Optimizing the Architecture of Artificial Neural Networks in Predicting Indian Stock Prices (original) (raw)
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2001
Neural networks are recognised as an effective tool for predicting stock prices (Shin & Han, 2000), but little is known about which configurations are best and for which indices. The present study uses genetic algorithms to find a near optimal learning rate, momentum, tolerance and network architecture for 47 indices listed on the Australian Stock Exchange (ASX). Some relationships were determined between stock index and neural network attributes, and important observations were made for the further development of a methodology for determining optimal neural network configurations.
Stock Market is the market for security where organized issuance and trading of Stocks take place either through exchange or over the counter in electronic or physical form. It plays an important role in canalizing capital from the investors to the business houses, which consequently leads to the availability of funds for business expansion. In this paper , we investigate to predict the daily excess returns of Bombay Stock Exchange (BSE) indices over the respective Treasury bill rate returns. Initially, we prove that the excess return time series do not fluctuate randomly. We are applying the prediction models of Autoregressive feed forward Artificial Neural Networks (ANN) to predict the excess return time series using lagged value. For the Artificial Neural Networks model using a Genetic Algorithm is constructed to choose the optimal topology. This paper examines the feasibility of the prediction task and provides evidence that the markets are not fluctuating randomly and finally, to apply the most suitable prediction model and measure their efficiency .
Journal of Intelligent Learning Systems and Applications (JILSA),Scientific Research Publishing (SRP www.scirp.org) , USA, ISSN Print: 2150-8402 , ISSN Online: 2150-8410, Vol. 4, No. 2, Pages 108-119, May 2012, 2012
Stock Market is the market for security where organized issuance and trading of Stocks take place either through ex- change or over the counter in electronic or physical form. It plays an important role in canalizing capital from the in- vestors to the business houses, which consequently leads to the availability of funds for business expansion. In this pa- per, we investigate to predict the daily excess returns of Bombay Stock Exchange (BSE) indices over the respective Treasury bill rate returns. Initially, we prove that the excess return time series do not fluctuate randomly. We are apply- ing the prediction models of Autoregressive feed forward Artificial Neural Networks (ANN) to predict the excess return time series using lagged value. For the Artificial Neural Networks model using a Genetic Algorithm is constructed to choose the optimal topology. This paper examines the feasibility of the prediction task and provides evidence that the markets are not fluctuating randomly and finally, to apply the most suitable prediction model and measure their effi- ciency.
Forecasting Indian Stock Market Using Artificial Neural Networks
The objective of this research is to predict the next day's opening value of Nifty-100 index of the National Stock Exchange (NSE) using Artificial Neural Networks (ANNs). The ANN is trained using Error Backpropagation Training Algorithm (EBPTA). The multilayer feedforward network is trained using the present day's closing values of seven input parameters which are Gold, Silver, Copper, Crude Oil, Natural Gas and Foreign Exchange (FOREX) rates. In addition to these six rates, the closing value of Nifty-100 index was incorporated as input using Simple Moving Average (SMA) model. The relationship between each input parameter and Nifty-100 index was studied and analyzed using correlation technique. This research proves that Nifty-100 index can be predicted using ANNs.
Integration of genetic algorithm with artificial neural network for stock market forecasting
2021
Traditional statistical as well as artificial intelligence techniques are widely used for stock market forecasting. Due to the nonlinearity in stock data, a model developed using the traditional or a single intelligent technique may not accurately forecast results. Therefore, there is a need to develop a hybridization of intelligent techniques for an effective predictive model. In this study, we propose an intelligent forecasting method based on a hybrid of an Artificial Neural Network (ANN) and a Genetic Algorithm (GA) and uses two US stock market indices, DOW30 and NASDAQ100, for forecasting. The data were partitioned into training, testing, and validation datasets. The model validation was done on the stock data of the COVID-19 period. The experimental findings obtained using the DOW30 and NASDAQ100 reveal that the accuracy of the GA and ANN hybrid model for the DOW30 and NASDAQ100 is greater than that of the single ANN (BPANN) technique, both in the short and long term.
