Do Banks Buy and Sell Recommendations Influence Stock Market Volatility? Evidence from the German DAX30

Transcription

1 Do Banks Buy and Sell Recommendations Influence Stock Market Volatility? Evidence from the German DAX30 forthcoming in European Journal of Finance Abstract We investigate the impact of good and bad news on stock market volatility. To this end, we utilize a novel data set of banks buy and sell recommendations for the German DAX30 stock market index, and estimate an EGARCH(1,1) model which features these recommendations as well as several other pertinent explanatory variables in the mean and variance equations. We find that in a rising market, buy recommendations lower the level of volatility and sell recommendations raise volatility, whereas the impact of news on stock market volatility is less clear-cut in a falling market. Keywords: Banks stock recommendations, EGARCH model, stock market volatility JEL classification: C53, E44, G11 Addresses: Torben W. Hendricks Bernd Kempa Christian Pierdzioch (corresponding author) Department of Economics University of Duisburg-Essen Germany Department of Economics University of Muenster Germany Department of Economics Saarland University Germany c.pierdzioch@mx.uni-saarland.de Phone: +49 (681) Fax: +49 (681)

2 1 1. Introduction There is an ongoing debate about the role of public information in asset markets. According to the efficient-market hypothesis of Fama (1970), financial analysts recommendations should be completely irrelevant as they produce no new information not already incorporated in asset prices. In contrast, if asset markets are less than fully efficient, such recommendations may in fact promote informational efficiency and impact the pricing of financial assets. By distinguishing which news is relevant for the valuation of an asset, financial analysts recommendations can then be seen as signals of new information in the market. As Womack (1996) suggests, revisions of financial analysts recommendations imply that they have assessed publicly available information and have come to the conclusion that the current asset price is not appropriate. However, the existing evidence implies that public information arrival, measured by publicly available economic and financial data, may not be able to explain the movements in asset prices very well. 1 Partly as a consequence, a growing number of studies have turned to investigating the impact of buy and sell recommendations on volatility rather than on prices. Jones and Lumsdaine (1998) detect short-lived excess volatility for U.S. bonds following macroeconomic announcements in daily data, while Janssen (2004) demonstrates that most of the volatility persistence, as observed by GARCH models, tends to disappear when news is included in the conditional variance equation. Kalev et al. (2004) study firm-specific announcements as a proxy for information flows and investigate the information-volatility relation using high-frequency data from the Australian Stock Exchange. Their analysis points to a positive and significant impact of the arrival rate of the selected news variables on the conditional variance of stock returns. In a related study, Antweiler and Frank (2004) find that even internet stock message boards can help predict market volatility. Most recently, 1 See, e.g., Graham and Harvey (1996) and Kutan and Aksoy (2004). Barber et al. (2003, 2006) point to the prevalence of buy relative to sell recommendations as a possible source of bias in analysts recommendations. In this regard, Hong and Kubik (2003) find that brokerage houses reward optimistic analysts who promote stocks.

3 2 Panchenko (2007) relates increased volatility directly to the updates of analysts recommendations. Another important strand of literature points to state dependence in asset price behaviour. In two influential papers, Black (1976) and Christie (1982) found that volatility increases with bad news and falls with good news. More recently, a number of studies also find state dependence of announcement effects in asset markets (see McQueen and Roley, 1993, Conrad et al., 2002, and Andersen et al., 2007). In these papers, bad news is found to have a stronger effect in good times compared to bad times, whereas the impact of good news appears to be similar across good as well as bad times. This paper adds to the existing evidence by focusing on a novel database on buy and sell stock recommendations of German, European as well as U.S. banks for shares contained in the German DAX30 stock market index. These recommendations are collected and disseminated through a daily internet newsletter called Aktienmarkt.net, which started operating in late 2001 and is available free of charge to any interested investor. The newsletter is dispatched at 6 p.m. every day and provides a summary of all stock recommendations which occurred throughout the trading day. As most stock recommendations relate to the 30 large German blue-chip companies contained in the DAX30 performance index, we restrict attention to this index. We investigate the impact of good and bad news on stock market volatility, represented by the occurrence of buy and sell recommendations, respectively, by means of an EGARCH(1,1) model. The EGARCH model is designed to discriminate between positive and negative returns, and is thus ideally suited to detect the potential asymmetries of the stock market response in the data. In our specification, the model features buy and sell recommendations as well as several other pertinent explanatory variables in the mean and variance equations.

