Particulate matter 2.5 (PM2.5) is considered the most dangerous air-polluting particle, causing premature death and inducing severe mental and physical diseases worldwide. PM2.5 affects stock market returns directly via the fundamental channel and indirectly via the behavioral channel. This study examines the effects of Bangkok’s PM2.5 pollution on the return on the Market for Alternative Investment index portfolio using a multivariate mediation analysis. Attention, awareness, mood, sentiment, and stress, the mediating variables known to influence investors’ behavior, were considered jointly and explicitly in the model. This study is the first to introduce stress as a behavioral mediator. The roles and effects of the behavioral mediators were identified, measured, and compared. Using daily data from August 1, 2016, to November 30, 2023, this study found that the total, direct, and indirect effects were not significant. Stress was the only behavioral mediator that significantly and positively contributed to the indirect effects. This result remains unchanged for different estimation techniques, sample periods, representative stock returns, and PM2.5 occurrence times.

ChatGPT is an artificial-intelligence chatbot. In addition to comprehending an image like a text prompt, it can understand complex prompts and exhibit human-level performance. It became the fastest-growing application in history, acquiring one million users within five days of release. However, despite its potential to improve productivity, job satisfaction, self-efficacy, and wages, it causes stress to individuals. This study examines the relationship between stress and ChatGPT in Thailand. Although stress is a severe health problem in the country, ChatGPT cannot be avoided as this application helps support the country’s targeted digital technology industry. The study uses a proxy for unobserved stress levels and ChatGPT concerns using Google’s search volume indexes. Based on daily samples from December 10, 2015, to May 31, 2023, regression analysis revealed that ChatGPT significantly increased stress levels. However, during the development sub-sample, the stress level decreased. Stress escalated in the early- and viral-use sub-samples, where the effect for the viral-use sub-sample was significantly higher. In the COVID-19 pandemic sub-sample, the effect was non-significant. The causality of ChatGPT in stress was confirmed by the contemporaneous-causality test.

A Kalman filtering regression model is proposed to resolve nonstationarity problems commonly found in certain performance variables, e. g. , trading volume, of event study analyses. Resolution is possible when the expected performance variables are allowed to move according to random walk processes. The model can be used for cases in which performance variables have deterministic or stochastic trends. The model is applied to examine the trading turnover behavior in the Thai stock and bond markets in the time around the military coups of 2006 and 2014. The model is successful; it passes validity tests, namely, the nonstationarity and parameter constancy tests. The findings suggest that the results reported by previous studies that failed to treat the stationanty problems are misleading.

Previous studies have found the significant adverse effects of coronavirus disease 2019 (COVID-19) on stock returns and volatility The effects varied with the confirmed cases and deaths However, the extent of the effects have never been measured exactly This study proposes a measurement model for the COVID-19 effects In the proposed model, stock returns in the COVID-19 period are weighted averages of pre-COVID-19 normal returns and COVID-19-induced returns The effects are measured by the contributing weights of the COVID-19-induced returns Kalman filtering is used to estimate the model for the world and Chinese markets, in combination with 10 markets – five most affected countries (United States, India, Brazil, Russia, and France) and five best recovering countries (Hong Kong, Australia, Singapore, Thailand, and South Korea) The sample returns are daily, obtained from the closing Morgan Stanley global investable market indexes The full period is from September 24, 2018, to October 30, 2020, whereas the COVID-19 period is from November 18, 2019, to October 30, 2020 The contributing weights are significant and close to 100% for all markets The COVID-19-induced returns replace the pre-COVID-19 normal returns;they are negatively auto-correlated and highly volatile The COVID-19-induced returns are new normal returns in the COVID-19 period © Copyright: The Author(s) This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons org/licenses/by-nc/4 0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited

During crises, investment re-allocation from risky to safe assets, constitutes a flight to quality market environment. This study investigates the flight to quality in Thailand from risky stocks to safe government bonds. It describes returns using the modified, conditional regression model, and extracts the unobserved abnormal returns using the Kalman filtering technique. Estimates of abnormal returns were used in tests for the Granger causality of stocks to bonds, and for investigating the significance of the contributions of abnormal returns to a decreasing correlation. Flight to quality implies these test hypotheses. The data are returns representative of stocks listed on the Stock Exchange of Thailand and of bonds registered on the Thai Bond Market Association. The full period runs from August 28, 2018 to June 30, 2020, whereas the COVID-19 period covers November 18, 2019, to June 30, 2020. The return correlation in the COVID-19 period is more negative than that in the pre-(COVID-19) period. Stocks Granger cause bonds. The contribution share of COVID-19 to the falling correlation is 89.2080%. While the joint Wald-test for the non-significance of COVID-19’s contributing correlations yields a p-value of 0.1144, the impulse response analyses suggest that they are all significant. Thailand has experienced flight to quality during the COVID-19 crisis. © 2021, ABAC Journal. All Rights Reserved.

