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

Hanxiao Wang and Huizi Ma ()
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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

Mathematics, 2022, vol. 10, issue 16, 1-18

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.

Keywords: Internet money funds; long short-term memory neural network model; liquidity-adjusted VaR; risk prediction; investment portfolio design (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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