Special Issue “Machine Learning in Insurance”

Asimit, Alexandru V.; Kyriakou, Ioannis; Nielsen, Jens Perch

doi:10.3390/risks8020054

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…ML is one of the in-vogue topics in empirical finance and actuarial science (Asimit et al, 2020;Dixon et al, 2020) for asset return prediction or portfolio choice (Coqueret & Guida, 2020;Akyildirim et al, 2021Akyildirim et al, , 2022. It is often seen as " (i) a diverse collection of high-dimensional models for statistical prediction, combined with (ii) so-called 'regularization' methods for model selection and mitigation of overfit, and (iii) efficient algorithms for searching among a vast number of potential model specifications" (Gu et al, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Forecast combinations for benchmarks of long-term stock returns using machine learning methods

Scholz

2022

Ann Oper Res

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Forecast combinations are a popular way of reducing the mean squared forecast error when multiple candidate models for a target variable are available. We apply different approaches to finding (optimal) weights for forecasts of stock returns in excess of different benchmarks. Our focus lies thereby on nonlinear predictive functions estimated by a fully nonparametric smoother with the covariates and the smoothing parameters chosen by cross-validation. Based on an out-of-sample study, we find that individual nonparametric models outperform their forecast combinations. The latter are prone to in-sample over-fitting and in consequence, perform poorly out-of-sample especially when the set of possible candidates for combinations is large. A reduction to one-dimensional models balances in-sample and out-of-sample performance.

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Section: Literature Reviewmentioning

confidence: 99%

Forecast combinations for benchmarks of long-term stock returns using machine learning methods

Scholz

2022

Ann Oper Res

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“…Finally, the conditional variance of long-term stock returns for both the one and five year horizons are predicted [40]. The latter is recently mentioned in the Special Issue on "Machine Learning in Insurance" as one of the ten high-quality articles developing new theoretical or practical advances in insurance [41].…”

Section: The Technical Motivationmentioning

confidence: 99%

Debt-by-Price Ratio, End-of-Year Economic Growth, and Long-Term Prediction of Stock Returns

Mousavi

2021

Mathematics

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With the prominent role of government debt in economic growth in recent decades, one would expect that government debt alongside economic growth to be a risk factor priced in the time series of stock returns. In this paper, this idea is investigated by applying a nonparametric model, namely, a local-linear kernel smoother with the aim of forecasting long-term stock returns where the model and smoothing parameters are chosen by cross-validation. While a wide range of predictive variables are examined, we find that our newly introduced debt-by-price ratio and the third to fourth quarter economic growth are robust predictors of stock returns, beating the well-known predictive variables in the literature by a significant difference. The combination of these two covariates can explain almost 30% variation of stock returns at a one-year horizon. This is very crucial considering the difficulty in capturing even a small proportion of movements in stock returns.

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Machine Learning in P&C Insurance: A Review for Pricing and Reserving

Blier-Wong

Cossette

Lamontagne

et al. 2020

Risks

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In the past 25 years, computer scientists and statisticians developed machine learning algorithms capable of modeling highly nonlinear transformations and interactions of input features. While actuaries use GLMs frequently in practice, only in the past few years have they begun studying these newer algorithms to tackle insurance-related tasks. In this work, we aim to review the applications of machine learning to the actuarial science field and present the current state of the art in ratemaking and reserving. We first give an overview of neural networks, then briefly outline applications of machine learning algorithms in actuarial science tasks. Finally, we summarize the future trends of machine learning for the insurance industry.

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Special Issue “Machine Learning in Insurance”

Abstract: It is our pleasure to prologue the special issue on “Machine Learning in Insurance”, which represents a compilation of ten high-quality articles discussing avant-garde developments or introducing new theoretical or practical advances in this field [...]

Cited by 3 publications

References 11 publications

Forecast combinations for benchmarks of long-term stock returns using machine learning methods

Forecast combinations for benchmarks of long-term stock returns using machine learning methods

Debt-by-Price Ratio, End-of-Year Economic Growth, and Long-Term Prediction of Stock Returns

Machine Learning in P&C Insurance: A Review for Pricing and Reserving

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