Who Moves the Market? A Study of Stock Prices and Sector Cashflows *

Transcription

1 Who Moves the Market? A Study of Stock Prices and Sector Cashflows * Brian Boyer bhb@byu.edu Lu Zheng luzheng@umich.edu February, 2003 We would like to thank Sugato Bhattacharyya, Randolph Cohen, Kenneth French, William Goetzmann, Roger Ibbotson, Grant McQueen, Tyler Shumway, Clemens Sialm, Rene Stulz, Paula Tkac, Vincent Warther, Toni Whited, and seminar participants at the University of Michigan Business School, Brigham Young University, and the American Finance Association meetings (2003) for useful comments. Contact information: Brian Boyer, Brigham Young University, Marriott School of Management, Provo UT, Phone: ; bhb@byu.edu. Lu Zheng, University of Michigan Business School, 701 Tappan St., Ann Arbor, MI, Phone: ; luzheng@umich.edu.

2 Who Moves the Market? A Study of Stock Prices and Sector Cashflows Abstract In this paper, we explore the relation between stock market returns and cash flows to the stock market from seven major investment sectors in the economy: Mutual Funds, Households, Pension Funds, Foreign Investors, Insurance Companies, Closedend Funds, and Other institutional investors. Our goal is to address the following questions: 1) Do the return-cashflow relations differ across sectors? 2) Are the differences caused by distinct trading behaviors, such as positive feedback trading, or the fact that trades of specific sectors systematically impact the overall level of the market? Using the Flow of Funds Accounts, we find that the quarterly contemporaneous relation between flow and return is positive and significant for Mutual Funds, Foreigners, Pension Funds and Insurance Companies. For example, over the entire sample we find that a one standard deviation realization in unexpected mutual fund flow corresponds with a 2.4 percent increase in the quarterly stock market return. We then develop a GMM framework to estimate higher frequency covariance measures based on a decomposition method developed in Sias, Starks and Titman (2001). We find that the positive contemporaneous quarterly covariance for Mutual Funds, Foreigners, and Insurance Companies is driven mainly by a strong contemporaneous monthly relation, suggesting that these sectors may exert price pressure on the market through their demand for stocks. The price impact appears to be temporary and is reversed in the subsequent months.

3 I. INTRODUCTION Empirical evidence indicates that trading behaviors vary across broad investor groups, such as households and institutions. In asset pricing models with heterogeneous agents, demand variables such as trading volume or fund flow can play an important role in determining asset prices. 1 In this paper, we study the joint behavior of the aggregate stock market return and net cashflows to the stock market from different investor groups. Our goal is to gain a better empirical understanding of the impact investor heterogeneity may have on asset prices. Specifically, we are interested in determining who is the marginal investor in the economy. If heterogeneous investors trade among themselves, whose behavior has significant price implications for the market portfolio? Recent studies document that individuals and various types of institutions exhibit different trading behaviors. Del Guercio (1996) finds that prudence restrictions cause different types of institutions to make distinctive investment decisions. Cohen (1998) provides evidence that institutions and individuals differ in their asset allocation decisions. In addition, Dennis and Strickland (2002) find that individuals and institutions exhibit different trading behaviors on days of high market volatility. Meanwhile, several studies find that trading by institutions in general may have important price effects on stocks. Badrinath, Kale and Noe (1995) and Sias and Starks (1997) relate institutional ownership to distinct lead-lag patterns in stock returns. Gompers and Metrick (2001) find that the increase in institutional ownership explains part of the disappearance of the historical small-company stock premium. Chakravarty (2000) provides evidence that institutional trades may impact stock prices because of superior information. Nofsinger and Sias (1999), Wermers (1999), 1 For example, see Campbell and Kyle (1993), Campbell, Grossman and Wang (1993), Wang (1993a, 1993b). 1

4 Cai and Zheng (2000), Griffin, Harris and Topaloglu (2001), and Sias, Starks and Titman (2001) all document a strong positive contemporaneous relation between institutional trading and stock returns. Other studies have focused primarily on determining whether flows into mutual funds have important market-wide price effects. Warther (1995) studies the relation between monthly flows into mutual funds and market returns from 1984 to 1993 and finds a significant contemporaneous relation between market returns and monthly mutual fund flows. Goetzmann and Massa (1998) examine the relation between daily flows of three Fidelity index funds and S&P 500 market returns from 1993 to Their results suggest that the market reacts to daily mutual fund demand. Edelen and Warner (2001) study the lead-lag relations between stock market returns and aggregate equity fund flows using daily flow data and intra-day returns from February 1998 through June They document a positive concurrent daily relation and show that this concurrent relation is mainly caused by returns responding to flows. In our study, aggregate net purchases of corporate equities are divided among seven major investment sectors in the economy: Mutual Funds, Households, Pension Funds, Foreign Investors, Insurance Companies, Closed-End Funds, and Other Institutions. Our data covers a broad sample of all investors in the economy and spans a long time period, from 1952 to We address the following questions: 1) Do the returncashflow relations differ across the seven investor groups? 2) Are the differences caused by distinct trading behaviors, such as positive feedback trading, or the fact that trades of specific sectors systematically impact the overall level of the market? While distinct sector trading behaviors are interesting to observe, researchers, practitioners and policy makers are more concerned about whether certain sectors move the stock market. The findings of this paper will provide insights into the asset pricing models with heterogeneous agents and the growing literature of investor behavior. The empirical results will also help regulators form policies based on sector specific 2

