Insights

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.

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".

Track QFII 'smart money' footprints in Taiwan large-cap stocks! Learn how the Foreign-Institutional Trading Concentration (conc_qfii) factor predicts returns.

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.

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.

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 article examines how corporate ESG performance affects bank lending decisions in Taiwan. Using TEJ’s TESG Ratings, the study finds that companies with stronger ESG performance generally receive more favorable lending conditions, including lower loan spreads, larger loan amounts, longer maturities, and a lower likelihood of collateral requirements. The findings also highlight the importance of balancing ESG initiatives with financial performance, as excessively high or low ESG performance may lead to less favorable lending terms.

TCRI Watchdog (WD) converts complex news and announcements into standardized quantitative alternative data. Building on our research into "Official Announcements (Source P)" and "Media News (Source N)," we have confirmed that disclosure channels directly dictate the speed and structure of market digestion. This chapter moves from macro "Event Categories" to micro "Source × Sub-category" dimensions to capture actionable Alpha within granular events. Focusing on high-sensitivity "Corporate Control Events," we analyze the signal heterogeneity between Source P and Source N. We further demonstrate how these high-precision signals assist investors in optimizing entry timing and hedging strategies.

While Part I establishes that news events generate identifiable market reactions, the informational content of news varies widely—from industry developments and financial disclosures to management changes and corporate crises. Event intensity alone is insufficient to explain these differences. Accordingly, this section decomposes news events into five categories (A, I, M, F, R) and examines whether markets respond systematically differently across news types.