Market Timing And Model Uncertainty: An Exploratory Study For The Swiss Stock Market

We use statistical model selection criteria and Avramov's (2002) Bayesian model averaging approach to analyze the sample evidence of stock market predictability in the presence of model uncertainty. The empirical analysis for the Swiss stock market is based on a number of predictive variables found important in previous studies of return predictability. We find that it is difficult to discard any predictive variable as completely worthless, but that the posterior probabilities of the individual forecasting models as well as the cumulative posterior probabilities of the predictive variables are time-varying. Moreover, the estimates of the posterior probabilities are not robust to whether the predictive variables are stochastically detrended or not. The decomposition of the variance of predicted future returns into the components parameter uncertainty, model uncertainty, and the uncertainty attributed to forecast errors indicates that the respective contributions strongly depend on the time period under consideration and the initial values of the predictive variables. In contrast to AVRAMOV (2002), model uncertainty is generally not more important than parameter uncertainty. Finally, we demonstrate the implications of model uncertainty for market timing strategies. In general, our results do not indicate any reliable out-of-sample return predictability. Among the predictive variables, the dividend-price ratio exhibits the worst external validation on average. Again in contrast to AVRAMOV (2002), our analysis suggests that the out-of-sample performance of the Bayesian model averaging approach is not superior to the statistical model selection criteria. Consequently, model averaging does not seem to help improve the performance of the resulting short-term market timing strategies.     

Investing for the Long Run when Returns Are Predictable

We examine how the evidence of predictability in asset returns affects optimal portfolio choice for investors with long horizons. Particular attention is paid to estimation risk, or uncertainty about the true values of model parameters. We find that even after incorporating parameter uncertainty, there is enough predictability in returns to make investors allocate substantially more to stocks, the longer their horizon. Moreover, the weak statistical significance of the evidence for predictability makes it important to take estimation risk into account; a long-horizon investor who ignores it may overallocate to stocks by a sizeable amount.     

The Predictability of Equity REIT Returns: Time Variation and Economic Significance

This article presents evidence on predictability of excess returns for equity REITs relative to the aggregate stock market, small-capitalization stocks, and T-bills using best-fit models from prior time periods. We find that excess equity REIT returns are far less predictable out-of-sample than in-sample. This inability to forecast out-of-sample is particularly true in the 1990s. Nevertheless, in the absence of transaction costs, active-trading strategies based on out-of-sample predictions modestly outperform REIT buy-and-hold strategies. However, when transaction costs are introduced, profits from these active-trading strategies largely disappear.     

Industry return predictability,timing and profitability

This paper aims to investigate the predictability of Australian industrial stock returns. Several identified economic variables are found to contain significant predictive power over industry portfolio returns in a Bayesian dynamic forecasting model. The Bayesian updating process was also applied in an investigation of out-of-sample prediction, timing ability and the profitability of an investment strategy of industry-rotation. When the predictor variables are employed in out-of-sample analysis, the predictive power is superior to the naïve prediction. The timing ability and profitability associated with predictability are also economically significant. When the industry momentum is examined, the results show that a group-rotation strategy can enhance the portfolio performance.     

Payout yield,risk, and mispricing: A Bayesian analysis

We develop a simple parametric model in which hypotheses about predictability, mispricing, and the risk-return tradeoff can be evaluated simultaneously, while allowing for time variation in both risk and expected return. Most of the return predictability based on aggregate payout yield is unrelated to market risk. We consider a range of Bayesian prior beliefs about the risk-return tradeoff and the extent to which predictability is driven by mispricing. The impact of these beliefs on an investor's certainty-equivalent return when choosing between a market index and riskless T-bills is economically significant, in both ex ante and out-of-sample analyses.     

Return predictability and intertemporal asset allocation: Evidence from a bias-adjusted VAR model

Within a VAR based intertemporal asset allocation model we explore the effects on return predictability and optimal asset allocation of adjusting VAR parameter estimates for small-sample bias. We apply a simple and easy-to-use analytical bias formula instead of bootstrap or Monte Carlo bias-adjustment. Regarding return predictability we show that bias-adjustment in the multivariate setup can yield very different results than in the univariate case. Furthermore, bias-correcting the VAR parameters has both quantitatively and qualitatively important effects on the optimal portfolio choice. For intermediate values of risk-aversion, the intertemporal hedging demand for bonds and stocks is heavily affected by the bias-correction. Utility calculations also show large effects of bias-adjustment, both in-sample and out-of-sample.     