5 price effects. Using a simple correlation test and regression analysis, we find that the quarterly contemporaneous relations between return and flow are positive and significant for Mutual Funds, Foreign Investors, and Pension Funds for the full sample period. In addition, the significant contemporaneous relations are mainly due to the unexpected component of cashflows. Forexample,overtheentiresamplewefind that a one standarddeviationrealizationinunexpectedmutualfundflow (approximately $6.1 billion in 1995) corresponds with a 2.4 percent increase in the quarterly stock market return. Next, we divide our sample period into two subsample periods: 1952 to 1983 and 1984 to The two subsample periods differ in many aspects, for example, the percentage equity ownership by the sectors, stock market returns, cashflow volatility, etc. The later period also corresponds to the sample period in Warther (1995). When we study the quarterly contemporaneous relations for the two subsample periods, we find that the positive relations for Mutual Funds and Foreign Investors are stronger in the second subperiod. In addition, unexpected cashflowsofinsurancecompaniesare positively and significantly related to stock market returns in the second subperiod. We identify three potential explanations for the positive contemporaneous relations between sector flows and stock market returns. First, a sector may move market prices through noninformational or liquidity trades. Noninformational traders shift theirdemandcurveforexogenousreasons. Other investors are willing to accommodate these trades only if there are price concessions since they are risk averse and are pushed away from their preferred portfolio positions. This explanation is consistent with models developed by Grossman and Miller (1988), Stoll (1978), Campbell, Grossman and Wang (1993), and Wang (1993a, 1993b). Cashflows reflect changes in demand. Hence, cashflows of market movers (those who initiate trades) should be positively correlated with market returns. On the other hand, cashflows of passive investors (liquidity providers or market mak- 3

6 ers) may be negatively correlated or uncorrelated with market returns, depending on whether price concessions are required to induce them to trade. Moreover, models with heterogeneous investors suggest that the price impact of noninformational trades will tend to be reversed subsequently to reflect the change in expected returns due to previous price concessions(campbell, Grossman and Wang 1993 and Wang 1993a). Hence, at higher frequencies, we may expect flows of noninformational market movers and subsequent returns to be negatively correlated. Second, a sector may move the market if it has superior information relative to other sectors and if information revealed through trading drives price changes (French and Roll, 1986; Barclay, Litzenberger, and Warner, 1990). This explanation is consistent with the models developed by Copeland and Galai (1983), Kyle (1985), Glosten and Milgrom (1985), Campbell, Grossman and Wang (1993), Wang (1993a, 1993b), Foster and Viswanathan (1996), and Back, Cao, and Willard (2000). In this case, we should also observe a positive contemporaneous relation between the sector flows of market movers and market returns. At higher frequencies however, the relation between flows of market movers and subsequent returns should be zero or positive as stock prices may continue to incorporate new information. Chakravarty (2000) and Sias, Starks and Titman (2001) find evidence supporting informed trading on individual stocks by institutional investors. Finally, the positive relation between flows and returns may be due to intra-quarter positive feedback trading. Warther (1995) finds no evidence of positive feedback trading for mutual funds at a monthly frequency. On the other hand, Edelen and Warner (2001) find some evidence that daily flows into mutual funds respond to lagged returns. However, using intra-day returns, they conclude that the positive contemporaneous daily relation between flowsandreturnsismostlycausedbyreturn responding to flow within the day. If sector flow chases returns, we may again observe a positive contemporaneous relation between flows and returns using lower frequency 4

7 data. At higher frequencies however, the relation between returns and subsequent flows should be positive while the relation between flow and subsequent returns should be zero. We estimate higher frequency lead-lag comovements in order to determine whether trades by the identified sectors do in fact have market-wide price impacts. The unfortunate drawback of our sector flow data is its low quarterly frequency. As a result, we are limited to observing quarterly relations between flow and returns and are unable to observe within quarter dynamics directly. Hence, to explore the flow-return dynamics within a quarter, we apply a covariance partitioning method developed in Sias, Starks and Titman (2001). The method allows us to utilize the higher frequency return data and decompose the quarterly covariances into components to estimate how much of the covariance arises from contemporaneous, lead, and lag cashflow-return relations respectively within a quarter. In this paper,wedevelopanestimationframework based on Hansen s (1982) Generalized Method of Moments (GMM) which makes efficient use of the data and also provides robust standard errors of the higher-frequency covariance estimates. Applying this method, we find that the monthly contemporaneous covariances are positive and significant for Mutual Funds and Foreign Investors. The monthly contemporaneous covariance is also positive and significant for Pension Funds during and for Insurance Companies during In addition, we find no evidence of positive feedback trading for any sector. In fact, for Mutual Funds we find that the monthly relation between returns and subsequent flows is significantly negative. This result is consistent with findings of mutual fund flows in Warther (1995). We also find a reversal in prices following the mutual fund, foreign and insurance flows, consistent with the price pattern of noninformational trades. Thus, our results suggest that the demand shocks of these sectors may exert temporary price pressure on the market and that such effects are stronger in the second subperiod of our data. 5

