Insights
In-depth research and data-driven insights on quantitative finance, factor investing, risk, and ESG from the TEJ research team.
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Fundamental Factor Research: Monthly Revenue Information – part1
The Taiwan equity market possesses a rare institutional advantage globally: under the Securities and Exchange Act, listed companies are required to announce and report their operational results for the preceding month by the 10th of each month (Exception: starting from FY2026, insurance companies and entities with insurance subsidiaries may extend their disclosure deadline to the 15th of each month). This is commonly referred to in the market as "Monthly Revenue".
Factor Research – Tracking Smart Money Footprints via Foreign Institutional Concentration – QFII Part 1
Track QFII ‘smart money’ footprints in Taiwan large-cap stocks! Learn how the Foreign-Institutional Trading Concentration (conc_qfii) factor predicts returns.
Empirical Research on Behavioral Factors in the Taiwan Stock Market: A Case Study of the Share Distribution
In an AI-driven Taiwan stock market, mastering chip distribution (ownership structure) is the key to profitability. This study delves into the Share Distribution data from the TDCC, transforming 15 tiers of shareholding data into behavioral finance factors such as investor attention, opinion dispersion, and retail speculation. By utilizing Fama–MacBeth two-stage regression and the alphalens-tej quantitative tool, we precisely validate the predictive power of psychological biases on stock returns, providing investors with actionable Alpha strategies and robust risk management solutions.
Insights
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Factor Investing
Discovering Investment Factors through Point-in-Time Audited Financial Database
This study employs TEJ’s Point-in-Time Audited Financial Database to construct a composite factor for stock selection in Taiwan’s equity market. By preserving financial data exactly as available at each historical moment, the framework avoids look-ahead bias and ensures empirical reliability. We find that higher-ranked portfolios deliver significant short-term excess returns, while predictive power weakens over longer horizons. The results highlight the practical value of Point-in-Time financial data for quantitative factor investing and underscore its role in building replicable, data-driven investment strategies.
Quant Data Science
[TQuant From 0 to 1 – Day 5] Introduction to Order Placement Methods in the TQuant Lab Backtesting System
In TQuant backtesting and live trading, order functions serve as the central link between strategy logic and capital management. Choosing the right order method not only makes the code cleaner and easier to read but also improves the efficiency of risk control and portfolio rebalancing. TQuant provides order functions across three dimensions — share quantity, capital amount, and portfolio weight. For each dimension, there are two variants: a basic order and a target order. In total, this gives us six order placement methods. In the following sections, we will explain the features, parameters, and recommended applications of each.
Market Knowledge & Data Guides
TEJ Point-in-Time Audited Financial Database – Rejecting “Peek-ahead” Backtesting
TEJ PIT Audited Financial Database eliminates look-ahead and survivorship bias with Point-in-Time data, full version retention, IFRS alignment, and 300+ ready-to-use ratios—delivering reliable backtesting and faster strategy development.
Quant Research
When Others Fear, I Enter: Anthony Melia’s Contrarian Strategy for Winning in the Market
In financial markets, Contrary Thinking is a timeless strategic wisdom. It stems from a simple yet profound observation: when most people are overly optimistic, the market is often overheated; when the crowd falls into fear, it may actually present a buying opportunity. However, contrarian investing has long remained at the level of a proverb, lacking concrete and quantifiable standards of action, making it difficult to implement in practice.
Factor Investing
Jabli–Watson Factor Model: The Growth Formula for Quantitative Momentum Investing
Gabriel Watson is a well-known American growth-momentum portfolio manager. In his early career, he worked at Morgan Stanley Investment Management and William O’Neil & Co., where he accumulated extensive market research experience. Since joining Black Rose Capital Management in 1998, Watson has broken away from the confines of traditional value investing and developed a systematic, rule-based stock-picking method centered on “revenue momentum and price strength.” He calls this framework “The Machine.” In an environment characterized by rapid information flow and swift capital rotation, this approach has enabled him to capture the powerful upward moves of leading stocks.
Market Knowledge & Data Guides
Investing in Emerging Markets: Opportunities, Risks & Strategy
Should you invest in emerging markets? Our emerging market investment guide covers the benefits, risks, and strategies to empower you with data-based decisions.
Market Knowledge & Data Guides
What are Alpha Signals & How are They Used in Trading?
Alpha signals are metrics that show portfolio outperformance potential. Learn how they’re generated and applied in trading, and the challenges of using them.
Factor Investing
Factor Investing Explained: Types of Factors & Strategy Guide
Factor investing targets quantifiable characteristics to improve returns. Explores common factors (macroeconomic and style types) and strategies in our guide.
Industry Insights
Turning Industry Rotation into Alpha: A Quant Backtest Strategy
In our previous article” Shipping Leads, Semiconductors Follow? “we identified a recurring pattern in Taiwan’s market: rallies in the shipping sector often precede gains in semiconductor stocks. This article builds on that insight by transforming the observed rotation sequence into a quantitative investment strategy.
Factor Investing
Factor Strategy – Capital Gain Overhang | Part 2
In the previous study, we examined the Capital Gain Overhang (CGO) factor, designed to capture the behavioral bias known as the Disposition Effect. By measuring the gap between current market prices and investors’ average cost basis, CGO quantifies unrealized gains and losses at the market level. Empirical tests in Taiwan’s equity market confirmed that CGO is a meaningful predictor of future returns: high-CGO stocks consistently outperformed low-CGO stocks, generating significant positive alpha beyond standard Fama–French models, especially over medium- to long-term horizons.
Factor Investing
Factor Research –Capital Gain Overhang | Part 1
The origins of the momentum anomaly have long been debated, with multiple competing explanations. Among them, one of the most influential behavioral interpretations attributes momentum to the Disposition Effect, a systematic bias in investor decision-making. This article focuses on the Capital Gain Overhang (CGO) factor, specifically designed to quantify this behavioral bias. Using the Taiwan equity market as a case study, we examine CGO’s predictive power as a stock selection indicator and evaluate its practical value through empirical analysis.
Quant Research
Shipping Leads, Semiconductors Follow? A Data-Driven View on Taiwan’s Sector Rotation
Analyze industry rotation between Taiwan’s shipping and semiconductor sectors using momentum and valuation factors. This study reveals how factor-based strategies capture cyclical shifts—and why semiconductors ultimately outperformed over time.