Model uncertainty,thick modelling and the predictability of stock returns

Recent financial research has provided evidence on the predictability of asset returns. In this paper we consider the results contained in Pesaran and Timmerman (1995), which provided evidence on predictability of excess returns in the US stock market over the sample 1959–1992. We show that the extension of the sample to the nineties weakens considerably the statistical and economic significance of the predictability of stock returns based on earlier data. We propose an extension of their framework, based on the explicit consideration of model uncertainty under rich parameterizations for the predictive models. We propose a novel methodology to deal with model uncertainty based on ‘thick’ modelling, i.e. on considering a multiplicity of predictive models rather than a single predictive model. We show that portfolio allocations based on a thick modelling strategy systematically outperform thin modelling. Copyright © 2005 John Wiley & Sons, Ltd.     

Forecasting stock returns an examination of stock market trading in the presence of transaction costs

The paper presents new evidence on the predictability of excess returns on common stocks for the Standard and Poor's 500 and the Dow Jones Industrial portfolios at the monthly, quarterly, and annual frequencies. It shows that recursive predictions obtained on the basis of the excess returns regressions are capable of correctly predicting a statistically significant proportion of the signs of the actual returns. The paper also shows that the switching portfolios constructed on the basis of the signs of the recursive predictions mean-variance dominate the respective market portfolios when trading takes place on a quarterly or annual basis. This result holds even under a high transaction cost scenario. However, due to the larger number of transactions at the monthly frequency the monthly switching portfolios only mean-variance dominate the respective market portfolios when transaction costs are zero or low.     

Non-linear prediction of security returns with moving average rules

Over the years, investors and the technical analysts have devised hundreds of technical market indicators in an effort to forecast the trend of a security market. Recent literature provides evidence that these rules may provide positive profits after accounting for transaction costs. This clearly contradicts the theory of the efficient market hypothesis which states that security prices cannot be forecasted from their past values or other past variables. This paper uses the daily Dow Jones Industrial Average Index from January 1963 to June 1988 to examine the linear and non-linear predictability of stock market returns with buy—sell signals generated from the moving average rules with a band between the short and the long averages. Strong evidence of non-linear predictability is found in the stock market returns by using the past buy and sell signals of these rules.     

Trading volume and prediction of stock return reversals: Conditioning on investor types' trading

We show that contrasting results on trading volume's predictive role for short‐horizon reversals in stock returns can be reconciled by conditioning on different investor types' trading. Using unique trading data by investor type from Korea, we provide explicit evidence of three distinct mechanisms leading to contrasting outcomes: (i) informed buying—price increases accompanied by high institutional buying volume are less likely to reverse; (ii) liquidity selling—price declines accompanied by high institutional selling volume in institutional investor habitat are more likely to reverse; (iii) attention‐driven speculative buying—price increases accompanied by high individual buying‐volume in individual investor habitat are more likely to reverse. Our approach to predict which mechanism will prevail improves reversal forecasts following return shocks: An augmented contrarian strategy utilizing our ex ante formulation increases short‐horizon reversal strategy profitability by 40–70% in the US and Korean stock markets.     

International transmission mechanism of stock market movements: evidence from emerging equity markets

This paper investigates the transmission patterns of stock market movements between developed and emerging market economies by estimating a four‐variable VAR model. The underlying economic fundamentals and trade links are considered as possible determinants of differences in transmission patterns. The results of the impulse response functions and variance decompositions indicate that significant links exist between the stock markets of the USA and Mexico and weaker links between the markets of the USA, Argentina, and Brazil. Differences in the patterns of stock market responses are consistent with differences in trade flows. The response of emerging markets to a shock to the US market lasts longer than that of a developed market such as the UK. While no single emerging market can affect the US stock market, the combined effect of emerging markets on the US stock market is found to be statistically significant. These findings can be linked to differences in the speed of information processing and to the institutional structure governing the market. Overall the findings suggest that the transmission of stock market movements is in accord with underlying economic fundamentals rather than irrational contagion effects. Copyright © 2000 John Wiley & Sons, Ltd.     

On the directional predictability of equity premium using machine learning techniques

This paper applies a plethora of machine learning techniques to forecast the direction of the US equity premium. Our techniques include benchmark binary probit models, classification and regression trees, along with penalized binary probit models. Our empirical analysis reveals that the sophisticated machine learning techniques significantly outperformed the benchmark binary probit forecasting models, both statistically and economically. Overall, the discriminant analysis classifiers are ranked first among all the models tested. Specifically, the high-dimensional discriminant analysis classifier ranks first in terms of statistical performance, while the quadratic discriminant analysis classifier ranks first in economic performance. The penalized likelihood binary probit models (least absolute shrinkage and selection operator, ridge, elastic net) also outperformed the benchmark binary probit models, providing significant alternatives to portfolio managers.     