8 Positive feedback trading may occur at intervals less than a month. Consequently, the positive relations we document should be viewed as an upper bound of the actual price impact of the sector flows. However, the results of Goetzmann and Massa (1998) and Edelen and Warner (2001) who study daily flows into mutual funds suggest that market returns respond to flows. Our results are therefore consistent with previous findings for mutual funds using higher frequency data. In addition, we also find that the cashflow-return relation for Foreign Investors is very similar to that for Mutual Funds. We interpret this result as interesting evidence that foreigners may alsofrequentlyplaytheroleofmarginalinvestor in the U.S. stock market. Meanwhile, flows from Insurance Companies and Pension Funds display some effects on the market during specific time periods. On the other hand, results for Households, Closedend Funds, and Other Institutions do not indicate that trades by these sectors have systematic market-wide price effects. We then decompose realized returns to examine which component of returns drives the positive contemporaneous correlations. Following Campbell (1991), Campbell and Shiller (1988a, 1988b), realized stock returns are decomposed into expected returns, dividend news, and future return news. A positive correlation between stock returns and news about future dividends does not reflect the change in price as a response to a demand shock but rather may reflect a change in cashflow in response to a supply shock of future dividends. If the positive correlation is due to demand shocks, it should reflect a negative correlation between flows and future return news. That is, if prices are discounted future dividends, and positive (negative) flows drive prices up (down) while dividends remain unchanged, then total returns at some point in the future must be lower (higher). The test results indicate that flows of Mutual Funds, Foreign Investors, and Insurance Companies negatively comove with contemporaneous news about future expected returns. We interpret these results as further evidence that Mutual Funds, Foreign Investors and Insurance Companies are market movers. 6

9 Overall, our empirical results indicate that trades of Mutual Funds, Foreign Investors and Insurance Companies exert price pressure on the market. Nevertheless, the price impact appears to be temporary and is reversed in the subsequent months. The rest of the paper is organized as follows: Section II discusses data and institutional history. Section III describes the methodologies and empirical results. Section IV concludes. II. DATA A. Sources of Data The primary data source for this study is the Flow of Funds Accounts, issued by the Board of Governors of the Federal Reserve System. The Flow of Funds Accounts records holdings and purchases of major assets by sectors in the U.S. economy starting in In our analyses, we use the end-of-quarter holdings and quarterly net purchases of equities for seven major investment sectors: Mutual Funds, Households and Non-profit Organizations, Pension Funds, Foreign Investors, Insurance Companies, Closed-End Funds, and Other Institutions. This study covers a 44-year time period, from the first quarter of 1952 through the last quarter of The net purchases of equity is a more direct measure of investment in the equity market than money flow into equity mutual funds as used in the fund flow literature, because purchases or sales of fund shares do not perfectly correspond to purchases or redemptions of equity investment. Researchers have used several different data sources to analyze the flow-return relation. Most papers on institutional trading and stock returns use the Spectrum quarterly institutional holdings data based on the 13F filings with the SEC. 2 The 2 For example, Badrinath, Kale and Noe (1995), Cai, Kaul and Zheng (2001), Gompers and Metrick (2001), Nofsinger and Sias (1999), Sias and Starks (1997), Sias, Starks and Titman (2001) 7

10 Spectrum data provides information on institutional holdings of individual stocks. The price effect documented in these studies are driven by the concentration of large trades in an individual stock. Such a price effect could be idiosyncratic and bear no relation to market returns. Different types of institutional investors have been studied using the Spectrum definitions of manager types. 3 However, these categorizations are noisy and problematic as pointed out by Gompers and Metrick (2001). Warther (1995, 1998) uses monthly mutual fund flow data provided by Investment Company Institute and focuses on the market return-flow relation of the mutual fund sector. Goetzmann and Massa (1998) and Edelen and Warner (2001) use high frequency daily flows and thus are able to address the lead-lag relation between flow and market returns. Nevertheless, the high frequency data sets can not address the issue of long run effectsastheycoverashortsampleperiodofalessbroadsample. Using the Flow of Funds Accounts, we analyze a broad sample including all major investment sectors for a long sample period of 44 years. The data we use is similar in spirit to the ICI mutual fund flow data used in Warther (1995, 1998), but for a broader sample including various investment sectors. The relatively clean classifications of investor types allow us to examine the differences in the market return-flow relations across sectors and identify the potential marginal investors in the economy. Test results using this broad sample should provide us with better understanding of the heterogeneity of the cashflow-market return relations for various investor groups in the US economy. In fact, conclusions drawn without considering all investment sectors of the economy only tell a partial story. The Flow of Funds Accounts data allows us to study the return-flow dynamics of foreign investors separately. The foreign investor sector is usually excluded or use the Spectrum holdings of institutions. Wermers (1999) uses the Spectrum holdings of individual mutual funds. 3 The five Spectrum manager types are: bank, insurance company, investment company (mutual fund), investment advisor, and other. 8

