Neural networks for stock price prediction (2018-05-29T00:00:00.000000Z)

TL;DR

This work survey and compare the predictive power of five neural network models, namely, back propagation (BP) neural network, radial basis function (RBF) neuralnetwork, general regression neural network (GRNN), support vector machine regression (SVMR), least squares support vectors machine regresssion (LS-VMR) and BP neural network consistently and robustly outperforms the other four models.

Abstract

Due to the extremely volatile nature of financial markets, it is commonly accepted that stock price prediction is a task full of challenge. However in order to make profits or understand the essence of equity market, numerous market participants or researchers try to forecast stock price using various statistical, econometric or even neural network models. In this work, we survey and compare the predictive power of five neural network models, namely, back propagation (BP) neural network, radial basis function (RBF) neural network, general regression neural network (GRNN), support vector machine regression (SVMR), least squares support vector machine regresssion (LS-SVMR). We apply the five models to make price prediction of three individual stocks, namely, Bank of China, Vanke A and Kweichou Moutai. Adopting mean square error and average absolute percentage error as criteria, we find BP neural network consistently and robustly outperforms the other four models.

Authors

Yu-Long Zhou

1 papers

Ren-Jie Han

1 papers

TL;DR

Abstract

Authors

References31 items

A back-analysis method using an intelligent multi-objective optimization for predicting slope deformation induced by excavation

The Influencing Factors, Regional Difference and Temporal Variation of Industrial Technology Innovation: Evidence with the FOA-GRNN Model

Fuzzy chance constrained least squares twin support vector machine for uncertain classification

Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Improved Cuckoo Search

Introduction to Forecasting

Forecasting Study of Shanghai’s and Shenzhen’s Stock Markets Using a Hybrid Forecast Method

Back propagation neural network with adaptive differential evolution algorithm for time series forecasting

LSSVM-ABC Algorithm for Stock Price prediction

Wind Power Forecasting Based on the BP Neural Network

Applications of GRNN Based on Particle swarm algorithm Forecasting Stock Prices

An efficient CMAC neural network for stock index forecasting

Forecasting stock indices with back propagation neural network

Enhanced stock price variation prediction via DOE and BPNN-based optimization

A hybrid model of self-organizing maps (SOM) and least square support vector machine (LSSVM) for time-series forecasting

On comparing three artificial neural networks for wind speed forecasting

Performing stock price prediction use of hybrid model

BIOLOGICAL BRAIN‐INSPIRED GENETIC COMPLEMENTARY LEARNING FOR STOCK MARKET AND BANK FAILURE PREDICTION 1

Rainfall‐runoff modelling using artificial neural networks: comparison of network types

Optimal partition algorithm of the RBF neural network and its application to financial time series forecasting

A tutorial on support vector regression

Tornado Detection with Support Vector Machines

A Tutorial on Support Vector Machines for Pattern Recognition

Support Vector Machines: Training and Applications

Predicting Time Series with Support Vector Machines

Support Vector Method for Function Approximation, Regression Estimation and Signal Processing

Introduction to financial forecasting

A general regression neural network

S and J

Application of Several Data-Driven Techniques for Predicting Groundwater Level

Statistics for engineering and information science,(2000)

Least Squares Support Vector Machines

Field of Study

Journal Information

Name

Volume

Venue Information

Name

Type

URL

Alternate Names