Abstract
Prediction of stock price is one of the most challenging tasks due to nonlinear nature of the stock data. Though numerous attempts have been made to predict the stock price by applying various techniques, yet the predicted price is not always accurate and even the error rate is high to some extent. Consequently, this paper endeavours to determine an efficient stock prediction strategy by implementing a combinatorial method of Grey Wolf Optimizer (GWO), Clustering and Non Linear Autoregressive Exogenous (NARX) Technique. The study uses stock data from prominent stock market i.e. New York Stock Exchange (NYSE), NASDAQ and emerging stock market i.e. Malaysian Stock Market (Bursa Malaysia), Dhaka Stock Exchange (DSE). It applies K-means clustering algorithm to determine the most promising cluster, then MGWO is used to determine the classification rate and finally the stock price is predicted by applying NARX neural network algorithm. The prediction performance gained through experimentation is compared and assessed to guide the investors in making investment decision. The result through this technique is indeed promising as it has shown almost precise prediction and improved error rate. We have applied the hybrid Clustering-GWO-NARX neural network technique in predicting stock price. We intend to work with the effect of various factors in stock price movement and selection of parameters. We will further investigate the influence of company news either positive or negative in stock price movement. We would be also interested to predict the Stock indices.
Highlights
For few decades, stock market is one of the best investment options for investors despite stock price being uncertain and unpredictable
Tsai et al have applied hybridization of artificial neural network (ANN) and decision tree (DT) data mining technique in creating stock price forecasting model for Taiwan stock market which provided them 77% accuracy which was higher than the single use of ANN or DT
This research attempts to construct a hybridized model for stock prediction and compare the performance of prediction between various stock markets
Summary
Stock market is one of the best investment options for investors despite stock price being uncertain and unpredictable. Xu has attempted to predict weekly stock price by mingling the conventional time series analysis technique with information from the Google trend website and the Yahoo finance website He had found that there is a correlation between the changes in weekly stock prices and the values of important news/events computed from the Google trend website. He argued that his result is better than traditional time series analysis he has agreed that his study has many drawbacks as well.
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