Essays on determinants spillovers and predictability of the South African stock returns

Following the recent recession, major global economies are still experiencing weak recoveries. The likelihood that the global economy may experience a double-dip recession driven by poor performance by advanced economies stresses the need for predicting the behaviour of leading indicators such as stock returns and equity premium. An understanding of market behaviour helps in guiding both policy and trading decisions. The main objective of this thesis is to assess the predictability, spillovers and determinants of stock returns in South Africa. Stock returns are determined by a number of financial and macroeconomic variables including valuation ratios (price-earnings ratio and price-dividend ratio), payout ratio, interest rates, the term spread, stock returns of South Africa‟s major trading partners, the inflation rate, money stock, industrial production and the employment rate, world oil production, the refiner acquisition cost of imported crude oil, global activity index, industrial stock returns and financial stock returns. A number of econometric models are used in investigating the determinants, predictability and spillovers of the stock returns – including; predictive regressions using in-sample and out-of-sample test statistics (t-statistics, MSE-F and the ENC-NEW, , utility gains, forecasting encompassing test); exponential smooth-transition autoregressive; Monte Carlo simulations; data-mining-robust bootstrap procedure; in-sample general-to-specific model selection, bootstrap aggregating, combining method (simple averages, discounting, clusters, principal components, Bayesian regression methods under the Gaussian and double-exponential prior); sign restriction VAR and a TVP-VAR model specification with stochastic volatility. The results show that firstly, the stock returns are determined by certain financial and macroeconomic variables (assessing both the statistical and economic significance). Secondly, South African stock returns react differently to different types of oil shocks – suggesting that the cause of the oil price shock is crucial in determining policy. The combination model forecasts, especially the Bayesian regression methods, outperform the benchmark model (AR(1)/random walk model). Further, the analysis does not only show evidence of significant spillovers to consumption and interest rate from the stock market, but, more importantly, it also highlights the fact that these effects have significantly varied over time.Thesis (PhD)--University of Pretoria, 2013.hb2013EconomicsUnrestricte

Does the source of oil price shocks matter for South African stock returns? A structural VAR approach

In this paper, we investigate the dynamic relationship between different oil price shocks and the South African stock market using a sign restriction structural vector autoregression (VAR) approach for the period 1973:01 to 2011:07. The results show that for an oil-importing country like South Africa, stock returns only increase with oil prices when global economic activity improves. In response to oil supply shocks and speculative demand shocks, stock returns and the real price of oil move in opposite directions. The analysis of the variance decomposition shows that the oil supply shock contributes more to the variability in real stock prices. The main conclusion is that different oil price shocks affect stock returns differently and policy makers and investors should always consider the source of the shock before implementing policy and making investment decisions.http://www.elsevier.com/locate/enecohb2016Economic

Macroeconomic variables and South African stock return predictability

We examine both in-sample and out-of-sample predictability of South African stock return using macroeconomic variables. We base our analysis on a predictive regression framework, using monthly data covering the in-sample period between 1990:01 and 1996:12, and the out-of sample period commencing from 1997:01 to 2010:06. For the in-sample test, we use the t-statistic corresponding to the slope coefficient of the predictive regression model, and for the out-of-sample tests we employ the MSE-F and the ENC-NEW test statistics. When using multiple variables in a predictive regression model, the results become susceptible to data mining. To guard against this, we employ a bootstrap procedure to construct critical values that account for data mining. Further, we use a procedure that combines the in-sample general-to-specific model selection with tests of out-of-sample forecasting ability to examine the significance of each macro variable in explaining the stock returns behaviour. In addition, we use a diffusion index approach by extracting a principal component from the macro variables, and test the predictive power thereof. For the in-sample tests, our results show that different interest rate variables, world oil production growth, as well as, money supply have some predictive power at certain short-horizons. For the out-of-sample forecasts, only interest rates and money supply show short-horizon predictability. Further, the inflation rate shows very strong out-of-sample predictive power from 6-month-ahead horizons. A real time analysis based on a subset of variables that underwent revisions, resulted in deterioration of the predictive power of these variables compared to the fully revised data available for 2010:6. The diffusion index yields statistically significant results for only four specific months over the out-of-sample horizon. When accounting for data mining, both the in-sample and the out-of-sample test statistics for both the individual regressions and the diffusion index become insignificant at all horizons. The general-to-specific model confirms the importance of different interest rate variables in explaining the behaviour of stock returns, despite their inability to predict stock returns, when accounting for data mining.http://www.elsevier.com/locate/ecmodhb2013ff201

South African stock return predictability in the context data mining : the role of financial variables and international stock returns

In this paper, we examine the predictive ability, both in-sample and the out-of-sample, for South African stock returns using a number of financial variables, based on monthly data with an in-sample period covering 1990:01 to 1996:12 and the out-of-sample period of 1997:01 to 2010:04. We use the t-statistic corresponding to the slope coefficient in a predictive regression model for in-sample predictions, while for the out-of-sample, the MSE-F and the ENC-NEW tests statistics with good power properties were utilised. To guard against data mining, a bootstrap procedure was employed for calculating the critical values of both the in-sample and out-of-sample test statistics. Furthermore, we use a procedure that combines in-sample general-to-specific model selection with out-ofsample tests of predictive ability to further analyse the predictive power of each financial variable. Our results show that, for the in-sample test statistic, only the stock returns for our major trading partners have predictive power at certain short and long run horizons. For the out-of-sample tests, the Treasury bill rate and the term spread together with the stock returns for our major trading partners show predictive power both at short and long run horizons. When accounting for data mining, the maximal out-of-sample test statistics become insignificant from 6-months onward suggesting that the evidence of the out-of-sample predictability at longer horizons is due to data mining. The general-tospecific model shows that valuation ratios contain very useful information that explains the behaviour of stock returns, despite their inability to predict stock return at any horizon. The model also highlights the role of multiple variables in predicting stock returns at medium- to long-run horizons.http://www.elsevier.com/locate/ecmodnf201

