Boongasame, Laor

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Boongasame, Laor
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Boongasame, Labor
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laor.bo@kmitl.ac.th

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    Item type:Publication,
    An Extension of Laor Weight Initialization for Deep Time-Series Forecasting: Evidence from Thai Equity Risk Prediction
    (2025-09-01)
    Petchpol, Katsamapol
    ;
    Boongasame, Laor
    This study presents a gradient-informed proxy initialization framework designed to improve training efficiency and predictive performance in deep learning models for time-series forecasting. The method extends the Laor Initialization approach by introducing backward gradient norm clustering as a selection criterion for input-layer weights, evaluated through a lightweight, architecture-agnostic proxy model. Only the numerical input layer adopts the selected initialization, while internal components retain standard schemes such as Xavier, Kaiming, or Orthogonal, maintaining compatibility and reducing overhead. The framework is evaluated on a real-world financial forecasting task: identifying high-risk equities from the Thai Market Surveillance Measure List, a domain characterized by label imbalance, non-stationarity, and limited data volume. Experiments across five architectures, including Transformer, ConvTran, and MMAGRU-FCN, show that the proposed strategy improves convergence speed and classification accuracy, particularly in deeper and hybrid models. Results in recurrent-based models are competitive but less pronounced. These findings support the method’s practical utility and generalizability for forecasting tasks under real-world constraints.
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    Enhancing Predictive Capabilities for Identifying At-Risk Stocks Using Multivariate Time-Series Classification: A Case Study of the Thai Stock Market
    (2025-01-01)
    Petchpol, Katsamapol
    ;
    Boongasame, Laor
    This study proposes a multivariate time-series classification approach using deep learning to predict stocks likely to be flagged by the Market Surveillance Measure List in the Thai stock market. Formulated as a binary classification problem, the model distinguishes At-Risk and Normal stocks based on two primary datasets: End-of-Day stock prices and Market Surveillance Measure List records, incorporating trading volumes and technical indicators. To address data imbalance, concept drift, and long-term dependencies, the framework integrates feature engineering, cost-sensitive learning, and rolling window training. Experimental results show deep learning models significantly outperform traditional baseline methods in capturing financial risk patterns. The study identifies models that effectively balance predictive accuracy with computational efficiency, with performance varying based on forecasting horizons. Despite improvements from specialized techniques, the study identifies challenges in long-term financial risk prediction. These findings support market surveillance, algorithmic trading, and portfolio risk management, with future work exploring explainable AI, adaptive learning, and alternative data sources to enhance interpretability and long-term forecasting.