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A New Multivariate Predictive Model for Stock Returns

Jianying Xie

arXiv 5 Oct 2021 · Econometrics

arXiv:2110.01873 · PDF · DOI · OpenAlex · Extracted main text

Abstract

One of the most important studies in finance is to find out whether stock returns could be predicted. This research aims to create a new multivariate model, which includes dividend yield, earnings-to-price ratio, book-to-market ratio as well as consumption-wealth ratio as explanatory variables, for future stock returns predictions. The new multivariate model will be assessed for its forecasting performance using empirical analysis. The empirical analysis is performed on S&P500 quarterly data from Quarter 1, 1952 to Quarter 4, 2019 as well as S&P500 monthly data from Month 12, 1920 to Month 12, 2019. Results have shown this new multivariate model has predictability for future stock returns. When compared to other benchmark models, the new multivariate model performs the best in terms of the Root Mean Squared Error (RMSE) most of the time.

Citation extraction

28
references
75
in-text mentions
28
distinct cited
0
self-citations
13,797
main-text words

appendix boundary found by appendix_titled_section at “Appendix” · 88% of the source is main text. Read the extracted text to check this.

Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
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Showing the top 10 of 28 scored citations.