38)

(0.00)***

UK p-value

0.06

0.72

-0.74

-0.93

-1.06

0.11

1.44

-1.73

-3.08

2.10

0.05

0.84

-0.72

-1.75

0.15

(0.49)

(0.48)

(0.35)

(0.27)

(0.32)

(0.23)

(0.03)**

(0.12)

(0.46)

(0.53)

(0.11)

(0.89)

France Coeff p-value

0.08

-1.92

-1.09

0.20

1.77

0.20

-2.38

-0.88

0.09

4.20

0.14

-2.49

-0.86

0.42

2.55

(0.11)

(0.36)

(0.86)

(0.14)

(0.05)**

(0.47)

(0.94)

(0.00)***

(0.02)**

(0.44)

(0.70)

(0.02)**

Germany Coeff p-value

0.16

-0.38

-0.56

-0.70

1.84

0.21

-0.11

-0.22

-1.05

2.74

0.17

-0.15

-0.58

-0.57

1.87

Coeff

(0.53)

(0.37)

(0.22)

(0.00)***

(0.87)

(0.77)

(0.13)

(0.00)***

(0.78)

(0.32)

(0.28)

(0.00)***

Italy p-value

0.16

-0.31

-0.08

-0.35

-0.44

0.20

-0.54

-0.56

-0.36

-0.28

0.17

-0.36

-0.16

-0.35

-0.42

(0.12)

(0.69)

(0.08)*

(0.03)**

(0.03)**

(0.02)**

(0.15)

(0.27)

(0.07)*

(0.41)

(0.09)*

(0.04)**

Japan Coeff p-value

regressed on equity fund flow at t, t-1, t-2 and t-3. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels respectively.

The below table reports the Model 1 of individual countries where aggregate market returns, value stocks returns and growth stocks returns are

Table 9: Model 1: Regression of stocks returns on net equity fund flow at Lag 0 to Lag 3

31

0.01

-0.14

(0.44) (0.73)

-0.83

0.00

-0.68

0.00

R2

Panel B: Value stocks returns Fund Flowt−1 0.66

0.34

Fund Flowt−3

Fund Flowt−2

Fund Flowt−3

R2

-0.86

0.00

Fund Flowt−3

R2

(0.39) 0.02

0.06

-1.35

1.16

Fund Flowt−2

(0.32)

0.79

0.00

-0.42

-0.02

Panel C: Growth stocks returns Fund Flowt−1 -0.22 (0.82)

(0.43)

(0.38)

-0.02

-0.69

0.87

Fund Flowt−2

(0.42)

0.30

(0.93)

(0.06)**

(0.25)

(0.51)

(0.97)

(0.82)

(0.96)

(0.22)

(0.57)

Canada Coeff p-value

US Coeff p-value Panel A: Aggregate market returns Fund Flowt−1 -0.05 (0.95)

Independent Variables

0.05

-0.08

-4.20

0.89

0.06

1.72

-8.11

3.75

0.07

0.56

-5.68

1.77

Coeff

(0.96)

(0.04)**

(0.60)

(0.46)

(0.00)***

(0.10)*

(0.75)

(0.00)***

(0.31)

UK p-value

0.04

0.58

-0.96

-1.19

0.08

1.71

-1.31

-2.58

0.05

0.86

-0.69

-1.72

(0.57)

(0.35)

(0.22)

(0.24)

(0.36)

(0.06)**

(0.45)

(0.53)

(0.11)

France Coeff p-value

0.05

-1.93

-0.71

0.50

0.05

-2.41

0.02

0.79

0.07

-2.51

-0.31

0.85

(0.11)

(0.55)

(0.67)

(0.07)*

(0.98)

(0.54)

(0.03)**

(0.78)

(0.46)

Germany Coeff p-value

0.01

-0.04

-0.38

-0.20

0.00

0.39

0.04

-0.30

0.00

0.19

-0.40

-0.06

(0.94)

(0.56)

(0.73)

(0.63)

(0.95)

(0.68)

(0.75)

(0.52)

(0.90)

Italy Coeff p-value

0.10

-0.35

-0.09

-0.38

0.18

-0.56

-0.57

-0.38

0.12

-0.40

-0.18

-0.38

(0.09)*

(0.65)

(0.06)*

(0.02)**

(0.02)**

(0.13)

(0.05)**

(0.39)

(0.07)*

Japan Coeff p-value

regressed on equity fund flow at t-1, t-2 and t-3. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels respectively.

