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Application of Multifactor Model to Stock Market Index Prediction using Multi-Task Deep Learning
  • 김하영 ;
  • 구형건 ;
  • 임준범 ;
  • 정계은 ;
  • 유재인
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Publication Year
2018-12
Journal
재무관리연구
Publisher
한국재무관리학회
Citation
재무관리연구, Vol.35 No.4, pp.45-67
Keyword
차원축소오토인코더장단기메모리네트워크주성분분석예측Dimensionality ReductionAutoencoderLong Short-Term Memory (LSTM) NetworkPrincipal Component Analysis (PCA)Prediction
Abstract
A multifactor model, extracting the common factors in returns and then tests whether expected returns are explained by the cross-sections of the loadings of security returns on the factors, have been popularly studied in cross-sectional return predictability in an efficient stock market. We deploy a long short-term memory (LSTM) networks in a multifactor model using individual stock returns in predicting out-of-sample return of the S&P 500 composite index from December, 2007 to December, 2010. We find that a LSTM network, a state-of-the art technique for sequence learning outperforms a factor regression by principal components. The outperformance, measured by the mean squared errors, is clear in predicting composite returns during the most recent financial crisis (January, 2008-June, 2009) when the LSTM is trained by data after dimensionality reduction by various autoencoders including denoising, and contractive autoencoder. Furthermore, we suggest a unique architecture of a multitasking network, consolidating an autoencoder and a LSTM network, resulting the best performance in application of a AE+LSTM network to a multifactor model.
ISSN
1225-0759
Language
Eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/34929
DOI
https://doi.org/10.22510/kjofm.2018.35.4.003
Type
Article
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