Anomalies and the Expected Market Return

Dong, Xi; Yan, Li; Rapach, David E.; Zhou, Guofu

doi:10.2139/ssrn.3562774

Cited by 8 publications

(7 citation statements)

References 83 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2017) Rapach and Zhou (2020). andDong et al (2020) use the LASSO and ENet to compute combination forecasts of the market return based on popular predictors from the time-series literature and numerous anomalies from the cross-sectional literature, respectively. Forecasting individual stock returns on the basis of firm characteristics in a panel framework, Freyberger et al (2020) and Gu et al (2020) employ machine-learning techniques -such as the nonparametric additive LASSO…”

mentioning

confidence: 99%

Forecasting: theory and practice

Petropoulos,

Apiletti,

Assimakopoulos

et al. 2020

Preprint

View full text Add to dashboard Cite

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice.

show abstract

mentioning

confidence: 99%

Forecasting: theory and practice

Petropoulos,

Apiletti,

Assimakopoulos

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…To date, these two literatures have evolved relatively independently." • Dong et al (2022): "The first examines whether firm characteristics can predict the cross-sectional dispersion in stock returns. These studies identify numerous equity market anomalies (e.g., Fama and French 2015;Harvey et al 2016;McLean and Pontiff 2016;Hou et al 2018).…”

Section: Related Literaturementioning

confidence: 99%

Machine learning techniques for cross-sectional equity returns’ prediction

et al. 2022

View full text Add to dashboard Cite

We compare the performance of the linear regression model, which is the current standard in science and practice for cross-sectional stock return forecasting, with that of machine learning methods, i.e., penalized linear models, support vector regression, random forests, gradient boosted trees and neural networks. Our analysis is based on monthly data on nearly 12,000 individual stocks from 16 European economies over almost 30 years from 1990 to 2019. We find that the prediction of stock returns can be decisively improved through machine learning methods. The outperformance of individual (combined) machine learning models over the benchmark model is approximately 0.6% (0.7%) per month for the full cross-section of stocks. Furthermore, we find no model breakdowns, which suggests that investors do not incur additional risk from using machine learning methods compared to the traditional benchmark approach. Additionally, the superior performance of machine learning models is not due to substantially higher portfolio turnover. Further analyses suggest that machine learning models generate their added value particularly in bear markets when the average investor tends to lose money. Our results indicate that future research and practice should make more intensive use of machine learning techniques with respect to stock return prediction.

show abstract

“…However, the ENet penalty term consists of two components, namely a LASSO component λ 1 and a ridge component λ 2 (Hoerl and Kennard, 1970). We follow Dong et al (2022) to select the value of the parameter that determines the degree of shrinkage, with the ENet coefficients being obtained by solving the following system min α,β 1 ,...,β k 1 2…”

Section: Elastic Netmentioning

confidence: 99%