The below table reports the Model 2 of individual countries where aggregate market returns, value stocks returns and growth stocks returns are

Table 10: Model 2: Regression of stock returns on net equity fund flow at Lag 1 to Lag 3

32

0.09

-0.73

(0.94) (0.00)***

0.77

0.43

0.70

0.73

0.40

R2

Panel B: Value stocks returns Expected Fund Flowt -0.06

6.63

Unexpected Fund Flowt−2

Unexpected Fund Flowt

Unexpected Fund Flowt−1

Unexpected Fund Flowt−2

R2

-0.39

0.78

0.38

Unexpected Fund Flowt−1

Unexpected Fund Flowt−2

R2

(0.37)

(0.68)

0.10

-1.02

0.88

2.27

7.58

Unexpected Fund Flowt

(0.00)***

-0.48

0.05

-0.09

-0.01

1.45

-0.63

Panel C: Growth stocks returns Expected Fund Flowt 0.02 (0.98)

(0.32)

(0.38)

(0.32)

(0.95)

0.31

-0.05

Unexpected Fund Flowt−1

(0.23)

(0.28)

(0.00)***

(0.63)

(0.91)

(0.98)

(0.01)***

(0.44)

(0.34)

(0.62)

(0.00)***

0.06

-1.72

1.68

6.55

-0.50

0.05

-5.53

2.13

5.97

2.03

0.07

-2.96

2.06

6.92

1.77

7.68

Unexpected Fund Flowt

(0.00)***

-0.10

-0.33

(0.67)

Coeff

Canada Coeff p-value

US Coeff p-value Panel A: Aggregate market returns Expected Fund Flowt -0.22 (0.80)

Independent Variables

the 10%, 5%, and 1% levels respectively.

(0.45)

(0.50)

(0.00)***

(0.79)

(0.07)*

(0.53)

(0.07)*

(0.44)

(0.21)

(0.42)

(0.00)***

(0.95)

UK p-value

0.05

-1.02

-0.91

-0.99

-0.90

0.11

-1.88

-2.42

2.21

-0.09

0.04

-0.68

-1.26

0.22

-1.35

(0.57)

(0.62)

(0.30)

(0.88)

(0.45)

(0.34)

(0.10)*

(0.99)

(0.73)

(0.53)

(0.83)

(0.84)

France Coeff p-value

0.07

-0.15

-0.54

1.61

-3.83

0.25

1.67

0.63

4.56

-5.10

0.13

0.50

0.17

2.68

1.82

(0.89)

(0.64)

(0.11)

(0.18)

(0.18)

(0.61)

(0.00)***

(0.87)

(0.66)

(0.88)

(0.01)**

(0.52)

Germany Coeff p-value

0.16

-0.17

0.11

1.83

-1.05

0.21

0.10

-0.27

2.75

0.32

0.17

-0.27

0.04

1.86

-0.27

Coeff

(0.79)

(0.88)

(0.00)***

(0.55)

(0.90)

(0.75)

(0.00)***

(0.87)

(0.67)

(0.95)

(0.00)***

(0.87)

Italy p-value

0.12

-0.18

-0.22

-0.53

0.63

0.09

-0.38

-0.29

-0.49

0.69

0.11

-0.19

-0.25

-0.54

0.53

(0.47)

(0.37)

(0.03)**

(0.20)

(0.23)

(0.36)

(0.12)

(0.27)

(0.47)

(0.33)

(0.04)**

(0.29)

Japan Coeff p-value

regressed on expected equity fund flow at t and unexpected equity fund flow at t, t-1 and t-2. *, **, and *** denote statistical significance at

The below table reports the Model 3 of individual countries where aggregate market returns, value stocks returns and growth stocks returns are

Table 11: Model 3: Regression of stock returns on expected equity fund flow at Lag 0 and unexpected equity fund flow at Lag 0 to Lag 2

33

0.09

-0.24

(0.80) (0.00)***

-0.20

0.44

0.00

0.38

R2

Panel B: Value stocks returns Returnst−1 -1.59

5.98

Returnst+1

Returnst

Returnst+1

R2

-0.18

0.39

Returnst+1

R2

(0.74) 0.08

0.54

3.09

5.08

Returnst

(0.00)***

0.47

0.05

-0.56

2.72

Panel C: Growth stocks returns Returnst−1 0.81 (0.13)