4 3 The remainder of the paper is structured as follows: Section 2 provides some descriptive statistics on our data, Section 3 describes our empirical strategy, Section 4 presents the results, and Section 5 concludes. 2. Descriptive statistics Our sample period runs from 1/3/2002 to 6/30/2009 and consists of 1906 daily data containing a total of 6135 recommendations, with 4971 buy recommendations and 1164 sell recommendations. 2 percentage returns. 3 Figure 1 displays the time series of the DAX30 together with its Studying this sample period is interesting because the DAX30 stock market index has been on a downward spin from the beginning of the sample until about the end of the first quarter This time span can clearly be associated with bad times, reflecting the aftermath of the bursting of the German IT stock market bubble which, as in several other countries, occurred at the beginning of the 2000s. This period is followed by a pronounced upward movement of the DAX30 index coupled with much reduced levels of volatility, starting in the second quarter of 2003 and lasting roughly until the fourth quarter of This second episode constitutes the good times in our investigation. Finally, the third period comprises the most recent financial and economic crisis, which started in the latter half of 2007 with the troubles in the U.S. subprime market. This bad times episode becomes clearly visible in our data in terms of a rapid decline of the DAX30 index coupled with a dramatic increase in the level of volatility starting at the beginning of 2008, and lasting right until the end of the sample period. 2 Buy recommendations include strong buy, buy, outperform, outperformer, market outperformer, overweight, add, accumulate and recommended list, whereas sell recommendations include sell, reduce, underperform, underperformer, market underperformer, and underweight. Recommendations are defined in terms of the number of buy and sell recommendations on a given day. We leave out hold recommendations such as neutral, market neutral, hold, market performer and equal weight. 3 All data except for the buy and sell recommendations are from Thomson Financial Datastream.

5 4 Insert Figure 1 about here. Table 1 provides descriptive statistics for the daily DAX30 stock market returns as well as banks buy and sell stock recommendations relating to the full sample as well as the three subperiods. Whereas the mean and median returns of the DAX30 have been positive at respectively % and % for the full sample, returns are highly negative in the bad times subsamples, with mean and median returns of % and % in the first subsample, and % and % in the second subsample. In contrast, returns are highly positive with returns of % and % in the good times subsample. The respective standard deviations of the DAX30 returns are also clearly and substantially above (below) average in the two bad times ( good times ) subsample(s), thus confirming the visual impression from Figure 1. The results for kurtosis and skewness indicate non-normality of the returns, which is verified by the Jarque-Bera (1980) test. Under the null hypothesis of a normal distribution, the statistic is distributed as chi-square with two degrees of freedom. Hence the reported probability for the Jarque-Bera test in Table 1 is the probability that the statistic exceeds the observed value under the null hypothesis. For the investigated return series, we reject the hypothesis of normality for the full sample as well as the three subsamples at the 5% level. Table 1 also reports on the relative frequencies of buy and sell recommendations in our data set. These make up, respectively, 81.03% and 18.97% of all recommendations in the full sample, corresponding to recent findings that analysts stock recommendations are biased upward (e.g., Barber et al., 2006, 2007, for the U.S., and Stotz, 2005, and Wallmeier, 2005, for Germany). This bias in favour of buy recommendations is also confirmed across the individual subperiods. Insert Table 1 about here.