Application of Genetic Algorithm and Neural Network in Forecasting with Good Data
Selection of effective input variables on decision making or forecasting problems, is one of the most important dilemmas in forecasting and decision making field. Due to research and problem constraints, we can not use all of known variables for forecasting or decision making in real world applications. Thus, in decision making problems or system simulations, we are trying to select important and effective variables as good data. In this paper we use a hybrid model of Genetic Algorithm (GA) and Artificial Neural Network (ANN) to determine and select effective variables on forecasting and decision making process. In this model we have used genetic algorithm to code the combination of effective variables and neural network as a fitness function of genetic algorithm. The introduced model is applied in a case study to determine effective variables on forecasting future dividend of the firms that are members of Tehran stock exchange. This model can be used in different fields such as financial forecasting, market variables prediction, intelligent robots decision making, DSS structures, etc.
Indian Stock Market Prediction Using Differential Evolutionary Neural Network Model
2012
This paper presents a scheme using Differential Evolution based Functional Link Artificial Neural Network (FLANN) to predict the Indian Stock Market Indices. The Model uses Back-Propagation (BP) algorithm and Differential Evolution (DE) algorithm respectively for predicting the Stock Price Indices for one day, one week, two weeks and one month in advance. The Indian stock prices i.e. BSE (Bombay Stock Exchange), NSE, INFY etc. with few technical indicators are considered as input for the experimental data. In all the cases, DE outperforms the BP algorithm. The Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) are calculated for performance evaluation. The MAPE and RMSE in case of DE are found to be very less in comparison to BP method. The simulation study has been done using Java-6 and NetBeans. Keywords-Stock Market Prediction; Functional Link Neural (FLANN); Differential evolution (DE); Back Propagation (BP) Algorithm; Least Mean Square (LMS) method.
Forecasting Portfolio Optimization using Artificial Neural Network and Genetic Algorithm
The 7th International Conference on Information and Communication Technology, ICoICT 2019, 2019
Investment has an important role in the economic growth of a country. The higher investment value obtained by a country, the faster the country is able to develop their prosperity. However, the investor faces some obstacle in investment activity to have a reasonable return and acceptable risk. In stock investments area, investors could increase chance of getting higher returns by making predictions and diversifying by forming a stock portfolio. Previous studies have stated that Artificial Neural Network (ANN), which are one of the machine learning models inspired by the activity of human brain cells have more advantages to predict the stock future value in terms of speed, accuracy, and the amount of data that can be processed compared to other stock prediction models. Diversification is a method of dividing investment funds into different index stocks, with the aim of reducing the investment risk. With thousands of stocks in the market, deciding which portfolio should be chosen is difficult. This study extends the scope of several previous studies, which are only limited to perform predictions using ANN or GA without forming an optimal stock portfolio. The objective of this study is to predict future stock values using ANN, then form those optimal stock portfolios using GA with aims to get the best optimization of maximal return and minimal risk value. The results of this study show, the implementation of GA as an alternative to the Single Index Model (SIM) method show better optimization index.
This paper proposes genetic algorithms (GAs) approach to feature discretization and the determination of connection weights for artificial neural networks (ANNs) to predict the stock price index. Previous research proposed many hybrid models of ANN and GA for the method of training the network, feature subset selection, and topology optimization. In most of these studies, however, GA is only used to improve the learning algorithm itself. In this study, GA is employed not only to improve the learning algorithm, but also to reduce the complexity in feature space. GA optimizes simultaneously the connection weights between layers and the thresholds for feature discretization. The genetically evolved weights mitigate the well-known limitations of the gradient descent algorithm. In addition, globally searched feature discretization reduces the dimensionality of the feature space and eliminates irrelevant factors. Experimental results show that GA approach to the feature discretization model outperforms the other two conventional models.