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Actuarial Science and Insurance Series: investment-and-risk

Forecasting benchmarks of long-term stock returns via machine learning

Research into machine learning techniques finds potential benefits for long-term saving strategies.

Author(s): Dr Ioannis Kyriakou - Cass Business School; Professor Jens Perch - Cass Business School; Parastoo Mousavi - Cass Business School; Michael Scholz - Department of Economics, University of Graz

Recent advances in pension product development seem to favour alternatives to the risk free asset often used as a performance standard for measuring the value generated by an investment.

To this end, for the paper Forecasting benchmarks of long-term stock returns via machine learning, the simplest machine learning technique was applied 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.

The researchers found that, net-of-inflation, the combined earnings-by-price and long-short rate spread formed the 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.

The working paper is available for download at the link below.

{Forecasting benchmarks of long-term stock returns via machine learning}{https://www.cass.city.ac.uk/__data/assets/pdf_file/0019/460504/forecasting-benchmarks-long-term-stock-returns-machine-learning.pdf}
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