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Table of Contents
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”.
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.
The TEJ Monthly Revenue Database aggregates monthly operational disclosures of listed companies sourced from the Market Observation Post System (MOPS). 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.
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:
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)
| Stock Code & Name | Revenue Month (YYMM) | Announcement Date | Monthly Revenue (NT$ Thousand) | Historical Remarks & PIT Implications |
| 2236 Patec | 202509 | 2025 / 10 / 13 | 739,569 | Initial Filing: The sole authentic data point available to the market on 2025/10/13. |
| 2236 Patec | 202509 | 2025 / 11 / 10 | 734,845 | Revision Record: Adjusted in accordance with CPA audit numbers, revising September revenue announcement downward. PIT trajectory fully retains both records. |
Figure 3-2: 2236 Patec-KY Discloses Revision of September 2025 Monthly Revenue on Market Observation Post System (MOPS)
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).
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.
Additionally, the database keeps historical data for delisted stocks, eliminating survivorship bias.
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.
This study constructs 7 distinct single-, dual-, and triple-factor strategies to evaluate fusion efficacy. The backtesting settings and parameters are as follows:
Variable ConstructionSURPS3MTA (Trend-Adjusted Standardized Unexpected 3-Month Cumulative Revenue Per Share): Normalizes 3-month cumulative per-share revenue relative to its historical 12-month baseline and deducts the long-term trend component. It measures “how much better current revenue is compared to historical normal levels” while removing pre-anticipated growth trajectories, focusing strictly on capturing revenue surprises.
REVOPY (Monthly Revenue Estimated Operating Profit to Market Value Ratio): Essentially an “operating profit yield” factor; higher values indicate cheaper market valuations given the company’s earnings.
Note: This indicator is omitted when revenue and operating profit margin are simultaneously negative.
MOM52WH (52-Week High Momentum): Calculated by dividing current stock price by its 52-week high price. Investors often treat previous highs as psychological anchor points, becoming hesitant to chase price rallies and leading to underreaction to positive news. Consequently, the closer the stock price is to its 52-week high, the higher its subsequent returns tend to be.
To investigate the cross-sectional distribution of factor values, stocks are sorted by factor values from smallest to largest into ten equal deciles (P1 lowest to P10 highest) on each trading day, as shown in Table 3-3.
SURPS3MTA is a standardized score with concentrated distribution, exhibiting heavy tails only in the extreme decile (P10) due to denominators approaching zero. REVOPY is a ratio metric, where middle deciles (P5–P7) show estimated operating profit yields around 4%–7%. Deciles P1 to P3 yield negative mean values, encompassing companies with negative estimated operating profits.
Table 3-3: Descriptive Statistics of Core Factor Deciles
Panel A: SURPS3MTA
| Decile | Minimum (Min) | Maximum (Max) | Mean | Standard Deviation (Std) | Observations (Count) | Percentage (%) |
| P1 | −225.83 | −0.97 | −2.72 | 3.45 | 463,331 | 10.03 |
| P2 | −3.11 | −0.42 | −1.53 | 0.42 | 461,930 | 10.00 |
| P3 | −2.45 | 0.00 | −1.09 | 0.40 | 461,764 | 9.99 |
| P4 | −2.02 | 0.37 | −0.75 | 0.41 | 461,884 | 10.00 |
| P5 | −1.72 | 0.66 | −0.42 | 0.43 | 462,294 | 10.00 |
| P6 | −1.39 | 0.95 | −0.08 | 0.45 | 461,389 | 9.98 |
| P7 | −1.05 | 1.34 | 0.28 | 0.46 | 461,657 | 9.99 |
| P8 | −0.67 | 1.85 | 0.70 | 0.46 | 462,012 | 10.00 |
| P9 | −0.22 | 2.64 | 1.23 | 0.46 | 461,612 | 9.99 |
| P10 | 0.46 | 9314.86 | 2.99 | 19.67 | 463,091 | 10.02 |
Panel B: REVOPY
| Decile | Minimum (Min) | Maximum (Max) | Mean | Standard Deviation (Std) | Observations (Count) | Percentage (%) |
| P1 | −238.0061 | −0.0430 | −0.2348 | 1.7790 | 459,587 | 10.03 |
| P2 | −0.1359 | −0.0019 | −0.0429 | 0.0206 | 458,217 | 10.00 |
| P3 | −0.0399 | 0.0253 | −0.0043 | 0.0105 | 457,938 | 9.99 |
| P4 | −0.0010 | 0.0467 | 0.0195 | 0.0091 | 458,251 | 10.00 |
| P5 | 0.0144 | 0.0673 | 0.0381 | 0.0095 | 458,494 | 10.00 |
| P6 | 0.0281 | 0.0876 | 0.0541 | 0.0102 | 457,571 | 9.98 |
| P7 | 0.0414 | 0.1094 | 0.0689 | 0.0109 | 457,980 | 9.99 |
| P8 | 0.0534 | 0.1317 | 0.0840 | 0.0122 | 458,101 | 10.00 |
| P9 | 0.0663 | 0.1720 | 0.1035 | 0.0155 | 457,868 | 9.99 |
| P10 | 0.0839 | 3.3816 | 0.1641 | 0.1168 | 459,206 | 10.02 |
Note: In this table, daily factor values are sorted and divided into equal deciles; constituent stocks within each decile are equal-weighted; statistical metrics represent summary values across all observations in that decile over the sample period.
