Projects & Research

A showcase of consulting engagements, financial models, and strategic implementations.

Technology & AI

An Integrated System for Gold Market Prediction and Risk Monitoring

Context & Problem

Gold prices are driven by complex cross-market dynamics, making them difficult to forecast and interpret. Traditional approaches lack integrated modeling and fail to capture regime shifts and systemic risk.

Methodology

I architect a multi-layer modeling system integrating supervised, unsupervised, and anomaly detection models on high-dimensional cross-market data, enabling dynamic forecasting and real-time scenario simulation.

Impact & Results

The system provides robust, forward-looking insights by combining prediction, regime identification, and risk monitoring. It improves forecast accuracy, detects market shifts, and enables real-time, scenario-based decision-making.

Technology & Business

How Specificity Shapes Stock Price Volatility — Evidence from the Stock Exchange of Thailand

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Context & Problem

This research examines whether the specificity of MD&A narratives helps reduce stock price volatility in the Thai market. While listed firms disclose more information than ever, much of it remains generic or boilerplate, increasing investors’ interpretation costs and uncertainty. The study focuses on whether clearer, more firm-specific disclosure can improve market understanding and reduce volatility.

Methodology

The study builds a panel of 1,504 quarterly MD&A reports from 31 SET50 firms during 2012–2024. Using an NLP and LLM-based framework, each sentence is classified by specificity, tone, forward-looking content, and disclosure topic. These textual signals are then linked with market and financial data through Two-Way Fixed Effects panel regressions to test their impact on future stock volatility.

Impact & Results

The findings show that more specific MD&A disclosure significantly reduces stock price volatility, especially over the longer inter-announcement period. Topic-level analysis shows that specificity matters most in business operations and external trends or uncertainties, where detailed explanations help investors better assess firm fundamentals and risks. The results suggest that market stability depends less on the amount of disclosure and more on the clarity, precision, and usefulness of the information provided.

RESEARCH

The Influence of Finfluencers on Investment Attitudes: A Cross-Generational Analysis and Psychological Mechanisms

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Context & Problem

Evaluate how Finfluencer credibility (expertise, trustworthiness, and attractiveness) shapes investors' cognitive, affective, and behavioral attitudes. The study addresses the growing reliance on social media for financial advice and the associated risks across different generations.

Methodology

Survey data analysis using a sample of 232 respondents. The study utilized Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM). A multi-group SEM analysis was applied to compare structural relationships and mechanisms across Gen X, Gen Y, and Gen Z.

Impact & Results

Finfluencers heavily impact cognitive learning, but direct behavioral action is low. Trustworthiness is the strongest driver of behavior (total effect beta=0.528.) The CAB model is confirmed with Affective driving Behavioral at beta=0.99. Gen X is the least influenced group.

Strategic Consulting

Lalamove Go-to-Market Strategy (CCD 2024)

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Context & Problem

Design a data-driven go-to-market and pricing strategy for Lalamove's B2B logistics expansion in Southeast Asian SME markets, addressing platform density and driver acquisition challenges.

Methodology

Competitive landscape mapping, TAM/SAM/SOM analysis, customer segmentation using logistics spend data, surge pricing elasticity modelling, and driver incentive funnel design.

Impact & Results

Proposed tiered subscription model projected to increase ARPU by 35%. Driver retention program design reduced modelled churn by 22%. Identified 3 underserved SME verticals (F&B, dry goods, pharma) for targeted acquisition.

Trade Policy Research

Non-Tariff Measures (NTMs) on Thai Rice Exports

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Context & Problem

Evaluate how international Non-Tariff Measures (sanitary standards, labelling, import quotas) uniquely affect Thailand's rice export volume and competitiveness in key markets (EU, Japan, US).

Methodology

Panel data analysis using NTM severity indices from UNCTAD TRAINS database cross-referenced with FAO trade flow data across 5 importer markets. Gravity model specification with fixed effects.

Impact & Results

SPS measures account for 40% of trade cost burden. Thai Hom Mali rice has 18% pricing premium resilience even under stringent NTMs, but volume-based quotas in Japan suppress export growth by an estimated 12% annually.

Financial Research

Macroeconomic Analysis of SET Index

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Context & Problem

Identify the core macroeconomic drivers influencing the volatility and valuation of the SET (Stock Exchange of Thailand) Index through empirical analysis.

Methodology

Multi-variable OLS regression combining GDP growth, inflation, interest rates, currency fluctuations, and foreign capital flows against SET index monthly returns. Robustness checks via heteroskedasticity-corrected standard errors.

Impact & Results

Identified 3 dominant predictors: USD/THB exchange rate, domestic policy rate changes, and current account balance. Model achieved R² of 0.71, suggesting 71% of SET variance is explained by the macro factors studied.

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