{"id":498,"date":"2026-09-02T09:00:00","date_gmt":"2026-09-02T09:00:00","guid":{"rendered":"https:\/\/www.tejwin.com\/en\/insights\/factor-strategy-integrating-broker-consensus-to-enhance-foreign-concentration-strategies-qfii-part-2\/"},"modified":"2026-09-29T09:01:38","modified_gmt":"2026-09-29T09:01:38","slug":"monthly-revenue-info-part1","status":"publish","type":"insight","link":"https:\/\/www.tejwin.com\/en\/insights\/monthly-revenue-info-part1\/","title":{"rendered":"Fundamental Factor Research: Monthly Revenue Information – part1"},"content":{"rendered":"\n
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Photo by Jakub \u017berdzicki<\/a> on Unsplash<\/a><\/p>\n\n\n\n

Data Sources and Taiwan Stock Market Framework<\/strong><\/p>\n\n\n\n

Taiwan’s Monthly Revenue Disclosure System<\/strong><\/h3>\n\n\n\n

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”.<\/p>\n\n\n\n

Benefiting from this regulation, investors can update the latest corporate revenue information every month at a frequency far higher than quarterly financial report fundamental data. Monthly revenue stands as one of the most frequently utilized fundamental signals in quantitative research in Taiwan.<\/p>\n\n\n\n

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\u2b50Explore more about fundamental data!<\/strong>\u2b50<\/a><\/div>\n<\/div>\n\n\n\n

Data Sources and Processing Flow<\/strong><\/h3>\n\n\n\n

The TEJ Monthly Revenue Database aggregates monthly operational disclosures of listed companies sourced from the Market Observation Post System (MOPS)<\/a><\/strong>. To satisfy the data requirements of quantitative backtesting, the TEJ database strictly adheres to a Point-in-Time (PIT) timestamp architecture. It accurately captures data at the “earliest disclosure point” to reconstruct the authentic market reaction in real-time, while comprehensively preserving the historical disclosure trajectories generated by subsequent revision filings for the same revenue month.<\/p>\n\n\n\n

Taking the revenue filing of stock 2236 Patec-KY in September 2025 (202509) as an example (Table 3-1), the TEJ PIT database fully records the initial announcement alongside subsequent revision trajectories for the same revenue month:<\/p>\n\n\n\n

Table 3-1: Illustrative Example of “Initial Filing and Revision Trajectory for the Same Month” in TEJ PIT Monthly Revenue Database (Taking 2236 Patec-KY as an Example)<\/p>\n\n\n\n

Stock Code & Name<\/strong><\/td>Revenue Month (YYMM)<\/strong><\/td> Announcement Date<\/strong><\/td>Monthly Revenue (NT$ Thousand)<\/strong><\/td>Historical Remarks & PIT Implications<\/strong><\/td><\/tr>
2236 Patec<\/strong><\/td>202509<\/strong><\/td>2025 \/ 10 \/ 13<\/strong><\/td>739,569<\/strong><\/td>Initial Filing:<\/strong> The sole authentic data point available to the market on 2025\/10\/13.<\/td><\/tr>
2236 Patec<\/strong><\/td>202509<\/strong><\/td>2025 \/ 11 \/ 10<\/strong><\/td>734,845<\/strong><\/td>Revision Record:<\/strong> Adjusted in accordance with CPA audit numbers, revising September revenue announcement downward.   PIT trajectory fully retains both records.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Figure 3-2: 2236 Patec-KY Discloses Revision of September 2025 Monthly Revenue on Market Observation Post System (MOPS)<\/p>\n\n\n\n

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Table 3-1 shows Patec-KY initially reported September revenue at NT$739,569 thousand on 2025\/10\/13, then revised it down to NT$734,845 thousand on 2025\/11\/10 (Figure 3-2).<\/p>\n\n\n\n

Without a PIT mechanism, overwriting the initial data causes look-ahead bias by using future revisions during backtests from 10\/13 to 11\/09. The TEJ PIT database retains the original 2025\/10\/13 release date and figure, ensuring backtests only access historically available data.<\/p>\n\n\n\n

Additionally, the database keeps historical data for delisted stocks, eliminating survivorship bias.<\/p>\n\n\n\n

Quantitative Factor Research<\/strong><\/h2>\n\n\n\n

Introduction<\/strong><\/h3>\n\n\n\n

Utilizing fundamental data to forecast equity returns has long been a cornerstone of quantitative investing. Do multi-factor portfolios genuinely outperform single-factor models? If so, which specific factor drives the contribution? This study focuses on two fundamental factors built upon monthly revenue: “SURPS3MTA”, which measures revenue momentum, and “REVOPY”, which measures operating profit yield. Concurrently, we introduce a third price-based factor: “52-Week High Momentum (MOM52WH)”. Taking the Taiwan market as an empirical testbed, this paper examines the predictive capabilities of these three factors and evaluates the practical value of multi-factor fusion through strategy backtests.<\/p>\n\n\n\n

Research Design<\/strong><\/h3>\n\n\n\n

This study constructs 7 distinct single-, dual-, and triple-factor strategies to evaluate fusion efficacy. The backtesting settings and parameters are as follows:<\/p>\n\n\n\n