A Novel Model for Stock Price Prediction Using Hybrid Neural Network

Senapati, Manas Ranjan; Das, Sumanjit; Mishra, Sarojananda

doi:10.1007/s40031-018-0343-7

Cited by 27 publications

(8 citation statements)

References 23 publications

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“…In recent studies, researchers and academics have employed artificial neural networks (ANN), hybrid neural networks (HNN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Natural Language Processing (NLP), and other methodologies to estimate stock values for a variety of purposes. Senapati [29] debuted Adaline Neural Network, a novel hybrid neural network for stock price prediction (ADNN). The neural network model used Particle Swarm Optimization (PSO) techniques to develop the hybrid model.…”

Section: -Literature Reviewmentioning

confidence: 99%

Fitting Multi-Layer Feed Forward Neural Network and Autoregressive Integrated Moving Average for Dhaka Stock Exchange Price Predicting

et al. 2022

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The stock market plays a vital role in the economic development of any country. Stock market performance can be measured by the market capitalization ratio as well as many other factors. The primary purpose of this study is to predict the movement of the stock market based on the total market capitalization of the Dhaka Stock Exchange (DSE) using autoregressive integrated moving average (ARIMA) models as well as artificial neural networks (ANN). The data set covers monthly time series data of total market capitalization from November 2001 to December 2018. This study also shows the best model for forecasting the movement of DSE market capitalization. The ARIMA (2,1,2) model is chosen from among the several ARIMA model combinations. From several artificial neural networks (ANN) models as a modern tool, a three-layer feed-forward topology using a backpropagation algorithm with five nodes in the hidden layer, one lag, and a learning rate equal to 0.01 is selected as the best model. Finally, these selected two models are compared based on the Root-Mean-Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Theil’s U statistic. The results showed that the estimated error of ANN is less than the estimated error of the traditional method. Doi: 10.28991/ESJ-2022-06-05-09 Full Text: PDF

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Section: -Literature Reviewmentioning

confidence: 99%

Fitting Multi-Layer Feed Forward Neural Network and Autoregressive Integrated Moving Average for Dhaka Stock Exchange Price Predicting

et al. 2022

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“…The authors obtained the promising result by optimizing the parameters of ELM with DE. Senapati et al (2018) integrated the Adaline neural network with modified PSO to optimize initial weights and bias of the neural network to foresee stock price of BSE by utilizing stock price time series data. Hu et al (2018) attempted to forecast the direction of S&P 500 and DJIA indices of US market by using stock prices and Google trends.…”

Section: Related Workmentioning

confidence: 99%

Swarm Intelligence Based Hybrid Neural Network Approach for Stock Price Forecasting

2021

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In this paper, a two-stage swarm intelligence based hybrid feed-forward neural network approach is designed for optimal feature selection and joint optimization of trainable parameters of neural networks in order to forecast the close price of Nifty 50, Sensex, S&P 500, DAX and SSE Composite Index for multiple-horizon (1-day ahead, 5-days-ahead and 10-days ahead) forecasting. Although the neural network can deal with complex non-linear and uncertain data but defining its architecture in terms of number of input features in the input layer, the number of neurons in the hidden layer and optimizing the weights is a challenging problem. The backpropagation algorithm is frequently used in the neural network and has a drawback to getting stuck in local minima and overfitting the data. Motivated by this, we introduce a swarm intelligence based hybrid neural network model for automatic search of features and other hlearnable neural networks' parameters. The proposed model is a combination of discrete particle swarm optimization (DPSO), particle swarm optimization (PSO) and Levenberg-Marquardt algorithm (LM) for training the feedforward neural networks. The DPSO attempts to search automatically the optimum number of features and the optimum number of neurons in the hidden layer of FFNN whereas PSO, simultaneously tune the weights and bias in different layers of FFNN. This paper also compares the forecasting efficiency of proposed model with another hybrid model obtained by integrating binary coded genetic algorithm and real coded genetic algorithm with FFNN. Simulation results indicate that the proposed model is effective for obtaining the optimized feature subset and network structure and also shows superior forecasting accuracy.

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“…The deep network now days popular in financial time series forecasting due to challenges in field of stock market and large number of features in times series data. Many researches had proposed deep learning network models to predict stock price some of implementations used recurrent NN (RNN) [46], conventional NN (CNN) [47] & long short term memory (LSTM) [48]. In [49] researcher compared the performance of DNN and tree based model in statistical commercialism.…”

Section: Literature Studymentioning

confidence: 99%

Advanced Deep Learning Framework for Stock Value Prediction

Das¹,

Mishra²

2019

IJITEE

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The main attractive feature to stock market is speedy growth of stock economic value in short yoke of time. The investor analyses the demonstration, estimated value and growth of organizations before investing money in market. The analysis may not be enough by using conventional process or some available methods suggested by different researches. In present days large number of stocks are available in market it is very difficult to study each stock by help of very few suggested foretelling methods. To know the anticipated stock value we need some advanced prediction technology for stock market. This paper introduce an advanced skillful method to plan and analyze the different organizers stock execution in market and prognosticate best suitable stock by predicting close price of stock. The projected arrangement is based on multilayer deep learning neural Network optimized by Adam optimizer. Recent 6 years (2010-2016) data of different organizations are applied to the model to demonstrate the skillfulness of the projected proficient method. From result it has been ascertained that the projected framework is best suited to all different data set of various sectors. The prediction error is very minimal as visible from outcome graph of framework

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A Novel Model for Stock Price Prediction Using Hybrid Neural Network

Cited by 27 publications

References 23 publications

Fitting Multi-Layer Feed Forward Neural Network and Autoregressive Integrated Moving Average for Dhaka Stock Exchange Price Predicting

Fitting Multi-Layer Feed Forward Neural Network and Autoregressive Integrated Moving Average for Dhaka Stock Exchange Price Predicting

Swarm Intelligence Based Hybrid Neural Network Approach for Stock Price Forecasting

Advanced Deep Learning Framework for Stock Value Prediction

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