Short-term returns and the predictability of Finnish stock returns

The predictability of Finnish stock returns is studied using the framework of Ferson and Harvey (1993). We use a conditional asset pricing model where risk premia and risk sensitivities are conditioned on a range of financial information variables. In particular, we study the effect of the return interval on the predictability of short-term stock returns. Using daily, weekly, and monthly Finnish size and industry-sorted portfolio returns, we find that the predictability of returns increases with the length of return interval, but so does the power of the conditional pricing model to explain the predictability. Consistent with earlier results, we report that the time variation in risk premium accounts for most of the predictability. However, the results show also there is a sizable positive interaction between beta and risk premium which seems to increase for smaller companies.asset pricing; predictability; return interval; time aggregation

International Stock Return Predictability Under Model Uncertainty

This paper examines return predictability when the investor is uncertain about the right state variables. A novel feature of the model averaging approach used in this paper is to account for finite-sample bias of the coefficients in the predictive regressions. Drawing on an extensive international dataset, we find that interest-rate related variables are usually among the most prominent predictive variables, whereas valuation ratios perform rather poorly. Yet, predictability of market excess returns weakens substantially, once model uncertainty is accounted for. We document notable differences in the degree of in-sample and out-of-sample predictability across different stock markets. Overall, these findings suggests that return predictability is not a uniform and a universal feature across international capital markets. --Stock Return Predictability,Bayesian Model Averaging,Model Uncertainty,International Stock Markets

Time-varying return predictability in the Chinese stock market

China's stock market is the largest emerging market all over the world. It is widely accepted that the Chinese stock market is far from efficiency and it possesses possible linear and nonlinear dependence. We study the predictability of returns in the Chinese stock market by employing the wild bootstrap automatic variance ratio test and the generalized spectral test. We find that the return predictability vary over time and significant return predictability is observed around market turmoils. Our findings are consistent with the Adaptive Markets Hypothesis and have practical implications for market participants.Comment: 11 Latex pages including 2 figures and 1 tabl

Return Predictability in the Treasury Market: Real Rates, Inflation, and Liquidity

Estimating the liquidity differential between inflation-indexed and nominal bond yields, we separately test for time-varying real rate risk premia, inflation risk premia, and liquidity premia in U.S. and U.K. bond markets. We find strong, model independent evidence that real rate risk premia and inflation risk premia contribute to nominal bond excess return predictability to quantitatively similar degrees. The estimated liquidity premium between U.S. inflation-indexed and nominal yields is systematic, ranges from 30 bps in 2005 to over 150 bps during 2008-2009, and contributes to return predictability in inflation-indexed bonds. We find no evidence that bond supply shocks generate return predictability.

The Implications of First-Order Risk Aversion for Asset Market Risk Premiums

Existing general equilibrium models based on traditional expected utility preferences have been unable to explain the excess return predictability observed in equity markets, bond markets, and foreign exchange markets. In this paper, we abandon the expected-utility hypothesis in favor of preferences that exhibit first-order risk aversion. We incorporate these preferences into a general equilibrium two-country monetary model, solve the model numerically, and compare the quantitative implications of the model to estimates obtained from U.S. and Japanese data for equity, bond and foreign exchange markets. Although increasing the degree of first-order risk aversion substantially increases excess return predictability, the model remains incapable of generating excess return predictability sufficiently large to match the data. We conclude that the observed patterns of excess return predictability are unlikely to be explained purely by time-varying risk premiums generated by highly risk averse agents in a complete markets economy.

The Dog That Did Not Bark: A Defense of Return Predictability

To question the statistical significance of return predictability, we cannot specify a null that simply turns off that predictability, leaving dividend growth predictability at its essentially zero sample value. If neither returns nor dividend growth are predictable, then the dividend-price ratio is a constant. If the null turns off return predictability, it must turn on the predictability of dividend growth, and then confront the evidence against such predictability in the data. I find that the absence of dividend growth predictability gives much stronger statistical evidence against the null, with roughly 1-2% probability values, than does the presence of return predictability, which only gives about 20% probability values. I argue that tests based on long-run return and dividend growth regressions provide the cleanest and most interpretable evidence on return predictability, again delivering about 1-2% probability values against the hypothesis that returns are unpredictable. I show that Goyal and Welch's (2005) finding of poor out-of-sample R2 does not reject return forecastability. Out-of-sample R2 is poor even if all dividend yield variation comes from time-varying expected returns.

Efficient Tests of Stock Return Predictability

Empirical studies have suggested that stock returns can be predicted by ï¬nancial variables such as the dividend-price ratio. However, these studies typically ignore the high persistence of predictor variables, which can make ï¬rst-order asymptotics a poor approximation in ï¬nite samples. Using a more accurate asymptotic approximation, we propose two methods to deal with the persistence problem. First, we develop a pretest that determines when the conventional t-test for predictability is misleading. Second, we develop a new test of predictability that results in correct inference regardless of the degree of persistence and is efficient compared to existing methods. Applying our methods to US data, we ï¬nd that the dividend-price ratio and the smoothed earningsprice ratio are sufficiently persistent for conventional inference to be highly misleading. However, we ï¬nd some evidence for predictability using our test, particularly with the earnings-price ratio. We also ï¬nd evidence for predictability with the short-term interest rate and the long-short yield spread, for which the conventional t-test leads to correct inference.

Sources of Predictability of European Stock Markets for High-Technology Firms

We study return predictability of stock indexes of blue chip firms and smaller hightechnology firms in Germany, France, and the United Kingdom during the second half of the 1990s. We measure return predictability in terms of first-order autocorrelation coefficients, and find evidence for return predictability of stock indexes of smaller hightechnology firms, but no evidence for return predictability of stock indexes of blue chip firms. Our findings suggest that a leading candidate for explaining the economic sources of return predictability of stock indexes of smaller high-technology firms is transaction costs

Predictability in Financial Markets: What Do Survey Expectations Tell Us?

There is widespread evidence of excess return predictability in financial markets. In this paper we examine whether this predictability is related to expectational errors. To consider this issue, we use data on survey expectations of market participants in the stock market, the foreign exchange market, and the bond and money markets in various countries. We find that the predictability of expectational errors coincides with the predictability of excess returns: when a variable predicts expectational errors in a given market, it typically predicts the excess return as well. Understanding expectational errors appears crucial for explaining excess return predictability.

Understanding stock return predictability

Over the period 1927:Q1 to 2005:Q4, the average CAPM-based idiosyncratic variance (IV) and stock market variance jointly forecast stock market returns. This result holds up quite well in a number of robustness checks, and we show that the predictive power of the average IV might come from its close relation with systematic risk omitted from CAPM. First, high lagged returns on high IV stocks predict low future returns on the market as a whole. Second, returns on a hedging portfolio that is long in stocks with low IV and short in stocks with high IV perform as well as the value premium in explaining the cross-section of stock returns. Third, realized variance of the hedging portfolio or of the value premium is closely correlated with the average IV, and these variables have similar predictive power for stock returns.Stock exchanges ; Stock - Prices