Prediction of Fund Net Value Based on ARIMA-LSTM Hybrid Model

TL;DR

This paper introduces an ARIMA-LSTM hybrid model for fund net value prediction, combining linear and nonlinear analysis to improve accuracy over traditional methods.

Contribution

The paper presents a novel hybrid approach that integrates ARIMA and LSTM models for more accurate fund net value prediction.

Findings

Hybrid model outperforms traditional methods in accuracy.

Effective extraction of linear and nonlinear data characteristics.

Applicable to real-world fund prediction scenarios.

Abstract

The net value of the fund is affected by performance and market, and the researchers try to quantify these effects to predict the future net value by establishing different models. The current prediction models usually can only reflect the linear variation law, poorly handled or selectively ignore their nonlinear characteristics, so the prediction results are usually less accurate. This paper uses a fund prediction method based on the ARIMA-LSTM hybrid model. After preprocessing the historical data, the first filter out the linear data characteristics with the ARIMA model, then pass the data to the LSTM model to extract the nonlinear characteristic by residual, and finally superposition the respective prediction values of the two models to obtain the prediction results of the hybrid model. Empirically shows that the methods in the paper are more accurate and applicable than traditional fund prediction methods.