Informatics, Data Mining, Econometrics and Financial Economics: A Connection

This short communication reviews some of the literature in econometrics and financial economics that is related to informatics and data mining. We then discuss some of the research on econometrics and financial economics that could be extended to informatics and data mining beyond the existing areas in econometrics and financial economics

Making Markowitz's Portfolio Optimization Theory Practically Useful

The traditional estimated return for the Markowitz mean-variance optimization has been demonstrated to seriously depart from its theoretic optimal return. We prove that this phenomenon is natural and the estimated optimal return is always $\sqrt{\gamma}$ times larger than its theoretic counterpart where $\gamma = \frac 1{1-y}$ with $y$ as the ratio of the dimension to sample size. Thereafter, we develop new bootstrap-corrected estimations for the optimal return and its asset allocation and prove that these bootstrap-corrected estimates are proportionally consistent with their theoretic counterparts. Our theoretical results are further confirmed by our simulations, which show that the essence of the portfolio analysis problem could be adequately captured by our proposed approach. This greatly enhances the practical uses of the Markowitz mean-variance optimization procedure

Making Markowitz's Portfolio Optimization Theory Practically Useful

The traditional estimated return for the Markowitz mean-variance optimization has been demonstrated to seriously depart from its theoretic optimal return. We prove that this phenomenon is natural and the estimated optimal return is always $\sqrt{\gamma}$ times larger than its theoretic counterpart where $\gamma = \frac 1{1-y}$ with $y$ as the ratio of the dimension to sample size. Thereafter, we develop new bootstrap-corrected estimations for the optimal return and its asset allocation and prove that these bootstrap-corrected estimates are proportionally consistent with their theoretic counterparts. Our theoretical results are further confirmed by our simulations, which show that the essence of the portfolio analysis problem could be adequately captured by our proposed approach. This greatly enhances the practical uses of the Markowitz mean-variance optimization procedure

Empirical Study on Conservative and Representative Heuristics of Hong Kong Small Investors Adopting Momentum and Contrarian Trading Strategies

Recently, a new Bayesian approach has been developed to explain some market anomalies. In this paper, we conduct a questionnaire survey to examine whether the theory holds empirically by studying the conservative and representative heuristics by Hong Kong small investors who adopt momentum and/or contrarian trading strategies. In addition, our study provides evidence for the small investors on their time horizon and risk tolerance when facing uncertainty in their investments. Our findings are useful to small investors in their investment decision making and useful to financial advisors in providing service to small investors

Making Markowitz's Portfolio Optimization Theory Practically Useful

The traditional estimated return for the Markowitz mean-variance optimization has been demonstrated to seriously depart from its theoretic optimal return. We prove that this phenomenon is natural and the estimated optimal return is always $\sqrt{\gamma}$ times larger than its theoretic counterpart where $\gamma = \frac 1{1-y}$ with $y$ as the ratio of the dimension to sample size. Thereafter, we develop new bootstrap-corrected estimations for the optimal return and its asset allocation and prove that these bootstrap-corrected estimates are proportionally consistent with their theoretic counterparts. Our theoretical results are further confirmed by our simulations, which show that the essence of the portfolio analysis problem could be adequately captured by our proposed approach. This greatly enhances the practical uses of the Markowitz mean-variance optimization procedure

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

Why did Warrant Markets Close in China but not Taiwan?

The paper bridges a gap in the literature by using moment analysis, CAPM statistics, stochastic dominance (SD) test, and volume analysis to examine investor preferences for warrants between China and Taiwan, and investigating why the market for warrants in China has to close while the market for Taiwan warrants is successful. Using moment analysis, it is shown that that buying China warrants has a higher likelihood of losses than its Taiwan counterpart. Using CAPM analysis, in general, both the Sharpe ratio and Jensen index for warrants from the Taiwan market are more reasonable, while that from the China market is too negative. On the other hand, the Treynor index for China warrants shows that China warrants are highly volatile. This could make investors avoid investing in China warrants which, in turn, could lead to its closure. Using SD analysis, though there is no arbitrage opportunity between the China and Taiwan warrant markets, it is shown that the markets for China and Taiwan warrants are not efficient, and second- and third-order risk averters prefer to invest in China warrants to warrants in Taiwan. This implies that the warrant issuers prefer to issue Taiwan warrants than China warrants. Using volume analysis, the China warrant market is much more active than the Taiwan warrant market. This could imply that there are more speculative activities in China than in Taiwan which, in turn, could lead to China’s decision to close its warrant market. The findings in the paper are useful for investors for investment decisions regarding Taiwan and China warrants, for academic analysis for modelling Taiwan and China warrants, and policy makers for policy making related to Taiwan and China warrants. In the future, China may rethink reopening warrant markets and learning from mature-covered warrant markets such as Taiwan how to inhibit excess speculation and educate warrant investors