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We propose a novel method of forecasting the level of informed trading at merger announcements. Informed traders typically take advantage of their knowledge of the forthcoming merger by trading heavily at announcement. They trade on positive volume or informed buys for cash mergers and negative volume or informed sells for stock mergers. In response, market makers set wider spreads and raise prices for informed buys and lower prices for informed sells. As liquidity traders trade on these prices, our vector autoregressive framework establishes the link between informed trading and liquidity trading through price changes. As long as the link holds, informed trading may be detected by measuring levels of liquidity trading. We observe the link during the −1 to +1 period for cash mergers and −1 to +5 period for stock mergers.

Several studies have observed that stocks tend to drop by an amount that is less than the dividend on the ex-dividend day, the so-called ex-dividend day anomaly. However, there still remains a lack of consensus for a single explanation of this anomaly. Different from other studies, this dissertation attempts to answer the primary research question: how can investors make trading profits from the ex-dividend day anomaly and how much can they earn? With this goal, I examine the economic motivations of equity investors through four main hypotheses identified in the anomaly's literature: the tax differential hypothesis, the short-term trading hypothesis, the tick size hypothesis, and the leverage hypothesis.

While the U.S. ex-dividend anomaly is well studied, I examine a long data window (1975–2010) of Thailand data. The unique structure of the Thai stock market allows me to assess all four main hypotheses proposed in the literature simultaneously. Although I extract the sample data from two data sources, I demonstrate that the combined data are consistently sampled. I further construct three trading strategies – “daily return,” “lag one daily return,” and “weekly return” – to alleviate the potential effect of irregular data observation.

I find that the ex-dividend day anomaly exists in Thailand, is governed by the tax differential, and is driven by short-term trading activities. That is, investors trade heavily around the ex-dividend day to reap the benefits of the tax differential. I find mixed results for the predictions of the tick size hypothesis and results that are inconsistent with the predictions of the leverage hypothesis.

I conclude that, on the Stock Exchange of Thailand, juristic and foreign investors can profitably buy stocks cum-dividend and sell them ex-dividend while local investors should engage in short sale transactions. On average, investors who employ the daily return strategy have earned significant abnormal return up to 0.15% (45.66% annualized rate) and up to 0.17% (50.99% annualized rate) for the lag one daily return strategy. Investors can also make a trading profit by conducting the weekly return strategy and earn up to 0.59% (35.67% annualized rate), on average.

The rapid change of technology has significantly affected the financial markets in Thailand. In order to enhance the market efficiency and liquidity, the Stock Exchange of Thailand (SET) has granted Thai stock brokers permission to develop and offer their customers algorithm and automatic stock trading. However, algorithm trading on SET was not widely adopted. This chapter intends to design and empirically estimate a model in explaining Thai investors’ acceptance of algorithm trading. The theoretical framework is based on the theory of reasoned action and technology acceptance model (TAM). A sample of 400 investors who have used online stock trading and 300 investors who have used algorithm stock trading were observed and analyzed using structural equations model (SEM) and generalized linear regression model (GLM) with a Logit specification. The results confirm that attitudes, subjective norm, perceived risks, and trust toward algorithm stock trading are factors determining investors’ behavior and acceptance of using algorithm stock trading. Investor’s perception and trust on algorithm stock trading as a trading strategy is a major factor in determining their perceived behavior and control, which affect their decision on whether to invest using algorithm trading. Accordingly, it can be concluded that Thai investors is willing to accept algorithm trading as a new financial technology, but still has concern about the reliability and profitable of this new stock trading strategy. Therefore, algorithm trading can be promoted by building investors’ trust on algorithm trading as a reliable and profitable trading strategy.

As of May 2022, the National Pension Service of Korea is the world's third-largest pension fund, with assets worth KRW912tn (approximately $US800bn). Of the KRW152tn (approximately $US133bn) invested in domestic equities, 45% is outsourced to external asset managers. Given the absence of prior research on the National Pension Service's (NPS's) management method, this study analyzes its trading strategies and market impact according to the fund management method from 2005 to 2022. The results are as follows: First, the stock characteristics selected by internal management using passive strategies are different from those selected by external management, in which various strategies are combined. Second, the contrarian investment strategy, which acts as a market stabilizer, is a characteristic of the external management trading pattern, while internal management increases volatility and does not improve liquidity. Third, there has been a change in the internal management strategy since 2016, when the fund management headquarters was relocated. This study is practically significant and distinctive in that it confirms the differences between the NPS's two investment methods in terms of trading strategies and market impact.

The purpose of this paper is to examine positive‐feedback (PF) behavior and its relationship to momentum profitability and information uncertainty.

Using the behavioral function of rational traders and feedback traders, the authors jointly estimate the mean and conditional variance equations of the GARCH model to derive the positive‐ and non‐positive‐feedback coefficients, respectively. In each six‐month period, the number of PF stocks were then calculated as a fraction of the total number of stocks in that period. The authors then investigate whether day‐to‐day PF trading activities vary across different momentum portfolios by calculating the percentage of PF stocks in each decile portfolio.

This study finds that about 9.4 per cent of stocks exhibit PF trading activities and that these activities have a more profound effect on stocks with a higher level of information uncertainty. The finding shows that the percentage of stocks with PF trading is higher in the portfolios of extreme losers than in the portfolios of extreme winners. The evidence suggests that stocks exhibiting PF trading activities subsequently experience significantly higher momentum returns.

This paper presents evidence to test whether a relationship exists between short‐term PF trading and future momentum profitability. Since PF traders tend to chase price movements, PF trading is more likely to cause stock prices to further diverge from the firm's fundamentals and, therefore, give rise to stock return momentum. This phenomenon appears to be more profound in this study when there is a higher level of information uncertainty.

