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Market makers, equipped with high-frequency trading capabilities, low-cost structures, and real-time creation/redemption privileges, are the primary participants in ETF premium–discount arbitrage. In contrast, retail investors face multiple constraints—information delays, transaction fees, and taxes—which make it difficult for them to enter the market promptly or profit effectively, even when an arbitrage opportunity is detected. Using the Yuanta Taiwan 50 ETF (0050) as an example, this study simulates the actual execution of premium–discount arbitrage by these two market participants based on historical data. By comparing performance under varying entry/exit thresholds and cost structures—and evaluating metrics such as win rate, return distribution, and drawdown risk—we aim to clarify an essential question:In this seemingly “risk-free arbitrage,” who truly captures the opportunity and earns consistent profits?

LazyBear is a highly influential indicator developer on the internationally renowned trading platform, TradingView. He has created a large number of popular custom technical indicators, and his open-source code has inspired countless quantitative traders and technical analysis enthusiasts around the world. LazyBear's indicators often focus on reducing the lag of traditional indicators and incorporate unique market observations to better capture trends and momentum. One of his representative works, the "Impulse MACD," is adopted here. This indicator is not a traditional Moving Average Convergence Divergence (MACD), but rather a significantly improved version. It uses a zero-lag Double Exponential Moving Average (DEMA) to respond more quickly to price changes and combines a smoothed high-low price channel (SMMA) to determine market "impulse." The core idea is that trading signals are more valuable only when price momentum aligns with the trend direction. This helps to filter out some of the noise typically found in ranging markets.

The concept of the Moving Average (MA) originates from the Dow Theory developed in the early 20th century. Dow Theory emphasizes that markets exhibit trends, and such trends can be observed through price movements themselves. From the Simple Moving Average (SMA) and Exponential Moving Average (EMA) to more sophisticated variants such as the Double Exponential Moving Average (DEMA) and Hull Moving Average (HMA), all these improvements aim to address the inherent lag problem of traditional moving averages, allowing them to reflect price trends more quickly or more smoothly.

The Maintenance Margin Ratio (MMR) is an important indicator used in financial markets to assess the risk conditions of margin accounts and to determine whether investors are approaching the threshold for a broker’s margin call. The purpose is to reduce the risk of investors being unable to repay their loans during periods of high market volatility. It is worth noting that the MMR provided by TEJ is calculated based on all margin accounts for each stock, reflecting the overall average risk exposure of the market.

In financial markets, Contrary Thinking is a timeless strategic wisdom. It stems from a simple yet profound observation: when most people are overly optimistic, the market is often overheated; when the crowd falls into fear, it may actually present a buying opportunity. However, contrarian investing has long remained at the level of a proverb, lacking concrete and quantifiable standards of action, making it difficult to implement in practice.

Gabriel Watson  is a well-known American growth-momentum portfolio manager. In his early career, he worked at Morgan Stanley Investment Management and William O’Neil & Co., where he accumulated extensive market research experience. Since joining Black Rose Capital Management in 1998, Watson has broken away from the confines of traditional value investing and developed a systematic, rule-based stock-picking method centered on “revenue momentum and price strength.” He calls this framework “The Machine.” In an environment characterized by rapid information flow and swift capital rotation, this approach has enabled him to capture the powerful upward moves of leading stocks.

In the previous study, we examined the Capital Gain Overhang (CGO) factor, designed to capture the behavioral bias known as the Disposition Effect. By measuring the gap between current market prices and investors’ average cost basis, CGO quantifies unrealized gains and losses at the market level. Empirical tests in Taiwan’s equity market confirmed that CGO is a meaningful predictor of future returns: high-CGO stocks consistently outperformed low-CGO stocks, generating significant positive alpha beyond standard Fama–French models, especially over medium- to long-term horizons.

The origins of the momentum anomaly have long been debated, with multiple competing explanations. Among them, one of the most influential behavioral interpretations attributes momentum to the Disposition Effect, a systematic bias in investor decision-making. This article focuses on the Capital Gain Overhang (CGO) factor, specifically designed to quantify this behavioral bias. Using the Taiwan equity market as a case study, we examine CGO’s predictive power as a stock selection indicator and evaluate its practical value through empirical analysis.

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.