Die Messung des Anlageerfolgs von Fondmanagern ist ein sowohl für Praktiker als auch für Forscher herausforderndes Thema. Das Ziel beider Gruppen ist nachhaltig erfolgreiche Manager, gute Performer, zu identifizieren. Die meisten Performancemaße beruhen auf der Annahme des konstanten Risikos über die Messungsperiode. Das Risiko wird wiederum als das Beta aus dem Capital Asset Pricing Model erfasst. Als Erweiterung solcher Modelle werden zusätzliche Faktoren, maßgeschnittene Benchmarks oder Nichtliniearitäten zwischen Risiken und Renditen erfasst usw. Die Dynamik der Wirtschaft und Kapitalmärkten ist für die Zwecke der Performancemessung von Ferson und Schaft in ein Modell eingeführt. Das Beta aus diesem Modell ist auf die Marktbedingungen bedingt und Fondsrenditen werden somit gegen eine dynamische Benchmark gemessen. Diese Studie berichtet aus einem solchen Modell ergebene Performancemaße (sog. Conditional Alphas) für 1192 deutsche Aktien- und Rentenmanager. Diese werden auch mit CAPM-basierten Alphas auf Persistenz verglichen. Zusätzlich wird die Fähigkeit des Managers richtig auf die verändernden Marktbedingungen zu reagieren aus der Dynamik des Beta extrahiert. Die Marktentwicklung wird durch vier anerkannte Indikatoren abgebildet (kurzfristiger Zins, Dividendenrendite, Laufzeitspread und Bonitätsspread). Die Ergebnisse befürworten die Nutzung von Modellen mit bedingtem Beta anstelle vom CAPM. Die Existenz einer dynamischen Investmentstrategie wurde anhand der Indikatoren bewiesen. Zum Schluss werden anhand der Erfahrungen aus dieser Studie konkrete Empfehlungen für die Erforschung von Performance anhand ähnlicher Modelle gegeben.

