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
Quantitative Factor Empirical Study in the Taiwan Stock Market — Broker Consensus Data: Target Price and Investment Recommendation Factors (Part 2)
The 'broker consensus data' examined in this study aggregates target price forecasts and investment recommendation (rating) information from various securities brokerages across the market. By consolidating the collective wisdom of analysts, it aims to eliminate the extreme forecast biases of individual brokers and provide objective forward-looking market expectations. Findings from Part 1 indicated that single factors—such as rating intensity (rating_rank) and breadth metrics (noa2)—are prone to rating clustering and distribution skewness; target price dispersion (disp1) acts as a short-term momentum catalyst; and revision factors suffer from sparse signals that limit their standalone stock selection utility. To overcome the vulnerabilities of single indicators to optimistic bias and idiosyncratic noise, this research implemented factor engineering to construct multi-factor cross-combinations.
Quant Research
Quantitative Factor Empirical Study in the Taiwan Stock Market– Broker Consensus Data: Target Price and Investment Recommendation Factors (Part 1)
Under the theoretical framework of the Efficient Market Hypothesis (EMH), stock prices should fully reflect all available information. However, information asymmetry remains widespread in actual capital markets, and market participants differ in their perception and access to corporate value information. As critical information intermediaries in capital markets, sell-side analysts continuously provide financial forecasts, investment ratings, and target prices through industry research, corporate visits, and financial modeling, exerting a tangible influence on market pricing. Nonetheless, the perspective of an individual analyst may still be influenced by investment banking relationships, herding behavior, or model assumption biases, thereby constraining their predictive power and information quality.
Factor Investing
Empirical Study on Behavioral Factors in the Taiwan Stock Market: An Analysis of Broker Branch Trading Factors (Part 2)
In the previous study, we verified the fundamental chip-analysis principle of "institutional/major players going momentum, retail investors going contrarian" in the Taiwan stock market, and identified the "Major Player Net Buying Strength (by Amount)" factor, which exhibited the highest winning rate and most stable performance. In this second part, we conduct backtests to evaluate the actual profitability of this major player factor across different holding periods, while analyzing how transaction fees and turnover rates impact final trading performance.
Factor Investing
Empirical Study on Behavioral Factors in the Taiwan Stock Market: An Analysis of Broker Branch Trading Factors (Part 1)
The Taiwan stock market is renowned for its high retail investor participation and exceptional market transparency. Every day after the market close, the Taiwan Stock Exchange (TWSE) and the Taipei Exchange (TPEx) publish detailed Broker Branch Trading Database. This dataset itemizes the exact buy and sell volume and transaction values executed by every brokerage branch across all listed equities. Compared with centralized depository shareholder distribution reports published weekly, brokerage branch data offers daily frequency, clear identification of market entities, and trade directionality. These features make it the premier tool for investors analyzing market participant positioning. However, many investors who trade based on branch flow fall into the trap of buying at market tops. Behavioral finance demonstrates that information asymmetries and psychological biases directly manifest in trading behaviors. On one hand, institutional investors and large accounts with genuine information advantages (smart money) execute sizable, concentrated orders, driving subsequent price momentum. On the other hand, when a stock gains widespread retail attention, excessive retail buying often drives crowd sentiment and asset congestion, causing prices to temporarily detach from fundamental value before undergoing mean reversion.
Market Knowledge & Data Guides
What are Quantitative Funds: How Quant Funds Work, Benefits & More
Learn all about quantitative funds, how their models select investments, common strategies, main benefits and risks, and how they compare with traditional funds.
Market Knowledge & Data Guides
10+ Most Common Hedge Fund Investing Strategies: Full Guide
Learn hedge fund investment strategies, including long-short equity, credit, arbitrage, and managed futures, plus their benefits and risks for investors.
Quant Research
Fundamental Factor Research: Monthly Revenue Information – part2
Based on preceding results, SURPS3MTA demonstrates clear return monotonicity and significant long-short portfolio Alpha. REVOPY possesses strong ranking predictive power distributed across the full cross-section. This study evaluates 7 backtesting strategies utilizing these two factors plus MOM52WH to test real-world execution feasibility and multi-factor fusion value.
Quant Research
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 Investing
Factor Strategy – Integrating Broker Consensus to Enhance Foreign Concentration Strategies – QFII Part 2
Boost your quantitative strategy with QFII concentration & broker consensus! Discover how the conc_qfii fusion strategy delivers a 30.12% annualized return in the Taiwan large-cap market.
Factor Investing
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.
Quant Research
James O’Shaughnessy’s Cornerstone Value Strategy: Unearthing High-Quality Gold Mines in Large Cap Revenue Stocks
James P. O’Shaughnessy demonstrated in his book What Works on Wall Street that market returns do not fully conform to the Efficient Market Theory, showing that selecting stocks with low P/B, low P/CF, and low P/S ratios significantly enhances long-term returns. Among his methodologies, the “Cornerstone Value Strategy” targets high-revenue, cash-rich large-cap stocks. It screens equities across four dimensions—size, cash flow quality, revenue scale, and relative valuation—and uses dividend yield ranking for final selection. Applying this strategy to the Taiwan stock market (excluding financial sector) with six quantitative filters and an annual July rebalancing protocol validates its capacity to deliver sustained alpha by balancing deep value with high shareholder yield.
Factor Investing
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.