A New Empirical Approach to Explain the Stock Market Yield: A Combination of Dynamic Panel Estimation and Factor Analysis

This paper presents an empirical approach that combines competing paradigms of modeling in empirical capital market research. The approach simultaneously estimates the explanatory power of fundamentals, expectations, and historic yield patterns, making it possible to test the extent to which the efficient market hypothesis, fundamental data analysis, and behavioral finance contribute to explaining stock market yield. The core of the approach is a dynamic panel model (Arellano-Bond estimator with an MA restriction of the residuals), complemented with an upstream factor analysis to reduce multicollinearity. Due to the complexity of the data set, a great many parameters that influence the yield can be determined. Highly significant parameter estimates are possible even though the information in the data set is interdependent. For the German stock market (the 160 companies listed in DAX, MDAX, SDAX, and TecDAX), the quarterly yield is analyzed for the period between 2004 and 2009. The model has high explanatory power for the entire observation period, even in light of the fact that the period includes the financial crisis of 2008

Volatility distribution in the S&P500 Stock Index

We study the volatility of the S&P500 stock index from 1984 to 1996 and find that the volatility distribution can be very well described by a log-normal function. Further, using detrended fluctuation analysis we show that the volatility is power-law correlated with Hurst exponent $\alpha\cong0.9$.Comment: 6 pages, 5 figure

Default Risk and Equity Returns: A Comparison of the Bank-Based German and the U.S. Financial System

In this paper, we address the question whether the impact of default risk on equity returns depends on the financial system firms operate in. Using an implementation of Merton's option-pricing model for the value of equity to estimate firms' default risk, we construct a factor that measures the excess return of firms with low default risk over firms with high default risk. We then compare results from asset pricing tests for the German and the U.S. stock markets. Since Germany is the prime example of a bank-based financial system, where debt is supposedly a major instrument of corporate governance, we expect that a systematic default risk effect on equity returns should be more pronounced for German rather than U.S. firms. Our evidence suggests that a higher firm default risk systematically leads to lower returns in both capital markets. This contradicts some previous results for the U.S. by Vassalou/Xing (2004), but we show that their default risk factor looses its explanatory power if one includes a default risk factor measured as a factor mimicking portfolio. It further turns out that the composition of corporate debt affects equity returns in Germany. Firms' default risk sensitivities are attenuated the more a firm depends on bank debt financing

Explaining and Benchmarking Corporate Bond Returns

We evaluate how different betas and characteristics related to default, term, and liquidity risk fare against one another in explaining the cross-section of corporate bond returns. We find that characteristics-credit rating, duration, and Amihud illiquidity measure-fare better. Yields add incremental explanatory power. Consistent with yields providing a timelier assessment of default risk than ratings, bonds with higher yields but similar credit ratings, durations and Amihud measures experience more subsequent ratings downgrades, fewer upgrades, and a higher frequency of defaults. Based on our findings, we present characteristic portfolios that can be used to benchmark individual bond and portfolio returns

Correlations in Economic Time Series

The correlation function of a financial index of the New York stock exchange, the S&P 500, is analyzed at 1 min intervals over the 13-year period, Jan 84 -- Dec 96. We quantify the correlations of the absolute values of the index increment. We find that these correlations can be described by two different power laws with a crossover time t_\times\approx 600 min. Detrended fluctuation analysis gives exponents $\alpha_1=0.66$ and $\alpha_2=0.93$ for $t<t_\times$ and $t>t_\times$ respectively. Power spectrum analysis gives corresponding exponents $\beta_1=0.31$ and $\beta_2=0.90$ for $f>f_\times$ and $f< f_\times$ respectively.Comment: 6 pages, 2 figure