11 bundled together with individual investors in the literature. On the other hand, academics have argued that foreign investors display distinct trading behavior and effects. For instance, Tesar and Werner (1995) document high turnover rate on foreign equity investments relative to turnover on domestic equity markets. Choe, Kho and Stulz (1999) study the possible destabilizing effect of foreign investor on stock prices in Korea. Dornbusch and Park (1995) argue that foreign investors follow positive feedback strategies and cause stock prices to overreact to changes in fundamentals. Boyer, Kuamgai, and Yuan (2001) provide evidence that international stock market crises are spread by the trading behavior of investors rather than fundamentals. Froot, O Connell and Seasholes (2001) document positive feedback trading as well as price impact by international investors. Thus, it is important to study the return-flow relation of foreign investors separately. The limitation of the Flow of Funds Accounts is its low frequency. Like the Spectrum data, the Flow of Funds Accounts report holdings and net purchases on a quarterly basis. The issue of intra-quarter lead-lag relation of flowandreturnisthus difficult to address. To mitigate the problem, we use a covariance decomposition method developed in Sias, Starks, Titman (2001) in a GMM framework to estimate, how much of the covariance arises from contemporaneous, lead and lag cashflowreturn relations within the quarter. We also decompose the realized returns following Campbell and Shiller (1988a,b) and Campbell (1991) to further explore the source of the cashflow-return relation. The Appendix documents the original data sources for each of the seven investment sectors. Data for the Flow of Funds Accounts come from a variety of government and nongovernment sources. Many of the data are published. Some are available to the public upon request. Others such as those gathered from individual depositoryinstitution financial reports are obtained from internal data bases maintained by offices within the Federal Reserve System. 9

12 The market returns are the total returns on the value-weighted CRSP stock portfolio. The interest rate variable is the three-month nominal risk free rate from CRSP. Dividend price ratios are also obtained from CRSP. Other macro-economic variables are provided by Ibbotson Associates. B. A Historical Look at the Players in the Market Figure 1 shows the percentage equity holdings of the major investment sectors for 1952 through The clear trend of ownership composition from 1952 to 1995 is the gradual increase in institutional stock holdings and the decrease in direct individual stock holdings. In 1952, Households held 91 percent of the equity market; Mutual Funds and Pension Funds together constituted only 3 percent of the market. During the recent four decades, Mutual Funds, Pension Funds, Insurance Companies, Other Financial Institutions, and Foreign Investors have all increased their equity stakes. The fastest-expanding sector over the whole period is Pension Funds, which grew from 1 percent in 1952 to 22 percent in The fast growth of pension fund equity holdings occurred from 1952 through In the 1990s, the dramatic expansion of mutual fund equity holdings took place. Mutual funds increased their equity ownership from 6 percent of the overall equity market in 1990 to 13 percent in Although Households, including nonprofit organizations, are still the major players in the equity market, they have been the net sellers of equity in the eighties 4 The mutual fund industry first appeared in the United States in the 1924 and grew steadily during the decades after World War II. Driven by the bull market in the 1960s, equity mutual funds more than tripled their assets, and bond mutual funds almost doubled theirs. However, there was little growth of equity funds in the 1970s because of the severe bear market caused by high inflation during the middle years of the decade. Along with the stock market, the mutual fund industry rebounded strongly in the 1980s, with steady cash inflow and an emergence of many new funds. In the 1990s, mutual funds, especially equity mutual funds, experienced an expansion in asset size and number of mutual fund share holders. 10

13 and nineties. Consequently, they directly owned 52 percent of the market in This decline in direct holdings of stocks by individuals has been offset by an increase in indirect holdings through such vehiclesaspensionplansandmutualfunds. Figure1alsoshowsasharpdeclineinequity holdings for Households and a sharp increase in equity holdings for Other Institutions during the first quarter of This spike is due to a change in the classification of equity holdings by Bank Personal Trusts. These were initially included under the Household sector but were counted in the Other Institutions sector beginning in Cohen (1998) uses the Flow of Funds Accounts to compare the asset allocation decision between equity and debt of individuals and institutions. He focuses on the asset allocation decision process of the two types of investors. For this purpose, he defines individual and institution based on whether the allocation decision is made by the security holder or a fund manager: individual, mutual funds and defined contribution plans are counted as household holdings and pension funds, banks, and insurance companies are counted as institutions. In our paper, we study the price impact and the return-chasing pattern of different sectors in the equity market. We form our sectors based on investment objectives, fiduciary responsibilities and information that can lead to different patterns and impact of trading. The primary variable analyzed in Cohen (1998) is the relative level of equity and debt investment for each sector, while our variable of interest is flows into equity market through different sectors. Unlike Cohen (1998), we do not study the interaction between the equity and the debt market. 5 Personal communication with staff at the Board of Governors of the Federal Reserve System. For our empirical tests, we checked to make sure that the results are not driven by this data reclassification. 11