(0.99)

(0.71)

0.02

4.53

5.77

Returnst

(0.00)***

0.27

(0.56)

(0.00)***

(0.61)

(0.58)

(0.00)***

(0.81)

(0.98)

(0.00)***

(0.83)

Canada Coeff p-value

US Coeff p-value Panel A: Aggregate market returns Returnst−1 0.72 (0.19)

Independent Variables

0.04

0.16

0.78

0.14

0.03

0.28

0.42

0.22

0.05

0.28

0.81

0.19

Coeff

(0.62)

(0.01)***

(0.67)

(0.26)

(0.09)*

(0.38)

(0.37)

(0.01)***

(0.54)

UK p-value

0.04

-1.37

-1.79

0.38

0.08

-2.15

1.71

-0.82

0.03

-2.08

0.00

-0.32

(0.37)

(0.24)

(0.80)

(0.05)**

(0.10)*

(0.44)

(0.14)

(0.99)

(0.82)

France Coeff p-value

0.11

-0.29

1.48

2.82

0.18

-0.16

3.48

0.99

0.12

0.15

2.46

2.28

(0.79)

(0.19)

(0.01)***

(0.87)

(0.00)***

(0.32)

(0.89)

(0.03)**

(0.05)**

Germany Coeff p-value

at t-1, t and t+1. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels respectively.

0.16

-1.51

7.83

0.97

0.24

-1.19

8.00

0.76

0.18

-1.09

9.03

1.15

Coeff

(0.52)

(0.00)***

(0.68)

(0.50)

(0.00)***

(0.67)

(0.65)

(0.00)***

(0.63)

Italy p-value

0.17

-11.10

-16.62

4.85

0.09

-8.56

-7.57

1.94

0.16

-11.62

-15.73

5.51

(0.07)*

(0.00)***

(0.39)

(0.09)*

(0.15)

(0.69)

(0.05)**

(0.00)***

(0.33)

Japan Coeff p-value

The below table reports regression of unexpected equity fund flow on aggregate market returns, value stocks returns and growth stocks returns

Table 12: Regression of unexpected equity fund flow on stocks returns at t-1, t and t+1

Table 13: Panel fixed effect regression: Closed-end equity fund discount The below table reports the panel fixed effect regression of value stocks returns and growth stocks returns on change in index of CEEF discount (∆V W D) and aggregate market returns in model 1 and change in index of CEEF discount (∆V W D), aggregate market returns, FTSE Banking, FTSE Basic materials, FTSE Industrials and FTSE Consumer goods indices in model 2. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels respectively. Model 1 Value stocks Growth stocks returns returns Coeff p-value Coeff p-value Intercept

0.09

∆ VWD

-0.19

(0.00)***

0.11

(0.00)***

-0.10

(0.05)**

0.07

(0.00)***

1.00

(0.00)***

1.03

(0.00)***

0.36

(0.00)***

1.29

(0.00)***

Banking

0.25

(0.00)***

-0.12

(0.00)***

Basic materials

0.29

(0.00)***

-0.11

(0.00)***

Industrials

0.08

(0.00)***

-0.01

(0.26)

Consumer goods

0.02

(0.29)

-0.01

(0.38)

Aggregate returns

R2

0.67

(0.78)

-0.04

(0.80)

Model 2 Value stocks Growth stocks returns returns Coeff p-value Coeff p-value

0.89

0.13

0.76

34

(0.67)

-0.04

0.91

(0.79)

Table 14: Model 1: Closed-end equity fund discount: Individual country effect The below table reports the regression of value stocks returns and growth stocks returns on change in index of CEEF discount ∆V W D and aggregate market returns. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels respectively. Intercept Coeff p-value Panel A: Value stocks returns U.S. 0.44 (0.02)** Canada 0.03 (0.91) U.K. 0.06 (0.81) France 0.02 (0.96) Germany 0.66 (0.02)** Italy -0.29 (0.52) Japan 0.76 (0.02)** Panel B: Growth stocks returns U.S. -0.08 (0.31) Canada -0.41 (0.03)** U.K. 0.05 (0.61) France 0.03 (0.87) Germany -0.09 (0.68) Italy -0.07 (0.62) Japan -0.34 (0.00)***

