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Swarm Intelligence Based Hybrid Neural Network Approach for Stock Price Forecasting

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Abstract

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 back-propagation 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 feed-forward 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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Abbreviations

ABC:

Artificial Bee Colony

ACO:

Ant Colony Optimization

ACRO:

Artificial Chemical Reaction Optimization

ADI:

Accumulation Distribution Index

ADX:

Average Directional Index

ANNs:

Artificial Neural Networks

ANFIS:

Adaptive Network Fuzzy Inference System

ARCH:

Autoregressive Conditional Heteroskedasticity

ARIMA:

Autoregressive Integrated Moving Average

ARMA:

Autoregressive Moving Average

ARV:

Average Relative Variance

ATR:

Average True Range

BBBC:

Big-Bang-Big Crunch (BBBC) Optimization

BCGA:

Binary Coded Genetic Algorithm

BFO:

Bacterial Foraging Optimization

BP:

Back-Propagation

BSE:

Bombay Stock Exchange

CCI:

Commodity Channel Index

CI:

Computational Intelligence

CL:

Close Price

DE:

Differential Evolution

DJIA:

Dow Jones Industrial Average

DNN:

Dynamic Neural Network

DPSO:

Discrete Particle Swarm Optimization

EC:

Evolutionary Computation

ELM:

Extreme Learning Machine

EMA:

Exponential Moving Average

ENN:

Elman Neural Network

ES:

Exponential Smoothing

FCRBFNN:

Fully Complex-valued Radial Basis Function Neural Network

FFNN:

Feed-Forward Neural Network

FL:

Fuzzy Logic

FLANN:

Functional Link Artificial Neural Network

GA:

Genetic Algorithm

GARCH:

Generalized Autoregressive Conditional Heteroskedasticity

GRNN:

Generalized Regression Neural Network

GWO:

Gray Wolf Optimization

HI:

High Price

HS:

Harmony Search

IHHO:

Improved Harris's hawks optimization

ISCA:

Improved Sine Cosine Algorithm

IT2FS:

Interval Type-2 Fuzzy System

%K:

Stochastic %K

LM:

Levenberg–Marquardt Algorithm

LMBP:

Levenberg–Marquardt Back-Propagation

LO:

Low Price

MAAPE:

Mean Arctangent Absolute Percentage Error

MACD:

Moving Average Convergence Divergence

MOEA:

Multi-objective Evolutionary Optimization

MOMM:

Multiobjective Model-Metric (MOMM) Learning

MSE:

Mean Squared Error

MTM:

Momentum

NAS:

Neural Architecture Search

NIA:

Nature Inspired Algorithm

OBV:

On-Balance Volume

OHLC:

Open High Low Close

OP:

Open Price

PSO:

Particle Swarm Optimization

QRNN:

Quantile Regression Neural Network

%R:

William %R

RBFNN:

Radial Basis Functional Neural network

RCGA:

Real Coded Genetic Algorithm

RMSE:

Root Mean Squared Error

RNN:

Recurrent Neural Network

ROC:

Rate of Change

RSI:

Relative Strength Index

RW:

Random Walk

SI:

Swarm Intelligence

SMA:

Simple Moving Average

SMAPE:

Symmetric Mean Absolute Percentage Error

TA:

Technical Analysis

TSI:

True Strength Index

UI:

Ulcer Index

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Authors and Affiliations

  1. School of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, 182320, India

    Gourav Kumar

  2. School of Mathematics, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, 182320, India

    Uday Pratap Singh

  3. Department of Computer Science and Engineering, Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, MP, 482005, India

    Sanjeev Jain

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  1. Gourav Kumar
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GK: Conceptualization, writing-original draft preparation, software, visualization. UPS: Methodology, result validation, writing- reviewing and editing. SJ: Writing- reviewing and supervision.

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Correspondence to Uday Pratap Singh.

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Kumar, G., Singh, U.P. & Jain, S. Swarm Intelligence Based Hybrid Neural Network Approach for Stock Price Forecasting. Comput Econ 60, 991–1039 (2022). https://doi.org/10.1007/s10614-021-10176-9

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