Quantitative Analysis

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.

Burton G. Malkiel is the Chemical Bank Chairman’s Professor of Economics at Princeton University. He previously worked in the investment banking division of Smith Barney & Co. and has served as a director of several large investment institutions, including The Vanguard Group and The Prudential Insurance Company of America. He was also appointed as a member of the U.S. President’s Council of Economic Advisers. In both academic and investment circles, he is a highly respected and influential figure.

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. 

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.

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.

In our previous article" Shipping Leads, Semiconductors Follow? "we identified a recurring pattern in Taiwan’s market: rallies in the shipping sector often precede gains in semiconductor stocks. This article builds on that insight by transforming the observed rotation sequence into a quantitative investment strategy.

On June 6, 2025, the TFA International Conference had been held at NTNU with the theme “Artificial Intelligence and Finance.” TEJ had delivered a seminar, “Enhancing Factor Investing Strategy Performance Using Machine Learning,” sharing empirical research on applying quantitative methods to Taiwan’s stock market.

Dividend arbitrage focuses on the price inefficiencies between dividend payouts and option pricing. This article will explore its execution process, examples, and challenges.

Dividend arbitrage focuses on the price inefficiencies between dividend payouts and option pricing. This article will explore its execution process, examples, and challenges.

Dividend arbitrage focuses on the price inefficiencies between dividend payouts and option pricing. This article will explore its execution process, examples, and challenges.

Quantitative data collection methods include surveys, interviews, observations, and dataset reviews. Explore the techniques, pros, and cons of each method.

In investment decision-making, price-volume factors are essential for investors to gain insights into market behavior. The relationship between price and trading volume supply and demand dynamics of an asset also reveals capital flows and shifts in market sentiment. These factors play a crucial role in capturing short-term opportunities and identifying potential risks in asset allocation.