UTILIZING ARTIFICIAL NEURAL NETWORK FOR PREDICTION IN THE NIGERIAN STOCK MARKET PRICE INDEX (original) (raw)
Related papers
PREDICTING THE NIGERIAN STOCK MARKET USING ARTIFICIAL NEURAL NETWORK
Forecasting a financial time series, such as stock market trends, would be a very important step when developing investment portfolios. This step is very challenging due to complexity and presence of a multitude of factors that may affect the value of certain securities. In this research paper, we have proved by contradiction that the Nigerian stock market is not efficient but chaotic. Two years representative stock prices of some banks stocks were analyzed using a feed forward neural network with back-propagation in Matlab 7.0. The simulation results and price forecasts show that it is possible to consistently earn good returns on investment on the Nigerian stock market using private information from an artificial neural network indicator.
Prediction of Stock Market in Nigeria Using Artificial Neural Network
— Prediction of Nigerian stock market is almost not done by any researcher and is an important factor which can be used to determine the viability of Nigerian stock market. In this paper, the prediction models were developed using Artificial Neural Network. The result of the prediction of Nigerian Stock Exchange (NSE) market index value of selected banks using Artificial Neural Network was presented. The multi-layer feed forward neural network was used, so that each output unit is told what its desired response to input signals ought to be. This work has confirmed the fact that artificial neural network can be used to predict future stock prices. The data collection period is from 2003 to 2006.
فصلنامه بورس اوراق بهادار, 2012
The main objective of this study is to find out whether an Artificial Neural Network (ANN) will be useful to predict stock market price, which is highly non-linear and uncertain. Specifically, this study will focus on forecasting TSE Price Index (TEPIX) as the most significant index of Iran Stock Market. Many data have been used as inputs to the network. These data are observations of 2000 days for a period of 9 years from 02/29/2000 to 12/03/2008. Data are divided into two categories; fundamental and technical data. The fundamental data used here are principal economic values like Dollar/Rials Exchange Rate, Gold price and Oil price. The technical data used are technical indices such as Moving Average (MA), Moving Average Convergence/Divergence (MACD), Relative Strength Index (RSI), Rate of Change (ROC), Momentum (MOM) and daily trading volume of stocks. The selected data are divided into training set and test set, in order to be entered into the network and the remaining 10% was used as the testing set. Training set consists 90% of data. Thi s classification uses 3 different approaches to assemble the training and test data, including random, deterministic and consecutive selection. Here, a feed-forward neural network (FFNN) with the most suitable algorithm for finance (i.e. Back Propagation algorithm) was used for the prediction. Predictions were made for the next day of TEPIX with a 3-4-1 topology and 1500 epochs. The performance of the ANN was evaluated by MSE. Finally, the results showed that ANN could properly recognize the relationships between fundamental and technical data and TEPIX, so that the prediction of the next day was quite possible.
Application of Neural Network in Analysis of Stock Market Prediction
2012
Predicting the stock market is very difficult since it depends on several known and unknown factors. So many methods like Technical analysis, Fundamental analysis, Time series analysis and statistical analysis etc. are all used to attempt to predict the price in the share market but none of these methods are proved as a consistently acceptable prediction tool. Artificial neural network (ANN), a field of Artificial Intelligence (AI), is relatively new, active and promising technique on finance problem such as stock exchange index prediction, bankruptcy prediction and corporate bond classification. ANN, is a popular way to identify unknown and unseen patterns in data which is suitable for share market prediction. We used Feedforward neural network trained by Back propagation algorithm to make prediction. The amalgamation of profit and time factors with training procedure made an improvement in forecasted result for Feedforward neural network.
2016
This research evaluates the performance of an Artificial Neural Network based prediction system that was employed on the Shanghai Stock Exchange for the period 21-Sep-2016 to 11-Oct-2016. It is a follow-up to a previous paper in which the prices were predicted and published before September 21. Stock market price prediction remains an important quest for investors and researchers. This research used an Artificial Intelligence system, being an Artificial Neural Network that is feedforward multi-layer perceptron with error backpropagation for prediction, unlike other methods such as technical, fundamental or time series analysis. While these alternative methods tend to guide on trends and not the exact likely prices, neural networks on the other hand have the ability to predict the real value prices, as was done on this research. Nonetheless, determination of suitable network parameters remains a challenge in neural network design, with this research settling on a configuration of 5:21:21:1 with 80% training data or 4-year of training data as a good enough model for stock prediction, as already determined in a previous research by the author. The comparative results indicate that neural network can predict typical stock market prices with mean absolute percentage errors that are as low as 1.95% over the ten prediction instances that was studied in this research.
STOCK PREDICTION SYSTEM USING ANN
Prediction of stock market returns is an important issue in finance. Artificial neural networks have been used in stock market prediction during the last decade. Predicting anything is very hard especially if the relationship between the inputs and outputs are non-linear in nature and stock price prediction is one of them. In this paper we have proposed stock prediction system, in this stock price prediction using multi-layer feed forward Artificial Neural Network (ANN). In this model we have used back propagation algorithm of ANN. As the closing price of any stock market already covers other attributes of the company, we have used historical stock prices (closing) for training the neural network. This paper was aimed at finding the best model for the prediction of Stock Exchange market values.
Indian Journal of Research in Capital Markets, 2019
This paper presents a computational approach for predicting the S&P CNX Nifty 50 Index. A neural network based model has been used in predicting the direction of the movement of the closing value of the index. The model presented in the paper also confirms that it can be used to predict price index value of the stock market. After studying the various features of the network model, an optimal model is proposed for the purpose of forecasting. The model has used the preprocessed data set of closing value of S&P CNX Nifty 50 Index. The data set encompassed the trading days from 1 st January, 2000 to 31st December, 2009. In the paper, the model has been validated across 4 years of the trading days. Accuracy of the performance of the neural network is compared using various out of sample performance measures. The highest performance of the network in terms of accuracy in predicting the direction of the closing value of the index is reported at 89.65% and with an average accuracy of 69.72% over a period of 4 years.
Academic Journal of Accounting and Economic Researches, 2016
The purpose of this research is the model fitness of predicting the companies' stock price using artificial neural networks (ANN) method of multilayer Perceptron with back propagation algorithm. The research population is Tehran Stock Exchange and National Iranian Copper Industries Company is considered as research sample. In order to model fitness, predicting of two cases is considered, in the first case, predicting occurred based on independent variables including the Tehran Stock Exchange price index, the price index of operating companies in the field of basic metals, the dollar exchange rate to Rial and monthly inflation rate and in the second case, predicting occurred based on the time series of past prices. The model of predicting stock price of National Iranian Copper Industries Company in the next day was studied and analyzed for each case individually on the fitted training data collection and then performance of fitted models in two cases, on the total testing data based on measuring criteria of error including mean absolute percentage error (MAPE), mean squared error (MSE) and root mean square error (RMSE). Evidence indicates the superiority of the predictive power of artificial neural network based on time series of the past prices. And to provide the predictive model the powerful software of MATLAB2014 is used.
A review of stock market prediction with Artificial neural network (ANN)
2013 IEEE International Conference on Control System, Computing and Engineering, 2013
Stock market is a promising financial investment that can generate great wealth. However, the volatile nature of the stock market makes it a very high risk investment. Thus, a lot of researchers have contributed their efforts to forecast the stock market pricing and average movement. Researchers have used various methods in computer science and economics in their quests to gain a piece of this volatile information and make great fortune out of the stock market investment. This paper investigates various techniques for the stock market prediction using artificial neural network (ANN). The aim of this paper is to provide a review of the applications of ANN in stock market prediction in order to determine what can be done in the future.