6 5 3. Empirical strategy In order to investigate the impact of good and bad news on stock market volatility, we separately add the number of the individual buy recommendations and sell recommendations for all the stocks contained in the DAX30 index on any given day, and then measure their aggregate impact on the returns and the volatility of the index. In order to account for the potential asymmetry in our estimation, we employ an EGARCH(1,1) model, as proposed by Nelson (1991). The mean and variance equations can be written as: RET ' t = X t θ + ε t, (1) 2 ' ε t 1 ε t 1 2 ( ) ω + χ + α + γ + β log( log σ t Z σ ), (2) = t t 1 σ t 1 σ t 1 where the mean equation, Eq. (1), models the return of the DAX30 index, RET t, as a function of a number of exogenous variables, X t, plus an error term, ε t. The set of exogenous variables contains own lagged returns (RET t-1 ), the lagged returns of the Dow-Jones index (DOW t-1 ), the German interbank overnight offered rate as a measure of the short-term interest rate (INT t ), trading volume as measured in terms of turnover by value (VOL t ), banks buy and sell recommendations (BUY t resp. SELL t ), as well as weekday dummies. The variance equation of the model is specified in Eq. (2), with a constant, ω, and a vector of exogenous variables, Z t, containing the buy and sell recommendations, the weekday dummies, the short-term interest rate, trading volume and the square of the lagged returns of the Dow-Jones index (DOW2 t-1 ), where the latter term is included to account for the general level of volatility in international financial markets. For γ 0, positive and negative realisations of the error term have different impacts on the log of the conditional variance, where γ < 0 implies that the absolute

7 6 effect of negative realisations of the residual is stronger than the absolute effect of positive realisations. 4. Results Results for the full sample as well as the three subsamples are contained in Tables 2 through 5, where residual diagnostics based on ARCH and Q test statistics are presented in the lower parts of the tables. The Q tests show no remaining serial correlation in any of the regressions. This indicates that the model captures well the observed behaviour of the stock market returns in our data. Because the test for remaining ARCH effects is significant in some cases, we computed robust standard errors (Bollerslev and Wooldridge, 1992). The estimation results show that the own lagged returns as well as the lagged returns of the Dow-Jones index are significant at the 1% level in the estimation of the mean equation for the full sample and the latter two subsample periods, and significance at the 10% level for the first subperiod. The coefficient for the lagged Dow-Jones returns is positive, demonstrating the trend-following behaviour of the DAX30 index to the US stock market. Trading volume has explanatory power in the last subsample, where it depresses stock returns. Similarly, buy recommendations turn out to be insignificant except for the last subperiod, in which they positively influence stock returns. In contrast, sell recommendations are significant and negatively influence stock returns for the full sample as well as the first bad times sample. We find no significant day-of-the-week effects in the mean equation. Turning to the variance equations of Tables 2 to 5, the EGARCH coefficients are statistically significant for all samples. Furthermore, there are significant asymmetric effects in all cases, as evidenced by the significant and negative asymmetry coefficient, γ, at the 1% significance level.

8 7 For the full sample in Table 2, the sell recommendations are found to significantly raise the level of stock market volatility, whereas the buy recommendations have the expected negative sign, but turn out to be insignificant. As for the evidence of the individual subsamples, buy recommendations are found to be insignificant in both bad times scenarios of Tables 3 and 5, and this is also true for the sell recommendations in the first subsample. However, sell recommendations significantly raise the level of stock market volatility in the second bad times episode. We interpret this finding as a reflection of the substantial degree of insecurity in the markets associated with the recent financial crisis. This interpretation is also supported by the significant coefficients of trading volume and the square of the lagged returns of the Dow-Jones index, which both raises the DAX30 stock market volatility in this period. Interestingly, both buy and sell recommendations are found to be highly significant in the good times scenario of Table 4, with buy recommendations lowering, and sell recommendations raising the level of stock market volatility. Insert Tables 2-5 about here. In terms of a suggested interpretation of our evidence, banks buy recommendations seem to be a contributing factor to resolving uncertainty and to building investor confidence, whereas sell recommendations appear to heighten the degree of uncertainty in the stock market. Moreover, our results imply that in absolute terms, the effect of sell recommendations is larger than the effect of buy recommendations. The stronger impact of sell recommendations on stock market volatility may be explained by the fact that banks provide buy recommendations more frequently than sell recommendations. Market participants, therefore, may pay more attention to sell recommendations relative to buy recommendations. Our estimation results further suggest that both banks buy and sell recommendations and the EGARCH terms have significant explanatory power for stock market volatility. It follows

