Management Information, Decision Sciences, and Financial Economics : a connection

The paper provides a brief review of the connecting literature in management information, decision sciences, and financial economics, and discusses some research that is related to the three cognate disciplines. Academics could develop theoretical models and subsequent econometric models to estimate the parameters in the associated models, and analyze some interesting issues in the three related disciplines

Big data, computational science, economics, finance, marketing, management, and psychology: connections

The paper provides a review of the literature that connects Big Data, Computational Science, Economics, Finance, Marketing, Management, and Psychology, and discusses some research that is related to the seven disciplines. Academics could develop theoretical models and subsequent econometric and statistical models to estimate the parameters in the associated models, as well as conduct simulation to examine whether the estimators in their theories on estimation and hypothesis testing have good size and high power. Thereafter, academics and practitioners could apply theory to analyse some interesting issues in the seven disciplines and cognate areas

Decision Sciences, Economics, Finance, Business, Computing, and Big Data: Connections

This paper provides a review of some connecting literature in Decision Sciences, Economics, Finance, Business, Computing, and Big Data. We then discuss some research that is related to the six cognate disciplines. Academics could develop theoretical models and subsequent econometric and statistical models to estimate the parameters in the associated models. Moreover, they could then conduct simulations to examine whether the estimators or statistics in the new theories on estimation and hypothesis have small size and high power. Thereafter, academics and practitioners could then apply their theories to analyze interesting problems and issues in the six disciplines and other cognate areas

Big Data, Computational Science, Economics, Finance, Marketing, Management, and Psychology: Connections

The paper provides a review of the literature that connects Big Data, Computational Science, Economics, Finance, Marketing, Management, and Psychology, and discusses some research that is related to the seven disciplines. Academics could develop theoretical models and subsequent econometric and statistical models to estimate the parameters in the associated models, as well as conduct simulation to examine whether the estimators in their theories on estimation and hypothesis testing have good size and high power. Thereafter, academics and practitioners could apply theory to analyse some interesting issues in the seven disciplines and cognate areas

Securities trading in multiple markets: the Chinese perspective

This thesis studies the trading of the Chinese American Depositories Receipts (ADRs) and their respective underlying H shares issued in Hong Kong. The primary intention of this work is to investigate the arbitrage opportunity between the Chinese ADRs and their underlying H shares. This intention is motivated by the market observation that hedge funds are often in the top 10 shareholders of these Chinese ADRs. We start our study from the origin place of the Chinese ADRs, China’s stock market. We pay particular attention to the ownership structure of the Chinese listed firms, because part of the Chinese ADRs also listed A shares (exclusively owned by the Chinese citizens) in Shanghai. We also pay attention to the market microstructures and trading costs of the three China-related stock exchanges. We then proceed to empirical study on the Chinese ADRs arbitrage possibility by comparing the return distribution of two securities; we find these two securities are different in their return distributions, and which is due to the inequality in the higher moments, such as skewness, and kurtosis. Based on the law of one price and the weak-form efficient markets, the prices of identical securities that are traded in different markets should be similar, as any deviation in their prices will be arbitraged away. Given the intrinsic property of the ADRs that a convenient transferable mechanism exists between the ADRs and their underlying shares which makes arbitrage easy; the different return distributions of the ADRs and the underlying shares address the question that if arbitrage is costly that the equilibrium price of the security achieved in each market is affected mainly by its local market where the Chinese ADRs/the underlying Hong Kong shares are traded, such as the demand for and the supply of the stock in each market, the different market microstructures and market mechanisms which produce different trading costs in each market, and different noise trading arose from asymmetric information across multi-markets. And because of these trading costs, noise trading risk, and liquidity risk, the arbitrage opportunity between the two markets would not be exploited promptly. This concern then leads to the second intention of this work that how noise trading and trading cost comes into playing the role of determining asset prices, which makes us to empirically investigate the comovement effect, as well as liquidity risk. With regards to these issues, we progress into two strands, firstly, we test the relationship between the price differentials of the Chinese ADRs and the market return of the US and Hong Kong market. This test is to examine the comovement effect which is caused by asynchronous noise trading. We find the US market impact dominant over Hong Kong market impact, though both markets display significant impact on the ADRs’ price differentials. Secondly, we analyze the liquidity effect on the Chinese ADRs and their underlying Hong Kong shares by using two proxies to measure illiquidity cost and liquidity risk. We find significant positive relation between return and trading volume which is used to capture liquidity risk. This finding leads to a deeper study on the relationship between trading volume and return volatility from market microstructure perspective. In order to verify a proper model to describe return volatility, we carry out test to examine the heteroscedasticity condition, and proceed to use two asymmetric GARCH models to capture leverage effect. We find the Chinese ADRs and their underlying Hong Kong shares have different patterns in the leverage effect as modeled by these two asymmetric GARCH models, and this finding from another angle explains why these two securities are unequal in the higher moments of their return distribution. We then test two opposite hypotheses about volume-volatility relation. The Mixture of Distributions Hypothesis suggests a positive relation between contemporaneous volume and volatility, while the Sequential Information Arrival Hypothesis indicates a causality relationship between lead-lag volume and volatility. We find supportive evidence for the Sequential Information Arrival Hypothesis but not for the Mixture of Distributions Hypothesis