Factor Investing

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.

This study examines whether incorporating the Short Interest Ratio (SIR) can improve the performance of a 52-week high momentum strategy in Taiwan. By comparing a baseline momentum model with two SIR-enhanced versions—one using SIR as a filter and another integrating it into a composite score—we find consistent gains in returns, lower volatility, and reduced drawdowns. The results show that SIR strengthens momentum strategies by identifying stocks under institutional short-selling pressure.

Factor investing targets quantifiable characteristics to improve returns. Explores common factors (macroeconomic and style types) and strategies in our guide.

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.

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.

TEJ showcases its Factor Library and Earnings Call Transcripts at Neudata Hong Kong 2025, highlighting Taiwan-focused quant data for institutional strategies.

Building on the statistical foundation presented in Part 1, this article explores how Idiosyncratic Volatility (IVOL) can be effectively applied in investment strategy design. We present two categories of approaches: a single-factor sorting model and a set of filter-enhanced momentum strategies. Through robust backtesting across two decades of Taiwan stock market data, we demonstrate how IVOL can improve risk-adjusted performance when used as a portfolio filter—especially when combined with momentum or dividend-based signals.

In recent years, the low-volatility anomaly has gained widespread attention for challenging traditional asset pricing theory. This article takes a closer look at one key driver behind the anomaly—Idiosyncratic Volatility (IVOL)—through a comprehensive analysis of the Taiwan stock market. Using point-in-time data from the TEJ Factor Library, we investigate the statistical behavior of IVOL, its relationship with stock characteristics, and its implications for cross-sectional return prediction. 

TEJ Factor Library provides a comprehensive set of pre-calculated quantitative factors designed for the Taiwan equity market. TEJ Factor Library covers over 100 localized factors across 11 major categories, enabling investors and researchers to efficiently conduct factor-based analysis, backtesting, and strategy development. Unlike raw data inputs, TEJ Factor Library delivers ready-to-use signals, significantly reducing the time required for data cleaning, factor construction, and alignment.

Discover how TEJ's Taiwan Market Data captivated global investors at the Neudata London Summit 2025, offering unique insights into Asia's financial landscape.

In investment decision-making, price-volume factors are essential for investors to gain insights into market behavior. The relationship between price and trading volume supply and demand dynamics of an asset also reveals capital flows and shifts in market sentiment. These factors play a crucial role in capturing short-term opportunities and identifying potential risks in asset allocation.

In recent years, as the stock prices of popular AI companies continue to reach new highs, investors are increasingly focused not only on these companies' operational status but also on the trading behavior of their insiders. Company insiders have more information compared to external investors, giving them an informational advantage when trading the company's stock.