14 III. METHODOLOGY AND EMPIRICAL RESULTS A. Quarterly cashflows and Stock Returns 1. Summary Statistics. We first study the quarterly contemporaneous relations between sector cashflows and stock market returns. The time series of flow data span 44 years. The measure of cashflow for sector i in quarter t is defined as the net purchase of stocks for sector i in quarter t divided by the total level of stock holdings of all sectors at the end of quarter t 1. This normalization measures new money relative to the total equity market capitalization and thus takes into account the overall price level of the equity market. It makes the time-series and cross-sectional sector observations comparable: cashflow t =(Net Purchase) t /(Total Level) t 1. (1) Note that the sector flows should add up to zero, after adjusting for the new issuance or buyback of stocks, which is only a small fraction of the equity market. Alternatively, we define flow as the net purchase of stocks for sector i in quarter t divided by the total level of stock holdings of sector i at the end of quarter t 1. All test results are qualitatively similar. In the paper, we report test results based on the cashflow definition in equation (1). The return series used is the value-weighted market portfolio from CRSP. The test results remain very similar when we use returns excluding dividends. We have additionally run all our tests using the excess return over the risk-free rate where the risk-free rate is the 3-month T-bill rate from CRSP. All results are again found to be quantitatively similar. In this paper, we report results using the total nominal return. In Table I, we report summary statistics for the market return and sector flow data. Panel A reports summary statistics fortheentiresamplefromthesecond 12

15 quarter of 1956 to the fourth quarter of The sample contains 175 quarters. The mean normalized flow is positive for all sectors except for Households and Closed- End Funds. Pension Funds have the highest mean normalized flow at 0.19 percent followed by Mutual Funds at 0.07 percent. The flow standard deviation is highest for Households at 0.40 percent and lowest for Closed-End Funds at 0.05 percent. Panel A also reports the correlations between the sector flows. The sector flows do not appear to be highly correlated. The highest correlation in Panel A of Table I is that between Pension Funds and Insurance Companies (0.189). In Table I and throughout the paper, we divide our sample period into two subsample periods. These two subperiods are from 1952 to 1983 and from 1984 to The later period corresponds to the fast growth of the mutual fund industry and the sample period in Warther (1995). Dividing the sample in this manner allows us to compare our results across the two periods. The mean normalized cashflow for Mutual Funds is ten-times higher in the second subperiod than in the first subperiod, percent versus percent, reflecting the rapid growth of the mutual fund industry over the second subperiod. The mean Household normalized flow is about four-times more negative in the second subperiod, percent versus percent, reflecting the general pattern of Households decreasing their direct stockholding and increasing their stockholding through mutual funds. There is also a rather large difference in the normalized flows across the two periods for the sector of Other Institutions, versus percent. The normalized flow of Foreign Investors, Insurance Companies, Pension Funds and Closed-End Funds appear to be comparable across the two periods. The mean quarterly total cashflow across all sectors is close to zero in the first subperiod, indicating an approximately fixed supply of stocks. In the second subperiod however, the mean quarterly total cashflow across all sectors is percent, probably reflecting the fact that corporate America issued substantial amounts 6 We lose one observation at the beginning of the sample when normalizing. 13

16 of debt and retired equity during the 1980 s. In addition, note that the standard deviation of flow is much higher for all sectors in the second subperiod than in the first, except for Closed-End Funds. Hence, another reason for splitting up the sample is to ensure that the results are not an aberration caused by nonstationarity in the data. 2. Contemporaneous Relations. Table II reports correlations between sector cashflows and stock market returns for each of the seven sectors. We decompose total flows into expected flows and unexpected flows using the VAR model that we describe in the next section. The expected flows are the forecasts of the VAR model, and the unexpected flows are the residuals of the model. The decomposition allows us to learn about the possible difference in how stock market returns relate to predictable versus unpredictable components of sector flows. We report the correlations of stock market returns with total cashflows, expected cashflows, andunexpectedcashflows for each sector respectively. In this table, we use both the standard Pearson estimate and the Spearman rank test. Since Spearman s method is based on ranks and is not sensitive to outliers and non-normality, it minimizes the effect of outliers on the test. In Panel A, which reports results for the entire sample period, cashflows of Mutual Funds, Foreign Investors, and Pension Funds are significantly and positively correlated with stock market returns. The correlations are negative for Households, Closed-End Funds and Other Institutions. As seen from the table, the results are robust to both the parametric and non-parametric correlation tests. A negative correlation indicates that the sector is a residual trader of the stock market or that it follows contrarian strategies. A lack of correlation suggests that the sector does not have systematic price impact on the stock market. We are particularly interested in examining the sectors of which cashflows display positive correlations with the stock 14

