Stock Return Predictability by Bayesian Model Averaging: Evidence from Stock Exchange of Thailand (original) (raw)

Bayesian Model Averaging and Forecasting

2011

This paper focuses on the problem of variable selection in linear regression models. I briefly review the method of Bayesian model averaging, which has become an important tool in empirical settings with large numbers of potential regressors and relatively limited numbers of observations. Some of the literature is discussed with particular emphasis on forecasting in economics. The role of the prior assumptions in these procedures is highlighted, and some recommendations for applied users are given.

8. Bayesian model averaging and forecasting

This paper focuses on the problem of variable selection in linear regression models. I briefly review the method of Bayesian model averaging, which has become an important tool in empirical settings with large numbers of potential regressors and relatively limited numbers of observations. Some of the literature is discussed with particular emphasis on forecasting in economics. The role of the prior assumptions in these procedures is highlighted, and some recommendations for applied users are given.

Forecasting Stock Returns Using Model‐Selection Criteria*

Economic Record, 2005

This paper examines the economic significance of return predictability in Australian equities. In light of considerable model uncertainty, formal model-selection criteria are used to choose a specification for the predictive model. A portfolio-switching strategy is implemented according to model predictions. Relative to a buy-and-hold market investment, the returns to the portfolio-switching strategy are impressive under several model-selection criteria, even after accounting for transaction costs. However, as these findings are not robust across other model-selection criteria examined, it is difficult to conclude that the degree of return predictability is economically significant. * The authors are grateful for the insightful comments and suggestions of an anonymous referee.

Bayesian Reconciliation of Return Predictability

arXiv (Cornell University), 2022

This article considers a stable vector autoregressive (VAR) model and investigates return predictability in a Bayesian context. The VAR system comprises asset returns and the dividend-price ratio as proposed in Cochrane (2008), and allows pinning down the question of return predictability to the value of one particular model parameter. We develop a new shrinkage type prior for this parameter and compare our Bayesian approach to ordinary least squares estimation and to the reduced-bias estimator proposed in Amihud and Hurvich (2004). A simulation study shows that the Bayesian approach dominates the reduced-bias estimator in terms of observed size (false positive) and power (false negative). We apply our methodology to annual CRSP value-weighted returns running, respectively, from 1926 to 2004 and from 1953 to 2021. For the first sample, the Bayesian approach supports the hypothesis of no return predictability, while for the second data set weak evidence for predictability is observed.

Forecast accuracy and economic gains from Bayesian model averaging using time-varying weights

Journal of Forecasting, 2010

Several Bayesian model combination schemes, including some novel approaches that simultaneously allow for parameter uncertainty, model uncertainty and robust time varying model weights, are compared in terms of forecast accuracy and economic gains using financial and macroeconomic time series. The results indicate that the proposed time varying model weight schemes outperform other combination schemes in terms of predictive and economic gains. In an empirical application using returns on the S&P 500 index, time varying model weights provide improved forecasts with substantial economic gains in an investment strategy including transaction costs. Another empirical example refers to forecasting US economic growth over the business cycle. It suggests that time varying combination schemes may be very useful in business cycle analysis and forecasting, as these may provide an early indicator for recessions.

A Bayesian Method of Forecast Averaging for Models Known Only by Their Historic Outputs: An Application to the BCRA´s REM

2006

Similar to other Central Banks, the BCRA publishes monthly a REM that summaries the forecasts and projections of a group of economic analysts and consultants who volunteer to participate in the program. The BCRA publishes only the median, and the standard deviation of the sample received. The logic for using these statistics is that all participants are to be treated equally. Under the assumption that some forecasters have better underlying models than others, one might be able to improve the accuracy of the aggregate forecast by giving greater priority to those who have historically predicted better. The BCRA does not have access to the models used to make the predictions, only the forecasts are provided. An averaging method that puts higher weights on the predictions of those forecasters who have done best in the past should be able to produce a better aggregate forecast. The problem is how to determine these weights. In this paper, we develop a Bayesian averaging method that can ...

Predicting Short-Term Interest Rates: Does Bayesian Model Averaging Provide Forecast Improvement

2011

This paper examines the forecasting qualities of Bayesian Model Averaging (BMA) over a set of single factor models of short-term interest rates. Using weekly and high frequency data for the one-month Eurodollar rate, BMA produces predictive likelihoods that are considerably better than the majority of the short-rate models, but marginally worse off than the best model in each dataset. We observe preference for models incorporating volatility clustering for weekly data and simpler short rate models for high frequency data. This is contrary to the popular belief that a diffusion process with volatility clustering best characterizes the short rate.

Incorporating Model Uncertainty and Model Instability in Forecasting Bond Risk Premia and Term Structure of Government Bond Yield – A Bayesian Model Averaging Approach

SSRN Electronic Journal, 2011

This research investigates the macro factors for forecasting (1) bond risk premia and (2) term structure of government bond yields by using Bayesian Model Averaging (BMA) based on empirical prior. Different from the traditional variable selection approach which advocates finding an "optimized" variable subset, BMA combines all model specifications with their posterior probability to handle model uncertainty. Our result shows strong empirical evidence that BMA outperforms the traditional model selection criteria. As market environment keep changing, the relationship between response variable and explanatory variable(s) changes too. The BMA based on empirical prior does not need subjective elicitation of priors and can be more adaptive to dynamic economic condition. Through explicitly incorporating model uncertainty and model instability, the BMA based on empirical prior can easily match artificial neural network, the state-of-the-art universal approximating method, in forecasting accuracy. Although we find there is no significant difference in forecasting performance between BMA and neural networks trained with Bayesian regularization, we find each method does offer unique information which could further improve the other method's forecasting performance. The performance of using BMA to forecast bond excess return is tested with both statistical measures and economic measures. Many existing yield curve models do not incorporate the role of macro factors (Diebold et al 2006). Diebold and Li (2006) demonstrated no existing model can outperform the random walk model in forecasting one-month-ahead yield curve. We apply BMA to forecast the government bond yield change and indicate BMA model can significantly outperform the random walk model at one-month-ahead horizon.