Financ Mark Portf Manag (2010) 24: 137–158 DOI 10.1007/s11408-010-0129-7
Do local analysts have an informational advantage in forecasting stock returns? Evidence from the German DAX30 T. Hendricks · B. Kempa · C. Pierdzioch
Published online: 1 April 2010 © Swiss Society for Financial Market Research 2010
Abstract Utilizing data from the German DAX30 stock index, we investigate whether local analysts have an informational advantage in forecasting stock returns. We analyze whether banks’ buy and sell recommendations improve on the out-ofsample predictability of daily stock returns and the market-timing ability of investors who base their decisions on such recommendations. We find that, indeed, in a few cases German banks do have better stock-forecasting ability for daily stock returns than do foreign banks. However, the value added of bank recommendations is generally small and sensitive to the model-selection criterion used by an investor in setting up a forecasting model for stock returns. Keywords Forecasting stock returns · Bank stock recommendations · Local analysts JEL Classification C53 · E44 · G11
T. Hendricks Department of Economics, Campus Essen, University of Duisburg-Essen, Universitätsstr. 12, 45117 Essen, Germany e-mail:
[email protected] B. Kempa () Department of Economics, University of Münster, Universitätsstr. 14-16, 48143 Münster, Germany e-mail:
[email protected] C. Pierdzioch Department of Economics, Saarland University, P.O. Box 15 11 50, 66041 Saarbrücken, Germany e-mail:
[email protected]
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1 Introduction There is a long-standing debate over whether financial analysts’ buy and sell recommendations for common stock have any investment value. In the seminal paper on the subject, Cowles (1933) analyzes the stock-picking skills of professional forecasters and demonstrates that the mean forecast did not perform any better than random and that even the most successful forecaster could not systematically beat the market. However, later studies find that such recommendations may yield positive abnormal returns, even after allowing for transactions costs (e.g., Groth et al. 1979; Bjerring et al. 1983). More recent studies on the recommendations of brokerage houses and security analysts (Stickel 1995; Womack 1996; Mikhail et al. 2004), the Wall Street Journal’s “Dartboard” column (Barber and Loeffler 1993; Liang 1999; Ferreira and Smith 2003), and the “superstar” money managers at Barron’s annual roundtable (Desai and Jain 1995) have confirmed the potential of professional forecasters’ recommendations to yield abnormal returns, although factoring in the associated trading costs often leaves such recommendations devoid of any investment value (Barber et al. 2001). A number of studies focus on analysts’ geographical location and the geographic diversification of their portfolios as decisive factors in their stock-forecasting ability. In terms of geographic diversification, Bolliger (2004) analyzes financial analysts’ forecast accuracy across 14 different European stock markets and finds forecasting accuracy negatively associated with the number of countries followed by analysts. As for analysts’ geographical location, Malloy (2005) finds an information advantage for local analysts in the United States, and Bae et al. (2008) show, for a large sample of countries, that local analysts have a significant information advantage over foreign analysts. Local analysts’ advantage is closely tied to the quality of disclosure and may arise from two different sources. First, location-induced information asymmetries may provide local analysts with an informational edge over foreign analysts. Second, there may be a lower demand for foreign analysts’ services because of a home bias in portfolio compositions (Healy and Palepu 2001). In particular, the demand for foreign analysts’ stock recommendations will be lower in countries where foreign ownership of companies is less prevalent. This lower demand may result in foreign analysts devoting fewer resources to information collection in the local market. This paper investigates whether stock recommendations by the banking industry generate potential investment value and, if so, whether this value is related to banks’ geographical location. Insofar as such recommendations influence investor perceptions and beliefs about financial conditions, they can be interpreted as a coincident financial indicator. The usefulness of coincident financial indicators for modeling and forecasting financial conditions is demonstrated by Chauvet and Potter (2000). The coincident financial indicator constructed by Chauvet and Potter requires implementation of highly nonlinear advanced econometric techniques. In this paper, we ask whether investors can, instead, use bank stock recommendations as an easy-tomeasure and readily observable coincident financial indicator. We analyze the impact of stock recommendations by German, European, and U.S. banks on the investment value of the German DAX30 stock index.
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We focus on Germany for two reasons. First, the structure of the German banking industry is unique. Kakes and Sturm (2002) classify the country’s industry in terms of the ownership structure separating public from private banks, the range of activities separating universal from specialized banks, and the organizational structure separating commercial, savings, and cooperative banks (the German three-pillar structure). This system has largely prevented any significant consolidation within the industry, with hostile takeover bids for German banks all but nonexistent (Schmidt 2004). As a consequence, the German banking industry is characterized by a multitude of smaller private banks, but there are very few specialized institutions. Thus the German case is a prime example of a banking industry in which foreign ownership of banks is modest. Our second reason for focusing on Germany is that we have compiled a unique data set on buy and sell stock recommendations of German and other 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 is available free of charge to any interested investor. We use the recursive forecasting approach developed by Pesaran and Timmermann (1995, 2000) to study the investment value of bank stock recommendations. This approach accounts for the fact that an investor, in real time, can choose among a large number of forecasting models for predicting stock returns. The forecasting models differ with respect to their predictor variables. While investors may use bank stock recommendations as a coincident financial indicator, it is likely that other predictor variables contain useful information about financial conditions that are not captured by bank stock recommendations. For this reason, banks’ buy and sell recommendations are just one among a large number of potentially useful predictor variables for stock returns. In real time, an investor thus must make an investment decision under uncertainty about financial conditions and, as a consequence, about the optimal forecasting model. The recursive forecasting approach stipulates that an investor solves this decision problem by recursively searching and optimizing over a large number of different forecasting models. The resulting search-and-updating process makes it possible to investigate whether using bank stock recommendations as a coincident indicator of the stock market would have systematically improved the out-of-sample forecasts of stock returns, an investor’s market-timing ability, and the performance of simple trading rules. Utilizing this approach, we find that German bank recommendations have better predictive power for daily DAX30 stock returns relative to those of foreign banks. However, the value added of bank recommendations is small and sensitive to the model-selection criterion used by an investor in setting up a forecasting model for stock returns. It thus would be rather difficult for an investor to exploit, in real time, German bank recommendations to improve market-timing ability and the performance of simple trading strategies. The remainder of the paper is structured as follows. Section 2 provides descriptive statistics. Section 3 contains a description of the recursive forecasting approach. Section 4 summarizes the estimation results and provides a number of robustness checks. We discuss our conclusion in Sect. 5.
