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Optimal Investment Portfolios for Internet Money Funds Based on LSTM and La-VaR: Evidence from China

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  • Hanxiao Wang

    (College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao 266590, China)

  • Huizi Ma

    (College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao 266590, China)

Abstract

The rapid development of Internet finance has impacted traditional investment patterns, and Internet money funds (IMFs) are involved extensively in finance. This research constructed a long short-term memory (LSTM) neural network model to predict the return rates of IMFs and utilized the value-at-risk (VaR) and liquidity-adjusted VaR (La-VaR) methods to measure the IMFs’ risk. Then, an objective programming model based on prediction and risk assessment was established to design optimal portfolios. The results indicate the following: (1) The LSTM model results show that the forecast curves are consistent with the actual curves, and the root-mean-squared error (RMSE) result is mere 0.009, indicating that the model is suitable for forecasting data with reliable time-periodic characteristics. (2) With unit liquidity cost, the La-VaR results match the actuality better than the VaR as they demonstrate that the fund-based IMFs (FUND) have the most significant risk, the bank-based IMFs (BANK) rank 2nd, and the third-party-based IMFs (THIRD) rank 3rd. (3) The programming model based on LSTM and the La-VaR can meet different investors’ preferences by adjusting the objectives and constraints. It shows that the designed models have more practical significance than the traditional investment strategies.

Suggested Citation

  • Hanxiao Wang & Huizi Ma, 2022. "Optimal Investment Portfolios for Internet Money Funds Based on LSTM and La-VaR: Evidence from China," Mathematics, MDPI, vol. 10(16), pages 1-18, August.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:16:p:2864-:d:885619
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    References listed on IDEAS

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