ChatGPT and Stress

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

Bangkok Traffic Congestion, Stressed Investors, and Thai Stock-Market Returns

Stress influences decision making. Stressed investors may trade in concert, driving stock market returns in a certain direction. This study examines the effect of Bangkok's traffic-induced stress on Thai stock market returns. The average Longdo traffic index during morning rush hours was used as the proxy for the level of stress. As Bangkok traffic affects only local investors, this study measures returns using the return on the Market for Alternative Investment (mai) index. Local investors have an average 96.96% share of the mai stocks’ trading volume. The sample data were taken from the period beginning on January 4, 2012, and ending on April 2, 2020. A test based on the artificial Hausman regression indicates that error-in-variable and omitted-variable problems are present in the estimation. Therefore, the generalized method of moments (GMM) regression—an instrumental variable (IV) regression, together with Racicot and Théoret’s (2010) two-step IVs, were chosen over the traditional ordinary least squares regression for this study. The IVs are informative and valid, with informativeness and validity R2 values of 0.9888 and 0.0000, respectively. The slope coefficient of stock returns on the traffic index was found to be negative and significant. Traffic-induced stress can drive stock market returns. Net selling by local institutional investors explains the significant traffic-induced stress effect in the stock market

Weather, Investor Sentiment, and Stock Returns in the Stock Exchange of Thailand

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

Weather-Driven Stock-Return Correlations

  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

(COVID-19)-Induced Flight to Quality

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

Gambling Attention and Retail Trading Volume

Retail investors pay limited attention to alternative gambling activities. More-attentive activities increase, whereas less-attentive activities decrease. However, attention is unobservable. Previous studies proxy gambling attention based on representative gambles, such as lotteries. These proxies incorporate general gambling and representative-gambling attention. Thus, previous studies have reported net effects. This study analyzes the effects of gambling attention on the trading of retail investors in the Stock Exchange of Thailand. Lotteries served as representative gambles. Gambling attention is decomposed into general gambling and lottery-specific components, enabling the study to separately estimate the effects of each component. Lotteries in Thailand offer fixed prizes. However, traditional proxies are not applicable. This study measures attention using the Google search volume index on a lucky-number query. The query is based on a superstitious belief that is unique to the Thai market. Using daily observations from August 6, 2008, to June 30, 2022, which totaled 3,388 observations, this study establishes that gambling attention has a net negative effect. When attention is decomposed, its general gambling and lottery-specific components exhibit positive and negative effects, respectively. Furthermore, the effect on the buying side was stronger than that on the selling side. During the COVID-19 pandemic, the lottery-specific effects became positive. Retail investors responded to lottery-specific attention through stock trading

Gambling-Motivated Market Attention and Stock Market Volatility

Retail investors show gambling preferences and pay greater attention to the market than individual stocks. Previous studies report a positive and significant relationship between market attention and volatility. This relationship results from the joint effects of attention to investment-motivated and gambling-motivated components. However, the separate roles of these two components have not yet been examined. Hence, this study applied principal component analysis to identify the gambling-motivated component from market attention and gambling-related variables. The investment-motivated component is the regression residual of the market’s attention paid to the gambling-motivated component. This study linearly relates these two components to volatility. The generalized method of moments regression was used to resolve endogeneity problems and biased estimates. The Google search volume index is a proxy for unobserved retail investors’ market attention. Using a daily sample of the Thai market from August 6, 2008, to September 30, 2022 (a total of 3,450 observations), this study found a positive relationship between market attention and stock market volatility. This relationship results from the positive effects of both investment-motivated and gambling-motivated components. Attention to gambling is more influential than attention to investment. The explanatory powers of gambling-attention and investment-attention for volatility were 81.33% and 18.67%, respectively. These effects were less pronounced during the COVID-19 pandemic

An Improved Linear Projection Approach to Estimate Daily Real Yields and Expected Inflations in a Latent Multifactor Interest Model

The study improves upon the linear projection approach to estimate daily real yields and expected inflations in a latent multifactor interest rate model. It estimates the projection coefficients for inflation factor exclusively from monthly inflation data, rather than from both inflation and nominal yield data, in order to lessen biasedness. Because these coefficients are the same as those in the daily model, the study uses them with daily nominal yield data to estimate the remaining parameters. Using Thailand’s data from March 1, 2001 to August 30, 2013, the study finds that the improved model can fit the nominal yields well. The term structure estimate of real yields has a normal shape, while that of expected inflations is flat. The inflation premiums are significant statistically and economically. They are ten times the ones reported in the past. Inflation premiums cannot be ignored in economic analyses for Thailand

Reactions of Thailand’s Stock Market to the 2020 U.S. Presidential Election

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

Instrumental-Variable Estimation Of Bangkokweather Effects In The Stock Exchange Of Thailand

The incorrect fxed-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. Artifcial Hausman instrumental-variable regressions successfully improve the quality of the analyses for ordinary least squares regressions when signifcant EIV problems are identifed and the regression results in a conflict. The study fnds signifcant air pressure and rainfall effects and empirically shows that the temperature effects reported by previous studies were induced by the fxed-effect assumption and are therefore incorrect