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2 Descriptive statistics Stock recommendations by German, European, and U.S. banks are conveniently collected and disseminated through a daily Internet newsletter for private investors called Aktienmarkt.net, which began publication in late 2001. The newsletter is dispatched at 6 p.m. every day and provides a summary of all major stock recommendations that were made throughout the trading day. In comparison to a data set such as IBES/First Call from Thomson Reuters, which for the most part covers the same recommendations but is used almost exclusively by professional investors, Aktienmarkt.net has the advantage of being widely disseminated, not to mention being free of charge to private as well as professional investors. This database thus conveniently summarizes, for any class of investors, the major publicly available stock recommendations for the German stock market. As most stock recommendations relate to the 30 large German blue-chip companies contained in the DAX30 performance index, we restrict our attention to this index. We group the spectrum of different stock recommendations into buy, sell, and hold recommendations, using only the first two categories in our empirical analysis.1 Based on this categorization, we investigate the out-of-sample predictability of the aggregate one-day-ahead stock returns of the DAX30 index. Figure 1 presents the time series of the DAX30 for the sample period, i.e., 1/2/2002 to 6/30/2006, and the daily returns on the DAX30. The returns have a sample maximum (minimum) of 7.85% (−6.14%). The unconditional returns distribution is skewed very little (0.14), but features a kurtosis that significantly exceeds that of a normal distribution (6.02) and thus has the “fat tails” property commonly found in financial market data. Table 1 presents (in percent) descriptive statistics for all (ALL) recommendations and the distribution of buy (BUY) and sell (SELL) recommendations for the 33 German (GER), 11 European (EURO), and 9 U.S. (US) banks included in our data set. When calculating EURO, we exclude German banks. Our sample consists of 1146 daily data with a total of 4186 recommendations, comprised of 2052, 283, and 1059 buy recommendations and 408, 114, and 270 sell recommendations by German, European, and U.S. banks, respectively. In Sect. 4, we investigate results for the buy and sell recommendations of foreign banks (FOREIGN), calculated as the sum of EURO and US, and therefore statistics for this subsample are also presented in Table 1. Buy and sell recommendations make up, respectively, 81.08% and 18.92% of all recommendations in our sample, confirming the familiar upward bias in analysts’ stock recommendations.2 The most buy recommendations are made by German banks—83.41% of all recommendations—whereas European banks make most of the sell recommendations (28.72%). The most active German bank (TOP 1), as well as the German TOP 3 and TOP 5, make, respectively, 9.60%, 24.73%, and 32.61% of all 1 Buy recommendations include “strong buy,” “buy,” “outperform,” “outperformer,” “market outper-
former,” “overweight,” “add,” “accumulate,” and “recommended list,” whereas sell recommendations include “sell,” “reduce,” “underperform,” “underperformer,” “market underperformer,” and “underweight.” We leave out hold recommendations such as “neutral,” “market neutral,” “hold,” “market performer,” and “equal weight.” 2 This observation conforms to recent finding that analysts’ recommendations are biased upward (e.g.,
Barber et al. 2006, 2007, for the U.S., and Stotz 2005, and Wallmeier 2005, for Germany).
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Fig. 1 Time series of the DAX30 and of returns. Note: Time series of the DAX30 is for the sample period from 1/2/2002 to 6/30/2006, with RET, the daily returns on the DAX30, calculated as 100 × (DAX30t − DAX30t−1 )/DAX30t−1
recommendations in the sample. As regards the distribution of the German buy (sell) recommendations, the corresponding figures are 10.05% (7.70%), 26.81% (19.57%), and 34.50 (28.28%). Very similar results are obtained for European, U.S., and foreign banks. For example, the TOP 5 FOREIGN banks make 26.58%, 30.81%, and 26.85% of the total BUY, SELL, and ALL recommendations. The evidence suggests that the recommendations are not strongly biased in favor of a few banks, but are fairly evenly distributed across the sample. In addition to banks’ buy and sell recommendations, we use a number of standard predictor variables for one-day-ahead stock returns. We include the returns of the Dow Jones industrial index (DOW) to control for developments in international financial markets, expecting a positive correlation due to the perceived trend-following behavior of the DAX30 index in regard to the U.S. stock market. We also include trading volume by value (VOL) as a general measure of market sentiment and market activity, and absolute returns on the DAX30 (VOLA) as an easy-to-compute measure of market volatility. Similarly to the variable DOW, the variables VOL and VOLA are easily observable real-time measures of market sentiment and information arrival. Because we are interested in whether bank stock recommendations are a useful coincident financial indicator, dropping these variables from our empirical forecasting models could easily result in an omitted variables bias. We include the Frankfurt interbank overnight offered rate (INT) as a measure of the riskless interest rate, expecting a substitution effect such that an increase in the interest rate should cause a rise in demand for bonds and, hence, a decline in the demand for stocks. The returns on the DAX30 (RET) account for potential autocorrelation of stock returns arising,
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Table 1 Descriptive statistics of banks’ buy and sell stock recommendations (in percent) BUY
SELL
ALL
Full sample
81.08
18.92
100.00
GER
83.41
16.59
100.00
EURO
71.28
28.72
100.00
US
79.68
20.32
100.00
FOREIGN
77.75
22.25
100.00
GER TOP 1
10.05
7.70
9.60
TOP 3
26.81
19.57
24.73
TOP 5
34.50
28.28
32.61
EURO TOP 1
2.80
6.57
3.51
TOP 3
5.77
11.62
6.88
TOP 5
7.13
13.64
8.36
US TOP 1
6.13
6.82
6.00
TOP 3
16.91
18.81
17.01
TOP 5
26.58
28.91
26.85
6.13
6.82
6.00
FOREIGN TOP 1 TOP 3
16.91
19.44
17.01
TOP 5
26.58
30.81
26.85
Note: BUY and SELL denote banks’ buy and sell recommendations; ALL is the aggregate of all recommendations. GER and EURO are the recommendations of German and European banks (excluding German banks), and US and FOREIGN denote the recommendations of U.S. banks and the group of European and U.S. banks combined (excluding German banks). TOP 1, TOP 3, and TOP 5 denote the respective fractions of the top one, top three and top five bank(s) with the most recommendations in the sample
for example, from technical or nonsynchronous trading. An investor can readily observe these predictor variables in real time. In addition, it is reasonable for an investor to consider the predictor variables as candidates for forecasting stock returns because similar predictor variables are analyzed in earlier literature (see, e.g., Christie 1982; Blume et al. 1994; Lee and Swaminathan 2000). The data are from Thomson Financial Datastream. Table 2 summarizes the results from estimating a regression equation with one-day-ahead stock returns as the dependent variable on a constant and on our predictor variables.3 We use the full sample to estimate the regression equation, employing the ordinary least squares technique. 3 We also estimated the regression equation for the two-days-ahead stock returns. This modification has no
significant effect on our estimation results.
