Insights

Discover how TEJ’s ex-dividend event data and financial indicators help forecast stock rebound probability. Learn to identify high-yield Taiwan stocks with stronger post-dividend performance using TEJ’s point-in-time quantitative datasets.

Howard Rothman is the Chief Investment Analyst at Vision Investment Advisor (VIA), a well-known investment firm on Wall Street. Under his leadership, the "Blue Chip Growth Account," launched in 1997, achieved a cumulative return of 49.27% by 2001—more than double the performance of the S&P 500 index over the same period—and consistently outperformed the market each year. As of December 2002, Rothman had become President of Vision Investment Advisor. He also served as Chairman and founding member of the National Introducing Brokers Association and was appointed to the Board of Directors of the National Futures Association (NFA) in 1990, serving three consecutive terms. He actively contributed to the NFA’s Legal and Compliance Committees, demonstrating his strong professional background and influential presence in the industry. Rothman's core investment philosophy is to “buy right and hold tight” or “buy strong and hold long.” He emphasizes selecting financially sound companies with stable and growing earnings, purchasing them at the right price, and holding them patiently for the long term. Without frequent portfolio adjustments, investors can achieve strong long-term returns. This simple yet resolute investment approach reflects Rothman’s practical wisdom and provides a clear, historically validated foundation for the strategy tested in this study.

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.

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. 

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.

In Part 1, we discussed the policy background driving the development of China’s IC design industry, including a comparison of the current status and trend analysis of selected IC firms in both Taiwan and China. In this section, we will further explore the development of the IC design industry from several perspectives, including a comparison of revenue trends between Taiwan and China. Additionally, we will examine the top 10 Taiwanese IC design companies with the highest and lowest R&D investments during the first three quarters of 2024, analyzing the differences in their R&D resource allocation. With the latest and most insightful analysis of the IC industry, you will not miss the hottest trends covered here.

In the field of investment, the "business cycle" has always been an important reference point. Fluctuations in the overall economy, corporate earnings, and market sentiment all show distinct characteristics during different stages of the cycle. Therefore, being able to grasp the movements of the business cycle can help investors more accurately adjust their asset allocations and gain a relative advantage in the market.

With the development of AI applications in recent years, many industries have benefited from it, and Taiwan's semiconductor industry is no exception. However, as the Chinese government continues to follow up and use policies to support local industries, the pressure on Taiwan's IC design companies is increasing day by day. How will Taiwanese manufacturers deal with the threat posed by the rapid rise of Chinese companies? This article is divided into two parts. The first part covers the policy background of China's IC design industry development, and includes a comparison of the current status and trend analysis of selected IC firms in Taiwan and China.

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.