The U.S. presidential election is one of the most important events in the world, to which the stock markets of other countries react. The 2020 U.S. presidential election was unique due to delayed vote counts, the incumbent president’s false election-fraud claims, and the violent riots at the U.S. Capitol Building. In this study, the reactions of Thailand’s stock market are examined using the event-conditioning method for event-study analyses. The sample period ranges from August 6, 2019, to January 28, 2021. The period overlaps the period of the COVID-19 pandemic and Thailand’s youth protest, thus constituting parameter-instability and confounding-event problems. This study relies on the international capital asset pricing model to mitigate the parameter-instability problem, as it constructs event-dummy control variables to resolve the confounding-event problem. The data comprises daily log returns of Morgan Stanley Global Investable Market Indices portfolios for Thailand and the world, in excess of the 1-month U.S. treasury bill rate. The reactions are found to be significant for the election, the final election results, and the presidential inauguration; they are non-significant for the Capitol riots and the incumbent president’s false claims. For the same events, there is dissimilarity between the reactions of the Thai and U.S. markets.

Gambling negatively affects the economy, and it brings unwanted financial, social, and health outcomes to gamblers. On the one hand, unemployment is argued to be a leading cause of gambling. On the other hand, gambling can cause unemployment in the second-order via gambling-induced poor health, falling productivity, and crime. In terms of significant effects, previous studies were able to establish an association, but not causality. The current study examines the time-sequence and contemporaneous causalities between lottery gambling and unemployment in Thailand. The Granger causality and directed acyclic graph (DAG) tests employ time-series data on gambling- and unemployment-related Google Trends indexes from January 2004 to April 2021 (208 monthly observations). These tests are based on the estimates from a vector autoregressive (VAR) model. Granger causality is a way to investigate causality between two variables in a time series. However, this approach cannot detect the contemporaneous causality among variables that occurred within the same period. The contemporaneous causal structure of gambling and unemployment was identified via the data-determined DAG approach. The use of time-series Google Trends indexes in gambling studies is new. Based on this data set, unemployment is found to contemporaneously cause gambling, whereas gambling Granger causes unemployment. The causalities are circular and last for four months.

This study extends the conditional regression model for event study analyses to include sub-events related to the events under investigation. The extended model ensures that all relevant sub-events are included in the event window and their significant effects are not averaged out. The model is applied to analyze the effects of Thailand’s 2019 general election on stock market performance. Information on the election day and the sub-event days before and after the election day contributed to the significant election effects. The inclusion of sub-events in the analysis is important and useful.

Previous studies on the effects of weather-driven moods on stock returns focus on significance tests but do not examine the time behaviors of these effects. This study estimates the vector autoregressive model to examine the time paths of the effects and analyzes whether the effects are temporary or permanent. Using the daily returns on the Stock Exchange of Thailand index portfolio and the weather conditions in Bangkok, this study finds that weather-driven mood effects exist and are permanent. Significant temporary effects are not observed.

This study investigates the behavior of foreign investors in the Stock Exchange of Thailand (SET) in the time of coronavirus disease 2019 (COVID-19) as to whether trading is abnormal, what strategy is followed, whether herd behavior is present, and whether the actions destabilize the market Foreign investors' trading behavior is measured by net buying volume divided by market capitalization, whereas the stock market behavior is measured by logged return on the SET index portfolio The data are daily from Tuesday, August 28, 2018, to Monday, May 18, 2020 The study extends the conditional-regression model in an event-study framework and extracts the unobserved abnormal trading behavior using the Kalman filtering technique It then applies vector autoregressions and impulse responses to test for the investors' chosen strategy, herd behavior, and market destabilization The results show that foreign investors' abnormal trading volume is negative and significant An analysis of the abnormal trading volume with stock returns reveals that foreign investors are not positive-feedback investors, but rather, they self-herd Although foreign investors' abnormal trading does not destabilize the market, it induces stock-return volatility of a similar size to normal trade The methodology is new;the findings are useful for researchers, local authorities, and investors © The Author(s)

Moods affect investors' attention, memory, and capacity to process information. inattentive investors delay the price adjustment process, thus leading to a positive autocorrelation of asset returns. In this study, I investigate the relationship between weather-induced moods and stock-return autocorrelation in the Stock Exchange of Thailandfrom January 2, 1991, to December 29, 2017. Only good moods contribute significantly to return autocorrelation.

COVID-19—the world’s most recent pandemic, has caused economic and financial crises globally. The situation is continually evolving overtime in a series of events, and stock markets must respond immediately to these evolving events with updates of expected cash flows and the real and perceived risks. This study asks how and how early the world and national markets react, and to which event or events in the series. Using the event-study method, based on returns on the world, French, German, Italian, Spanish, U.K., U.S., Chinese, Philippine, and Thai stocks, the study found significant, negative reactions to the disease. The reactions were to COVID-19’s extensive media coverage and pandemic declaration, not to the evolving events and situations when they actually occur.

