A neural network with a case based dynamic window for stock trading prediction (original) (raw)

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.

Stock Market Data Analysis and Future Stock Prediction using Neural Network

Share market is one of the most unpredictable and place of high interest in the world. There are no significant methods exist to predict the share price. Mainly people use three ways such as fundamental analysis, statistical analysis and machine learning to predict the share price of share market but none of these methods are proved as a consistently acceptable prediction tool. So developing a prediction tool is one of the challenging tasks as share price depends on many influential factor and features. In this paper, we propose a robust method to predict the share rate using neural network based model and compare how it differ with the actual price. For that we collect the share market data of last 6 months of 10 companies of different categories, reduce their high dimensionality using Principal Component Analysis (PCA) so that the Backpropagation neural network will be able to train faster and efficiently and make a comparative analysis between Dhaka Stock Exchange (DSE) algorithm and our method for prediction of next day share price. In order to justify the effectiveness of the system, different test data of companies stock are used to verify the system. We introduce a robust method which can reduce the data dimensionality and predict the price based on artificial neural network.

Stock Prediction using Neural Network

Today neural networks have been integrated into most fields and are a very important analytical tool. Neural networks are trained without the restriction of a model to derive parameters and discover relationships, driven and shaped solely by the nature of the data. This has profound implications and applicability to the finance field. Multilayer neural network has been successfully applied to the time series forecasting. Steepest descend, a popular learning algorithm for back propagation network, converges slowly and has the difficulty in determining the network parameters. In fact, artificial neural networks have been widely used for forecasting financial markets. However, such applications to Indian stock markets are scarce. This paper applies neural network models to predict the daily returns of the BSE (Bombay Stock Exchange) Sensex. Multilayer perceptron network is used to build the daily return’s model and the network is trained using Multiple linear regression (MLR) provides a better alternative for weight initialization. It is found that the predictive power of the network model is influenced by the previous day’s return than the first three-day’s inputs. The study shows that satisfactory results can be achieved when applying neural networks to predict the BSE Sensex. However, the proposed Multilayer perceptron network with MLR weight initialization requires a lower computation cost and learns better....

Neural Networks as a Decision Maker for Stock Trading: A Technical Analysis Approach

International Journal of Smart Engineering System Design · , 2003

There has been a growing interest in applying neural networks and technical analysis indicators for predicting future stock behavior. However, previous studies have not practically evaluated the predictive power of technical indicators by employing neural networks as a decision maker to uncover the underlying nonlinear pattern of these indicators. The objective of this paper is to investigate if using these indicators as the input variables to a neural network will provide more accurate stock trend predictions, and whether they will yield higher trading pro¢ts than the traditional technical indicators. Three neural networks are examined in the study to predict the short-term trend signals of three stocks across different market industries. The overall results indicate that the proportion of correct predictions and the pro¢tability of stock trading guided by these neural networks are higher than those guided by their benchmarks.

Artificial Neural Network Approach for Stock Price and Trend Prediction

Nowadays, Demand of forecasting stock market price is increasing at a higher rate than the ever before as more people are getting connected to the stock business. As many criteria play more or less strong inductive role over the stock market, the trend and price always keep changing here. So, it is challenging to predict exact price value. But some Data mining and Machine learning techniques can be implemented to do this challenging task to predict stock market price and trend. In this study, Artificial Neural Network (ANN) is used along with windowing operator; which is highly efficient for working with time series data for predicting stock market price and trend. This study is done on Wal-Mart Stores Inc. (WMT) a listed company of New York Stock Exchange. Five years historical dataset (2010-2015) is used to undertake the experiments of this study. According to the result of this study Artificial Neural Network (ANN) can produce a rational result with a small error.