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Table 2 Full-sample regression results Variables Constant
Coefficient
Standard error
t-statistic 3.31∗∗∗
0.96
0.29
BUY (GER)
−0.02
0.04
−0.68
SELL (GER)
−0.16
0.08
−2.09∗∗
BUY (EURO)
−0.05
0.09
−0.54
SELL (EURO)
−0.21
0.18
−1.17
BUY (US)
−0.03
0.049
−0.68
SELL (US)
−0.01
0.12
−0.03
RET
−0.21
0.05
−4.09∗∗∗
0.36
0.09
4.09∗∗∗
< 0.00
< 0.00
0.06
0.06
0.90
−0.28
0.13
−2.17∗∗
DOW VOL VOLA INT Adjusted R-squared
0.04
F -statistic
Durbin–Watson statistic
2.01
Prob(F -statistic)
−1.25
75.25 0.00
Note: BUY and SELL are banks’ buy and sell recommendations, DOW is the returns of the Dow Jones industrial index, VOL is trading volume by value, VOLA is absolute returns on the DAX30, INT is the Frankfurt interbank overnight offered rate, and RET is the returns on the DAX30. The t -statistics are based on White heteroskedasticity-consistent standard errors. Asterisks ∗∗ (∗∗∗ ) denote significance at the 5 (1) percent level
The estimation results show that the returns (RET), the returns of the Dow Jones index (DOW), and the short-term interest rate (INT) have statistically significant predictive power for one-day-ahead stock returns and are correctly signed. German bank sell recommendations (SELL) significantly predict one-day-ahead stock returns; however, German bank buy recommendations (BUY) have no predictive power. Moreover, the incremental predictive power of German bank stock recommendations outperforms the incremental predictive value of the stock recommendations of European and U.S. banks, a result possibly due to the fact that German banks may receive more attention by the general public in Germany and are thus more visible to investors. This special attention may be based on an informational advantage of German banks regarding the business strategies of the companies included in the DAX30 index. Alternatively, the special attention may reflect a home bias in information processing on the part of investors. Thus, the regression results suggest that our data support the hypothesis of an informational advantage of local analysts in the stock-forecasting ability of banks.4 4 Kerl and Walter (2008) argue that an underwriting relationship may constitute a conflict of interest and
might tempt an analyst to paint an overly optimistic picture of the company’s prospects to secure current and future business deals from the company. Lai and Teo (2008) specifically compare domestic and foreign analyst recommendations. They show that domestic analysts in emerging countries have more shares of underwriting business and as a result are substantially more optimistic compared to foreign analysts. Therefore, their sell recommendations contain more information than their buy recommendations. In addition, their sell (buy) recommendations contain more (less) information than those of foreign analysts.
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However, the adjusted coefficient of determination of the regression equation is relatively small, suggesting that it is difficult to forecast stock returns. This raises the question of whether an investor can exploit the predictive value of bank stock recommendations to improve out-of-sample forecasts of stock returns in real time.
3 Recursive forecasting of stock returns We describe the recursive modeling approach in two steps. First, we show how the approach can be used to forecast stock returns in real time. Second, we use the approach to assess, in statistical and economic terms, the predictive power of banks’ stock recommendations. 3.1 Forecasting stock returns We consider an investor who uses the predictor variables described in Sect. 2 for forecasting stock returns. On any individual day, the investor must determine how to optimally combine the predictor variables so as to predict one-day-ahead stock returns. Hence, the investor must reach a decision under uncertainty about the optimal forecasting model. We assume that the investor reaches this decision by employing a recursive modeling approach (Pesaran and Timmermann 1995). With K potential predictor variables, the investor searches over all possible 2K models to identify the optimal forecasting model. As time progresses and new data and information become available, the investor recursively restarts this search to examine forecasting models of the following format: rt+1 = βi Xt,i + εt+1,i ,
(1)
where i = 1, 2, . . . , 2K denotes a model index, rt+1 denotes (excess) stock returns on day t + 1, and Xt,i denotes the predictor variables under model i. We assume that the investor uses data from 2002 as a training period to initialize the recursive forecasting of stock returns. Given the large number of forecasting models being estimated each day (we estimated more than 4,100,000 models to compute the results reported in this paper), the investor needs a model-selection criterion to identify the optimal forecasting model. We consider four model-selection criteria: the adjusted coefficient of determination (ACD), the Akaike information criterion (AIC), the Bayesian information criterion (BIC), and a direction-of-change criterion (DCC). The definition of the ACD modelselection criterion is given by ACDt,i = 1 −
Tt − 1 (1 − CODt,i ), Tt − kt,i
(2)
where CODt,i denotes the coefficient of determination under model i, Tt denotes the number of observations available on day t, and kt,i represents the number of regressors considered under model i on day t. The optimal forecasting model is the one that maximizes the ACD model-selection criterion. The AIC model-selection crite-
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rion (Akaike 1973) and the BIC model-selection criterion (Schwarz 1978) are defined as e et,i 2kt,i t,i AICt,i = ln + , (3) Tt Tt BICt,i = ln
e et,i t,i
Tt
+
kt,i ln Tt , Tt
(4)
where et,i denotes the estimated residuals under model i. The optimal forecasting models minimize the AIC and BIC model-selection criteria. Finally, to implement the DCC model-selection criterion, the number of correct in-sample forecasts of the sign of one-day-ahead stock returns is counted for every forecasting model and the following formula is computed: DCCt,i
Tt 1 I (rs )I (ˆrs,i ) + 1 − I (rs ) 1 − I (ˆrs,i ) , = Tt
(5)
s=1
where rt denotes the actual stock returns, rˆt,i represents the forecast of stock returns implied by model i, and I (x) denotes an indicator function that assumes the value 1 when x > 0, and 0 otherwise. The optimal forecasting model is the one that maximizes the DCC model-selection criterion. 3.2 The predictive power of banks’ stock recommendations Application of the four model-selection criteria gives four sequences of optimal forecasting models and four sequences of one-day-ahead forecasts of stock returns. We apply the Fair–Shiller (FS) test (Fair and Shiller 1990) to compare the informational content of the four sequences of forecasts for one-day-ahead stock returns. To this end, we estimate a regression of one-day-ahead stock returns on a constant, the forecasts derived from a model featuring the stock recommendations of German banks, and the forecasts derived from an alternative model. As alternative models, we consider a model featuring EURO, a model featuring US, a model featuring FOREIGN, and a model that does not contain any BUY and SELL recommendations. In addition, we apply the tests developed by Cumby and Modest (1987) and Pesaran and Timmermann (1992) to analyze the implications of bank stock recommendations for an investor’s market-timing ability. The Cumby–Modest (CM) test requires estimating a regression of actual realized stock returns on a constant and a dummy variable that assumes the value 1 if the forecast of stock returns is positive, 0 otherwise. The Pesaran–Timmermann (PT) test is a nonparametric test that has an asymptotically standard normal distribution. The null hypothesis is that there is no information in the forecasts of stock returns over the sign of subsequent actual realized stock returns. Statistical measures of forecasting performance do not need to be strongly correlated with the profits that may be generated from forecasts using trading strategies (Leitch and Tanner 1991). In other words, measures of forecasting accuracy that analyze the investment value of forecasts may give results that are different from measures of forecasting accuracy derived from statistical criteria. For example, tests of
