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The purpose of this paper is to investigate how stock characteristics influence investor trading behavior and psychological pitfalls.

This study employs the methods of Solt and Statman (1989) and Kumar (2009) to examine investor trading activities.

Good companies do not usually have good stocks, while lottery-type stocks show better price performance than other stocks. Due to the representativeness and affect heuristics, the stocks of good companies are frequently transacted, while the low-priced stocks are infrequently transacted. Moreover, investors may display the gambler’s fallacy in the trade of stocks of good companies and the overconfidence and self-attribution bias in the trade of lottery-type stocks.

Investors trading lottery-type stocks demonstrate greater maturity than those that trade stocks of good companies; however, psychological pitfalls still dominate investor trading behavior.

The representativeness heuristic of “stocks of good companies are good stocks” results in the inclusion of stocks of good companies in a portfolio and poorer price performance, whereas the inclusion of lottery-type stocks in a portfolio brings higher returns within a short period of time.

Compared to earlier studies that focussed on the price performance of stocks of good companies and investor trading behavior in relation to lottery-type stocks, this study aims to investigate the influence of stock characteristics on price performance, trading activities, and psychological pitfalls.

Stock selection has long been identified as a challenging task. This line of research is highly contingent upon reliable stock ranking for successful portfolio construction. The purpose of this paper is to employ the methods from computational intelligence (CI) to solve this problem more effectively.

The authors develop a risk-adjusted strategy to improve upon the previous stock selection models by two main risk measures – downside risk and variation in returns. Moreover, the authors employ the genetic algorithm for optimization of model parameters and selection for input variables simultaneously.

It is found that the proposed risk-adjusted methodology via maximum drawdown significantly outperforms the benchmark and improves the previous model in the performance of stock selection.

Future work considers an extensive study for the risk-adjusted model using other risk measures such as Value at Risk, Block Maxima, etc. The authors also intend to use financial data from other countries, if available, in order to assess if the method is generally applicable and robust across different environments.

The authors expect this risk-adjusted model to advance the CI research for financial engineering and provide an promising solutions to stock selection in practice.

The originality of this work is that maximum drawdown is being successfully incorporated into the CI-based stock selection model in which the model's effectiveness is validated with strong statistical evidence.

The purpose of this paper is to conduct an assessment of the research productivity of the accounting and finance community in UK higher education institutions (HEIs) during 1991-2010 using 44 high-quality accounting and finance journals.

The authors follow Chan et al. (2011) to use their 22 finance journals. For accounting journals, the paper includes a set of 24 accounting journals that were used in a global accounting ranking study by Chan et al. (2007). The paper uses the number of coauthors (n) and coaffiliations (M) to derive the weighted articles as the measurement metric.

In general, the research output in terms of weighted articles steadily increases during the 20-year period. The University of Manchester, London School of Economics, and London Business School are the top-three HEIs using 44 accounting and finance journals for the full sample. The authors also find that it is a challenge to publish multiple articles. If an author is able to manage five total appearances, he/she is in the top 16 percent among the 1,447 UK authors. Furthermore, the paper finds that many highly productive authors are able to move to different jobs during the 20-year period.

The assessment of research productivity is, unavoidably, based on a set of selected accounting and finance journals. Hence, no matter what journal screening criteria the paper uses, there is always a subjective element in the process. If other journals or more/less journals were to be included in a similar study, different results may emerge. As a way to extend the value of the research, it would be interesting to obtain broader institutional knowledge, such as the tenure requirements of HEIs in UK, and information on the institutions where faculty members obtained their doctoral degrees, so that the authors can better evaluate the research productivity among accounting and finance community in the UK.

The paper conducts an assessment of the research productivity of accounting and finance community in UK HEIs during 1991-2010 using 44 high-quality accounting and finance journals. The study fills the gap of the extant literature to compliment the assessment of the UK accounting and finance departments in RAEs.

The purpose of the paper is to investigate the price interrelationship between the Taiwanese and US financial markets.

The trivariate GJR‐GARCH (1,1) model and event study were employed to investigate volatility asymmetry and overreaction phenomenon, respectively.

The empirical results show that return volatility reveals the asymmetric phenomenon, and the holding period returns on US index futures from the opening of the US index futures electronic trading to the opening of the Taiwanese stock market are an important reference for investors in the Taiwanese stock market. Additionally, the paper presents an overreaction of the Taiwan Stock Exchange Capitalization Weighted Stock Index to a drastic price rise of E‐min NASDAQ 100 Index futures at the opening of the Taiwanese stock market.

This paper deletes the observations arising from the different national holidays of the USA and Taiwan, to have the same number of observations in both markets, which might contaminate the empirical results.

Investors in the Taiwanese stock market tend to pay more attention to the fluctuations in the share prices of high‐technological companies in the USA.

Most of the previous studies regarding price transmission between the Taiwanese and US stock markets focused mainly on the Taiwanese market reactions to the overnight returns of the US market. This paper enlarges the current field by examining the lead‐lag relationship, the volatility asymmetry, and the overreaction phenomenon between the Taiwanese and US financial markets according to the most updated US stock index information.

This chapter investigates attention theory by examining retail investors' true intention to purchase. Attention theory indicates that investors, and especially retail investors, typically invest in stocks about which they are aware. Previous studies test attention theory by analyzing stock price behavior or trading volume. However, stock prices and trading volume are primarily driven by institutional investors rather than retail investors. We examine investor attention using initial public offering (IPO) subscriptions in Taiwan because only retail investors are allowed to subscribe to Taiwanese IPOs. We use media coverage as a measure of passive retail investor attention and Google search volume as a measure of active retail investor attention. Our results reveal that active attention has a more profound relationship with retail investor IPO subscriptions than passive attention does. Additionally, information about the value of IPOs taken from trading prices in the pre-IPO market mitigates the effects of attention theory.