Quant

In the field of growth investing, Derwood S. Chase Jr. is undeniably a legendary and representative figure. With an investment career spanning nearly half a century, Chase held a strong academic background—graduating from the University of Virginia and earning an MBA from Harvard Business School in 1954. 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. Under his leadership, the firm managed over $1.4 billion in assets by 2001. From 1990 to 2001, its institutional large-cap growth portfolio achieved an impressive annualized return of 16.38%, significantly outperforming the S&P 500 and the Russell 1000 Growth Index over the same period.

In recent years, the low-volatility anomaly has gained widespread attention for challenging traditional asset pricing theory. This article takes a closer look at one key driver behind the anomaly—Idiosyncratic Volatility (IVOL)—through a comprehensive analysis of the Taiwan stock market. Using point-in-time data from the TEJ Factor Library, we investigate the statistical behavior of IVOL, its relationship with stock characteristics, and its implications for cross-sectional return prediction. 

Building on the statistical foundation presented in Part 1, this article explores how Idiosyncratic Volatility (IVOL) can be effectively applied in investment strategy design. We present two categories of approaches: a single-factor sorting model and a set of filter-enhanced momentum strategies. Through robust backtesting across two decades of Taiwan stock market data, we demonstrate how IVOL can improve risk-adjusted performance when used as a portfolio filter—especially when combined with momentum or dividend-based signals.

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.

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

This article will explore what quantitative data is, outlining its meaning, types, and examples, while briefly viewing its collection and analysis methods.

Event-driven investing exploits pricing inefficiencies caused by significant corporate events. Discover common event-driven strategies and examples below.

what is the right way to invest? If you’ve had the same doubts, you’re not alone. While seeking answers, I stumbled upon a term that was entirely new to me: quantitative finance. Learning this field helps resolve personal financial challenges and enhances career competitiveness. More importantly, it sharpens your logical thinking and data analysis skills—benefits that are hard to overlook. TQuant Lab provides everything you need to get started efficiently and effectively. Let’s embark on this journey together and explore the endless possibilities of quantitative trading!

This article will explore the common types of quantitative investment strategies as well as the advantages and potential challenges of this trading model.

This guide will introduce the types of data analysis used in quantitative research, then discuss relevant examples and applications in the finance industry.

In recent years, as the stock prices of popular AI companies continue to reach new highs, investors are increasingly focused not only on these companies' operational status but also on the trading behavior of their insiders. Company insiders have more information compared to external investors, giving them an informational advantage when trading the company's stock.