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market timing analyze whether a model helps investors forecast the sign of returns and, thus, the direction of change of future stock prices. If there are 100 trades with very small returns and one trade with a large negative return (e.g., a crash of the market), it may happen that a forecasting model correctly predicts the signs of the 100 small returns, but misses the crash. Based solely on the statistical evaluation of market timing, such a forecasting model would be brilliant! In terms of investment value, however, the very same forecasting model would be an utter disaster. This example is not meant to imply that investment value is a better guide for selecting a forecasting model than a careful study of the market-timing ability of the model (in fact, a crash may be an unlikely but influential outlier), but it does illustrate that it is useful to study both the statistical and the economic forecasting performance of a forecasting model. We therefore study an investor who can use the forecasts of stock returns implied by the optimal forecasting models to set up four simple trading rules. The four trading rules are based on the optimal forecasts of stock returns computed by applying the ACD, AIC, BIC, and DCC model-selection criteria. Every trading rule requires the investor to invest in stocks if the forecasts of one-day-ahead stock returns are positive, and otherwise to invest in bonds. In addition, the investor does not use leverage when making an investment decision. It follows that an investor buys stocks at the end of month t if rˆt+1 > 0, and bonds if rˆt+1 < 0. For tracking an investor’s sequence of investments over time, four different cases need to be considered. • Case 1: The investor invests in stocks at the end of day t, and continues to invest in stocks at the end of day t + 1. In this case, we have rˆt+1 > 0 and rˆt+2 > 0. • Case 2: The investor invests in stocks at the end of day t, but buys bond at the end of day t + 1. In this case, we have rˆt+1 > 0 and rˆt+2 < 0. • Case 3: The investor invests in bonds at the end of day t, but buys stocks at the end of day t + 1. In this case, we have rˆt+1 < 0 and rˆt+2 > 0. • Case 4: The investor invests in bonds at the end of day t, and continues to invest in bonds at the end of day t + 1. In this case, we have rˆt+1 < 0 and rˆt+2 < 0. The four cases correspond to the four cases analyzed by Pesaran and Timmermann (1995: 1219). The performance of the four trading rules can be compared in terms of Sharpe’s ratios (Sharpe 1966). For every trading rule, Sharpe’s ratio is defined as SRj = r¯j /SDj , where SRj denotes Sharpe’s ratio under trading rule j, r¯j denotes mean excess portfolio returns under trading rule j , and SDj denotes the standard deviation of portfolio returns under trading rule j .
4 Results Table 3 summarizes how often the various predictor variables are included in the optimal forecasting models for stock returns. We present results (in percent) for forecasting models that use BUY and SELL as potential predictor variables for all banks in Panel A, and for German, European, and U.S. banks separately in Panels B, C,
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and D, respectively.5 We also report results for forecasting models that feature only foreign banks (Panel E) and for models that feature no bank stock recommendations at all (Panel F). Corroborating the results presented in Sect. 2, returns (RET), the short-term interest rate (INT), and the returns on the Dow Jones (DOW) are the predictor variables most frequently included in the optimal forecasting models. Under the ACD model-selection criterion, bank SELL recommendations are more often included in the optimal forecasting models than are bank BUY recommendations. The same result obtains for German banks under the AIC model-selection criterion. In contrast, under the DCC model-selection criterion, BUY recommendations tend to be more often included in the optimal forecasting models than SELL recommendations. Finally, our results suggest that under the BIC model-selection criterion, bank stock recommendations are never included in the optimal forecasting model. This result is not surprising because (i) it is difficult to forecast one-day-ahead stock returns, and (ii) the BIC model-selection criterion is known to select a parsimonious optimal forecasting model. It is worth noting that because bank stock recommendations are never included in the optimal forecasting model under the BIC model-selection criterion, the results for the percentage inclusion of RET, DOW, and INT in Panels A–E are identical to the results for the model that never considers BUY and SELL as potential predictor variables (Panel F). Similarly, under the AIC model-selection criterion, BUY and SELL recommendations of foreign banks, European banks, and U.S. banks are never included in the optimal forecasting models, meaning that the results in Panels C–E will be identical to those reported in Panel F. It turns out that the results for the entire sample of banks reported in Panel A are determined to a large extent by the influence of the German banks reported in Panel B. In fact, under the ACD and AIC model-selection criteria, foreign banks’ buy and sell recommendations are hardly ever used in the optimal forecasting models, the sell recommendations of European banks being an exception. The buy and sell recommendations of European and U.S. banks are frequently included in the optimal forecasting models only when the DCC model-selection criterion is used. Table 4 summarizes the results of the Fair–Shiller test. The results suggest that the model featuring the BUY and SELL recommendations of German banks systematically fares better than the alternative models under the AIC model-selection criterion. Under the ACD and DCC model-selection criteria, the forecasts from a model featuring the recommendations of German banks do not contain more precise information regarding one-day-ahead stock returns than the forecasts implied by the alternative models. We do not report results for the BIC model-selection criterion because BUY and SELL are never included in the optimal forecasting models under the BIC modelselection criterion. Because stock recommendations of non-German banks are never included in the optimal forecasting models under the AIC model-selection criterion, the results in Table 4 do not change when one uses EURO, US, or FOREIGN stock recommendations in setting up the Fair–Shiller test. 5 We also estimated forecasting models including either BUY or SELL recommendations one at a time,
but the results are very similar to those reported in the paper.