9 8 that the asymmetric response of stock market volatility to buy recommendations (i.e., good news) and sell recommendations (i.e., bad news) contains information over and above the kind of asymmetries that have been documented in many earlier empirical studies of stock market volatility based on the EGARCH and similar models. In terms of economic theory, the reaction of stock market volatility to buy and sell recommendations is consistent with leverage volatility theories (Christie, 1982). Good news lower stock market volatility, while bad news increase stock market volatility. The significance of buy and sell recommendations in good times suggests that the asymmetric volatility response curve implied by a conventional EGARCH model not featuring bank s stock recommendations does not capture all the leverage in the data. As for the bad times it seems that the volatility response curve becomes even more asymmetric than in good times insofar as, if anything, only bad news may have an effect on stock market volatility. 5. Conclusion We have utilized a novel data set of banks buy and sell recommendations for the German DAX30 index to investigate whether such recommendations influence stock market volatility. To this end, we have estimated an EGARCH (1,1) model which feature the buy and sell recommendations as well as a number of other pertinent explanatory variables in the mean and variance equations. In contrast to the traditional literature that focuses solely on prices, in this paper we concentrate on the impact of the observed news variables on volatility. As for the news variables, we find that only the sell recommendations exert a significant influence on volatility in the full sample. However, we find that in a rising market, interpreted as the good times in our sample, buy recommendations (constituting the good news in our sample) lower the level of volatility and sell recommendations (the bad news) raise volatility, whereas the impact of news on stock market volatility is less clear-cut in a falling market,

10 9 interpreted as the bad times in the sample. Interestingly, we also find the impact of good news to be stronger in good times relative to bad times. In general, public information appears to play an important role in stock markets, and the present paper confirms this evidence for the German DAX30 stock market index. Our analysis demonstrates that news variables impact not only stock returns but also the level of stock market volatility. In particular, our evidence points to the importance of investigating the effects of public information arrival on returns and volatility in an integrated framework. We have shown that the EGARCH model presented in this paper appears to be particularly well-suited to accomplish this task. By influencing asset prices as well as volatility, public information constitutes an important variable for any investment decision in asset markets. The accumulation of empirical evidence regarding the effects of public information arrival should in future foster a better understanding of the general impact of public news in financial markets. By improving the transparency of price and volatility determination in such markets, it should also contribute to improving the optimization of financial and portfolio management strategies.

11 10 References Andersen, T., Bollerslev, T., Diebold, F., Vega, C. (2007), Real-time price discovery in stock, bond and foreign exchange markets, Journal of International Economics 73, Antweiler, W., Frank, M.Z. (2004), Is all that talk just noise? The information content of internet stock message boards, Journal of Finance 59, Barber, B., Lehavy, R., McNichols, M., Trueman, B. (2003), Reassessing the returns to analysts recommendations, Financial Analysts Journal 59, Barber, B., Lehavy, R., McNichols, M., Trueman, B. (2006), Buys, holds, and sells: The distribution of investment banks stock ratings and the implications for the profitability of analysts recommendations, Journal of Accounting and Economics 41, Barber, Brad M., Lehavy, R., Trueman, B. (2007), Comparing the stock recommendation performance of investment banks and independent research firms, Journal of Financial Economics 85, Black, F. (1976), Studies in stock price volatility changes, Proceedings of the 1976 Business Meeting of the Business and Economic Statistic Section, American Statistical Association, Bollerslev, T., Wooldridge, J.M. (1992), Quasi-maximium likelihood estimation and inference in dynamic models with time varying covariances, Econometric Reviews 11, Christie, A.A. (1982), The stochastic behaviour of common stock variances: Value, leverage and interest rate effects, Journal of Financial Economics 10, Conrad, J., Cornell, B., Landsman, W.R. (2002), When is bad news really bad news? Journal of Finance 57, Fama, E.F. (1970), Efficient capital markets: A review of theory and empirical work, Journal of Finance 25, Graham, J.R., Harvey, C.R. (1996), Market timing ability and volatility implied in investment newsletters asset allocation recommendations, Journal of Financial Economics 42, Hong. H., Kubik, J.D. (2003), Analyzing the analysts: Career concerns and biased earnings forecasts, Journal of Finance 58, Janssen, G. (2004), Public information arrival and volatility persistence in financial markets, European Journal of Finance 10, Jarque, C.M., Bera, A.K. (1980), Efficient test for normality, heteroskedasticity, and serial independence of regression residuals, Economics Letters 6, Jones, C. and Lumsdaine, R. (1998), Macroeconomic news and bond market volatility, Journal of Financial Economics 47,