17 market returns, because these sectors can potentially affect stock prices. However, as discussed above, a positive correlation is consistent with 1) noninformational sector demand affecting stock prices; 2) sectors having superior information and timing their trades 3) sectors following positive feedback trading strategies. We will devote much effort in exploring the source of the positive correlations later in the paper. As we see from the correlations of the decomposed flows with returns, the results for the total flows are mainly driven by the unexpected flow component for Mutual Funds, Foreign Investors and Pension Funds. In addition, the unexpected flows of Insurance Companies are also positively and significantly correlated with market returns. Panel B and C report correlation statistics for the subsample periods. These results suggest that the positive correlations between the stock market returns and unexpected flows are higher both in magnitude and in significance for Mutual Funds and Foreign Investors in the later period. This finding is consistent with the hypothesis that these sectors affect stock prices and that their effects are more pervasive during the later period in which they play a more important role in the equity market. In Table III, we report estimated regression coefficients of stock market returns on the time series of seven sector cashflows using OLS. Numbers in parenthesis are t-statistics. Standard errors are calculated using the approach of Newey and West (1987) assuming the error structure is possibly autocorrelated up to eight lags. The findings are consistent with those of Table II. Cashflows of Mutual Funds, Foreign Investors and Pension Funds are positively related to stock market returns, and the results are driven mainly by the unexpected flow components. Unexpected flows of Insurance Companies are also positively correlated with market returns. Using the summary statistics from Table I and the regression coefficients of Table III, we can get an idea of the economic significance of the cashflow-return relations. For example, the coefficient for total mutual fund flow reported in Panel A of Table III is approximately 9.8, implying that a one standard deviation realization in nor- 15

18 malized mutual fund flow (0.142 percent or $11.8 billion in 1995) corresponds with a 1.39 percent increase in the quarterly stock market return. The coefficient on ForeignInvestorsisapproximately27.9implying a one standard deviation realization in normalized foreign flow (0.076 percent or $6.3 billion in 1995) corresponds with a 2.12 percent increase in the quarterly return. Using the results for unexpected flow in Panel A, the coefficient on mutual funds is approximately 32.3 implying a one standarddeviationrealizationinunexpectedmutualfundflow (0.073 percent or $6.1 billion in 1995) 7 corresponds with a 2.4 percent increase in the quarterly stock market return, while for Foreign Investors, the coefficient is approximately 37.3 implying a one standard deviation realization in unexpected foreign flow(0.062percentor $5.2 billion in 1995) is associated with a 2.3 percent increase in the quarterly market return. Since Edelen and Warner (2001) have found some evidence that flows follow market returns, the positive relations we document should be viewed as an upper bound of the actual price impact of the sector flows. It is interesting to note that the cashflow-return relation for Foreign Investors is as strong, if not stronger than that for Mutual Funds and Pension Funds, despite the disproportionate attention and coverage given to these sectors by academics and the popular press. This finding is consistent with the evidence in Froot, O Connell and Seasholes (2001) that the sensitivity of local stock prices to foreign inflowsispositiveandlarge. In the second subsample period, we observe higher regression coefficients for Mutual Funds and Foreign Investors using unexpected flow than in the first subsample period. It is also interesting to note the difference in R-squared across the different subsamples for the unexpected flow regressions. In the first subsample, unexpected mutual fund flow explains a mere 2.9 percent of the return variance while unexpected foreign flow explains only 1.8 percent. In the second subsample however, the unexpected flows for mutual funds and foreigners both explain about 38 percent of the variance in returns. 7 Summary statistics for unexpected flow are not reported in this paper. 16

19 The relations between flows and returns for Mutual Funds and Foreign Investors are found to be stronger during the later period in which their equity holdings are larger and they trade perhaps more actively. For Pension Funds, the regression coefficients using unexpected flow are similar across the two periods, 11.5 versus 12.5, and the R-squared using unexpected flow is only slightly higher during the second period, 0.03 versus For Insurance Companies, the magnitude of the regression coefficients using unexpected flow is higher in the first subperiod and the significance levels are similar across the two periods. We also examine the contemporaenous relations between flowandreturncontrolling for changes in aggregate supply of stocks, where supply is measured as the quarterly total cashflow across all sectors. When we include the changes in aggregate stock supply as a control variable in the regressions, the results reported in Table III stay virtually unchanged. Hence, the positive contemporaneous relations between sector flows and stock returns are due to demand rather than supply shocks. In summary, we find evidence that cashflows of Mutual Funds, Foreign Investors, Pension Funds and unexpected cashflows of Insurance Companies are positively correlated with stock market returns. These relations are found to be stronger over the second subperiod for Mutual Funds and Foreign Investors. Subsections B and C are devoted to understanding the source of these positive correlations. 2. VAR Model. We use a VAR approach to study the lead-lag relations between quarterly sector cashflows and stock returns. Let y t+1 be a k 1 vector of variables observed at time t+1 or earlier that help forecast future returns and flows. The vector y t+1 is assumed to follow a first-order VAR y t+1 = Ay t +w t+1. (2) where A is a k k matrix of coefficients and w t+1 is a k 1 vector of error terms. 17

