Value Investing Research

Regression is a tool that can turn you into a fool

By |July 27th, 2023|Empirical Methods, Research Insights, Factor Investing, Value Investing Research|

Running regressions on past returns is a great tool for academic researchers who understand this approach's nuance, assumptions, pitfalls, and limitations. However, when factor regressions become part of a sales effort and/or are put in the hands of investors/advisors/DIYers, "the tool can quickly turn you into a fool."

And the Winner Is: Examining Alternative Value Metrics

By |July 7th, 2023|Research Insights, Factor Investing, Larry Swedroe, Value Investing Research|

Although the most efficient way to implement a value strategy may need to be clarified, it is clear that value has withstood the test of time and that some implementations are superior to others. The evidence suggests that P/B is not an efficient metric as a standalone criteria. Instead, value strategies that use P/B should include at least a measure of profitability while managing sector - and security-level diversification.

How factor exposure changes over time: a study of Information Decay

By |April 17th, 2023|Quality Investing, Factor Investing, Research Insights, Basilico and Johnsen, Academic Research Insight, Value Investing Research, Momentum Investing Research, Low Volatility Investing|

Factor strategies need to be rebalanced in order to maintain their factor exposure. But different factors decay at different rates and this affects how they should be rebalanced. For example, momentum needs to be rebalanced more than value. This study digs into these questions.

The Value Factor and Deleveraging

By |January 13th, 2023|Larry Swedroe, Factor Investing, Research Insights, Value Investing Research|

How do you separate the signal from the noise? To have confidence that a factor premium, or strategy, isn’t just the result of data mining - a lucky/random outcome - we recommended that you should require evidence that the premium has been not only persistent over long periods of time and across economic regimes, but also pervasive across sectors, countries, geographic regions and even asset classes; robust to various definitions (for example, there has been both a value and a momentum premium using many different metrics); survives transactions costs; and has intuitive risk- or behavioral-based explanations for the premium to persist.

The Role of the Secular Decline in Interest Rates in Asset Pricing Anomalies

By |November 10th, 2022|Research Insights, Value Investing Research, Macroeconomics Research|

Jules van Binsbergen, Liang Ma and Michael Schwert, authors of the September 2022 study “The Factor Multiverse: The Role of Interest Rates in Factor Discovery,” posed an interesting question: Are the findings of at least some of the reported anomalies the direct result of the 40-year secular decline in global interest rates and thus not really anomalies?

Momentum Everywhere, Including in Factors

By |July 14th, 2022|Factor Investing, Larry Swedroe, Research Insights, 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.

Does Intangible-Adjusted Book-to-Market Work?

By |July 6th, 2022|Research Insights, Factor Investing, Basilico and Johnsen, Academic Research Insight, Value Investing Research|

The book-to-market ratio has been widely used to explain the cross-sectional variation in stock returns, but the explanatory power is weaker in recent decades than in the 1970s. I argue that the deterioration is related to the growth of intangible assets unrecorded on balance sheets. An intangible-adjusted ratio, capitalizing prior expenditures to develop intangible assets internally and excluding goodwill, outperforms the original ratio significantly. The average annual return on the intangible-adjusted highminus-low (iHML) portfolio is 5.9% from July 1976 to December 2017 and 6.2% from July 1997 to December 2017, vs. 3.9% and 3.6% for an equivalent HML portfolio

Combining Factors in Multifactor Portfolios

By |June 30th, 2022|Larry Swedroe, Research Insights, Factor Investing, 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, AI and 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.

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