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

Abstract: This paper 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

Do stock prices impact consumption and interest rate in South Africa? Evidence from a time-varying vector autoregressive model

This paper investigates the existence of spillovers from stock prices onto consumption and the interest rate for South Africa using a time-varying vector autoregressive (TVP-VAR) model with stochastic volatility. In this regard, we estimate a three-variable TVP-VAR model comprising of real consumption growth rate, the nominal three-months Treasury bill rate and the growth rate of real stock prices. We find that the impact of a real stock price shocks on consumption is in general positive, with large and significant effects observed at the one-quarter ahead horizon. However, there is also evidence of significant negative spillovers from the stock market to consumption during the financial crisis, at both short and long-horizons. Monetary policy response to stock price shocks has been persistent, and strong especially post-the financial liberalization in 1985, but became weaker during the financial crisis. Overall, we provide evidence of significant time-varying spillovers on consumption and interest rate from the stock market.http://emf.sagepub.comhb201

The impact of oil shocks on the South African economy

The recent increases in oil prices have raised the importance of studying the effects of oil supply and demand shocks on an economy. The purpose of this paper is to investigate the impact of the oil supply and demand shocks on the South African economy using a sign restriction-based structural Vector Autoregressive (VAR) model. Our results show that an oil supply shock has a short-lived significant impact only on the inflation rate, while the impact on the other variables is statistically insignificant. Supply disruptions result in a short-term increase in the domestic inflation rate with no reaction from the monetary policy. An aggregate demand shock results in short- to medium-term improvements in domestic output and the real exchange rate. The effect is statistically insignificant for the inflation rate as well as the monetary policy instrument. The inflation rate and the real exchange rate react negatively to an oil-specific demand shock, while output is positively related to unanticipated changes in oil price due to speculations. Our results highlight the importance of understanding the source of the oil price movements, since an oil price increase necessarily does not imply a negative effect on the economy.http://www.tandfonline.com/loi/uesb202017-08-31hb2016Economic

Can economic uncertainty, financial stress and consumer sentiments predict U.S. equity premium?

This article attempts to examine whether the equity premium in the United States can be predicted from a comprehensive set of 18 economic and financial predictors over a monthly out-of-sample period of 2000:2–2011:12, using an in-sample period of 1990:2–2000:1. To do so, we consider, in addition to the set of variables used in Rapach and Zhou (2013), the forecasting ability of four other important variables: the US economic policy uncertainty, the equity market uncertainty, the University of Michigan’s index of consumer sentiment, and the Kansas City Fed’s financial stress index. Using a more recent dataset compared to that of Rapach and Zhou (2013), our results from predictive regressions show that the newly added variables do not play any significant statistical role in explaining the equity premium relative to the historical average benchmark over the out-of-sample horizon, even though they are believed to possess valuable informative content about the state of the economy and financial markets. Interestingly, however, barring the economic policy uncertainty index, the three other indexes considered in this study yield economically significant out-of-sample gains, especially during recessions, when compared to the historical benchmark.http://www.elsevier.com/locate/intfin2015-11-30hb201

DSGE model-based forecasting of modelled and nonmodelled inflation variables in South Africa

Inflation forecasts are a key ingredient for monetary policy-making – especially in an inflation targeting country such as South Africa. Generally, a typical Dynamic Stochastic General Equilibrium (DSGE) only includes a core set of variables. As such, other variables, for example alternative measures of inflation that might be of interest to policy-makers, do not feature in the model. Given this, we implement a closed-economy New Keynesian DSGE model-based procedure which includes variables that do not explicitly appear in the model.We estimate such a model using an in-sample covering 1971Q2 to 1999Q4 and generate recursive forecasts over 2000Q1 to 2011Q4. The hybrid DSGE performs extremely well in forecasting inflation variables (both core and nonmodelled) in comparison with forecasts reported by other models such as AR(1). In addition, based on ex-ante forecasts over the period 2012Q1–2013Q4, we find that the DSGE model performs better than the AR(1) counterpart in forecasting actual GDP deflator inflation.http://www.tandfonline.com/loi/raec202016-05-30hb201

Valuation ratios and stock price predictability in South Africa : is it there?

Using monthly South African data for 1990:01-2009:10, this paper, to the best of our knowledge, is the first to examine the predictability of real stock prices based on valuation ratios, namely, price-dividend and price-earnings ratios. We cannot detect either short-horizon or long-horizon predictability; that is, the hypothesis that the current value of a valuation ratio is uncorrelated with future stock price changes cannot be rejected at both short- and long-horizons based on bootstrapped critical values constructed from linear representations of the data. We find, via Monte Carlo simulations, that the power to detect predictability in finite samples tends to decrease at long horizons in a linear framework. Though Monte Carlo simulations applied to exponential smooth-transition autoregressive (ESTAR) models of the price-dividend and price-earnings ratios, show increased power, the ability of the non-linear framework in explaining the pattern of stock price predictability in the data does not show any promise both at short- and long-horizons, just as in the linear predictive regressions.http://www.mesharpe.com/mall/results1.asp?ACR=RE