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 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 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.
Insights
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Quant Research
Three Major Institutional Investors’ Position-Based Trading Strategy for TAIEX Futures
This strategy is based on tracking the flow of so-called “smart money” in the market, referring to the positions of the three major institutional investors in Taiwan: foreign investors, investment trusts, and proprietary traders. The strategy assumes that when these three institutions hold a strong and consistent view of the market direction, following their lead has a higher probability of success.
Quant Research
ETF Premium-Discount Arbitrage: Market Maker vs. Retail Performance
Market makers, equipped with high-frequency trading capabilities, institutional-grade cost structures, and real-time creation/redemption privileges, are the primary participants in ETF premium–discount arbitrage. In contrast, Non-Institutional Participants face multiple constraints—including information latency and higher transaction frictions—which make it difficult to capture arbitrage opportunities promptly or profitably.
Event & Alternative Signals
TCRI Watchdog Part2：How Different Types of Corporate Events Shape Market Reactions
TCRI Watchdog classifies all events into 5 major categories: Accounting, Industry Prospects, Management & Governance, Market Transactions, and Crisis Events. We analyze how each category affects stock prices, compare their reaction magnitudes and persistence, and highlight which types of information serve as the most important early-warning signals for investors.
Event & Alternative Signals
How Major Announcements Drive Stock Price Volatility：Event Study of the TCRI Watchdog “P” Type Event－Part 1
Discover how TCRI Watchdog quantifies material announcements and reveals the asymmetric market impact of event intensity. Learn why negative events drive deeper, longer price reactions and how investors can use event-based signals to enhance risk monitoring and strategy design. We find that higher-ranked portfolios deliver significant short-term excess returns, while predictive power weakens over longer horizons. The results highlight the practical value of Point-in-Time financial data for quantitative factor investing and underscore its role in building replicable, data-driven investment strategies.
Quant Research
Impulse MACD Futures Trading Strategy
LazyBear is a highly influential indicator developer on the internationally renowned trading platform, TradingView. He has created a large number of popular custom technical indicators, and his open-source code has inspired countless quantitative traders and technical analysis enthusiasts around the world. LazyBear’s indicators often focus on reducing the lag of traditional indicators and incorporate unique market observations to better capture trends and momentum. One of his representative works, the “Impulse MACD,” is adopted here. This indicator is not a traditional Moving Average Convergence Divergence (MACD), but rather a significantly improved version. It uses a zero-lag Double Exponential Moving Average (DEMA) to respond more quickly to price changes and combines a smoothed high-low price channel (SMMA) to determine market “impulse.” The core idea is that trading signals are more valuable only when price momentum aligns with the trend direction. This helps to filter out some of the noise typically found in ranging markets.
Quant Research
Golden Cross Futures Trading Strategy（MTX）
The concept of the Moving Average (MA) originates from the Dow Theory developed in the early 20th century. Dow Theory emphasizes that markets exhibit trends, and such trends can be observed through price movements themselves. From the Simple Moving Average (SMA) and Exponential Moving Average (EMA) to more sophisticated variants such as the Double Exponential Moving Average (DEMA) and Hull Moving Average (HMA), all these improvements aim to address the inherent lag problem of traditional moving averages, allowing them to reflect price trends more quickly or more smoothly.
Market Knowledge & Data Guides
Panic or Opportunity? Spotting Market Turning Points Through Margin Maintenance Ratios
In the Taiwan stock market, credit trading (margin buying and short selling) serves as a critical mechanism for observing market leverage and investor sentiment. Unlike institutional investors with ample capital, retail investors often face capital constraints and therefore resort to margin buying to increase their purchasing power. Consequently, margin data serves as a key barometer for retail trading heat. Key indicators for monitoring credit trading include daily margin/short volume, balance, margin utilization rates, and—most crucially—the Margin Maintenance Ratio (MMR). The MMR is a core metric used to assess the risk status of margin accounts and determine how close investors are to a margin call. Brokers set specific MMR thresholds to mitigate default risks during periods of high market volatility.
Factor Investing
Discovering Investment Factors through Point-in-Time Audited Financial Database
This study employs TEJ’s Point-in-Time Audited Financial Database to construct a composite factor for stock selection in Taiwan’s equity market. By preserving financial data exactly as available at each historical moment, the framework avoids look-ahead bias and ensures empirical reliability. We find that higher-ranked portfolios deliver significant short-term excess returns, while predictive power weakens over longer horizons. The results highlight the practical value of Point-in-Time financial data for quantitative factor investing and underscore its role in building replicable, data-driven investment strategies.
Quant Data Science
[TQuant From 0 to 1 – Day 5] Introduction to Order Placement Methods in the TQuant Lab Backtesting System
In TQuant backtesting and live trading, order functions serve as the central link between strategy logic and capital management. Choosing the right order method not only makes the code cleaner and easier to read but also improves the efficiency of risk control and portfolio rebalancing. TQuant provides order functions across three dimensions — share quantity, capital amount, and portfolio weight. For each dimension, there are two variants: a basic order and a target order. In total, this gives us six order placement methods. In the following sections, we will explain the features, parameters, and recommended applications of each.
Market Knowledge & Data Guides
TEJ Point-in-Time Audited Financial Database – Rejecting “Peek-ahead” Backtesting
TEJ PIT Audited Financial Database eliminates look-ahead and survivorship bias with Point-in-Time data, full version retention, IFRS alignment, and 300+ ready-to-use ratios—delivering reliable backtesting and faster strategy development.
Quant Research
When Others Fear, I Enter: Anthony Melia’s Contrarian Strategy for Winning in the Market
In financial markets, Contrary Thinking is a timeless strategic wisdom. It stems from a simple yet profound observation: when most people are overly optimistic, the market is often overheated; when the crowd falls into fear, it may actually present a buying opportunity. However, contrarian investing has long remained at the level of a proverb, lacking concrete and quantifiable standards of action, making it difficult to implement in practice.
Factor Investing
Jabli–Watson Factor Model: The Growth Formula for Quantitative Momentum Investing
Gabriel Watson is a well-known American growth-momentum portfolio manager. In his early career, he worked at Morgan Stanley Investment Management and William O’Neil & Co., where he accumulated extensive market research experience. Since joining Black Rose Capital Management in 1998, Watson has broken away from the confines of traditional value investing and developed a systematic, rule-based stock-picking method centered on “revenue momentum and price strength.” He calls this framework “The Machine.” In an environment characterized by rapid information flow and swift capital rotation, this approach has enabled him to capture the powerful upward moves of leading stocks.