Asset Pricing with Return Extrapolation

Macroeconomic attention and stock market return predictability

This thesis consists of three empirical studies on what drives stock market dynamics. The first empirical study explores the effect of crude oil price changes on the stock market returns of oil-exporting countries and oil-importing countries as well as those of a number of global stock indices. Using the Ordinary Least Squares (OLS) approach as well as the more robust Quantile Regression (QR) approach to explore the relationship between crude oil and stock market dynamics. The empirical findings suggest that the QR approach provides further insights compared to the OLS approach. For instance, the QR approach is able to identify specific quantiles where a significant relation exists. In particular crude oil price increases tend to have a negative impact on the stock market returns for some oil-exporting countries (such as Mexico, Iraq, Ecuador, and Venezuela) and a positive effect for other oil-exporting countries (such as Brazil and Algeria). However, the OLS approach suggests that these relationships are insignificant at the level of the mean. Overall, the empirical findings confirm that the QR approach can reveal more information about the relationship between crude oil price changes and stock market return across different quantiles of their distribution.The second study explores the extent to which implied volatility extracted from commodity markets and developed stock markets can predict the implied volatility of stock markets in BRICS countries. Using daily data from 2011 to 2016 and employing the newly developed Bayesian Graphical Vector Autoregressive (BGVAR) model of Ahelegbey et al. (2016) which does not suffer from over-parameterization and the identification problems associated with traditional VAR frameworks, this study finds that implied volatilities extracted from global and regional stock markets have a significant predictive power over the implied volatilities in BRICS stock markets. However, the predictive power of implied volatility from commodity markets are significant only in the case of South Africa.The third empirical study analyses the relationship between illiquidity and stock market returns in the G7 and BRICS countries. More specifically, this study explore the extent to which the Amihud (2002) illiquidity measure can improve the explanatory power of three commonly used asset pricing models, namely the Capital Asset Pricing Model (CAPM), the Fama-French three-factor model and the Carhart four-factor model. The empirical analysis is based on 15 years of monthly data on the returns of seven stock portfolios: 100 largest companies (Largest100), small value (S/V), small neutral (S/N), small growth (S/G) stocks, big value (B/V) stocks, big neutral (B/N) stocks, and big growth (B/G) stocks. The findings suggest that incorporating illiquidity as an additional factor results in a significant improvement in the explanatory power of these asset pricing models across several of the sample countries (8 countries in the case of the CAPM and Carhart four-factor model, and 6 countries in the case of the Fama-French three-factor model). For example, in the US adding illiquidity to the CAPM leads to an increase of the goodness of fit by 2.6% in the B/V portfolio, and for the Fama-French three-factor model the goodness of fit increases by up to 3% in all portfolios Moreover, the goodness of fit increases in all portfolios in the US by adding illiquidity to the Carhart four-factor model, with an up to 36% increase in the B/N portfolio.

In order to accelerate the learning ability of neural network structure parameters and improve the prediction accuracy of deep learning algorithms, an evolutionary algorithm, based on a prior Gaussian mutation (PGM) operator, is proposed to optimize the structure parameters of a gated recurrent unit (GRU) neural network. In this algorithm, the sensitivity learning process of GRU model parameters into the Gaussian mutation operator, used the variance of the GRU model parameter training results as the Gaussian mutation variance to generate the optimal individual candidate set. Then, the optimal GRU neural network structure is constructed using the evolutionary algorithm of the prior Gaussian mutation operator. Moreover, the PGM-EA-GRU algorithm is applied to the prediction of stock market returns. Experiments show that the prediction model effectively overcomes the GRU neural network, quickly falling into a local optimum and slowly converging. Compared to the RF, SVR, RNN, LSTM, GRU, and EA-GRU benchmark models, the model significantly improves the searchability and prediction accuracy of the optimal network structure parameters. It also validates the effectiveness and the progressive nature of the PGM-EA-GRU model proposed in this paper with stock market return prediction.

Category-specific EPU indices, macroeconomic variables and stock market return predictability

The paper provides an updated evidence of the linkage between stock market and macroeconomic factors in Pakistan. The sample period is from January 2011 to November 2017. Macroeconomic variables used are money supply, exchange rate, treasury bill rate, inflation and industrial production. Generalized autoregressive conditional heteroscedasticity (GARCH) models have been used to examine the impact of macroeconomic factors on stock market return and stock market volatility. Findings suggest that macroeconomic factors have an impact on stock market volatility. The fluctuations in inflation and money supply negatively influence the volatility of stock market returns. In contrast, industrial production positively affects the fluctuations of stock market returns. The findings are important for shareholders, investors, regulatory authorities and policymakers.

