Momentum Investing Research

Momentum Factor Investing: 30 years of Out of Sample Data

By |October 31st, 2022|Research Insights, Basilico and Johnsen, Academic Research Insight, Momentum Investing Research|

In this article, the author examines the research published over the last 30 years on momentum and its theoretical credibility. One of the original momentum articles was published by Jegadeesh and Titman in 1993, and is considered the seminal work on the topic. The research review contained in this publication begins with the 1993 work and confines itself to only the highest quality journals among the plethora of work that has been published on momentum.

Momentum Investing: How Did Momentum Investing Perform After the Previous Two Valuation Peaks?

By |September 16th, 2022|Research Insights, Factor Investing, Momentum Investing Research|

How did Momentum investing perform after the previous two valuation peaks?

This article answers this question.

Key finding: Momentum investing also performed well following episodes when value stocks were cheap. Of course, momentum portfolios did not perform nearly as well as value portfolios, but they did still beat the generic market.

We dig into the details below.

Alpha from Short-Term Signals

By |August 18th, 2022|Research Insights, Factor Investing, Academic Research Insight, Momentum Investing Research|

Short-term alpha signals are generally dismissed in traditional asset pricing models, primarily due to market friction concerns. However, this paper demonstrates that investors can obtain a significant net alpha by combining signals applied on a liquid global universe with simple buy/sell trading rules. The composite model consists of short-term reversal, short-term momentum, short-term analyst revisions, short-term risk, and monthly seasonality signals. The resulting alpha is present across regions, translates into long-only applications, is robust to incorporating implementation lags of several days, and is uncorrelated to traditional Fama-French factors.

What Drives Momentum and Reversal?

By |August 8th, 2022|Price Pressure Factor, Overnight Returns Research, Research Insights, Factor Investing, Basilico and Johnsen, Academic Research Insight, Momentum Investing Research|

How information affects asset prices is of fundamental importance. Public information flows through news, while private information flows through trading. We study how stock prices respond to these two information flows in the context of two major asset pricing anomalies— short-term reversal and momentum. Firms release news primarily during non-trading hours, which is reflected in overnight returns. While investors trade primarily intraday, which is reflected in intraday returns. Using a novel dataset that spans almost a century, we find that portfolios formed on past intraday returns display strong reversal and momentum. In contrast, portfolios formed on past overnight returns display no reversal or momentum. These results are consistent with underreaction theories of momentum, where investors underreact to the information conveyed by the trades of other investors.

Avoiding Momentum Crashes

By |August 4th, 2022|Research Insights, Factor Investing, Larry Swedroe, Academic Research Insight, Momentum Investing Research|

Across markets, momentum is one of the most prominent anomalies and leads to high risk-adjusted returns. On the downside, momentum exhibits huge tail risk as there are short but persistent periods of highly negative returns. Crashes occur in rebounding bear markets, when momentum displays negative betas and momentum volatility is high. Based on ex-ante calculations of these risk measures we construct a crash indicator that effectively isolates momentum crashes from momentum bull markets. An implementable trading strategy that combines both systematic and momentum-specific risk more than doubles the Sharpe ratio of original momentum and outperforms existing risk management strategies over the 1928–2020 period, in 5 and 10-year sub-samples, and an international momentum portfolio.

Momentum Everywhere, Including in Factors

By |July 14th, 2022|Research Insights, Factor Investing, Larry Swedroe, Trend Following, Academic Research Insight, Value Investing Research, Momentum Investing Research|

Managed portfolios that exploit positive first-order autocorrelation in monthly excess returns of equity factor portfolios produce large alphas and gains in Sharpe ratios. We document this finding for factor portfolios formed on the broad market, size, value, momentum, investment, prof- itability, and volatility. The value-added induced by factor management via short-term momentum is a robust empirical phenomenon that survives transaction costs and carries over to multi-factor portfolios. The novel strategy established in this work compares favorably to well-known timing strategies that employ e.g. factor volatility or factor valuation. For the majority of factors, our strategies appear successful especially in recessions and times of crisis.

Combining Factors in Multifactor Portfolios

By |June 30th, 2022|Research Insights, Factor Investing, Larry Swedroe, Value Investing Research, Momentum Investing Research|

Reschenhofer’s findings demonstrate the important role that portfolio construction rules (such as creating efficient buy and hold ranges or imposing screens that exclude stocks with negative momentum) play in determining not only the risk and expected return of a portfolio but how efficiently the strategy can be implemented (considering the impact of turnover and trading costs)—wide (narrow) thresholds reduce (increase) portfolio turnover and transactions costs, thereby increasing after-cost returns and Sharpe ratios. His findings also provide support for multiple characteristics-based scorings to form long-only factor portfolios, encouraging the combination of slow-moving characteristics (such as value, investment and/or profitability) conditional on fast moving characteristics (such as momentum), to reduce portfolio turnover and transactions cost. Fund families such as AQR, Avantis, Bridgeway and Dimensional use such an approach, integrating multiple characteristics into their portfolios conditional on momentum signals.

