Effects of COVID-Induced Public Anxiety on European Stock Markets: Evidence From a Fear-Based Algorithmic Trading System

The effect of COVID-induced public anxiety on stock markets, particularly in European stock market returns, is examined in this research. The search volumes for the notion of COVID-19 gathered by Google Trends and Wikipedia were used as proxies for COVID-induced public anxiety. COVID-induced public anxiety was shown to be linked with negative returns in European stock markets when a panel data method was used to a sample of data from 14 European stock markets from January 2, 2020 to September 17, 2020. Using an automated trading system, we used this finding to suggest investment methods based on COVID-induced anxiety. The findings of back-testing indicate that these techniques have the potential to generate exceptional profits. These results have significant consequences for government officials, the media, and investors.

Multiscale Partial Correlation Clustering of Stock Market Returns

This study proposes a wavelet procedure for estimating partial correlation coefficients between stock market returns over different time scales. The estimated partial correlations are subsequently used in a cluster analysis to identify, for each time scale, groups of stocks that exhibit distinct market movement characteristics and are therefore useful for portfolio diversification. The proposed procedure is demonstrated using all the major S&P 500 sector indices as well as precious metals and energy sector futures returns during the last decade. The results suggest cluster formations that vary by time scale, which entails different stock selection strategies for investors differing in terms of their investment horizon orientation.

Do average higher moments predict aggregate returns in emerging stock markets?

PurposeIt has been demonstrated in the US market that expected market excess returns can be predicted using the average higher-order moments of all firms. This study aims to empirically test this theory in emerging markets.Design/methodology/approachTwo measures of average higher moments have been used (equal-weighted and value-weighted) along with the market moments to predict subsequent aggregate excess returns using the linear as well as the quantile regression model.FindingsThe authors report that both equal-weighted skewness and kurtosis significantly predict subsequent market returns in two countries, while value-weighted average skewness and kurtosis are significant in predicting returns in four out of nine sample markets. The results for quantile regression show that the relationship between the risk variable and aggregate returns varies along the spectrum of conditional quantiles.Originality/valueThis is the first study that investigates the impact of third and fourth higher-order average realized moments on the predictability of subsequent aggregate excess returns in the MSCI Asian emerging stock markets. This study is also the first to analyze the sensitivity of future market returns over various quantiles.

Stock Market Responses to Monetary and Fiscal Policies

This study analyzes the short-run and long-run effects of interaction between fiscal and monetary policies on stock market performance in four emerging Asian economies, which are China, India, Indonesia, and Malaysia, by using ARDL model. The study covers the period of 2003:Q1-2020:Q1. The findings from this study show monetary and fiscal policies play an important role in determining stock market returns. Also, the results theoretically support Richardian neutrality hypothesis for China and Indonesia, Keynesian positive effect hypothesis for India, and classical crowding out effect hypothesis for Malaysia, and interest channel of monetary transmission mechanism only for China.

Labor Market Returns to Vocational Secondary Education

We study labor market returns to vocational versus general secondary education using a regression discontinuity design created by the centralized admissions process in Finland. Admission to the vocational track increases initial annual income, and this benefit persists at least through the mid-thirties, and present discount value calculations suggest that it is unlikely that life cycle returns will turn negative through retirement. Moreover, admission to the vocational track does not increase the likelihood of working in jobs at risk of replacement by automation or offshoring. Consistent with comparative advantage, we observe larger returns for people who express a preference for vocational education. (JEL D15, I21, I26, J24, J31, O33)

The Role of Big Data Research Methodologies in Describing Investor Risk Attitudes and Predicting Stock Market Performance

The purpose of this chapter is to compare the performance of a deep learning modeling technique to predict market performance compared to conventional prediction modeling techniques. A secondary purpose of this chapter is to describe the degree to which financial risk tolerance can be used to predict future stock market performance. Specifically, the models used in this chapter were developed to test whether aggregate investor financial risk tolerance is of value in establishing risk and return market expectations. Findings from this chapter's examples also provide insights into whether financial risk tolerance is more appropriately conceptualized as a predictor of market returns or as an outcome of returns.

Subjective Return Expectations, Perceptions, and Portfolio Choice

Exploiting a representative sample of the French population by age, wealth, and asset classes, we document novel facts about their expectations and perceptions of stock market returns. Both expectations and perceptions of returns are very dispersed, significantly lower than their data counterparts, and a substantial portion of the variation in the former is explained by dispersion in the latter. Consistent with portfolio choice models under incomplete information, a conditional risk-return trade-off explains the intensive margin, while at the extensive margin, only expected returns matter. Despite accounting for survey measurement error in subjective return expectations, ’muted sensitivities’ at both portfolio choice margins obtain, getting consistently (i) bigger when excluding informed non-participants, and (ii) smaller, for inertial and professionally delegated portfolios.

Expiration-Day Effects of Index Futures in a Frontier Market: The Case of Ho Chi Minh Stock Exchange

This study employs OLS, GARCH and EGARCH regression models to test the expiration-day effects of index stock futures on market returns, volatility and trading volume for the Ho Chi Minh Stock Exchange (HOSE). Data used in this study is from a daily return series of the VN30-Index for the period from 10August 2017 through 30 June 2020. The results derived from GARCH(1,1) and EGARCH(1,1) models consistently confirm that Index futures expiration-day effects on market returns exists in the HOSE. Specifically, the average market return for expiration days is significantly lower than other trading days, by 0.13% at the 5% level of significance. However, the results obtained from the regression models indicate that the expiration-day has no impact on market volatility and trading volume.