Forecasting benchmarks of long-term stock returns via machine learning

Abstract

Recent advances in pension product development seem to favour alternatives to the risk free asset often used in the financial theory as a performance standard for measuring the value generated by an investment or a reference point for determining the value of a financial instrument. To this end, in this paper, we apply the simplest machine learning technique, namely, a fully nonparametric smoother with the covariates and the smoothing parameter chosen by cross-validation to forecast stock returns in excess of different benchmarks, including the short-term interest rate, long-term interest rate, earnings-by-price ratio, and the inflation. We find that, net-of-inflation, the combined earnings-by-price and long-short rate spread form our best-performing two-dimensional set of predictors for future annual stock returns. This is a crucial conclusion for actuarial applications that aim to provide real-income forecasts for pensioners.

Emerging Trends and Innovation Modes of Internet Finance—Results from Co-Word and Co-Citation Networks

“…Kyriakou et al studied the application of machine learning in stock return research [63]. Moat et al found that the data on the change of page views of Wikipedia's financial pages contained the early signs of the stock market trend, so the online data were useful in the information collection stage before the decision [64]. 1 represents the cited times in the its cluster we selected, not the real cited times.…”

Early box office prediction in China’s film market based on a stacking fusion model

“…Its goal is to develop non-biological systems to perform tasks that usually require human intelligence. Artificial intelligence and big data technology have shaped various aspects of business and management (Grover et al 2020;Kyriakou et al 2019). For example, Akter et al 2020introduced a Netflix's recommendation system, which first mines user data and then adopts different models to determine the most suitable recommendation system.…”

A Note on Combining Machine Learning with Statistical Modeling for Financial Data Analysis

“…The latter is until today an open question; presently, cross-validation-type methods are the most popular ones. For kernel based methods, see Heidenreich et al (2013) and Köhler et al (2014) for a review or Nielsen and Sperlich (2003) in the context of forecasting in finance.…”

“…Following Nielsen and Sperlich (2003), Kyriakou et al (2019), and Mammen et al (2019), you could use local linear regression in Step 1 for both functions, combined with the validated R 2 for the bandwidth choice. Obviously, any method known as ML, including LASSO variable selection, can be applied in this step.…”

“…The Editors of this Special Issue also urge practitioners not to ignore what has already been learned about financial data when using presumably fully automatic ML methods. Regarding financial data for example, Buch-Larsen et al (2005), Bolancé et al (2012), Scholz et al (2015Scholz et al ( , 2016, and Kyriakou et al (2019) (among others) have shown the significant gains in estimation and prediction when including prior knowledge in nonparametric prediction. The first two showed how knowledge-driven data transformation improves nonparametric estimation of distribution and operational risk; the third paper used parametrically-guided ML for stock return prediction; the fourth imputed bond returns to improve stock return predictions; and the last proposed comparing different theory-driven benchmark models regarding their predictability.…”