This paper tests whether fluctuations in investors' attention affect stock return comovement with national and global markets, and which stocks are most affected. We measure fluctuations in investor attention using 59 high-profile soccer matches played during stock market trading hours at the three editions of the FIFA World Cup between 2010 and 2018. Using intraday data for more than 750 firms in 19 countries, we find that distracted investors shift attention away from firm-specific and from global news. When movements in global stock markets are large, the pricing of global news reverts back to normal, but firm-specific news keep being priced less, leading to increased comovement of stock returns with the national stock market. This increase is economically large, and particularly strong for those stocks that typically comove little with the national market, thereby leading to a convergence in betas across stocks.

Prior literature has documented that investor attention and constraints on that attention are associated with the pricing of stocks. We introduce the concept of attention comovement, which is the extent to which investor attention for a firm is explained by attention paid to the firm's industry and the market in general. We find that attention comovement is non-trivial for the average firm and is related to firm characteristics, such as size and visibility. We also find that the comovement of investor attention has market consequences, in that it is positively associated with excess stock return comovement. Finally, we show that a firm's earnings announcement contributes to the transfer of attention from one firm to its peer firms. Our results provide insights about the information flows underlying return comovement and aid in understanding the micro- and macro-nature of investor attention.

The study of the comovement between asset returns reflects an ongoing effort by economists to understand investment risk in financial markets. Building on previous findings, in the current thesis I provide some new evidence on this topic with a focus on large-cap stocks and highlight an innovative way to evaluate the statistical significance of comovement asymmetry. In the first part of the thesis, I revisit the question of how large-cap stock return comovement varies with volatility and market returns. I propose the use of an eigenvalue-based measure of comovement in a multivariate semi-Markov-switching framework. I conduct various model evaluation checks and compare the new results with that based on a benchmark. I estimate models with two to four regimes and consider the impact of sample selection and outlier reduction. Contrary to the sweeping sentiment that comovement is highest when market is down and volatile, I illustrate the significance of comovement differential across states and find in most case studies evidence that suggests otherwise. In the second part, I propose a test of asymmetric stock return comovement across states. The test can be viewed as a variation of Kendall's [unknown mathematical symbol] conditional on the state and has an asymptotic X^2-distribution. A refined version of the test is derived based on the Markov chain theory of regenerative cycles which substantially improves finite sample size and power. I show that the test has power against local alternatives, which is nonetheless compromised due to a finite sample convergence bound put on the implied local alternative data generating process. I evaluate the new test against traditional correlation-based measures and demonstrate power attrition due to nuisance parameters when states are ignored. I find that asymmetric tail dependence becomes much less significant when considered state by state. A list of related tests is given as an extension at the end.

We study the stock return comovements from two different perspectives, one being trading behaviour-induced return comovements and the other volatility-induced return comovements. Following Baker and Wurglur (2006), we construct an investor sentiment index and examine whether it has relationship with return comovements induced by investor's trading behaviour and market volatility. We find that a correlated trading behaviour along with investor sentiment significantly determines excess stock returns. Also stocks with high volatility exhibit higher return comovement properties compared to low volatilie stocks. In a cross-sectional framework, we find higher level of market uncertainty characterized by more biased investor sentiment induces highly correlated trading behaviour and thereby generates stronger correlated returns, causing stronger return comovements. The findings from our study imply that irrational and idiosyncratic sentiment of market participants, particularly which of investors, causes significant return comovement.

This book presents frontier research on the use of computational methods to model complex interactions in economics and finance. Artificial Intelligence, Machine Learning and simulations offer effective means of analyzing and learning from large as well as new types of data. These computational tools have permeated various subfields of economics, finance, and also across different schools of economic thought. Through 16 chapters written by pioneers in economics, finance, computer science, psychology, complexity and statistics/econometrics, the book introduces their original research and presents the findings they have yielded. Theoretical and empirical studies featured in this book draw on a variety of approaches such as agent-based modeling, numerical simulations, computable economics, as well as employing tools from artificial intelligence and machine learning algorithms. The use of computational approaches to perform counterfactual thought experiments are also introduced, which help transcend the limits posed by traditional mathematical and statistical tools. The book also includes discussions on methodology, epistemology, history and issues concerning prediction, validation, and inference, all of which have become pertinent with the increasing use of computational approaches in economic analysis.

The wavelet is a powerful mathematical tool that plays an important role in science and technology. This book looks at some of the most creative and popular applications of wavelets including biomedical signal processing, image processing, communication signal processing, Internet of Things (IoT), acoustical signal processing, financial market data analysis, energy and power management, and COVID-19 pandemic measurements and calculations. The editor’s personal interest is the application of wavelet transform to identify time domain changes on signals and corresponding frequency components and in improving power amplifier behavior.