Table 3-4 details average forward returns (converted to daily equivalent return rates) and long-short spread returns (Spread = P10 − P1) for deciles of the two core factors across different holding periods.
Average forward returns and decile cumulative returns across holding periods are summarized in the charts. Return analysis shows that SURPS3MTA exhibits perfect monotonic increasing properties with positive long-short spread (P10−P1) returns. Conversely, REVOPY decile returns display compressed flattening; P10 exhibits no absolute dominance, and P1 cumulative returns crossed upward above middle deciles post-2021 (Figure 3-7).
Table 3-4: Average Forward Returns of Core Factor Deciles Across Holding Periods
Panel A: SURPS3MTA
| Decile | 1D | 5D | 10D | 21D |
| P1 (Bottom) | −0.018% | 0.004% | 0.007% | 0.009% |
| P2 | 0.009% | 0.023% | 0.023% | 0.023% |
| P3 | 0.010% | 0.022% | 0.024% | 0.026% |
| P4 | 0.026% | 0.036% | 0.038% | 0.043% |
| P5 | 0.038% | 0.045% | 0.046% | 0.046% |
| P6 | 0.044% | 0.050% | 0.052% | 0.056% |
| P7 | 0.063% | 0.067% | 0.068% | 0.068% |
| P8 | 0.092% | 0.091% | 0.090% | 0.091% |
| P9 | 0.113% | 0.104% | 0.101% | 0.101% |
| P10 (Top) | 0.168% | 0.132% | 0.124% | 0.116% |
| Spread (P10−P1) | 0.187% | 0.128% | 0.117% | 0.107% |
Panel B: REVOPY
| Decile | 1D | 5D | 10D | 21D |
| P1 (Bottom) | 0.054% | 0.064% | 0.064% | 0.065% |
| P2 | 0.043% | 0.049% | 0.049% | 0.051% |
| P3 | 0.042% | 0.049% | 0.049% | 0.050% |
| P4 | 0.053% | 0.055% | 0.056% | 0.058% |
| P5 | 0.055% | 0.060% | 0.059% | 0.061% |
| P6 | 0.053% | 0.057% | 0.055% | 0.056% |
| P7 | 0.052% | 0.057% | 0.056% | 0.056% |
| P8 | 0.062% | 0.064% | 0.065% | 0.066% |
| P9 | 0.068% | 0.066% | 0.064% | 0.063% |
| P10 (Top) | 0.066% | 0.059% | 0.056% | 0.055% |
| Spread (P10−P1) | 0.012% | −0.004% | −0.008% | −0.010% |
Note: Values represent daily equivalent return rates; transaction costs are excluded.
Figure 3-5: Average Forward Return of Core Factor Deciles Across Four Holding Periods (Daily Equivalent)
Figure 3-6: Cumulative Return Curves of SURPS3MTA Deciles (1-Day Holding, Daily Rebalancing)
(Transaction costs excluded; vertical axis represents cumulative return multiple; chart highlights P10 and P1, with P2–P9 rendered in light gray)
Figure 3-7: Cumulative Return Curves of REVOPY Deciles (1-Day Holding, Daily Rebalancing)
(Transaction costs excluded; vertical axis represents cumulative return multiple; chart highlights P10 and P1, with P2–P9 rendered in light gray; here P10 is not the highest at period end, nor P1 the lowest)
Monthly returns of the long-short portfolio (P10 − P1) were regressed against CAPM, Fama-French Three-Factor (FF3), and Five-Factor (FF5) models to evaluate intercept (Alpha) significance.
