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Should you invest in emerging markets? Our emerging market investment guide covers the benefits, risks, and strategies to empower you with data-based decisions.

In the context of sector rotation, we have observed an intriguing phenomenon: the strengthening of the shipping index often appears to precede the rally in semiconductors. If such a relationship can be verified and transformed into an actionable logic, it may serve as a valuable clue for investment decisions. Building on the empirical analysis presented in the previous article, “Observing Sector Rotation through Data: Deconstructing the Leading and Lagging Relationship between Shipping and Semiconductors,” this paper further explores how the hypothesis of “shipping leading, semiconductors following” can be translated into a concrete asset allocation strategy. We integrate business cycle indicators, industry performance metrics, and temporal conditions to construct a backtesting framework that simulates historical trading scenarios. By evaluating the strategy’s performance, we aim to address a central question: if shipping truly signals upcoming movements in semiconductors, how should we ride this relay race?

Analyze industry rotation between Taiwan’s shipping and semiconductor sectors using momentum and valuation factors. This study reveals how factor-based strategies capture cyclical shifts—and why semiconductors ultimately outperformed over time.

Discover how Derwood Chase’s value-momentum strategy—favoring low P/E stocks with strong price trends—delivers long-term outperformance in Taiwan’s market. Backtest results show strong alpha and lower drawdowns, proving the power of disciplined factor investing.

TEJ showcases its Factor Library and Earnings Call Transcripts at Neudata Hong Kong 2025, highlighting Taiwan-focused quant data for institutional strategies.

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.

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. 

Factor Library – TEJ's Factor Database for Quantitative Investing

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.

Discover how TEJ's Taiwan Market Data captivated global investors at the Neudata London Summit 2025, offering unique insights into Asia's financial landscape.