Text Sample: Chapter 4.3, The Hypotheses: In this section, I will concretize the objective of this study to relate it to the models presented in the previous section. As previously stated, the objective is to identify dynamic strategies by looking at responses to public information. If it is proved that there are dynamic strategies within the presented models, accounting for their effects on performance would be useful in order to obtain more realistic and more persistent measures. Public variables reflect the state of the market and are known even to the wide investment public. Since investors use the same variables when they form their expectations regarding returns and risks, using the same variables is plausible when evaluating the performance of fund managers. Both the existence of dynamic strategies and their effects will be substantiated in the hypotheses. These are based the informative value of the used proxies, as discussed in section 2.4. Hypothesis 1: Funds employ dynamic beta strategies. The coefficients on the interaction variables, consisting of excess returns of market indices multiplied by public information variables will be statistically significant. This is interpreted as evidence of dynamic strategy. Hypothesis 2: The responses to information variables will be different for equity and bond fund managers. A manager who wants to reduce his exposure to market risk can hold cash or deposit it e.g. for a month. The short-term rate is therefore an alternative to the investment strategy that the fund manager employs. The Euribor doesn’t exactly match the fixed-deposit rate available to every fund manager, but serves as its proxy. Bond markets are more directly linked to the short end of the yield curve, which is why the bond manager will respond to the effect consisting of (expectedly) positively correlated variables, the bond index and the lagged Euribor, more strongly. This is why I expect a positive coefficient for the bond fund groups. The same coefficient resulting from regressions on equity fund groups will probably be negative and not as large as the one obtained from the regressions on bond funds. I expect negative coefficients because high short-term rates, which are associated with less risk than the stock market is, make equities less attractive. The expectation on the magnitude is derived from the existence of both high and low interest rate phases during bull market periods. The dividend yield is a proxy for the expected performance of publicly listed companies i.e. a stock market performance predictor. As described earlier, the dividend yield captures long-term expectations. A positive effect on equities and long-term bond portfolios is expected. Bond managers who can switch their portfolio to hold short-term bonds will probably have more possibilities to respond to changes in the dividend yield and so achieve benefits. The term spread is a proxy for the preferences of long- vs. short term investments. Stock and long-term bond portfolios will profit from larger spreads, which should be reflected in a positive coefficient. Since bond funds are not sorted by maturity, a consentaneous effect will probably not be found. The other interest rate based variable, the default spread, carries similar information to the dividend yield. However, the used grouping doesn’t provide insight into which fund managers would be able to make use of its changes. Hypothesis 3: Performance measures based on the conditional model are more significant and more persistent. Conditional alpha will be closer to zero than its unconditional counterpart. Also, alphas from conditional models will be accompanied by higher significance. This hypothesis is based on the expectation that manager skills are better accounted for, paired with the belief that the average manager neither outperforms nor underperforms the market. If the information is chosen well, different levels of market dynamics (based on expectations for different horizons) will be modeled. This would lead to a more realistic value of the alpha, which is why I expect it to be significant and close to zero. It is further plausible that an alpha, which better incorporates manager skills, is more persistent. Section 4.6.2 will be dedicated to the analysis of persistence. Hypothesis 4: Conditional timing coefficients will be positive. Negative timing coefficients will be removed after controlling for time-variation in beta risk associated with public information. Excursus: Panel Data Estimation: I provide an excursus on panel data estimation procedures used later in this study. The basis for this excursus is provided in chapters on panel data analysis from textbooks of Wooldridge and Greene . Panel Data Specifics: I estimate the empirical models for the four fund groups which I treat as four datasets. Deciding which estimation method should be used relies on appropriate tests from the econometric toolbox. These tests are performed separately for each group. The four datasets are unbalanced panels with 192 monthly panel waves (I use the term ‘unbalanced’ to emphasize that there aren’t observations for each fund in each panel wave). Fund identification numbers are used as the panel variable and the variable ‘period’ with 192 different values as the time variable. Since funds with missing observations within the examination period have already been removed, the sample contains only those funds that start later than January 1991 or seize to exist before December 2006. This is why Stata regards the used data sets as ‘strongly balanced’. Panel data are data which have both time-series and cross sectional properties. As in time series, the returns of a certain fund in two consecutive months could be correlated. This is called serial correlation. It is induced either by unknown characteristics which are constant over time or unknown time-dependent characteristics which themselves are serially correlated. OLS estimation works with a set of assumptions, some of which may be wrong when dealing with a set of panels. One of the most problematic assumptions is that of constant variance of the error term, namely homoscedasticity. Independence of the error terms is assumed across observations, although many can be traced back to the same fund. This fact should be accounted for by using a model that allows the homoscedasticity assumption to be relaxed. More precisely, the model needed is one that takes into account that there are repeated observations within the same unit that is fund returns are generated by the same manager. Treating panel data as a cross section is called pooling, a method commonly used in literature on asset pricing and performance. I employ a pooled OLS regression to provide basis for the analysis using more appropriate methods.

• Asset Management

• Mutual Fund Performance

• Conditional performance evaluation

• Mutual Fund Strategy

• Performance Persistence

Nikola Jelicic, Dipl. Kfm., wurde 1984 in Belgrad geboren. Nach seinem Bachelor in Wirtschaftswissenschaften in Belgrad, setzte er sein Studium an der Universität zu Köln fort. Der Autor hat sich für die Schwerpunkte Finanzierung, Corporate Finance und Spezielle Volkswirtschaft entschieden, um sich im letzten Jahr seines Studiums auf die empirische Forschung in Asset Management zu konzentrieren. Der Autor strebt eine Promotion im Bereich der Liquidität und ihren Einfluss auf die Preisbildung an. Seine praktischen Erfahrungen sammelte er in zwei großen deutschen Konzernen und arbeitet derzeit in Serbien an der Entwicklung eines liquiden Anleihenmarktes und Liquiditätsmanagements des Staatshaushalts. Er ist auch Gründer einer Finanzberatung.