An analysis on the predictability of CAPM beta for momentum returns

This paper demonstrates that the forecasted capital asset pricing model (CAPM) beta of momentum portfolios explains a large portion of the return, ranging from 40% to 60% for stock‐level momentum, and from 30% to 50% for industry‐level momentum. Beta forecasts are from a realized beta estimator using daily returns over the prior year. Periods such as 1969–1989 have been found in earlier studies to contain abnormal profits from momentum trading; however, we show that these were spuriously generated by measurement error in systematic risk. These results cast further doubt on the ability of standard momentum trading strategies to generate abnormal profits.     

A tug of war of forecasting the US stock market volatility: Oil futures overnight versus intraday information

This study is the first to examine the impacts of overnight and intraday oil futures cross-market information on predicting the US stock market volatility the high-frequency data. In-sample estimations present that high overnight oil futures RV can lead to high RV of the S&P 500. Moreover, negative overnight returns are more powerful than positive components, implying the existence of the leverage effect. From statistical and economic perspectives, out-of-sample results indicate that the decompositions of overnight oil futures and intraday RVs, based on signed intraday returns, can significantly increase the models' predictive ability. Finally, when considering the US stock market overnight effect, the decompositions are still useful to predict volatility, especially during high US stock market fluctuations and high and low EPU states.     

Forecasting price delay and future stock returns: The role of corporate social responsibility

This paper investigates the role of corporate social responsibility (CSR) performance in forecasting companys' stock prices and future returns. The forecasting analysis identifies a negative association between CSR performance and proxies of price delay. The negative CSR–delay association is weak for state‐owned enterprises (SOEs) because of their politically oriented motivation of CSR activities, but significantly strong for non‐SOEs. Furthermore, we find that forecasting delayed firms is expected to have higher future returns. In particular, the returns premium is most attributable to the CSR component of delay, compared with the non‐CSR component. Taken together, these results suggest that CSR performance plays a positive role in enhancing stock price efficiency, and a potential explanation is that CSR performance can be considered as additional information for equity predictions.     

Trading volume and realized volatility forecasting: Evidence from the China stock market

The existing contradictory findings on the contribution of trading volume to volatility forecasting prompt us to seek new solutions to test the sequential information arrival hypothesis (SIAH). Departing from other empirical analyses that mainly focus on sophisticated testing methods, this research offers new insights into the volume-volatility nexus by decomposing and reconstructing the trading activity into short-run components that typically represent irregular information flow and long-run components that denote extreme information flow in the stock market. We are the first to attempt at incorporating an improved empirical mode decomposition (EMD) method to investigate the volatility forecasting ability of trading volume along with the Heterogeneous Autoregressive (HAR) model. Previous trading volume is used to obtain the decompositions to forecast the future volatility to ensure an ex ante forecast, and both the decomposition and forecasting processes are carried out by the rolling window scheme. Rather than trading volume by itself, the results show that the reconstructed components are also able to significantly improve out-of-sample realized volatility (RV) forecasts. This finding is robust both in one-step ahead and multiple-step ahead forecasting horizons under different estimation windows. We thus fill the gap in studies by (1) extending the literature on the volume-volatility linkage to EMD-HAR analysis and (2) providing a clear view on how trading volume helps improve RV forecasting accuracy.     

Stock market volatility and the forecasting performance of stock index futures

This study attempts to apply the general equilibrium model of stock index futures with both stochastic market volatility and stochastic interest rates to the TAIFEX and the SGX Taiwan stock index futures data, and compares the predictive power of the cost of carry and the general equilibrium models. This study also represents the first attempt to investigate which of the five volatility estimators can enhance the forecasting performance of the general equilibrium model. Additionally, the impact of the up‐tick rule and other various explanatory factors on mispricing is also tested using a regression framework. Overall, the general equilibrium model outperforms the cost of carry model in forecasting prices of the TAIFEX and the SGX futures. This finding indicates that in the higher volatility of the Taiwan stock market incorporating stochastic market volatility into the pricing model helps in predicting the prices of these two futures. Furthermore, the comparison results of different volatility estimators support the conclusion that the power EWMA and the GARCH(1,1) estimators can enhance the forecasting performance of the general equilibrium model compared to the other estimators. Additionally, the relaxation of the up‐tick rule helps reduce the degree of mispricing. Copyright © 2008 John Wiley & Sons, Ltd.     

Predicting the Distribution of Stock Returns: Model Formulation,Statistical Evaluation,VaR Analysis and Economic Significance

A large literature has investigated predictability of the conditional mean of low‐frequency stock returns by macroeconomic and financial variables; however, little is known about predictability of the conditional distribution. We look at one‐step‐ahead out‐of‐sample predictability of the conditional distribution of monthly US stock returns in relation to the macroeconomic and financial environment. Our methodological approach is innovative: we consider several specifications for the conditional density and combinations schemes. Our results are as follows: the entire density is predicted under combination schemes as applied to univariate GARCH models with Gaussian innovations; the Bayesian winner in relation to GARCH‐skewed‐t models is informative about the 5% value at risk; the average realised utility of a mean–variance investor is maximised under the Bayesian winner as applied to GARCH models with symmetric Student t innovations. Our results have two implications: the best prediction model depends on the evaluation criterion; and combination schemes outperform individual models. Copyright © 2015 John Wiley & Sons, Ltd.     

