Commodity Prices and Forecastability of International Stock Returns over a Century: Sentiments versus Fundamentals with Focus on South Africa

Abstract

We forecast real stock returns of South Africa over the monthly period of 1915:01 to 2021:03 using real oil, gold and silver prices, based on an autoregressive type distributed lag model that controls for persistence and endogeneity bias. Oil price proxies for fundamentals, while gold and silver prices capture sentiments. We find that the metrics for fundamentals and sentiments both predict real stock returns of South Africa, with nonlinearity, modelled by decomposing these prices into their respective positive and negative counterparts, playing an important role in terms of forecasting when a longer out-of-sample period spanning over three-quarters of a century is used. When compared to fundamentals, sentiments, particularly real gold prices, have a relatively stronger role to play in forecasting real stock returns. Further, the predictability of stock returns emanating from fundamentals and sentiments is in line with the findings over the same period derived for two other advanced markets namely, the United Kingdom (UK) and the United States (US), but the stock market of another emerging economy, i.e., India covering 1920:08 to 2021:03, unlike South Africa, is found to be completely unpredictable. In other words, South Africa, in terms of its predictability, behaves like a developed stock market. Finally, given the importance of platinum and palladium for South Africa, our forecasting exercise based on their real prices over 1968:01 to 2021:03, depicts strong predictive content for real stock returns, thus again highlighting the importance of behavioral variables. However, these prices do not necessarily contain additional information over what is already available in gold, silver and oil real prices. Our results have important implications for academicians, investors and policymakers.

Climate Risks and Stock Market Volatility over a Century in an Emerging Market Economy: The Case of South Africa

“…In order to model and predict the volatility of the South African stock market, researchers have traditionally used various variants of the popular GARCH model. A comprehensive review of this literature is beyond the scope and objective of this paper, but the interested reader can refer to the works of [33,[38][39][40][41][42][43][44][45][46][47][48][49][50][51][52], and the references cited therein. In terms of the international literature on modeling and predictability of stock market volatility, see [33,[53][54][55][56][57] for detailed reviews.…”

“…A comprehensive review of this literature is beyond the scope and objective of this paper, but the interested reader can refer to the works of [33,[38][39][40][41][42][43][44][45][46][47][48][49][50][51][52], and the references cited therein. In terms of the international literature on modeling and predictability of stock market volatility, see [33,[53][54][55][56][57] for detailed reviews. In these studies, researchers have thus far utilized either univariate approaches, especially when dealing with daily data, as well as various macroeconomic and financial predictors when modeling low-frequency, i.e., monthly, volatility.…”

“…Having said this, like [33], we control for the role of fundamentals-and sentimentsbased information via the West Texas Intermediate (WTI) oil and precious metals (gold and silver) prices. The oil price (or its returns) is a good proxy of macroeconomic and financial predictors because of its potential to move stock prices through its impact on changes in expected cash flows and/or the discount rate, output, monetary and fiscal policy, and macroeconomic and financial uncertainties [34,35].…”

Drivers of Realized Volatility for Emerging Countries with a Focus on South Africa: Fundamentals versus Sentiment

“…The choice of South Africa as the main component of our case study is driven not only by the availability of stock market data spanning over four decades in our analyses (1980 to 2020), but also because the need to conduct a focussed analysis of the South African stock market is warranted due to its sophistication. Moreover, South Africa is one of the largest exporters of highly financialized strategic commodities such as coal, chrome, diamond, gold, ilmenite, iron ore, manganese, palladium, platinum, rutile, vanadium, vermiculite, and zirconium [18]. Being a commodity-based economy, South Africa is globally well-integrated, and in light of the dominance of the US economy in the world financial system, changes in its macroeconomic fundamentals and behavioural components are likely to affect international financial markets in general and the South African stock market in particular, besides domestically, given that asset prices are functions of the state variables of the economy, shaped by the dynamics of fundamentals and sentiments [19,20].…”

Oil-Price Uncertainty and International Stock Returns: Dissecting Quantile-Based Predictability and Spillover Effects Using More than a Century of Data

“…Naturally, the existing literature on forecasting international stock returns is vast, based on a wide array of (linear and nonlinear) models and (macroeconomic, financial, technical, and behavioral) predictors. Hence, providing a detailed review is beyond the scope of this paper; interested readers should refer to [21][22][23][24][25][26][27][28][29][30], for further information regarding this area of research. Even though the role of the oil price and/or returns in forecasting stock returns has been extensively analyzed [31][32][33][34], our contribution to this important and significant area of research is that we are the first to incorporate the role of oil-price uncertainty in forecasting international stock returns of advanced and emerging countries using over a century of data.…”