This study proposed a state-space model that allows time-varying weather effects on asset returns. It resolves the model misspecification of the unrealistic, fixed effect assumption commonly made by previous weather studies. The model was applied to examine the weather effects on Thai government bond returns from July 2, 2001, to December 30, 2015. Kalman filtering was used in the estimation. The study found that the weather effects were time-varying. They were wandering in the early sample period but disappearing in the later period. The effects were not co-integrated with the market’s inefficiency levels.

A well-specified and complete empirical model for weather effects, based on a rigorous noise-trader-risk theory, was developed. Using the daily data on the Stock Exchange of Thailand index portfolio and Bangkok weather variables from February 17, 1992 to December 30, 2016, significant effects of weather on both stock returns and volatility were found. Further investigation revealed that the effect on stock returns was temporary. Because weather effects were driven by sentiment, the significant effect suggested the important role of noise traders in price formation in the Stock Exchange of Thailand.

The behavior of the Thai bond market in the time surrounding the 2006 and 2014 military coups is examined using the event-study method. Unlike the stock and foreign exchange market results, the bond market results are not affected by possible crony capitalism and seasonal international trade demands. Most variables, except for the 2014 abnormal return, reacted positively to these coups. Foreign investors did not panic; they were net buyers even before the coup occurrence dates. The behavior of returns and net foreign volume before the occurrence dates suggests leakage of the information about the planned coups.

The coordinated trading of weather-sensitive investment drives stock returns and links the return correlations with weather variables. This study tested whether the correlations in the Stock Exchange of Thailand can be explained by Bangkok’s weather variables. Using daily data from September 3, 2002, to December 29, 2017, it was found that the correlation of the returns on the Stock Exchange of Thailand 50 and the Market for Alternative Investment index portfolios has a significant relationship with Bangkok’s weather. The significant variables are a subset of those variables that drive return volatility.

Market efficiency evolves with changing market conditions. Moreover, if the conditions are weekday dependent, the efficiency can be day-seasonal. In this study, I test for the day-seasonal efficiency of the Thai stock market and examine how it behaves over time. Using the daily returns on the Stock Exchange of Thailand index portfolio from April 30, 1975, to December 29, 2017, I find that the day-seasonal efficiency exists. However, it disappears as the efficiency of the market improves. The day-seasonal efficiency is empirically explained by the positive feedback strategies. The market has a delayed response to the information from foreign investors’ trading volume.

The incorrect fixed-effect assumption, missing-data problem, omitted-variable problem, and errors-in-variables (EIV) problem are estimation problems that are generally found in studies on weather effects on asset returns. This study proposes an approach that can address these problems simultaneously. The approach is demonstrated by revisiting the effects on the Stock Exchange of Thailand. The sample shows daily data from 2 January 1991 to 30 December 2015. Artificial Hausman instrumental-variable regressions successfully improve the quality of the analyses for ordinary least squares regressions when significant EIV problems are identified and the regression results in a conflict. The study finds significant air pressure and rainfall effects and empirically shows that the temperature effects reported by previous studies were induced by the fixed-effect assumption and are therefore incorrect.

This study proposed a model for setting self-discipline saving rates in a risk-management framework and applied it to Thai income earners. The model involved financial planning, incorporating stochastic lifetime incomes, expenses, savings, and investment returns, together with mortality and morbidity data. The self-discipline saving rate was set so the probability that the bequest was less than the funeral expenses was at a pre-determined, low, acceptable level. The resulting rate was higher for females than for males, and it increased with age. When the rate was possible, the median net bequest of funeral expenses was positive for both females and males of all ages. Therefore, if earners follow the self-discipline saving rule, they are likely to have sufficient funds to cover the expenses of their own funeral.

Weather effects exist, as weather influences investors’ mood, compelling them to raise or lower asset prices. These effects are indirect, as weather affects returns via mood – weather does not directly affect returns. Weather effects are, in fact, weather-induced mood effects. In this study, I estimated a model of weather-induced mood effects in its full form, which was the only way to identify and estimate the model. The model in its reduced from had exactly the same form as did the direct weather-effect model.Using the daily returns on Thailand’s stock index portfolios, this study found that all weather variables – air pressure, cloud cover, ground visibility, rainfall, relative humidity, temperature, and wind speed – significantly affected mood. The mood effects on the returns were time varying; they were wandering and significant in the early sample period – up to 2009 – but disappearing in the later period – from 2010 onward. The findings help to explain why previous studies reported insignificant effects on returns from certain weather variables even though the psychological literature suggested that these variables were important.