The impact of oil price shocks on the stock market return and volatility relationship

This paper studied the impact of inflation rates and US Dollar exchange rates in Indonesian stock market return volatility in the period of 2002-2012. Daily data of stock market return, inflation rates and US Dollar exchange rates were used to estimate the daily volatility of the stock return. The data of stock market price, inflation rates and US Dollar exchange rates were taken from Yahoo Finance, Indonesia statistics bureau and central bank of Indonesia, respectively. The result showed that both inflation rates and US Dollar exchange rates did not have a significant impact on the Indonesian stock market return volatility. Inflation rates contributed 0.03% change in daily stock market return volatility whereas a 1% appreciation of Rupiah contributed to a 0.0000001% change in daily stock market return volatility. This research finding was different from the result in Nigeria where Yaya’s & Shittu’s (2010) research had the same variables. Inflation rates and US Dollar exchange rates were to examine the impact on Nigeria stock market index return volatility.

The volatility of yields reflects the degree of risk in the market, and at the same time the volatility of yields is viewed as a measure of information, and high volatility is generally accompanied by large market information shocks. Therefore, it is particularly important to study the volatility of China's stock market returns so as to find out where the problems lie. In this paper, the CSI 300 index is taken as the research object, and a total of 2,671 daily closing prices from 4 January 2013 to 29 December 2023 are taken as the sample, and the GARCH family model is used to analyse this sample data, so as to derive the volatility characteristics of the CSI 300 index and to make static forecasts.

Many empirical studies have analysed the effect of good news and bad news on equity market return volatility using both developed and emerging markets data, with scant literature for frontier stock markets. This study evaluates how news affects stock market return volatility in a frontier market using Uganda data. It specifically analyses the reaction of stock return volatility to news filtering into a frontier market using the exponential generalized autoregressive conditional heteroscedasticity (GARCH) model on daily data ranging from 1 September 2011 to 31 December 2017. Estimates of the shape parameter from generalized error distribution indicate the existence of leptokurtic return distribution. Results from the exponential GARCH model show that the effect of bad news and good news on the frontier market return volatility differs, thus suggesting existence of leverage effect in the period studied. Overall results from the study suggest that positive news impacts stock market returns volatility more than negative news of the same magnitude. An important implication of our results is that investors, analysts, brokers and dealers should be conscious of the nature of news filtering into the stock market as such information might improve their expected volatility forecast.

I use the consumer's budget constraint to derive a relationship between stock market returns, the residuals of the trend relationship among consumption, aggregate wealth and labour income, and three major sources of risk: future changes in the housing consumption share, future labour income growth and future consumption growth. I model the joint dynamics of changes in the housing consumption share, consumption–growth, wealth growth, income growth, asset returns, consumption–wealth ratio and dividend–price ratio, and show that asset returns largely reflect expectations about long-run risk. On the other hand, unexpected shocks play a negligible role in the context of forecasting future asset returns. Combining the intertemporal budget constraint and the forecasting properties of an informative Vector Autoregression (VAR), one can, therefore, generate the predictability of many economically motivated variables developed in the literature on asset pricing, and accommodate the implications of a wide class of o...

Chapter 2 - The Effects of Natural Disasters on Stock Market Return and Volatility in Hong Kong

Entropy and predictability of stock market returns

Time-Varying Rare Disaster Risk and Stock Returns

This paper develops a method of using financial data to understand the expected impact of trade policy on welfare and other real variables when the policy has heterogeneous and uncertain impacts on firms. We embed a firm-level specific factors model in a consumption capital asset pricing model to map expected cash-flow movements into expected movements in productivity, wages, and welfare. Using our framework, we find that the U.S.-China trade war caused increases in uncertainty and large declines in U.S. stock prices, expected cash flows, and expected productivity. We estimate that the expected decline in U.S. welfare is 4.9 percentage points.

We propose a heuristic switching model of an asset market where the agents’ choice of heuristic is consistent with their individual risk aversion. They choose between a fundamentalist and a trend-following rule to form expectations about the price of a risky asset. Given their risk aversion, agents make a deterministic trade-off between mean and variance both in choosing a forecasting heuristic and determining the number of risky assets to buy. Heterogeneous risk preferences can lead to diverse choices of heuristic. Using empirical estimates for the distribution of risk aversion, simulations show that the resulting time-varying heterogeneity of expectations can give rise to chaotic dynamics: irregular booms and busts in the asset price without exogenous shocks. Small, stochastic price shocks lead to larger asset price bubbles and can make stable solutions explosive. We prove that a representative agent cannot capture our model.