20 Since a higher order VAR can always be stacked into a first-order rendition, the first-order assumption is not restrictive. We use the Bayesian Information Criterion (BIC) and an analysis of the residuals to select the appropriate lag structure. The variables included in y t+1 are the quarterly market return, the quarterly sector flows, the dividend-price ratio, the relative short term interest rate, and a constant to estimate the intercept. Since sector flows approximately sum to zero, flows for Other Institutions are excluded from the set of explanatory variables as a precaution against multicollinearity. The dividend-price ratio is used following Fama French (1988) and Campbell and Shiller (1988a,b). If prices are discounted future dividends, then changes in the dividend price ratio should reflect changes in future expected returns. The variable is measured as total dividends paid over the previous year divided by the current stock price. The relative short term interest rate is used because many authors, including Fama and Schwert (1977) and Campbell (1987) have found that the short term interest rate helps forecast returns. This variable is measured as the difference between the three month risk-free rate from CRSP and its one-year backward moving average. These measures for the dividend price ratio and the relative short term interest rate are discussed in Campbell (1991). The parameters of (2) are estimated by OLS with standard errors calculated using the approach of Newey and West (1987) assuming the error structure is possibly autocorrelated up to eight lags. Hence, the standard errors are consistent even in the presence of heteroscedasticity and autocorrelation. We make no further assumptions about the nature of the error terms. Consistency of parameter estimates only requires that y t+1 be stationary. Stationary tests of each series based on the unit root tests of Dickey-Fuller reject the null hypothesis that the time series is non-stationary at the one percent level. Using BIC and an analysis of the residuals to select the appropriate lag structure, we select a model with two lags. Results using additional lags are qualitatively similar. The model is estimated using all data in our sample from

21 to1995, as well as over the two subperiods from 1956 to 1983 and from 1983 to Table IV reports the VAR coefficients for the entire sample. All sector flows appear to be positively autocorrelated. However, we do not find much evidence for cross effects between sector flows. In addition, we find no significant relation between returns and lagged cashflows or between cashflows and lagged returns on a quarterly basis, except that mutual fund flows are negatively correlated with the two-quarter lagged return. Using a Wald test to study the Granger-causality between stock market returns and sector cashflows, we find no evidence that stock market returns Granger cause any of the sector cashflows or that cashflows of any sector Granger cause returns. Given the lack of a significant lead-lag relation between quarterly flows and returns, this result is not surprising. Any lead-lag effects among these variables are likely to be carried out very quickly and therefore difficult to detect using quarterly data. To get a better sense of the cause-effect relationship, higher frequency data is needed. We explore methods of estimating higher frequency covariances in the next section. In addition to the lagged returns and cashflows, other variables may also be correlatedwithmarketreturnsandcashflows. 8 Omitting such variables may cause some misleading results since lagged returns and cashflows may just be proxies for other factors. We check the robustness of our VAR results by adding five variables which reflect economic and demographic conditions. The variables are: population median age, median family income and inflation. We find that the magnitude of the coefficients changes very little when we add these conditioning variables. 8 For example, a number of papers, including Rozeff (1984), Campbell and Shiller (1988a, b), Fama and French (1988) and Bekaert and Hodrick (1992) document some evidence that dividend yields are positively correlated with future stock returns. Bakshi and Chen (1994) suggests that a rise in average age predicts a rise in risk premiums. 19

22 B. A Time Decomposition of Covariance Given that we have found positive correlations between quarterly cashflows and quarterly market returns for some sectors, we now investigate the cause-effect relation between sector flows and market returns. If high frequency data on sector cashflows were available, it would be straight forward to determine if the correlation between cashflow and returns is based on some lead-lag relation. Unfortunately we do not have this convenience, and hence, must employ a methodology which allows us to indirectly estimate the relation between sector cashflows and market returns. Specifically, we use a method discussed in Sias, Starks, and Titman (2001) based on the additive property of covariances. Estimation is carried out using a framework developed in this paper based on GMM which makes efficient use of the data. We begin with a simple example and then describe the method in general. Let C q be the total flow of some sector over quarter q for q =1,...Q. Divide each quarter into monthly intervals and let r t and c t respectively be the market return and sector flow measured over month t for t =1,..., 3Q. We do not observe monthly flow, however, by definition, quarterly flows are the sum of monthly flows: C q+1 = c 3q+1 + c 3q+2 + c 3q+3. (3) Suppose market returns and sector flows are covariance stationary. We are particularly interested in the covariance between c t and r t+z : Cov(c t,r t+z ), γ(z). (4) From (3) it follows that Cov(C q+1,r 3q+1+z )=γ(z)+γ(z 1) + γ(z 2). (5) In addition we have: Cov(C q+1,r 3q+z )=γ(z 1) + γ(z 2) + γ(z 3). (6) 20

23 Subtracting (6) from (5) we have that Cov(C q+1,r 3q+1+z ) Cov(C q+1,r 3q+z )=γ(z) γ(z 3). (7) It follows that if γ(z 3) is zero, then γ(z) can be found by estimating Equation (7). If γ(z 3) is nonzero, then additional covariance terms between quarterly flows and lagged returns are needed to cancel out γ(z 3) as described below. The same methodcanbeusedtoestimateγ(z) using leading returns: Cov(C q+1,r 3q+3+z ) Cov(C q+1,r 3q+4+z )=γ(z) γ(3). We now describe the method generally. Let C q+1 be the total flow of some sector from time q to q + 1 observed at time q +1forq =1,...Q. Divide each time interval into equally spaced subintervals and let r t and c t respectively be the market return and sector flow measured over the higher frequency time periods for t =1,..., Q. Note that by definition, X C q+1 = c q +δ. (8) δ=1 Suppose market returns and sector flows are covariance stationary. We are particularly interested in the covariance between c t and r t+z : Cov(c t,r t+z ), γ(z). (9) We do not observe high frequency cashflows c t so direct estimation of γ(z) isnot possible.however,from(8)itfollowsthat X Cov(C q+1,r q +z )= γ(z δ) (10) for some fixed value of z. Define γ F (z) andγ L (z) as FX γ F (z) Cov C q+1,r (q+k) +z Cov Cq+1,r (q+k) +z+1 (11) k=1 δ=1 XL 1 γ L (z) Cov C q+1,r (q k) +z+1 Cov Cq+1,r (q k) +z. (12) k=0 21

