market data

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.

Should you invest in emerging markets? Our emerging market investment guide covers the benefits, risks, and strategies to empower you with data-based decisions.

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.

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.

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.

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

Discover how TEJ’s ex-dividend event data and financial indicators help forecast stock rebound probability. Learn to identify high-yield Taiwan stocks with stronger post-dividend performance using TEJ’s point-in-time quantitative datasets.

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. 

The Zipline engine, integrated within TQuant Lab, offers a high-quality and realistic backtesting framework. It leverages four core functions—initialize、handle_data、analyze、run_algorithm——to construct a comprehensive simulation environment. These components enable trading strategies to dynamically adjust to market conditions, incorporating elements such as slippage and transaction costs to ensure that backtest results closely reflect real-world performance. This article begins with a brief overview of the four key components and their applications. It then focuses in detail on the initialize function, exploring its specific configurations and role within the backtesting process.

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