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An integrative extraction approach for index-tracking portfolio construction and forecasting under a deep learning framework

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Abstract

This paper proposed a fusion model of the deep long- and short-term memory network named as deep LSTM and the stochastic dominance named as SD filter method to construct an index-tracking portfolio. We present a practical model that provides investors for portfolio construction using the deep LSTM model on extracting stock features and integrating the dimension deduction ability of the SD approach to the fusion model. Three main conclusions are drawn. First, deep learning in a supervised framework can work in portfolio management with our tuned model. Second, our deep LSTM model with an SD selection filter has enhanced the feature extraction ability to construct an index-tracking portfolio. Third, the linear activator, rectified linear unit, cooperated with SD methods can better reduce the estimation errors than the nonlinear activator in our deep LSTM model. These findings can implement on the portfolio construction in the neural network field. Additionally, entropy suggests to evaluate the learning effect of forecasting. The SD methods also are indicated for prosecuting in other neural network models to extract features of time series data, like transformer-based models.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Authors and Affiliations

  1. International Business Administration Program, Providence University, Taichung, Taiwan, Republic of China

    Yu-Ju Wang

  2. Department of Statistics, Feng Chia University, 100 Wenhua Rd, Taichung, 407102, Taiwan, Republic of China

    Liang-Hong Wu

  3. Institute of Technology Management, National Chung Hsing University, 250, Kuo Kuang Rd, Taichung, 402, Taiwan, Republic of China

    Liang-Chuan Wu

Authors
  1. Yu-Ju Wang
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  2. Liang-Hong Wu
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  3. Liang-Chuan Wu
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Contributions

Yu-Ju Wang: Conceptualization of adopting the index tracking portfolio; writing original draft preparation text, tables, and figures. Conceptualization of LSTM design; validation and supervision. Liang-Hong Wu: Original conceptualization of bridging LSTM and stochastic dominance. Conceptualization of LSTM design; validation; visualization of experimental results; and supervision. Conceptualization of stochastic dominance. Liang-Chuan Wu: Revised and edited the manuscript.

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Correspondence to Liang-Hong Wu.

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Wang, YJ., Wu, LH. & Wu, LC. An integrative extraction approach for index-tracking portfolio construction and forecasting under a deep learning framework. J Supercomput 80, 2047–2066 (2024). https://doi.org/10.1007/s11227-023-05538-z

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  • DOI: https://doi.org/10.1007/s11227-023-05538-z

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