TQuant Lab

In investment practice, industry rotation remains a core strategy for capital allocation and stock selection. As the economic cycle evolves, capital often shifts from leading sectors to lagging ones, creating a structural rotation across industries. For example, in the early stages of economic recovery, cyclical sectors like shipping and steel tend to react first. As the economy expands and corporate capital expenditures rise, growth-oriented sectors such as semiconductors and technology often take the lead. While this rotation logic is widely referenced in macroeconomic analysis, accurately identifying the starting and ending points of such trends requires support from quantitative tools and empirical data. This article analyzes historical data from various industry indices and applies statistical tools such as Transfer Entropy and rolling correlation coefficients to examine whether stable lead-lag relationships exist between sectors. It also explores the potential role of economic indicators, such as business cycle signal lights, in shaping inter-sector dynamics.

In the field of growth investing, Derwood S. Chase Jr. is undeniably a legendary and representative figure. In 1958, he founded Chase Investment Counsel Corporation, a firm focused on managing corporate pension funds, trust funds, and individual retirement plans, deliberately avoiding any banking-related business. From 1990 to 2001, its institutional large-cap growth portfolio achieved an impressive annualized return of 16.38%. Through a strategy that combined growth and stability, Chase established a long-term return model that could endure across market cycles, providing a benchmark for investors navigating volatile markets.

2025 International Conference of Taiwan Finance Association (TFA) is held at National Taiwan Normal University (NTNU) on June 6.This year's annual conference adopts the theme "Artificial Intelligence and Finance," exploring the applications of finance from multiple perspectives. TEJ was invited to deliver a practical seminar titled "Enhancing Factor Investing Strategy Performance Using Machine Learning," sharing empirical research findings on the application of quantitative analysis methods in Taiwan's stock market.

With the rapid development of the high-tech industry, technology stocks have increasingly become the focus of the market. While these stocks offer significant growth potential, they also come with high volatility and substantial investment risk. Investors seeking high returns may face major losses if they fail to properly assess the associated risks. Therefore, effectively measuring and managing the downside risk of high-tech stocks has become a crucial component of sound investment decision-making.

In the field of investing, business cycles have always served as an important reference. Whether it's fluctuations in the macroeconomy or the ups and downs of corporate earnings, these cycles play a crucial role. Charles Brandes, a distinguished disciple of Benjamin Graham, founded Brandes Investment Partners in 1974 and has since grown its assets under management from $130 million to over $75 billion. The firm’s Brandes Global Equity Fund achieved an impressive 20-year annualized return of 17.91%, significantly outperforming the MSCI World Index, and has received Morningstar's five-star rating along with numerous international awards. Another flagship product, the AGF International Value Fund, has also demonstrated outstanding long-term performance. Brandes himself has been repeatedly ranked among the world’s top fund managers.

The Zipline engine, integrated within TQuant Lab, offers a high-quality and realistic backtesting framework. It leverages four core functions—initialize、handle_data、analyze、run_algorithm——to construct a comprehensive simulation environment. These components enable trading strategies to dynamically adjust to market conditions, incorporating elements such as slippage and transaction costs to ensure that backtest results closely reflect real-world performance. This article begins with a brief overview of the four key components and their applications. It then focuses in detail on the initialize function, exploring its specific configurations and role within the backtesting process.

Traditional Ichimoku strategies rely on fixed parameters (9-26-52) and visual interpretation, making them inflexible in adapting to different market conditions. XGBoost learns complex high-dimensional relationships between different data points and enhances the filtering and decision-making process of trading signals. This article use XGBoost to enhance investment performance of Ichimoku Cloud.

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.

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