∆ VWD Coeff p-value

Aggregate returns Coeff p-value

R2

-0.36 -0.47 0.20 -0.21 -0.23 -0.11 -0.03

(0.01)*** (0.00)*** (0.44) (0.20) (0.07)* (0.51) (0.76)

0.74 0.90 1.06 1.07 0.98 1.08 1.12

(0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)***

0.66 0.57 0.66 0.70 0.72 0.75 0.69

0.16 0.31 0.41 0.13 0.02 0.10 0.01

(0.01)*** (0.00)*** (0.00)*** (0.17) (0.85) (0.06)* (0.79)

1.03 1.21 0.90 0.96 1.04 1.00 0.99

(0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)***

0.96 0.89 0.87 0.84 0.84 0.97 0.95

35

36

0.33

0.12

0.16

0.03

0.16

0.78

Aggregate returns

Banking

Basic materials

Industrials

Consumer goods

R2

1.03

-0.01

-0.10

0.11

-0.00

0.96

Aggregate returns

Banking

Basic materials

Industrials

Consumer goods

R2

(0.82)

(0.00)***

(0.00)***

(0.41)

(0.00)***

0.94

-0.01

0.02

-0.20

-0.35

1.68

0.19

0.14

∆ VWD

(0.01)***

-0.31

0.79

-0.02

0.14

0.57

0.19

Panel B: Growth stocks returns Intercept -0.09 (0.20)

(0.00)***

(0.64)

(0.00)***

(0.00)***

(0.00)***

0.03

-0.17

-0.30

∆ VWD

(0.01)***

0.25

(0.45)

(0.36)

(0.00)***

(0.00)***

(0.00)***

(0.01)***

(0.03)**

(0.48)

(0.00)***

(0.00)***

(0.00)***

(0.73)

(0.17)

(0.29)

Canada Coeff p-value

U.S. Coeff p-value Panel A: Value stocks returns Intercept 0.49 (0.00)***

0.88

0.02

-0.02

-0.05

-0.08

1.06

0.34

0.02

0.67

-0.09

0.07

0.08

0.01

0.96

0.26

0.10

Coeff

significance at the 10%, 5%, and 1% levels respectively.

(0.18)

(0.49)

(0.06)*

(0.00)***

(0.00)***

(0.00)***

(0.81)

(0.03)**

(0.20)

(0.19)

(0.90)

(0.00)***

(0.33)

(0.67)

U.K. p-value

0.89

-0.09

-0.12

-0.06

-0.18

1.44

0.09

-0.01

0.85

0.38

0.22

0.20

0.34

-0.12

-0.02

0.17

(0.03)**

(0.02)**

(0.33)

(0.00)***

(0.00)***

(0.28)

(0.96)

(0.00)***

(0.00)***

(0.01)***

(0.00)***

(0.38)

(0.84)

(0.55)

France Coeff p-value

0.87

-0.10

-0.13

-0.23

-0.10

1.60

-0.04

-0.07

0.79

0.18

-0.17

0.18

0.29

0.46

-0.14

0.79

(0.01)***

(0.03)**

(0.00)***

(0.01)***

(0.00)***

(0.64)

(0.71)

(0.00)***

(0.03)**

(0.01)***

(0.00)***

(0.00)***

(0.20)

(0.00)***

Germany Coeff p-value

0.98

-0.07

-0.01

-0.04

-0.13

1.23

0.03

-0.22

0.85

0.31

0.12

0.11

0.26

0.28

0.09

0.49

Coeff

(0.00)***

(0.55)

(0.06)*

(0.00)***

(0.00)***

(0.48)

(0.05)**

(0.00)***

(0.01)***

(0.10)*

(0.00)***

(0.02)**

(0.50)

(0.21)

Italy p-value

0.97

-0.03

0.08

-0.18

-0.05

1.18

0.02

-0.37

0.84

-0.06

-0.14

0.58

0.17

0.49

-0.07

0.90

Coeff

(0.29)

(0.03)**

(0.00)***

(0.00)***

(0.00)***

(0.50)

(0.00)***

(0.44)

(0.21)

(0.00)***

(0.00)***

(0.00)***

(0.38)

