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.     

A CEO roundtable on making mergers succeed

The announcement in January of the merger between America Online and Time Warner marked the convergence of the two most important business trends of the last five years--the rise of the Internet and the resurgence of mergers and acquisitions. M&A activity is at a fever pitch, spurred in large part by the breathtaking influx of capital into the Internet space. And all signs indicate the trend will only accelerate. Against this background, an impressive group of experienced deal makers came together to share their experiences of what makes mergers work. They were assembled in Scottsdale, Arizona, under the auspices of the M&A Group, a professional society formed in 1999 for CEOs interested in M&A as a business strategy. Participants included top executives from Internet start-ups like Teligent; venture capital firms like Baroda Ventures; financial institutions like Merrill Lynch and PricewaterhouseCoopers; and major corporations like Allstate, Tyco International, SmithKline Beecham, Rohm and Haas, VF, Crown Cork & Seal, and Hughes Space and Communications. The spirited and surprisingly frank discussion cut a wide swath, considering issues such as whether most mergers fail to pan out as well as expected, how to increase the odds of success, the nuts and bolts of the integration process, the trade-offs between acquiring a company and growing from within, the importance of cultural issues, and why anyone would want to be on the board of a merged company.     

The relationship between healthcare expenditure and disposable personal income in the US states: a fractional integration and cointegration analysis

This paper analyses the empirical relationship between inflation and growth using a panel data estimation technique, multiple-regime panel smooth transition regression, which takes into account the nonlinearities in the data. By using a panel data set for 10 countries in the Southern African Development Community permitting us to control for unobserved heterogeneity at both country and time levels, we find that a statistically significant negative relationship exists between inflation and growth for inflation rates above the critical threshold levels of 12 and 32% which are endogenously determined. Furthermore, we remedy the cross-section dependence with the common correlated effects estimator.     

The role of news-based uncertainty indices in predicting oil markets: a hybrid nonparametric quantile causality method

A recent strand in the literature emphasizes the role of news-based economic policy uncertainty (EPU) and equity market uncertainty (EMU) as drivers of oil price movements. Against this backdrop, this paper uses a kth-order nonparametric quantile causality test, to analyse whether EPU and EMU predict stock returns and volatility. Based on daily data covering the period of 2 January 1986 to 8 December 2014, we find that, for oil returns, EPU and EMU have strong predictive power over the entire distribution barring regions around the median, but for volatility, the predictability virtually covers the entire distribution, with some exceptions in the tails. In other words, predictability based on measures of uncertainty is asymmetric over the distribution of oil returns and its volatility.     

International stock return predictability: Is the role of U.S. time-varying?

This study investigates the predictability of 11 industrialized stock returns with emphasis on the role of U.S. returns. Using monthly data spanning 1980:2–2014:12, we show that there exist multiple structural breaks and nonlinearities in the data. Therefore, we employ methods that are capable of accounting for these and at the same time date stamping the periods of causal relationship between the U.S. returns and those of the other countries. First we implement a subsample analysis which relies on the set of models, data set and sample range as in Rapach et al. (J Finance LXVIII(4):1633–1662, 2013). Our results show that while the U.S. returns played a strong predictive role based on the OLS pairwise Granger causality predictive regression and news-diffusion models, its role based on the adaptive elastic net model is weak. Second, we implement our preferred model: a bootstrap rolling window approach using our newly updated data on stock returns for each countries, and find that U.S. stock return has significant predictive ability for all the countries at certain sub-periods. Given these results, it would be misleading to rely on results based on constant-parameter linear models that assume that the relationship between the U.S. returns and those of other industrialized countries are permanent, since the relationship is, in fact, time-varying, and holds only at specific periods.     

The Role of Current Account Balance in Forecasting the US Equity Premium: Evidence From a Quantile Predictive Regression Approach

The purpose of this paper is to investigate whether the current account balance can help in forecasting the quarterly S&P500-based equity premium out-of-sample. We consider an out-of-sample period of 1970:Q3 to 2014:Q4, with a corresponding in-sample period of 1947:Q2 to 1970:Q2. We employ a quantile predictive regression model. The quantile-based approach is more informative relative to any linear model, as it investigates the ability of the current account to forecast the entire conditional distribution of the equity premium, rather than being restricted to just the conditional-mean. In addition, we employ a recursive estimation of both the conditional-mean and quantile predictive regression models over the out-of-sample period which allows for time-varying parameters in the forecast evaluation part of the sample for both of these models. Our results indicate that unlike as suggested by the linear (mean-based) predictive regression model, the quantile regression model shows that the (changes in the) real current account balance contains significant out-of-sample information when the stock market is performing poorly (below the quantile value of 0.3), but not when the market is in normal to bullish modes (quantile value above 0.3). This result seems to be intuitive in the sense that, when the markets are performing average to well, that is performing around the median and above of the conditional distribution of the equity premium, the excess return is inherently a random-walk and hence, no information, from a predictor (changes in the real current account balance) is able to predict the equity premium.