24 Using (10) we can write: γ F (z) = γ(z) γ (z + F ) γ L (z) = γ(z) γ(z L). Suppose that covariances decay to zero such that γ(w) =0for w w 0 where w 0 is some positive finite integer (w 0 is henceforth referred to as the threshold ). Then the following implications hold: if F w 0 z if L w 0 + z then γ F (z) =γ(z) (13) then γ L (z) =γ(z). We use the relations of Equations (11) and (12) to estimate covariances between market returns and sector flows. Two econometric approaches are used. First, sample moments are used to obtain point estimates and a simple bootstrap procedure is performed to test for significance. The second approach is based on Hansen s (1982) generalized method of moments (GMM). Estimation by Sample Moments. First, Sample moments are used to estimate the covariance between quarterly flows and monthly returns. Estimates of the covariance between monthly flows and monthly returns can then be obtained using Equations (11) and (12). This is done by choosing a threshold w 0 and then estimating γ F (z) andγ L (z) using the smallest integers F and L which satisfy the boundary conditions of (13). The estimate of γ(z) isthentaken to be the average of these two estimates. We check the robustness of our results over a wide range of thresholds w 0. In Table V, we present estimates of γ(z) forz ( 2, 1, 0, 1, 2) using monthly data and a threshold w 0 = 15. These tables also report boot-strapped p-values for the covariance estimates. To create these p-values, we generated one thousand 22

25 samples of returns by drawing random observations of monthly market returns with replacement and uniform probability. The time series of market returns from which observations were drawn is from January 1945 through December Estimates of γ(z) are then obtained for each random sample using the quarterly sector flows. The results reported in Table V help to disentangle the cause-effect relation between sector flows and market returns. 9 Panel A reports results for the entire sample, ; Panel B reports results for the first subperiod, ; Panel C reports results for the second subperiod, Across all panels in Table V, estimates of the contemporaneous covariance between monthly mutual fund flow and the monthly market return, γ(0), are positive and statistically significant at the 5 percent level. This finding is consistent with the evidence in Sias, Starks and Titman (2001), Goetzmann and Massa (1998) and Edelen and Warner (2001). Sias, Starks and Titman (2001) suggest that the positive quarterly contemporaneous relation between individual stock returns and institutional trades are mainly concurrent. Goetzmann and Massa (1998) document evidence that daily flows of three Fidelity index funds affect S&P 500 returns rather than follow the market returns. Edelen and Warner (2001) document a positive concurrent daily relation and show that this concurrent relation reflects mutual fund flow affecting stock market returns. Consistent with Tables II and III, the estimated contemporaneous covariance for Mutual Funds is much stronger over the second subperiod (0.328) than over the first subperiod (0.066) probably due to the more important role Mutual Funds played in the equity market during the second subperiod. Interestingly, similar results are found for Foreign Investors. Estimates of the contemporaneous covariance between monthly foreign flows and the monthly market 9 Covariance estimates are scaled by a factor of 10,000 for convenience in presentation. 10 Results for expected and unexpected flows are not reported in this section since these can only be measured using information at the beginning of each quarter (not each month). 23

26 return, γ(0), are positive and significant at the 5 percent level in Panels A and B, and at the 10 percent level in Panel C. Again, the magnitude of the covariance is larger over the second subperiod (Panel C). These results indicate that foreign flow may also have an important impact on market prices, despite the disproportionate attention and coverage given to these sectors by academics and the popular press. We find a positive contemporaneous monthly relation for Pension Funds in the first subperiod, whereas a similar positive relation for Insurance Companies in the second subperiod. These results indicate that Pension Funds and Insurance Companies may have market-wide impact in specific timeperiods. We find no evidence for positive feedback trading on a monthly basis. The estimated covariance between returns and subsequent flows, γ( 1), is not significant for any sector. In addition, for Mutual Funds and Foreign Investors, we find that flows are negatively related to subsequent returns, γ(1) < 0andγ(2) < 0, though the relation is at best marginally significant. As discussed above, a negative relation between flows and future returns supports the idea that demand shocks originating from these sectors impact market prices and weakens the premise that information is being revealed through their trades. Thus, our results so far suggest that the positive contemporaneous correlations are mainly due to noninformational demand shocks of mutual funds and foreign investors exerting price pressure on the market. We check the robustness of our results over a wide range of thresholds w 0. The results are qualitatively consistent. We also estimate weekly covariances using weekly return data. Unfortunately, the covariance estimates using weekly partitioning method are not significant and hence, do not allow us to draw conclusions about the cashflowreturn relation at weekly intervals. Estimation by GMM. Alternatively, we use GMM to jointly estimate γ(z) forz ( 2, 1, 0, 1, 2). As- 24