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Table 3 Inclusion of predictor variables in the optimal forecasting models (in percent) Panel A: Model with BUY and SELL (all banks) Variables
ACD
AIC
BIC
DCC
BUY (GER)
48.94
0.00
0.00
81.79
SELL (GER)
99.66
92.40
0.00
78.99
BUY (EURO)
0.00
0.00
0.00
52.18
SELL (EURO)
60.22
0.00
0.00
89.50
BUY (US)
0.00
0.00
0.00
37.54
SELL (US)
1.12
0.00
0.00
53.85
RET
100.00
100.00
75.08
91.84
VOL
85.47
77.65
0.00
93.74
DOW
100.00
100.00
75.08
99.78
VOLA
55.87
0.00
0.00
61.45
INT
92.63
91.73
15.75
94.97
BIC
DCC
Panel B: Model with BUY and SELL (German banks) Variables
ACD
AIC
BUY (GER)
47.15
0.00
0.00
73.63
SELL (GER)
99.66
92.40
0.00
67.71
RET
100.00
100.00
75.08
82.12
VOL
85.47
77.65
0.00
95.20
DOW
100.00
100.00
75.08
99.66
VOLA
56.65
0.00
0.00
50.39
INT
92.85
91.73
15.75
90.39
BIC
DCC
Panel C: Model with BUY and SELL (European banks) Variables
ACD
AIC
BUY (EURO)
0.00
0.00
0.00
40.89
SELL (EURO)
41.56
0.00
0.00
90.39
RET
100.00
100.00
75.08
63.35
VOL
84.92
72.29
0.00
95.08
DOW
100.00
100.00
75.08
99.44
VOLA
52.85
0.00
0.00
42.12
INT
92.51
89.05
15.75
89.16
BIC
DCC
Panel D: Model with BUY and SELL (U.S. banks) Variables
ACD
AIC
BUY (US)
0.00
0.00
0.00
67.71
SELL (US)
1.56
0.00
0.00
68.60
RET
100.00
100.00
75.08
62.90
VOL
84.92
72.29
0.00
95.87
DOW
100.00
100.00
75.08
99.22
VOLA
56.98
0.00
0.00
68.27
INT
92.51
89.05
15.75
92.07
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Table 3 (Continued) Panel E: Model with BUY and SELL (foreign banks) Variables
ACD
AIC
BIC
DCC
BUY (FOREIGN)
0.00
0.00
0.00
64.92
SELL (FOREIGN)
0.00
0.00
0.00
50.28
RET
100.00
100.00
75.08
70.39
VOL
84.92
72.29
0.00
78.77
DOW
100.00
100.00
75.08
98.55
VOLA
56.98
0.00
0.00
50.84
INT
92.51
89.05
15.75
90.84
DCC
Panel F: Model without BUY and SELL Variables
ACD
AIC
BIC
RET
100.00
100.00
75.08
63.69
VOL
84.92
72.29
0.00
95.64
DOW
100.00
100.00
75.08
98.55
VOLA
56.98
0.00
0.00
66.93
INT
92.51
89.05
15.75
90.17
Note: ACD denotes the adjusted coefficient of determination, AIC denotes the Akaike information criterion, BIC denotes the Bayesian information criterion, and DCC denotes the direction-of-change criterion. GER denotes the recommendations of German banks, EURO denotes the recommendations of European banks (excluding German banks), US denotes the recommendations of U.S. banks, and FOREIGN denotes the recommendations of U.S. banks and European banks (excluding German banks). For definitions of the other predictor variables, see Sect. 2. The investor uses one year (2002) of daily data as a training period to start the recursive forecasting of stock returns
The results of tests for market timing (Table 5) indicate that the good performance of a model featuring the BUY and SELL recommendations of German banks as potential predictor variables under the AIC model-selection criterion does not imply that using bank stock recommendations significantly improves an investor’s markettiming ability. The results of the CM and PT tests for market timing are significant, irrespective of whether BUY and SELL recommendations are used as potential predictor variables for one-day-ahead stock returns. Rejection of the null hypothesis of no market-timing implies that the forecasting models being analyzed contain information useful for forecasting the sign of subsequent actual realized stock returns. The important point is that in terms of market timing, a forecasting model that uses the stock recommendations of German banks as potential predictor variables for stock returns does not fare systematically better than the models featuring the stock recommendations of European or U.S. banks. It follows that an investor’s market-timing ability depends on the predictor variables RET, INT, and DOW, rather than on banks’ BUY and SELL recommendations. As shown in Table 3, in addition to BUY and SELL, the predictor variables RET, INT, and DOW are often included in the optimal forecasting models. It follows that these readily observable simple real-time measures of market sentiment have a significant impact on an investor’s market-timing ability.