Can Machine Learning Identify Future Outperforming Active Equity Funds?

By |June 23rd, 2022|Research Insights, Factor Investing, Larry Swedroe, Trend Following, Academic Research Insight, Machine Learning, Value Investing Research, Momentum Investing Research|

We show, using machine learning, that fund characteristics can consistently differentiate high from low-performing mutual funds, as well as identify funds with net-of-fees abnormal returns. Fund momentum and fund flow are the most important predictors of future risk-adjusted fund performance, while characteristics of the stocks that funds hold are not predictive. Returns of predictive long-short portfolios are higher following a period of high sentiment or a good state of the macro-economy. Our estimation with neural networks enables us to uncover novel and substantial interaction effects between sentiment and both fund flow and fund momentum.

Does Emerging Markets Investing Make Sense?

By |June 17th, 2022|Factor Investing, Research Insights, Key Research, Value Investing Research, Momentum Investing Research, Active and Passive Investing, Macroeconomics Research|

The analysis above suggests that portfolios that include or exclude emerging allocations are roughly the same. For some readers, this may be a surprise, but for many readers, this may not be "news." That said, even if the data don't strictly justify an Emerging allocation, the first principle of "stay diversified" might be enough to make an allocation.

Of course, the assumptions always matter.

The Unintended Consequences of Single Factor Strategies

By |June 10th, 2022|Quality Investing, Research Insights, Factor Investing, Larry Swedroe, Value Investing Research, Momentum Investing Research|

Since the 1992 publication of “The Cross-Section of Expected Stock Returns” by Eugene Fama and Kenneth French factor-based strategies and products have become an integral part of the global asset management landscape. While “top-down” allocation to factor premiums (such as size, value, momentum, quality, and low volatility) has become mainstream, questions remain about how to efficiently gain exposure to these premiums. Today, many generic factor products, often labeled as “smart beta”, completely disregard the impact of other factors when constructing portfolios with high exposures to any single factor. However, recent research, such as 2019 study “The Characteristics of Factor Investing” by  David Blitz and Milan Vidojevic, has shown that single-factor portfolios, which invest in stocks with high scores on one particular factor, can be suboptimal because they ignore the possibility that these stocks may be unattractive from the perspective of other factors that have demonstrated that they also have higher expected returns.

Short-term Momentum

By |June 3rd, 2022|Research Insights, Factor Investing, Larry Swedroe, Academic Research Insight, Momentum Investing Research|

We document a striking pattern in U.S. and international stock returns: double sorting on the previous month’s return and share turnover reveals significant short-term reversal among low-turnover stocks, whereas high-turnover stocks exhibit short-term momentum. Short-term momentum is as profitable and as persistent as conventional price momentum. It survives transaction costs and is strongest among the largest, most liquid, and most extensively covered stocks. Our results are difficult to reconcile with models imposing strict rationality but are suggestive of an explanation based on some traders underappreciating the information conveyed by prices.

Strategies to Mitigate Tail Risk

By |May 26th, 2022|Quality Investing, Crisis Alpha, Research Insights, Factor Investing, Larry Swedroe, Trend Following, Academic Research Insight, Momentum Investing Research|

Investors care about more than just returns. They also care about risk. Thus, prudent investors include consideration of strategies that can provide at least some protection against adverse events that lead to left tail risk (portfolios crashing). The cost of that protection (the impact on expected returns) must play an important role in deciding whether to include them. For example, buying at-the-money puts, a strategy that eliminates downside risk, should have returns no better than the risk-free rate of return, making that a highly expensive strategy.

Trend Following: Timing Fast and Slow Trends

By |May 19th, 2022|Research Insights, Factor Investing, Larry Swedroe, Trend Following, Academic Research Insight, Momentum Investing Research|

A large body of evidence demonstrates that momentum, including time-series momentum (trend following), has improved portfolio efficiency. Research has found that there are a few ways to improve on simple trend-following strategies. Techniques that have been found to improve Sharpe ratios and reduce tail risk include volatility scaling and combining fast and slow signals as well as combining long-term reversals. These have been incorporated by many fund managers into investment strategies. Cheng, Kostyuchyk, Lee, Liu and Ma provided evidence that machine learning could be used to further improve results. With that said, a word of caution on the use of machine learning is warranted. The powerful tools and the easy access to data now available to researchers create the risk that machine learning studies will find correlations that have no causation and thus the findings could be nothing more than a result of torturing the data. To minimize that risk, it is important that findings not only have rational risk- or behavioral-based explanations for believing the patterns identified will persist in the future, but they also should be robust to many tests. In this case, investors could feel more confident in the results if their findings were robust to international equities and other asset classes (such as bonds, commodities and currencies).

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