The purpose of this paper is to identify factors that influence the intention to use internet stock trading among investors in Malaysia.

A structured questionnaire was used to collect data from investors who are aware of internet stock trading in Malayisa. Out of 300 questionnaire only 144 were usable.

Findings show that attitude, subjective norm and perceived behavioral control has a direct positive relationship towards behavioral intention to use internet stock trading. The theory of planned behavior can be used to explain variation in behavioral intention and actual usage.

More variations of results could be gained through a wider coverage of respondents. Other factors such as descriptive norm and perceived usefulness should be used to increase the explanatory power of the dependent variable. A comparison of the same study of explanatory power between other intention‐based model could give another valuable contribution.

This study will provide information on factors that influence and affect investor's intention to use online stock trading. In addition, the result of the study could serve as a guideline by online stock broking organizations in understanding the factors and program that need to be instilled to increase online stock trading among current retail investors and future investors.

Not much has been written on understanding intention to use internet stock trading in a developing country.

The purpose of this paper is to investigate the impact of the frequency of information acquisition on the frequency of stock trading. The authors also examined if the Big Five personality traits of investor influence the association between information acquisition and stock trading behavior.

The authors adopted NEO Five-Factor Inventory (Costa and McCrae, 1989) inventory to measure the Big Five personality traits of investors and examined the data collected from 541 individual investors of the Chinese stock market. To overcome the potential endogeneity bias, the authors followed two-stage least square method for estimating endogenous covariate by employing instrumental variable analysis. The authors performed probit regression to evaluate the moderating influence of investor personality traits on the association between information acquisition and stock trading behavior. The authors also performed several other tests to check the robustness of the key findings.

This research confirmed the previous findings that the more frequently investors acquire information, the more often they trade in stocks. Moreover, the authors added to the existing literature by providing empirical evidence that the Big Five personality traits moderate the relationship of information acquisition with stock trading behavior. Information acquisition tends to increase stock trading frequency in investors with conscientiousness, extraversion and agreeableness traits. On the other hand, it also has the tendency to decrease the intensity of stock trading in investors with openness and neuroticism traits.

The theoretical model in this study seeks to explain that the psychological factor, namely, investor personality, influences the way an investor interprets signals from information which in turn influences the investor decision to trade in securities. This research suggests that psychological characteristics of investors can be of relevance for policy makers in their attempts to improve their business in the financial services industry.

This study combines both information search literature and behavioral finance literature to investigate whether or not the information acquisition that relates to investors’ asset allocation decisions is influenced by investor personality. The study offers new theoretical insights into investors’ behavior due to the characteristics of the Chinese stock market which are uniquely different from other stock markets in the world. No previous study has been conducted so far in the Chinese stock market to explore variations in the impact of investors’ information acquisition on their stock trading by the Big Five personality and this paper strives to fill this research gap.

The purpose of this paper is to examine whether, and to what extent, specific personality traits drive investors’ trading behaviour.

This study investigates these assumptions in an innovative way by employing an integrated model and using structural equation modelling analysis to examine them simultaneously as they would occur in the complex real world environment.

The results provide strong evidence that these personality traits influence investors’ trading behaviour and stock trading performance. The most powerful relationships are found to be those between over-confidence and stock trading volume, frequency and performance.

To the best of the authors’ knowledge there is no any similar study. This paper is the authors’ original unpublished work and it has not been submitted to any other journal for reviews.

This paper aims to propose a method of forecasting the level of informed trading at merger announcements by permitting liquidity traders to adjust their trading based upon signals from informed traders. Informed traders typically take advantage of their knowledge of forthcoming mergers by trading heavily at announcement. For cash mergers, they respond to a positive signal by purchasing stock, and for stock mergers, they respond to a negative signal by selling stock. In response, exchanges (market makers) set wider spreads (charge higher transaction fees) for informed buyers. Uninformed traders are subject to such excessive fees unless they can accurately predict the period during which such fees are charged.

This paper proposes a technique by which uninformed traders may make predictions by creating a vector autoregressive framework that links informed and liquidity trading through price changes.

For cash mergers, transaction fees remained excessive for days −1 to +1. For stock mergers, fees remained high on days −1 to +1, started declining on days 2 and 3, and vanished on days 4 and 5.

Most theoretical models of informed trading have viewed informed trading and liquidity trading as tangentially linked. This study finds a direct link between these two trading activities.

Uninformed traders may wish to limit their trading until after day +1 for both types of mergers.

This paper defines the time period during which transactions costs for traders are at the maximum level. Short sellers have more information about the direction of stock movements and may sell during days of informed selling set forth by this study and repurchase stock afterwards.

What information do members of Congress (politicians) use when they trade stocks? The purpose of this paper is to attempt to answer this question by investigating the relationship between an aggregate measure of trading by members of Congress (aggregate congressional trading) and future stock market returns.

The authors follow the empirical framework used in academic work on corporate insiders. In particular, they aggregate 61,998 common stock transactions by politicians over the 2004–2010 period and estimate time series regressions at a monthly frequency with heteroskedasticity and autocorrelation robust t-statistics.

The authors find that aggregate congressional trading predicts future stock market returns, suggesting that politicians use economy-wide (i.e. macroeconomic) information in their stock trades. The authors also present evidence that aggregate congressional trading is related to the growth rate of industrial production, suggesting that industrial production serves as a potential channel through which aggregate congressional trading predicts future stock market returns.

To the best of the authors’ knowledge, this study is the first to document a relationship between aggregate congressional trading and stock market returns. The media and scholarly attention on politicians’ trades have mostly focused on the question of whether politicians have superior information on individual firms. The results from this study suggest that politicians’ informational advantage may go beyond individual firms such that they potentially have superior information on the overall trajectory of the economy as well.