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Table 4 Fair–Shiller comparisons of information in forecasts ACD
AIC
DCC
GER versus EURO BUY and SELL (GER)
0.49
0.57∗
0.23
t-statistic
1.39
1.92
0.89
BUY and SELL (EURO)
0.01
−0.14
0.30
t-statistic
0.01
−0.39
0.96
GER versus US BUY and SELL (GER)
0.39
0.57∗
0.32
t-statistic
1.12
1.92
1.23
BUY and SELL (US)
0.12
−0.14
0.14
t-statistic
0.30
−0.39
0.46
GER versus FOREIGN BUY and SELL (GER)
0.28
0.57∗
0.25
t-statistic
0.82
1.92
1.01
BUY and SELL (FOREIGN)
0.26
−0.14
0.29
t-statistic
0.64
−0.39
0.99
GER versus model without BUY and SELL BUY and SELL (GER)
0.28
0.57∗
0.27
t-statistic
0.82
1.92
1.11
BUY and SELL (without)
0.26
−0.14
0.25
t-statistic
0.64
−0.39
0.88
Note: ACD denotes the adjusted coefficient of determination, AIC denotes the Akaike information criterion, and DCC denotes the direction-of-change criterion. Results for the BIC model-selection criterion are not reported because BUY and SELL are never included in the optimal forecasting models under that criterion. GER denotes the recommendations of German banks, EURO denotes the recommendations of European banks (excluding German banks), US denotes the recommendations of U.S. banks, and FOREIGN denotes the recommendations of U.S. banks and European banks (excluding German banks). The Fair–Shiller test requires estimation of a regression equation of realized one-day-ahead stock returns on a constant, the forecasts implied by a model featuring the BUY and SELL recommendations of German banks as potential predictor variables of stock returns, and the forecasts of an alternative model. For brevity’s sake, we do not report the estimates of the constant. As alternative models, we consider models featuring EURO, US, FOREIGN, and a model that does not contain any BUY and SELL recommendations. The t -statistics are based on Newey–West robust standard errors. The investor uses one year (2002) of daily data as a training period to start the recursive forecasting of stock returns. An asterisk ∗ denotes significance at the 10 percent level
In line with the results reported in Tables 3 and 4, the results of the market-timing tests under the BIC model-selection criterion do not change when one replaces GER with EURO, US, or FOREIGN, because the optimal forecasting models never feature any stock recommendations. A similar argument applies in the case of the AIC model-selection criterion, where only BUY and SELL recommendations of German banks are included in the optimal forecasting models.
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Table 5 Tests for market timing ACD
AIC
BIC
DCC
Model with BUY and SELL (ALL) CM
2.63∗∗∗
2.51∗∗∗
2.88∗∗∗
2.58∗∗∗
PT
3.08∗∗∗
3.36∗∗∗
3.90∗∗∗
3.98∗∗∗
Model with BUY and SELL (GER) CM
2.90∗∗∗
2.51∗∗∗
2.88∗∗∗
2.30∗∗
PT
3.28∗∗∗
3.36∗∗∗
3.90∗∗∗
3.21∗∗∗
Model with BUY and SELL (EURO) CM
2.47∗∗∗
2.23∗∗
2.88∗∗∗
3.50∗∗∗
PT
2.87∗∗∗
3.20∗∗∗
3.90∗∗∗
3.43∗∗∗
Model with BUY and SELL (US) CM
2.42∗∗∗
2.23∗∗
2.88∗∗∗
2.42∗∗∗
PT
2.55∗∗∗
3.20∗∗∗
3.90∗∗∗
2.56∗∗∗
Model with BUY and SELL (FOREIGN) CM
2.64∗∗∗
2.23∗∗
2.88∗∗∗
2.67∗∗∗
PT
2.70∗∗∗
3.20∗∗∗
3.90∗∗∗
2.94∗∗∗
Model without BUY and SELL CM
2.64∗∗∗
2.23∗∗
2.88∗∗∗
2.24∗∗
PT
2.70∗∗∗
3.20∗∗∗
3.90∗∗∗
2.22∗∗∗
Note: ACD denotes the adjusted coefficient of determination, AIC denotes the Akaike information criterion, BIC denotes the Bayesian information criterion, and DCC denotes the direction-of-change criterion. ALL denotes the recommendations of all banks irrespective of their geographical location, GER denotes the recommendations of German banks, EURO denotes the recommendations of European banks (excluding German banks), US denotes the recommendations of U.S. banks, and FOREIGN denotes the recommendations of U.S. banks and European banks (excluding German banks). The investor uses one year (2002) of daily data as a training period to start the recursive forecasting of stock returns. CM = test for market timing developed by Cumby and Modest (1987). This test requires estimating a regression of realized stock returns on a constant and a dummy variable that assumes the value 1 if the forecast of stock returns is positive, 0 otherwise. We report the t -statistics of the dummy variables. The t -statistics are based on heteroskedasticity-consistent standard errors. PT = test for market timing developed by Pesaran and Timmermann (1992). The Pesaran–Timmermann test has an asymptotically standard normal distribution. The null hypothesis is that there is no information in the forecasts of stock returns over the sign of subsequent actual realized stock returns. Asterisks ∗∗ (∗∗∗ ) denote significance at the 5 (1) percent level
Table 6 summarizes the performance of simple trading rules.6 Under the BIC model-selection criterion, bank stock recommendations are never included in the optimal forecasting models, implying that Sharpe’s ratios and terminal wealth are identical for all models. Under the ACD and AIC model-selection criteria, the Sharpe’s 6 We assume that transaction costs are zero. The relative performance of the trading rules does not change
when transaction costs are factored in. Transaction costs lower Sharpe’s ratio and terminal wealth and, thus, affect the performance of the trading rules relative to the performance of a simple buy-and-hold trading rule.
