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.
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Quant Research
Derwood Chase’s Growth Momentum Stock-Picking Strategy: The Intersection of Value and Momentum
Discover how Derwood Chase’s value-momentum strategy—favoring low P/E stocks with strong price trends—delivers long-term outperformance in Taiwan’s market. Backtest results show strong alpha and lower drawdowns, proving the power of disciplined factor investing.
Event & Alternative Signals
Forecasting Dividend Rebound Probability with Ex-Dividend Event Studies
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.
Quant Research
The Wisdom of Blue-Chip Stocks: Howard Rosman’s Prudent Path to Wealth
Rothman’s core investment philosophy is to “buy right and hold tight” or “buy strong and hold long.” He emphasizes selecting financially sound companies with stable and growing earnings, purchasing them at the right price, and holding them patiently for the long term. Without frequent portfolio adjustments, investors can achieve strong long-term returns. This simple yet resolute investment approach reflects Rothman’s practical wisdom and provides a clear, historically validated foundation for the strategy tested in this study.
Factor Investing
Factor Strategy – Idiosyncratic Volatility | Part 2
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.
Factor Investing
Factor Research – Idiosyncratic Volatility | Part 1
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.
Quant Research
Michael Murphy’s Risk Assessment Rules for Investing in High-Tech Stocks
With the rapid development of the high-tech industry, technology stocks have increasingly become the focus of the market. While these stocks offer significant growth potential, they also come with high volatility and substantial investment risk. Investors seeking high returns may face major losses if they fail to properly assess the associated risks. Therefore, effectively measuring and managing the downside risk of high-tech stocks has become a crucial component of sound investment decision-making.
Quant Research
Charles Brandes’ Value Investing Principles : Building a Portfolio with a Margin of Safety
In the field of investing, business cycles have always served as an important reference. Whether it’s fluctuations in the macroeconomy or the ups and downs of corporate earnings, these cycles play a crucial role. Charles Brandes, a distinguished disciple of Benjamin Graham, founded Brandes Investment Partners in 1974 and has since grown its assets under management from $130 million to over $75 billion. The firm’s Brandes Global Equity Fund achieved an impressive 20-year annualized return of 17.91%, significantly outperforming the MSCI World Index, and has received Morningstar’s five-star rating along with numerous international awards. Another flagship product, the AGF International Value Fund, has also demonstrated outstanding long-term performance. Brandes himself has been repeatedly ranked among the world’s top fund managers.
Industry Insights
AI Boom Benefits Taiwan: Can Taiwanese IC Design Firms Sustain Their Competitive Edge Amid China’s Semiconductor Autonomy? (Part 2)
In Part 1, we discussed the policy background driving the development of China’s IC design industry, including a comparison of the current status and trend analysis of selected IC firms in both Taiwan and China. In this section, we will further explore the development of the IC design industry from several perspectives, including a comparison of revenue trends between Taiwan and China. Additionally, we will examine the top 10 Taiwanese IC design companies with the highest and lowest R&D investments during the first three quarters of 2024, analyzing the differences in their R&D resource allocation. With the latest and most insightful analysis of the IC industry, you will not miss the hottest trends covered here.
Quant Research
From Business Cycle Indicators to Asset Rotation: A Quantitative Strategy to Avoid Bear Markets
In the field of investment, the “business cycle” has always been an important reference point. Fluctuations in the overall economy, corporate earnings, and market sentiment all show distinct characteristics during different stages of the cycle. Therefore, being able to grasp the movements of the business cycle can help investors more accurately adjust their asset allocations and gain a relative advantage in the market.
Industry Insights
AI Boom Benefits Taiwan: Can Taiwanese IC Design Firms Sustain Their Competitive Edge Amid China’s Semiconductor Autonomy?(Part 1)
With the development of AI applications in recent years, many industries have benefited from it, and Taiwan’s semiconductor industry is no exception. However, as the Chinese government continues to follow up and use policies to support local industries, the pressure on Taiwan’s IC design companies is increasing day by day. How will Taiwanese manufacturers deal with the threat posed by the rapid rise of Chinese companies? This article is divided into two parts. The first part covers the policy background of China’s IC design industry development, and includes a comparison of the current status and trend analysis of selected IC firms in Taiwan and China.
Quant Data Science
【TQuant : From 0 to 1 – Day 4】 Core Architecture of Backtesting: What are the key settings for Initialize?
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.
Quant Data Science
[TQuant from 0 to 1 – Day 3] Building a Comprehensive Investment Data Perspective: Stock Pool Screening and Data Retrieval with TejToolAPI
Preface In financial investment, mastering accurate and comprehensive data is an indispensable skill, and effectively managing stock pools and acquiring stock price data is the key to unlocking this skill. With the tools and APIs provided by TQuant Lab, we can easily define screening criteria, quickly establish stock pools that meet specific requirements, and retrieve […]