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 suggest that return predictability is neither a uniform, nor a universal feature across international capital markets.     

Implementing statistical criteria to select return forecasting models: what do we learn?

Statisticl model selection criteria provide an informed choiceof the model with best external (i.e., out-of-sample) validity.Therefore they guard against overfitting ('data snooping').We implement several model selection criteria in order to verifyrecent evidence of predictability in excess stock returns andto determine which variables are valuable predictors. We confirmthe presence of in-sample predictability in an internationalstock market dataset, but discover that even the best predictionmodels have no out-of-sample forecasting power. The failureto detect out-of-sample predictability is not due to lack ofpower.     

Predictive regressions with time-varying coefficients

We evaluate predictive regressions that explicitly consider the time-variation of coefficients in a comprehensive Bayesian framework. For monthly returns of the S&P 500 index, we demonstrate statistical as well as economic evidence of out-of-sample predictability: relative to an investor using the historic mean, an investor using our methodology could have earned consistently positive utility gains (between 1.8% and 5.8% per year over different time periods). We also find that predictive models with constant coefficients are dominated by models with time-varying coefficients. Finally, we show a strong link between out-of-sample predictability and the business cycle.     

Tactical Industry Allocation and Model Uncertainty

We use Bayesian model averaging to analyze industry return predictability in the presence of model uncertainty. The posterior analysis shows the importance of inflation and earnings yield in predicting industry returns. The out‐of‐sample performance of the Bayesian approach is, in general, superior to that of other statistical model selection criteria. However, the out‐of‐sample forecasting power of a naive i.i.d. forecast is similar to the Bayesian forecast. A variance decomposition into model risk, estimation risk, and forecast error shows that model risk is less important than estimation risk.     

Out-of-Sample Predictability in International Equity Markets: A Model Selection Approach

For 13 major international stock markets, there is much evidence of out-of-sample predictability for growth stocks especially when evaluated with economic criteria, and to a noticeably lesser extent for general stock markets and value stocks. Our results shed light on the recent debate about stock return predictability, using different assets (growth-style indexes), forecasting variables (past returns), forecasting models (nonlinear models), and alternative forecasting evaluation criteria (economic criteria). Our analysis suggests that (growth) stock returns might be predictable.     

Predicting stock returns: A regime-switching combination approach and economic links

This paper introduces a regime-switching combination approach to predict excess stock returns. The approach explicitly incorporates model uncertainty, regime uncertainty, and parameter uncertainty. The empirical findings reveal that the regime-switching combination forecasts of excess returns deliver consistent out-of-sample forecasting gains relative to the historical average and the Rapach et al. (2010) combination forecasts. The findings also reveal that two regimes are related to the business cycle. Based on the business cycle explanation of regimes, excess returns are found to be more predictable during economic contractions than during expansions. Finally, return forecasts are related to the real economy, thus providing insights on the economic sources of return predictability.     

Hedge fund return predictability; To combine forecasts or combine information?

While the majority of the predictability literature has been devoted to the predictability of traditional asset classes, the literature on the predictability of hedge fund returns is quite scanty. We focus on assessing the out-of-sample predictability of hedge fund strategies by employing an extensive list of predictors. Aiming at reducing uncertainty risk associated with a single predictor model, we first engage into combining the individual forecasts. We consider various combining methods ranging from simple averaging schemes to more sophisticated ones, such as discounting forecast errors, cluster combining and principal components combining. Our second approach combines information of the predictors and applies kitchen sink, bootstrap aggregating (bagging), lasso, ridge and elastic net specifications. Our statistical and economic evaluation findings point to the superiority of simple combination methods. We also provide evidence on the use of hedge fund return forecasts for hedge fund risk measurement and portfolio allocation. Dynamically constructing portfolios based on the combination forecasts of hedge funds returns leads to considerably improved portfolio performance.     