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Table 6 Performance of simple trading rules ACD
AIC
BIC
DCC
Model with BUY and SELL (ALL) SHARPE
0.11
0.10
0.16
0.11
WEALTH
255.60
250.92
211.43
258.90
Model with BUY and SELL (GER) SHARPE
0.11
0.10
0.16
0.10
WEALTH
268.33
250.92
211.43
240.22
Model with BUY and SELL (EURO) SHARPE
0.10
0.10
0.16
0.13
WEALTH
245.23
239.43
211.43
301.19
Model with BUY and SELL (US) SHARPE
0.10
0.10
0.16
0.10
WEALTH
242.32
239.43
211.43
242.19
Model with BUY and SELL (FOREIGN) SHARPE
0.10
0.10
0.16
0.11
WEALTH
252.69
239.43
211.43
263.06
Model without BUY and SELL SHARPE
0.10
0.10
0.16
0.10
WEALTH
252.69
239.43
211.43
231.31
Note: ACD denotes the adjusted coefficient of determination, AIC denotes the Akaike information criterion, BIC denotes the Bayesian information criterion, and DCC denotes the direction-of-change criterion. ALL denotes the recommendations of all banks irrespective of their geographical location, GER denotes the recommendations of German banks, EURO denotes the recommendations of European banks (excluding German banks), US denotes the recommendations of U.S. banks, and FOREIGN denotes the recommendations of U.S. banks and European banks (excluding German banks). The investor uses one year (2002) of daily data as a training period to start the recursive forecasting of stock returns. On every day, the investor selects four optimal forecasting models according to the ACD, AIC, BIC, and DCC modelselection criteria. For switching between stocks and bonds, the investor uses information on the optimal one-day-ahead stock return forecasts implied by the optimal forecasting models. When the optimal oneday-ahead stock return forecasts are positive (negative), the investor invests only in shares (bonds), not in bonds (shares). The investor does not make use of short selling, nor does the investor use leverage when reaching an investment decision. SHARPE = Sharpe’s ratio, defined as the ratio of the mean and standard deviation of excess portfolio returns. Mean excess portfolio returns were computed from the first period in which a forecast of stock returns is computed to the end of the sample. WEALTH = terminal wealth. Initial wealth is 100 monetary units
ratios are nearly identical regardless of whether BUY and SELL are considered as potential predictor variables for stock returns. The model featuring the BUY and SELL recommendations of German banks fares better than the other models in terms of terminal wealth. Under the DCC model-selection criterion, a model featuring the BUY and SELL recommendations of European banks as potential predictor variables for stock returns outperforms the other models in terms of the Sharpe’s ratio and terminal wealth.
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Table 7 Inclusion of banks’ stock recommendations in the optimal forecasting models in the subsample June 2003–June 2006 (in percent) Variables
ACD
AIC
BIC
DCC
BUY (GER)
91.28
78.29
0.00
96.85
SELL (GER)
2.78
0.00
0.00
58.63
BUY (EURO)
45.45
25.97
0.00
55.10
SELL (EURO)
0.00
0.00
0.00
54.92
BUY (US)
42.86
0.00
0.00
33.02
SELL (US)
0.00
0.00
0.00
36.73
BUY (GER)
91.28
78.66
0.00
99.81
SELL (GER)
2.78
0.00
0.00
48.42
BUY (EURO)
48.05
25.97
0.00
54.55
SELL (EURO)
0.00
0.00
0.00
50.46
BUY (US)
33.21
0.00
0.00
76.25
SELL (US)
0.00
0.00
0.00
37.29
BUY (FOREIGN)
0.00
0.00
0.00
66.23
SELL (FOREIGN)
3.15
0.00
0.00
32.65
All banks
German banks
European banks
U.S. banks
Foreign banks
Note: ACD denotes the adjusted coefficient of determination, AIC denotes the Akaike information criterion, BIC denotes the Bayesian information criterion, and DCC denotes the direction-of-change criterion. GER denotes the recommendations of German banks, EURO denotes the recommendations of European banks (excluding German banks), US denotes the recommendations of U.S. banks, and FOREIGN denotes the recommendations of U.S. banks and European banks (excluding German banks). For brevity’s sake, data on the inclusion of the other predictor variables introduced in Sect. 2 are not reported. The investor uses one year of daily data for the period June 2003–June 2004 as a training period to start the recursive forecasting of stock returns
Figure 1 shows that stock market volatility in 2002 and in the first half of 2003 was higher than during the rest of the sample. Forecasting stock returns during market phases characterized by high stock market volatility might be more difficult than forecasting stock returns during market phases when stock market volatility is relatively low. The switch between a turbulent market phase at the beginning of the sample and a tranquil market phase in the remainder of the sample implies that the informational content of bank stock recommendations may have changed over time. To account for possible changes in the informational content of bank stock recommendations, we drop the data for the period 1/3/2002 to 5/30/2003 from our sample and implement the recursive forecasting approach for the subsample 6/1/2003 to 6/30/2006. In contrast to the results for the full sample, in Table 7 the results for the subsample indicate that BUY recommendations are more often included in the optimal fore-
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Table 8 Fair–Shiller comparisons of information in forecasts (subsample June 2003–June 2006) ACD
AIC
DCC
GER versus EURO BUY and SELL (GER)
0.52
0.19
0.55∗∗
1.31
0.46
2.41
BUY and SELL (EURO)
−0.03
0.30
0.10
t-statistic
−0.09
0.72
0.50
0.30
−0.32
0.57∗∗
−0.68
2.15
t-statistic
GER versus US BUY and SELL (GER) t-statistic
0.69
BUY and SELL (US)
0.19
0.82∗
0.07
t-statistic
0.43
1.75
0.34
0.03
−0.32 −0.68
GER versus FOREIGN BUY and SELL (GER)
0.66∗∗∗
t-statistic
0.06
BUY and SELL (FOREIGN)
0.48
0.82∗
−0.04
2.93
t-statistic
0.93
1.75
−0.22
0.05
−0.32
0.60∗∗
−0.68
2.42
GER versus model without BUY and SELL BUY and SELL (GER) t-statistic
0.10
BUY and SELL (without)
0.45
0.82∗
0.04
t-statistic
0.89
1.75
0.20
Note: ACD denotes the adjusted coefficient of determination, AIC denotes the Akaike information criterion, and DCC denotes the direction-of-change criterion. Results for the BIC model-selection criterion are not reported because BUY and SELL are never included in the optimal forecasting models under that criterion. GER denotes the recommendations of German banks, EURO denotes the recommendations of European banks (excluding German banks), US denotes the recommendations of U.S. banks, and FOREIGN denotes the recommendations of U.S. banks and European banks (excluding German banks). The Fair–Shiller test requires estimation of a regression equation of realized one-day-ahead stock returns on a constant, the forecasts implied by a model featuring the BUY and SELL recommendations of German banks as potential predictor variables of stock returns, and the forecasts of an alternative model. For brevity’s sake, we do not report the estimates of the constant. As alternative models, we consider models featuring EURO, US, FOREIGN, and a model that contains no BUY and SELL recommendations. The t -statistics are based on Newey–West robust standard errors. The investor uses one year of daily data for the period June 2003–June 2004 as a training period to start the recursive forecasting of stock returns. Asterisks ∗ (∗∗ , ∗∗∗ ) denote significance at the 10 (5, 1) percent level
