Yokrattanasak, Jiraphat

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Yokrattanasak, Jiraphat
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jiraphat.yo@kmitl.ac.th

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    Stock Movement Prediction Using Machine Learning Based on Technical Indicators and Google Trend Searches in Thailand
    (2023-03-01)
    Saetia, Kittipob
    ;
    Yokrattanasak, Jiraphat
    Machine learning for stock market prediction has recently been popular for identifying stock selection strategies and providing market insights. In this study, we adopted machine learning algorithms to analyze technical indicators, and Google Trends search terms based on the Thai stock market. This study uses three datasets, which are technical indicators, Google Trends search terms, and a combination of the two. The objectives were to study and identify the factors in stock selection, develop and evaluate portfolio selection models using keyword proxies from the three datasets mentioned, and compare the performance of the selected algorithms. In the prediction process, we discovered that the combination of technical indicators and Google Trends search terms while applying Logistic Regression, Random Forest, and Extreme Gradient Boosting (XGBoost) exhibited the highest ROC curves. For success prediction rate and annualized return, Random Forest and XGBoost were almost similar but still different. While XGBoost performs well during a period of market critical conditions (COVID-19), Random Forest performs marginally better than XGBoost during normal market conditions in terms of average success rate.
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    Machine Learning-Driven Portfolio Optimization Using Money Flow Index-Based Sentiment Signals
    (2026-05-01)
    Singsiri, Prapassara
    ;
    Yokrattanasak, Jiraphat
    Market indices serve as a benchmark for performance comparison, guide asset allocation decisions, and reflect overall market sentiment and economic conditions, thereby influencing investment strategies by representing a segment of the market. Unquestionably, investor sentiment impacts price movement. In this paper, the objectives were to study the effectiveness of the Money Flow Index (MFI) in enhancing the performance of predictive analysis by capturing market psychology, developing an investment strategy, and analyzing the performance of the method mentioned. This study applies machine learning algorithms with technical indicators and optimizes portfolio allocation based on three notable market indices in Southeast Asia (SEA): SET50 in Thailand, STI in Singapore, and VN30 in Vietnam. Firstly, we combined technical indicators with machine learning—Support Vector Classifier (SVC), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—by comparing datasets with and without MFI over the period from 2013 to 2023. The results showed that XGBoost with MFI delivered the best predictive performance across three indices. These findings indicate that MFI significantly enhances prediction accuracy, even during volatile market conditions (COVID-19). Additionally, the predictions were integrated into the Markowitz Mean-Variance (MV) model to construct an optimal portfolio, which was then benchmarked against an equal-weight portfolio (1/N). Ultimately, the findings demonstrate that incorporating the machine learning predictions into the MV framework efficiently generates wealth.
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    Comparison between two different cardiovascular models during a hemorrhagic shock scenario
    (2020-07-01)
    Curcio, Luciano
    ;
    Cusimano, Valerio
    ;
    D'Orsi, Laura
    ;
    Yokrattanasak, Jiraphat
    ;
    De Gaetano, Andrea
    Hemorrhagic shock is a form of hypovolemic shock determined by rapid and large loss of intravascular blood volume and represents the first cause of death in the world, whether on the battlefield or in civilian traumatology. For this, the ability to prevent hemorrhagic shock remains one of the greatest challenges in the medical and engineering fields. The use of mathematical models of the cardiocirculatory system has improved the capacity, on one hand, to predict the risk of hemorrhagic shock and, on the other, to determine efficient treatment strategies. In this paper, a comparison between two mathematical models that simulate several hemorrhagic scenarios is presented. The models considered are the Guyton and the Zenker model. In the vast panorama of existing cardiovascular mathematical models, we decided to compare these two models because they seem to be at the extremes as regards the complexity and the detail of information that they analyze. The Guyton model is a complex and highly structured model that represents a milestone in the study of the cardiovascular system; the Zenker model is a more recent one, developed in 2007, that is relatively simple and easy to implement. The comparison between the two models offers new prospects for the improvement of mathematical models of the cardiovascular system that may prove more effective in the study of hemorrhagic shock.