Results indicate that after controlling for known risk factors, SURPS3MTA delivers a statistically significant FF5 Alpha of 1.81% (unabsorbed by market, size, value, or profitability factors), whereas REVOPY exhibits weak Alpha in long-short spread regressions (marginally significant at -0.46%).
Table 3-8: Risk Factor Model Regression Alpha of Core Factors P1, P10, and Long-Short Portfolios
Panel A: SURPS3MTA
| Model | P1 | P10 | Long-Short (P10−P1) |
| CAPM | −0.94%*** (−3.73) | 0.89%*** (3.85) | 1.83%*** (10.05) |
| FF3 | −1.13%*** (−10.71) | 0.68%*** (4.68) | 1.81%*** (10.25) |
| FF5 | −1.11%*** (−9.01) | 0.70%*** (4.06) | 1.81%*** (8.07) |
Panel B: REVOPY
| Model | P1 | P10 | Long-Short (P10−P1) |
| CAPM | 0.14% (0.38) | 0.04% (0.15) | −0.10% (−0.35) |
| FF3 | −0.20% (−1.13) | −0.23% (−1.43) | −0.03% (−0.12) |
| FF5 | 0.07% (0.33) | −0.39%** (−2.25) | −0.46%* (−1.70) |
Note: Alpha is the monthly regression intercept in %/month; numbers in parentheses indicate Newey-West adjusted t-statistics; significance levels: *** p<0.01, ** p<0.05, * p<0.1; n = 137 months.
Measuring via Information Coefficient (IC) reveals an “IC vs. Alpha divergence” phenomenon: REVOPY outperforms SURPS3MTA in IC metrics (21D reaches 0.0604 vs 0.0577), yet its long-short Alpha is only marginally significant. This is because REVOPY’s predictive information spreads evenly across the entire cross-section rather than concentrating in extreme deciles (P1, P10); thus, purely longing P10 substantially underestimates its ranking value.
Table 3-9: Statistical Summary of Core Factor Information Coefficients (IC) Across Holding Periods
Panel A: SURPS3MTA
| Metric | 1D | 5D | 10D | 21D |
| IC Mean | 0.0191 | 0.0331 | 0.0431 | 0.0577 |
| IC Std | 0.0572 | 0.0599 | 0.0605 | 0.0611 |
| Risk-Adjusted IC | 0.334 | 0.552 | 0.713 | 0.944 |
| IC > 0 (%) | 64.1 | 72.4 | 76.7 | 83.9 |
| IC t-value (Newey-West) | 16.50 | 14.38 | 14.47 | 13.39 |
| IC Mean (Size-Neutral) | 0.0183 | 0.0317 | 0.0417 | 0.0566 |
| IC Mean (Industry-Neutral) | 0.0184 | 0.0308 | 0.0395 | 0.0519 |
Panel B: REVOPY
| Metric | 1D | 5D | 10D | 21D |
| IC Mean | 0.0307 | 0.0466 | 0.0532 | 0.0604 |
| IC Std | 0.0583 | 0.0717 | 0.0784 | 0.0851 |
| Risk-Adjusted IC | 0.526 | 0.651 | 0.679 | 0.709 |
| IC > 0 (%) | 70.7 | 74.9 | 76.6 | 76.7 |
| IC t-value (Newey-West) | 21.94 | 15.50 | 13.15 | 9.78 |
| IC Mean (Size-Neutral) | 0.0252 | 0.0370 | 0.0428 | 0.0502 |
| IC Mean (Industry-Neutral) | 0.0273 | 0.0424 | 0.0487 | 0.0552 |
Note: Risk-Adjusted IC = IC Mean / IC Std; t indicates Newey-West adjusted values; Size-Neutral = cross-sectional regression residuals of factor against log market cap; Industry-Neutral = factor value minus cross-sectional mean of corresponding main industry.
Table 3-9 presents overall sample period averages, masking whether predictive power accumulates steadily or concentrates in specific years. Figure 3-10 plots 21-day holding period ICs cumulatively over time: SURPS3MTA cumulative IC rises at a steady pace, whereas REVOPY exhibits stagnation during 2020.
Figure 3-10: Cumulative Information Coefficients of Core Factors at 21-Day Holding Period
(The curve represents the daily accumulation of 21D ICs, with its slope proportional to the average IC over that period.)
