Factor Trends and Cycles

Bearish trends or deep corrections in international equity markets starting in 2022 and rising interest rates worldwide brought investors’ attention back to not only once-proclaimed dead factor investing. From long-run and short run, during different market cycles, different factors behave differently. What’s fortunate is that it is pretty predictable to some extent. Andrew Ang, Head of Factor Investing Strategies at BlackRock, in his Trends and Cycles of Style Factors in the 20th and 21st Centuries (2022), used Hodrick-Prescott (HP) filter and spectral analysis to investigate different models to draw some general conclusions on most-widely used factors. We will take a look at a few of quite the most interesting ones of them.

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Anomaly Discovery and Arbitrage Trading

Today, we will look closer into the hood of life expectancy of investment strategies and try to answer the critical question on which many, in some sense, if not all, trading strategies are built: what happens with anomalies after their discovery? The paper’s authors, with the sweet, simple name Anomaly Discovery and Arbitrage Trading, analyze a stylized model of anomaly discovery, which has implications for both asset prices and arbitrageurs’ trading. Their original research produced an arbitrageur-based asset pricing model that shows that discovering an anomaly reduces the correlation between the returns of its long- and short-leg portfolios: HFs (professional arbitrageurs) use to increase (unwind) such trades when their wealth increases (decreases), further supporting the view that the discovery effects work through arbitrage trading. This effect is more substantial when arbitrageurs’ wealth is more volatile.

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Comparison of Commodity Momentum Strategy in the U.S. and Chinese Markets

The commodity momentum strategy is a crucial driving force behind Commodity Trading Advisor (CTA) strategies, as it capitalizes on the persistence of price trends in various commodity markets. By identifying and exploiting these trends, CTAs can achieve robust returns and diversification benefits. In their new paper, John Hua FAN and Xiao QIAO (February 2023) present their perspective and understanding of cross-country and cross-sector influences on the behavior of commodity momentum beyond established commodity fundamentals focusing on U.S. and China markets.

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Price Momentum or Factor Momentum: What Leads What?

Continuing our research of different factor allocations and models, we will look at the evergreen momentum effect closer. Cakici, Fieberg, Metko, and Zaremba’s (January 2023) paper contributes to the never-ending debate of the chicken-or-egg problem of what comes first: Does the stock price momentum originate from the factor momentum? The study reexamined the relationship between the factor and price momentum on an extensive sample of 95 years of data from 51 countries. And what are the main takeaways? Let’s find out …

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Avoid Equity Bear Markets with a Market Timing Strategy – Part 3

In the last third installment, we will finish exploring the world of market timing strategies (see parts 1 & 2). We will focus on yield curve predictors and incorporate all three ideas (price-based, macro-economic, and yield curve predictors) into one final trading strategy that yields an annual return above that of the stock market while doubling its Sharpe ratio and reducing maximal drawdown by two thirds.

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Which Factors Drive the Hedge Fund Returns: A Machine Learning Approach

Arbitrage is a central concept in finance. It is defined as simultaneous long and short positions in similar assets to exploit mispricing. Hedge funds experienced fast growth over the past three decades, as real-world arbitrageurs as a group. As they increasingly influence the financial market, it is important to understand the economic drivers of hedge fund returns. Therefore we would like to present a paper dealing with the development of a parsimonious factor model, based on anomalies, to explain hedge fund returns.

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Time Series Variation in the Factor Zoo

Factor investing and detailed allocation according to different sets of factors are lively researched topics with many unanswered and open questions. Many views are often conflicting and from both radical sides — on one, that only a few factors should be necessary to explain the cross-section of mean returns, which is attractive, especially because of its simplicity; on the other, that you can use complex (authors examine the 161 “clear predictors” and 44 “likely predictors”) combinations of factors from less known and unorthodox models, but falling into dangerous and often unexamined “factor zoo” with many undesirable, unexamined and non-controllable outcomes. A huge gap is often seen in finance between the theory of academia and practical applications (by PMs [portfolio managers]), and so is especially present in this one. Let’s take a look at what the complexity of factors does for various equities pricing models.

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An Analysis of Rebalancing Performance Dispersion

The theme of rebalancing in longer-term investing is neglected but important as it influences the overall portfolio’s performance and risk. Unfortunately, many investors are inconsistent in choosing dates for their rebalances of portfolios, resulting in hardly predictable results (whether positively or negatively affecting it), and not contributing to handling risk management properly. The following article presents our analysis of the impact of rebalancing on portfolio returns. It also serves as an introduction to the methodology for an upcoming Quantpedia Pro report that our users would be able to use to quickly assess the impact of the rebalancing period on any selected combination of trading strategies, custom equity curves, and ETFs.

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Size Factor vs. Monetary Policy Regime

We have brought attention to the importance of evaluating factors models in different market regimes, and now, we will take a closer look at the size factor. Size [SMB (small minus big)] factor is a popular investment choice for asset investigation by many portfolio managers worldwide. The Size earned prominence in Fama and French’s three and five-factor models, and enjoy the continued discussion about its place in today’s portfolio construction. But it’s crucially important for investors seeking to capture the Size premium to realize that it is dependent on the monetary policy being pursued by the Federal Reserve, as the monetary easing seems to induce a Size premium.

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