The Predictabilty Of German Stock Returns

Judith Klähn proves that some of the most important variables in predicting U.S. equity returns are not significant for the German stock market. She shows that the composition of Germany's investor base plays an important role, and she outlines the variables crucial for the German stock market.

Diploma Thesis from the year 2007 in the subject Business economics - Banking, Stock Exchanges, Insurance, Accounting, grade: 1,3, University of Tubingen, language: English, abstract: This paper develops a multifactor model for explaining the difference in average returns for the German stock market in the period between July 1990 and June 2007. The methodology of Fama and French (1993) is adopted to determine possible common risk factors in that market. Despite the enormous and strong stock markets movements and the high volatility during that period, the three factors RM-RF, SMB and HML seem to be able to capture cross-sectional variation in average returns for portfolios formed on various sorting criteria based on publicly available financial data. In addition, the analysis shows a negative (risk?) premium for small size stocks, which is a surprising result since it contradicts previous studies for the German, but also international markets. For stocks with a high book-to-market value, a strong positive premium is found. This value effect is consistent over time and statistically significant. Positive premiums seem to exist for high E/P and C/P stocks as well. These market anomalies show that returns are indeed predictable in the German market over long time horizons. High BM, E/P and C/P stocks do outperform stocks with low ratios in these categories significantly and consistent over time. However, the evidence in this analysis highlights that the common explanation in rational asset-pricing models of an outperformance due to some economic risk factors that are proxied by HML and SMB must be strongly questioned. Portfolios consisting of value stocks outperform growth portfolios in all possible states of the stock market. This evidence is contradictory to the 'marginal value of wealth' assumption in the rational asset pricing models presented. Additionally, there is a January effect in stock returns which cannot be captured by a risk-based, rational asset

Diploma Thesis from the year 2007 in the subject Business economics - Banking, Stock Exchanges, Insurance, Accounting, grade: 1,3, University of Tubingen, language: English, abstract: This paper develops a multifactor model for explaining the difference in average returns for the German stock market in the period between July 1990 and June 2007. The methodology of Fama and French (1993) is adopted to determine possible common risk factors in that market. Despite the enormous and strong stock markets movements and the high volatility during that period, the three factors RM-RF, SMB and HML seem to be able to capture cross-sectional variation in average returns for portfolios formed on various sorting criteria based on publicly available financial data. In addition, the analysis shows a negative (risk?) premium for small size stocks, which is a surprising result since it contradicts previous studies for the German, but also international markets. For stocks with a high book-to-market value, a strong positive premium is found. This value effect is consistent over time and statistically significant. Positive premiums seem to exist for high E/P and C/P stocks as well. These market anomalies show that returns are indeed predictable in the German market over long time horizons. High BM, E/P and C/P stocks do outperform stocks with low ratios in these categories significantly and consistent over time. However, the evidence in this analysis highlights that the common explanation in rational asset-pricing models of an outperformance due to some economic risk factors that are proxied by HML and SMB must be strongly questioned. Portfolios consisting of value stocks outperform growth portfolios in all possible states of the stock market. This evidence is contradictory to the ‘marginal value of wealth’ assumption in the rational asset pricing models presented. Additionally, there is a January effect in stock returns which cannot be captured by a risk-based, rational asset pricing model. Thus, the evidence suggests that it is in fact investor irrationality which is causing differences in average returns across stocks. RM-RF, SMB and HML can be described as common factors helping to explain return differences, but it is very likely that it is not underlying economic risk, but investor behavior which is causing the presented market anomalies and return predictability.

Research Paper (postgraduate) from the year 2015 in the subject Business economics - Banking, Stock Exchanges, Insurance, Accounting, grade: 17 (1,3), University of St Andrews (School of Management), course: Investment and Portfolio Management, language: English, abstract: Empirical evidence of stock return predictability obtained by financial ratios or macroeconomic factors has received substantial attention and remains a controversial topic to date. This is no surprise given that the existence of return predictability is not only of interest to practitioners but also introduces severe implications for financial models of risk and return. Founded on the assumption of efficient capital markets, research on capital asset pricing models has instigated this emergence of stock return predictability factors. Analysing these factors categorically, this paper will provide a balanced discussion of advocates as well as sceptics of stock return predictability. This essay will commence by firstly outlining the fundamental assumptions of an efficient capital market and its implications for return predictability. Subsequently, a thorough focus will be placed on the most significant predictability factors, including fundamental financial ratios and macroeconomic indicators as well as the validity of sampling methods used to attain return forecasts. Lastly this essay will reflect on the findings while proposing areas of further research.

The highly prized ability to make financial plans with some certainty about the future comes from the core fields of economics. In recent years the availability of more data, analytical tools of greater precision, and ex post studies of business decisions have increased demand for information about economic forecasting. Volumes 2A and 2B, which follows Nobel laureate Clive Granger's Volume 1 (2006), concentrate on two major subjects. Volume 2A covers innovations in methodologies, specifically macroforecasting and forecasting financial variables. Volume 2B investigates commercial applications, with sections on forecasters' objectives and methodologies. Experts provide surveys of a large range of literature scattered across applied and theoretical statistics journals as well as econometrics and empirical economics journals. The Handbook of Economic Forecasting Volumes 2A and 2B provide a unique compilation of chapters giving a coherent overview of forecasting theory and applications in one place and with up-to-date accounts of all major conceptual issues. Focuses on innovation in economic forecasting via industry applications Presents coherent summaries of subjects in economic forecasting that stretch from methodologies to applications Makes details about economic forecasting accessible to scholars in fields outside economics

The present work advances the research on hedge fund returns in three main areas. Firstly, their statistical properties are assessed in order to understand by what degree the returns of this alternative asset class are subject to non-normality, autocorrelation and heteroscedasticity. Secondly, state-of-the-art econometric approaches are used for the purpose of analyzing whether and to what extent monthly hedge fund returns are forecastable. Thirdly, an effort is made to identify and explain which economic risks affect the performance of the different hedge fund strategy styles in which way. The empirical results suggest that monthly hedge fund returns are forecastable by means of multivariate regression models which rely on economic predictors such as changes in interest rates or changes in business outlooks. Accounting for the fact that hedge fund returns are non-normally distributed, heteroscedastic and time-varying in their exposure to pervasive risk factors, the devised econometric models are found to deliver significant out-of-sample predictive power. The thesis at hand also documents that the interdependencies between the monthly changes of envisaged risk factors and the subsequent hedge fund returns remain remarkably stable throughout time. In essence, the performance of hedge funds appears to be sensitive to common business cycle movements. Altogether, the results are relevant to researchers in search of a description and application of contemporary return prediction methods as well as to investors in need of a better understanding of the drivers of hedge fund returns.