(0.00)***

Japan p-value

market returns, FTSE Banks, FTSE Basic materials, FTSE Industrials and FTSE Consumer goods indices. *, **, and *** denote statistical

The below table reports the regression of value stocks returns and growth stocks returns on change in index of CEEF index ∆V W D, aggregate

Table 15: Model 2: Closed-end equity fund discount: Individual country effect

Table 16: Panel Fixed effects regression: Equity Put Call Ratio The below table reports the results of panel fixed effect regression of aggregate market returns, value stock returns and growth stock returns on equity put-call ratio for the period January 2002 to December 2007. The equity PCR is equity put volume divided by equity call volume and equity put volume. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels respectively. Rt

Rt+1

Rt+2

Rt+3

Panel A: Aggregate market returns PCR p-value R2 Obs

-0.07 (0.00)*** 0.02 504

-0.03 (0.26) 0.01 497

-0.01 (0.78) 0.01 490

-0.04 (0.13) 0.01 483

Panel B: Value stocks returns PCR p-value R2 Obs

-0.12 (0.00)*** 0.04 504

-0.06 (0.04)** 0.01 497

-0.02 (0.49) 0.01 490

-0.07 (0.01)*** 0.01 483

Panel C: Growth stocks returns PCR p-value R2 Obs

-0.05 (0.02)** 0.02 504

-0.02 (0.38) 0.01 497

-0.01 (0.72) 0.01 490

-0.03 (0.29) 0.01 483

37

Table 17: Equity Put Call Ratio: Individual Country Effect The below table reports an individual country effect of equity put-call ratio on aggregate market returns, value stocks returns and growth stocks returns for the period from which equity PCR data is first available. The equity PCR is equity put volume divided by equity call volume and equity put volume. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels respectively. Rt Rt+1 Coeff p-value Coeff p-value Panel A: Aggregate market returns U.S. -0.12 (0.00)*** -0.07 (0.06)* Canada -0.33 (0.00)*** 0.25 (0.04)** U.K. -0.36 (0.00)*** -0.04 (0.68) France -0.24 (0.01)*** -0.07 (0.45) Germany -0.30 (0.01)*** -0.00 (0.97) Italy -0.06 (0.60) 0.10 (0.40) Japan 0.10 (0.69) 0.03 (0.29)

Rt+2 Coeff p-value

Rt+3 Coeff p-value

-0.06 0.23 -0.06 -0.12 -0.05 0.06 0.03

(0.10)* (0.07)* (0.49) (0.25) (0.69) (0.60) (0.17)

-0.03 -0.02 -0.16 -0.11 -0.09 -0.20 0.03

(0.45) (0.85) (0.07)* (0.27) (0.44) (0.07)* (0.27)

Panel B: Value stocks returns U.S. -0.07 (0.02)** Canada -0.61 (0.00)*** U.K. -0.52 (0.00)*** France -0.34 (0.00)*** Germany -0.27 (0.04)** Italy -0.15 (0.31) Japan -0.04 (0.30)

-0.02 0.09 -0.11 -0.16 -0.02 0.14 0.00

(0.47) (0.57) (0.34) (0.20) (0.88) (0.35) (0.90)

-0.04 0.09 -0.12 -0.18 -0.05 0.15 0.04

(0.23) (0.54) (0.33) (0.16) (0.70) (0.32) (0.22)

-0.02 -0.08 -0.31 -0.23 -0.13 -0.19 0.06

(0.46) (0.59) (0.00)*** (0.07)* (0.35) (0.21) (0.12)

Panel C: Growth stocks U.S. -0.13 Canada -0.25 U.K. -0.30 France -0.12 Germany -0.28 Italy -0.07 Japan 0.01

-0.08 0.46 -0.07 -0.06 -0.01 0.13 0.03

(0.04)** (0.00)*** (0.41) (0.52) (0.94) (0.30) (0.17)

-0.07 0.37 -0.12 -0.06 -0.10 0.05 0.03

(0.08)* (0.03)** (0.17) (0.50) (0.46) (0.67) (0.22)

-0.03 0.03 -0.13 -0.01 -0.15 -0.20 0.02

(0.39) (0.82) (0.11) (0.90) (0.26) (0.10)* (0.43)

returns (0.00)*** (0.11) (0.00)*** (0.18) (0.04)** (0.57) (0.59)

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