Factor Models of Stock Returns: GARCH Errors versus Time‐Varying Betas

This paper investigates the implications of time‐varying betas in factor models for stock returns. It is shown that a single‐factor model (SFMT) with autoregressive betas and homoscedastic errors (SFMT‐AR) is capable of reproducing the most important stylized facts of stock returns. An empirical study on the major US stock market sectors shows that SFMT‐AR outperforms, in terms of in‐sample and out‐of‐sample performance, SFMT with constant betas and conditionally heteroscedastic (GARCH) errors, as well as two multivariate GARCH‐type models. Copyright © 2016 John Wiley & Sons, Ltd.     

Forecasting stock volatility in the presence of extreme shocks: Short-term and long-term effects

This paper introduces a novel generalized autoregressive conditional heteroskedasticity–mixed data sampling–extreme shocks (GARCH-MIDAS-ES) model for stock volatility to examine whether the importance of extreme shocks changes in different time ranges. Based on different combinations of the short- and long-term effects caused by extreme events, we extend the standard GARCH-MIDAS model to characterize the different responses of the stock market for short- and long-term horizons, separately or in combination. The unique timespan of nearly 100 years of the Dow Jones Industrial Average (DJIA) daily returns allows us to understand the stock market volatility under extreme shocks from a historical perspective. The in-sample empirical results clearly show that the DJIA stock volatility is best fitted to the GARCH-MIDAS-SLES model by including the short- and long-term impacts of extreme shocks for all forecasting horizons. The out-of-sample results and robustness tests emphasize the significance of decomposing the effect of extreme shocks into short- and long-term effects to improve the accuracy of the DJIA volatility forecasts.     

Economic and statistical measures of forecast accuracy

This paper argues in favour of a closer link between the decision and the forecast evaluation problems. Although the idea of using decision theory for forecast evaluation appears early in the dynamic stochastic programming literature, and has continued to be used with meteorological forecasts, it is hardly mentioned in standard academic texts on economic forecasting. Some of the main issues involved are illustrated in the context of a two‐state, two‐action decision problem as well as in a more general setting. Relationships between statistical and economic methods of forecast evaluation are discussed and links between the Kuipers score used as a measure of forecast accuracy in the meteorology literature and the market timing tests used in finance are established. An empirical application to the problem of stock market predictability is also provided, and the conditions under which such predictability could be explained in the presence of transaction costs are discussed. Copyright © 2000 John Wiley & Sons, Ltd.     

基于动态技术差异的国际分工理论及对中国的国际分工地位的启示

技术可以通过改变产出从而影响一国的国际分工地位。基于动态技术差异的国际分工理论是运用经济学中的动态分析方法，强调了技术这一生产要素导致国际分工的一系列理论。本文通过对这些相关理论和模型的综述，突出了由于技术在时间和过程变动中体现出的差异给各国带来国际分工地位的不同，进而阐述了动态技术差异给我国的国际分工地位所带来的启示。     

Estimating the Out‐of‐Sample Predictive Ability of Trading Rules: A Robust Bootstrap Approach

In this paper, we provide a novel way to estimate the out‐of‐sample predictive ability of a trading rule. Usually, this ability is estimated using a sample‐splitting scheme, true out‐of‐sample data being rarely available. We argue that this method makes poor use of the available data and creates data‐mining possibilities. Instead, we introduce an alternative.632 bootstrap approach. This method enables building in‐sample and out‐of‐sample bootstrap datasets that do not overlap but exhibit the same time dependencies. We show in a simulation study that this technique drastically reduces the mean squared error of the estimated predictive ability. We illustrate our methodology on IBM, MSFT and DJIA stock prices, where we compare 11 trading rules specifications. For the considered datasets, two different filter rule specifications have the highest out‐of‐sample mean excess returns. However, all tested rules cannot beat a simple buy‐and‐hold strategy when trading at a daily frequency. Copyright © 2015 John Wiley & Sons, Ltd.     

国际科技合作新趋势对中国科技发展的挑战及其对策

随着国际科技合作新趋势的不断发展,其对我国政府的作用、国家科技体制和科研环境、国家科学研究、产业研发、国家安全、知识产权建设等提出了严峻的挑战。面对挑战,我们应该积极调整政府在国际科技合作中的作用;深化科研体制改革,全面推进科技体制创新;形成有层次的国际合作交流体系;积极调整企业国际科技合作;加强国家的安全建设和高度重视知识产权建设。