Investing Research Articles

What is the state of machine learning in finance? In their July 2023 paper entitled “Financial Machine Learning”, Bryan Kelly and Dacheng Xiu survey studies on the use of machine learning in finance to further its reputation as an indispensable tool for understanding financial markets. They focus on the use of machine learning for statistical… Keep Reading

During 1989 through 2019, the S&P 500 Index generated 5.5% real annual return, compared to just 2.5% annual real growth in U.S. gross domestic product (GDP). How can this disconnect happen? Can it continue? In the June 2023 version of his paper entitled “End of an Era: The Coming Long-Run Slowdown in Corporate Profit Growth… Keep Reading

Below is a weekly summary of our research findings for 7/24/23 through 7/28/23. These summaries give you a quick snapshot of our content the past week so that you can quickly decide what’s relevant to your investing needs. Subscribers: To receive these weekly digests via email, click here to sign up for our mailing list.

Which previously researched variables or combinations of such variables best predict long-term U.S. stock market returns? In their June 2023 paper entitled “Estimating Long-Term Expected Returns”, Rui Ma, Ben Marshall, Nhut Nguyen and Nuttawat Visaltanachoti compare abilities of several yield, yield/growth, valuation variables and combinations across these categories of variables to predict 10-year and 20-year… Keep Reading

With monthly market state specified as positive (negative) when prior-month market excess return relative to U.S. Treasury bill (T-bill) yield is positive (negative), “Equity Factor Performance Following Positive and Negative Market Returns” reports that average monthly market excess return is 0.83% (10.0% annualized) positive market states and 0.05% (0.6% annualized) for negative states during August… Keep Reading

Do stock return anomalies perform differently after positive and negative monthly market returns? In their July 2023 paper entitled “The Market State, Mispricing and Asset Pricing Anomalies”, Michael Di Carlo and Ilias Tsiakas examine the role of the overall market state in estimating returns for stock return anomalies, specifying the market state as positive (negative)… Keep Reading

Are intraday stock market exchange-traded funds (ETF), stock sector ETFs and individual stock returns exploitably predictable at short horizons? In their June 2023 paper entitled “Intraday Stock Predictability Everywhere”, Fred Liu and Lars Stentoft study intraday U.S. equity return predictability using machine learning methods. Specifically, they: Consider the market portfolio represented by SPDR S&P 500… Keep Reading

Below is a weekly summary of our research findings for 7/17/23 through 7/21/23. These summaries give you a quick snapshot of our content the past week so that you can quickly decide what’s relevant to your investing needs. Subscribers: To receive these weekly digests via email, click here to sign up for our mailing list.

What modeling techniques help avoid biases/overfitting in use of machine learning to predict stock returns? In his July 2023 paper entitled “Less is More? Reducing Biases and Overfitting in Machine Learning Return Predictions”, Clint Howard explores how modeling choices affect machine learning as applied to predicting next-month stock returns, as follows: He considers 11 machine… Keep Reading

Does an expectation of an unusually large number of firm defaults in the coming year usefully predict stock and bond market returns? In their May 2023 paper entitled “Systematic Default and Return Predictability in the Stock and Bond Markets”, Jack Bao, Kewei Hou and Shaojun Zhang apply an iterative process to estimate the probability that… Keep Reading