12 11 Kalev, P.S., Liu, W.M., Pham, P.K., Jarnecic, E. (2004), Public information arrival and volatility of intraday stock returns, Journal of Banking and Finance 28, Kutan, A.M., Aksoy, T. (2004), Public information arrival and emerging markets returns and volatility, Multinational Finance Journal 8, McQueen, G., Roley, V.V. (1993), Stock prices, news and business conditions, Review of Financial Studies 6, Nelson, D.B. (1991), Conditional heteroskedasticity asset returns: A new approach, Econometrica 59, Panchenko, V. (2007), Impact of analysts recommendations on stock performance, European Journal of Finance 13, Stotz, O. (2005), Active portfolio management, implied expected returns, and analyst optimism. Financial Markets and Portfolio Management 19, Wallmeier, M. (2005), Analysts earnings forecasts for DAX100 firms during the stock market boom of the 1990s, Financial Markets and Portfolio Management 19, Womack, K.L. (1996), Do brokerage analysts recommendations have investment value? Journal of Finance 51,

13 12 Figures and Tables Figure 1. Time series of the DAX 30 index and of the DAX 30 stock market returns DAX30 9,000 8,000 7,000 Bad Times Good Times Bad Times 6,000 5,000 4,000 3,000 2, RETURNS Note: The full sample period extends from 1/2/2002 to 6/30/2009, with RETURNS computed by logdifferencing the stock price index, multiplied by a factor of 100. The vertical lines separate the three subsamples analysed in the empirical section of the paper.

14 13 Table 1. Descriptive statistics of the DAX 30 stock market returns and banks buy and sell stock recommendations full sample: 1/3/2002 to 6/30/ st bad times subsample: 1/3/2002 to 3/30/2003 good times subsample: 1/4/2003 to 12/28/ nd bad times subsample: 2/1/2008 to 6/30/2009 Observations Mean Median Maximum Minimum Std. Dev Skewness Kurtosis Jarque-Bera Probability BUY 4971 (81.03%) 1188 (77.04%) 2762 (83.70%) 1021 (78.96%) SELL 1164 (18.97%) 354 (22.96%) 538 (16.30%) 272 (21.04%) TOTAL Note: BUY and SELL denote banks buy and sell recommendations, and TOTAL constitutes the aggregate of all recommendations in the samples. Numbers in brackets are in percent. The results for kurtosis and skewness indicate non-normality of the returns, which is verified by the Jarque-Bera (1980) test.

15 14 Table 2. EGARCH (1,1) model estimates, full sample: 1/3/2002 to 6/30/2009 Variable Coefficient Std. Error z-statistic Prob. Mean Equation Constant RET t DOW t INT t VOL t BUY t SELL t MONDAY t TUESDAY t THURSDAY t FRIDAY t Variance Equation Constant α γ EGARCH(1) BUY t SELL t MONDAY t TUESDAY t THURSDAY t FRIDAY t INT t VOL t DOW2 t Log-Likelihood R-squared Q(1) Q(5) Q(10) ARCH(1) ARCH(5) ARCH(10) Notes: Estimates are based on an EGARCH (1,1) model specification, see Section 3 for details. RET are the returns of the DAX30, DOW are the returns of the Dow-Jones industrial index, INT is the Frankfurt interbank overnight offered rate, VOL is trading volume measured in terms of turnover by value, and BUY and SELL are banks buy and sell recommendations. MONDAY, TUESDAY, THURSDAY and FRIDAY are weekday dummies. Q(x) and ARCH(x) stand for the residual diagnostic tests for remaining serial correlation and ARCH effects for the lag length x. The z-statistics are based on Bollerslev-Wooldridge robust standard errors and Prob represents the p-value.

