Deep Learning Algorithm-Based Financial Prediction Models

Helin Jia,Wei Wang

doi:10.1155/2021/5560886

Abstract

In this paper, a new FEPA portfolio forecasting model is based on the EMD decomposition method. The model is based on the special empirical modal decomposition of financial time series, principal component analysis, and artificial neural network to model and forecast for nonlinear, nonstationary, multiscale complex financial time series to predict stock market indices and foreign exchange rates and empirically investigate this hot area in financial market research. The combined forecasting model proposed in this paper is based on the idea of decomposition-reconstruction synthesis, which effectively improves the model’s prediction of internal financial time series. In this paper, we select the CSI 300 Index and foreign exchange rate as the empirical market and data and establish seven forecasting models to make predictions about the short-term running trend of the closing price. The interval EMD decomposition algorithm is introduced in this paper, considering both high and low prices to be contained in the input and output. By analyzing the closing price, high and low prices of the stock index at the same time, the volatility of this interval time series of the index and its trend can be better captured.

Highlights

Despite the rapid development of information science and technology and computer networks in the past mortal decade, interdisciplinary research promoting cross fertilization of disciplines, and the application of research methods used in many other disciplines to the financial market forecasting, it is still very difficult to predict the current and future of financial markets
For the CSI 300 Index, several forecasting models are built in this paper model, and the forecasting effect and empirical analysis of various models are described, including data sources, analysis of empirical results, eigenvalues and cumulative contribution rates after principal component analysis, performance criteria of forecasting models, empirical results, and discussion [21]. e FEPA model is compared with the reference model selected in this paper. e empirical results show that the FEPA model has significantly higher forecasting performance compared with linear models such as ARIMA
E model consists of three parts; the first part of the financial time-series empirical modal decomposition of financial time series data is decomposed to generate multilayer IMF time series; the set of IMF series is transformed by principal component analysis for dimensionality reduction, and an artificial neural network model is built for forecasting

Summary

Introduction

Despite the rapid development of information science and technology and computer networks in the past mortal decade, interdisciplinary research promoting cross fertilization of disciplines, and the application of research methods used in many other disciplines to the financial market forecasting, it is still very difficult to predict the current and future of financial markets. E foreign exchange index series is a very complex interval financial time series with numerous noises, and it is self- difficult for the author to use special data mining methods to extract valid historical information sets from this complex interval time series and build mathematical models to predict the future trend of the stock index. E main nonlinear mathematical models currently used to forecast financial markets are artificial neural networks, support vector machines, genetic algorithms, wavelet analysis, and empirical model analysis. Many of the combinatorial models that people build to predict financial markets are still based on artificial intelligence neural networks, which remain as a hot area of research and a challenging topic for experts and scholars. To find and build a more accurate model for financial market forecasting, scholars have invested a lot of effort into the algorithms of mathematical models and developed various neural networks based on unique algorithms [11]. Compared with the EMD-BPNN model, the FEPA model improves to some extent

A General Architecture for Deep Learning in Financial Market Forecasting

Forecast Analysis of CSI 300 Index Empirical Results

Deep Learning-Based Analysis of Empirical Forex Results’ Prediction

Findings

Conclusion