Predicting stock returns

This paper studies whether incorporating business cycle predictors benefits a real time optimizing investor who must allocate funds across 3,123 NYSE-AMEX stocks and cash. Realized returns are positive when adjusted by the Fama-French and momentum factors as well as by the size, book-to-market, and past return characteristics. The investor optimally holds small-cap, growth, and momentum stocks and loads less (more) heavily on momentum (small-cap) stocks during recessions. Returns on individual stocks are predictable out-of-sample due to alpha variation, whereas the equity premium predictability, the major focus of previous work, is questionable.     

Out-of-Sample Equity Premium Predictability in South Africa: Evidence from a Large Number of Predictors

This article uses a predictive regression framework to examine the out-of-sample predictability of South Africa’s equity premium, using a host of financial and macroeconomic variables. We employ various methods of forecast combination, bootstrap aggregation (bagging), diffusion index (principal component), and Bayesian regressions to allow for a simultaneous role of the variables under consideration, besides individual predictive regressions. We assess both the statistical and economic significance of the individual predictive regressions, combination methods, bagging, principal components, and Bayesian regressions. Our results show that forecast combination methods and principal component regressions improve the predictability of the equity premium relative to the benchmark autoregressive model of order one (AR[1]). However, the Bayesian predictive regressions are found to be the standout performers with the models outperforming the individual regressions, forecast combination methods, bagging and principal component regressions, both in terms of statistical (forecasting) and economic (utility) gains.     

Stock return forecasting: Some new evidence

This paper makes three contributions to the literature on forecasting stock returns. First, unlike the extant literature on oil price and stock returns, we focus on out-of-sample forecasting of returns. We show that the ability of the oil price to forecast stock returns depends not only on the data frequency used but also on the estimator. Second, out-of-sample forecasting of returns is sector-dependent, suggesting that oil price is relatively more important for some sectors than others. Third, we examine the determinants of out-of-sample predictability for each sector using industry characteristics and find strong evidence that return predictability has links to certain industry characteristics, such as book-to-market ratio, dividend yield, size, price earnings ratio, and trading volume.     

In-sample vs. out-of-sample tests of stock return predictability in the context of data mining

We undertake an extensive analysis of in-sample and out-of-sample tests of stock return predictability in an effort to better understand the nature of the empirical evidence on return predictability. We find that a number of financial variables appearing in the literature display both in-sample and out-of-sample predictive ability with respect to stock returns in annual data covering most of the twentieth century. In contrast to the extant literature, we demonstrate that there is little discrepancy between in-sample and out-of-sample test results once we employ out-of-sample tests with good power. While conventional wisdom holds that out-of-sample tests help guard against data mining, Inoue and Kilian [Inoue, A., Kilian, L., 2004. In-sample or out-of-sample tests of predictability: which one should we use? Econometric Reviews 23, 371–402.] recently argue that in-sample and out-of-sample tests are equally susceptible to data mining biases. Using a bootstrap procedure that explicitly accounts for data mining, we still find that certain financial variables display significant in-sample and out-of-sample predictive ability with respect to stock returns.     

Out-of-sample equity premium predictability and sample split–invariant inference

For a comprehensive set of 21 equity premium predictors we find extreme variation in out-of-sample predictability results depending on the choice of the sample split date. To resolve this issue we propose reporting in graphical form the out-of-sample predictability criteria for every possible sample split, and two out-of-sample tests that are invariant to the sample split choice. We provide Monte Carlo evidence that our bootstrap-based inference is valid. The in-sample, and the sample split invariant out-of-sample mean and maximum tests that we propose, are in broad agreement. Finally we demonstrate how one can construct sample split invariant out-of-sample predictability tests that simultaneously control for data mining across many variables.     

Are corporate bond market returns predictable?

This paper examines the predictability of corporate bond returns using the transaction-based index data for the period from October 1, 2002 to December 31, 2010. We find evidence of significant serial and cross-serial dependence in daily investment-grade and high-yield bond returns. The serial dependence exhibits a complex nonlinear structure. Both investment-grade and high-yield bond returns can be predicted by past stock market returns in-sample and out-of-sample, and the predictive relation is much stronger between stocks and high-yield bonds. By contrast, there is little evidence that stock returns can be predicted by past bond returns. These findings are robust to various model specifications and test methods, and provide important implications for modeling the term structure of defaultable bonds.