16 15 Table 3. EGARCH (1,1) model estimates, bad times subsample: 1/3/2002 to 3/30/2003 Variable Coefficient Std. Error z-statistic Prob. Mean Equation Constant RET t DOW t INT t VOL t BUY t SELL t MONDAY t TUESDAY t THURSDAY t FRIDAY t Variance Equation Constant α γ EGARCH(1) BUY t SELL t MONDAY t TUESDAY t THURSDAY t FRIDAY t INT t VOL t DOW2 t Log-Likelihood R-squared Q(1) Q(5) Q(10) ARCH(1) ARCH(5) ARCH(10) Notes: Estimates are based on an EGARCH (1,1) model specification, see Section 3 for details. RET are the returns of the DAX30, DOW are the returns of the Dow-Jones industrial index, INT is the Frankfurt interbank overnight offered rate, VOL is trading volume measured in terms of turnover by value, and BUY and SELL are banks buy and sell recommendations. MONDAY, TUESDAY, THURSDAY and FRIDAY are weekday dummies. Q(x) and ARCH(x) stand for the residual diagnostic tests for remaining serial correlation and ARCH effects for the lag length x. The z-statistics are based on Bollerslev-Wooldridge robust standard errors and Prob represents the p-value.

17 16 Table 4. EGARCH (1,1) model estimates, good times subsample: 4/1/2003 to 12/28/2007 Variable Coefficient Std. Error z-statistic Prob. Mean Equation Constant RET t DOW t INT t VOL t BUY t SELL t MONDAY t TUESDAY t THURSDAY t FRIDAY t Variance Equation Constant α γ EGARCH(1) BUY t SELL t MONDAY t TUESDAY t THURSDAY t FRIDAY t INT t VOL t DOW2 t Log-Likelihood R-squared Q(1) Q(5) Q(10) ARCH(1) ARCH(5) ARCH(10) Notes: Estimates are based on an EGARCH (1,1) model specification, see Section 3 for details. RET are the returns of the DAX30, DOW are the returns of the Dow-Jones industrial index, INT is the Frankfurt interbank overnight offered rate, VOL is trading volume measured in terms of turnover by value, and BUY and SELL are banks buy and sell recommendations. MONDAY, TUESDAY, THURSDAY and FRIDAY are weekday dummies. Q(x) and ARCH(x) stand for the residual diagnostic tests for remaining serial correlation and ARCH effects for the lag length x. The z-statistics are based on Bollerslev-Wooldridge robust standard errors and Prob represents the p-value.

18 17 Table 5. EGARCH (1,1) model estimates, bad times subsample: 2/1/2008 to 6/30/2009 Variable Coefficient Std. Error z-statistic Prob. Mean Equation Constant RET t DOW t INT t VOL t BUY t SELL t MONDAY t TUESDAY t THURSDAY t FRIDAY t Variance Equation Constant α γ EGARCH(1) BUY t SELL t MONDAY t TUESDAY t THURSDAY t FRIDAY t INT t VOL t DOW2 t Log-Likelihood R-squared Q(1) Q(5) Q(10) ARCH(1) ARCH(5) ARCH(10) Notes: Estimates are based on an EGARCH (1,1) model specification, see Section 3 for details. RET are the returns of the DAX30, DOW are the returns of the Dow-Jones industrial index, INT is the Frankfurt interbank overnight offered rate, VOL is trading volume measured in terms of turnover by value, and BUY and SELL are banks buy and sell recommendations. MONDAY, TUESDAY, THURSDAY and FRIDAY are weekday dummies. Q(x) and ARCH(x) stand for the residual diagnostic tests for remaining serial correlation and ARCH effects for the lag length x. The z-statistics are based on Bollerslev-Wooldridge robust standard errors and Prob represents the p-value.