casting models than are SELL recommendations. Moreover, as reported in Table 8, the Fair–Shiller test reveals that under the DCC model-selection criterion, accounting for the stock recommendations of German banks as potential predictor variables improves the informational content of forecasts of one-day-ahead stock returns. As indicated in Table 9, accounting for BUY and SELL as potential predictor variables for stock returns improves both Sharpe’s ratios and terminal wealth only under the DCC model-selection criterion. The size of the improvement depends on the ge-
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Table 9 Performance of simple trading rules in the subsample June 2003–June 2006 ACD
AIC
BIC
DCC
Model with BUY and SELL (ALL) SHARPE
0.15
0.19
0.16
0.14
WEALTH
173.05
181.72
170.50
167.54
Model with BUY and SELL (GER) SHARPE
0.15
0.15
0.14
0.17
WEALTH
176.01
174.71
170.50
180.12
Model with BUY and SELL (EURO) SHARPE
0.15
0.16
0.14
0.14
WEALTH
172.03
180.84
170.50
166.25
Model with BUY and SELL (US) SHARPE
0.15
0.16
0.14
0.13
WEALTH
178.05
179.53
170.50
166.43
Model with BUY and SELL (FOREIGN) SHARPE
0.14
0.16
0.14
0.13
WEALTH
170.05
179.53
170.50
163.18
Model without BUY and SELL SHARPE
0.14
0.16
0.14
0.11
WEALTH
170.05
179.53
170.50
154.58
Note: ACD denotes the adjusted coefficient of determination, AIC denotes the Akaike information criterion, BIC denotes the Bayesian information criterion, and DCC denotes the direction-of-change criterion. ALL denotes the recommendations of all banks irrespective of their geographical location, GER denotes the recommendations of German banks, EURO denotes the recommendations of European banks (excluding German banks), US denotes the recommendations of U.S. banks, and FOREIGN denotes the recommendations of U.S. banks and European banks (excluding German banks). The investor uses one year of daily data for period June 2003–June 2004 as a training period to start the recursive forecasting of stock returns. In every week, the investor selects four optimal forecasting models according to the ACD, AIC, BIC, and DCC model-selection criteria. For switching between stocks and bonds, the investor uses information on the optimal one-day-ahead stock return forecasts implied by the optimal forecasting models. When the optimal one-day-ahead stock return forecasts are positive (negative), the investor invests only in shares (bonds), not in bonds (shares). The investor does not make use of short selling, nor does the investor use leverage when reaching an investment decision. SHARPE = Sharpe’s ratio, defined as the ratio of the mean and standard deviation of excess portfolio returns. Mean excess portfolio returns were computed from the first period in which a forecast of stock returns is computed to the end of the sample. WEALTH = terminal wealth. Initial wealth is 100 monetary units
ographical location of the banks, and is larger for German banks than for foreign banks. As in the full sample, the inclusion of BUY and SELL in the set of predictor variables for stock returns again results in the most noticeable improvement in terms of terminal wealth under the DCC model-selection criterion. However, as the improvement depends on the model-selection criterion, it would be difficult for an investor to use bank stock recommendations to set up a profitable trading strategy in real time.
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5 Conclusion Utilizing data on the German DAX30 stock index, we investigated whether local analysts have an informational advantage based on the stock-forecasting ability of banks. To this end, we analyzed whether banks’ buy and sell recommendations constitute a coincident financial indicator of the stock market that improves on the out-of-sample predictability of daily stock returns and the market-timing ability of investors who base their decisions on such recommendations. We focused on Germany for two reasons. First, the three-pillar structure of the German banking industry has largely prevented any significant consolidation, to the disadvantage of foreign banks. Second, we were able to compile a unique data set on individual buy and sell recommendations of German, European, and U.S. banks for shares contained in the German DAX30 stock market index. We used the recursive forecasting approach developed by Pesaran and Timmermann (1995, 2000) to analyze whether using bank stock recommendations as a predictor variable in real time would have systematically improved the out-of-sample forecasts of stock returns, an investor’s market-timing ability, and the performance of simple trading rules. Supporting the hypothesis of a local advantage in the stockforecasting ability of banks, we find that German bank recommendations for the DAX30 have better predictive power for daily stock returns relative to those of foreign banks. However, the value added of bank recommendations is small and sensitive to the model-selection criterion used by an investor in setting up a forecasting model for stock returns. It thus would be difficult, if not impossible, for an investor to exploit, in real time, German bank recommendations to improve market-timing ability and the performance of simple trading strategies. Acknowledgements
We thank two anonymous referees for helpful comments.
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T. Hendricks et al. T. Hendricks is an instructor and research fellow at the Chair for Macroeconomics in the faculty of Business Administration and Economics at the University of Duisburg-Essen, Campus Essen, Germany. His main research interests are in the areas of monetary policy and financial markets. He has published on the empirics of monetary policy transmission, financial liquidity, and asset price behavior.
B. Kempa is Professor of International Economics at the University of Münster, Germany. His primary research interests are in the fields of international economics, monetary economics, and international finance. His publications include both theoretical and empirical studies on exchange rate volatility, monetary policy in open economies, European monetary integration, and the credit channel of monetary policy.
C. Pierdzioch is Professor of Macroeconomics and International Economics at Saarland University, Germany. His research interests include macroeconomics, monetary economics, financial economics, and international economics. He has published theoretical and empirical studies on monetary and fiscal policy in open economies, real-time forecasting of returns and volatility of stock prices, international interdependencies of stock markets, and speculative bubbles.
