Abstract
A stock market is an aggregation of buyers and sellers where issuance, buying, and selling of stocks happen. Predicting stock price is a significant concern due to volatility. Historical stock price and historical price data reveal the effect of such factors. Since stock data is time series and prediction can be made accurately with time series forecasting model. LSTM (Long Short Term Memory) model, a particular kind of RNN (Recurrent Neural Network), based on time series forecasting used to predict stock price. LSTM doesn’t have long term dependencies because of its distinctive structure. The study focuses on major IT firms considering the company’s low and high prices. But, mid-price, which is a mean of the low and close price, is considered for the prediction. LSTM based methodology employing mid-price is effective in predicting values compared to other attributes and accuracy of prediction using the LSTM model. We conclude with the present model is more efficient in stock price prediction with a decrease in mean square error.
Highlights
The stock market provides the platform for the investors to buy and sell investments avenues, especially equities
Results of Infosys Stock Price Prediction Here, 3800 samples of Infosys stock prices are considered in this model
Standard Averaging method and LSTM model were employed on these samples
Summary
The stock market provides the platform for the investors to buy and sell investments avenues, especially equities. Prediction of stock prices has gain major attention in academia and industry as well This attention towards prediction is majorly due to market volatility and less awareness of the companies among investors. Predicting stock prices is a complex task as it involves various factors including rational and irrational, physical and psychological, political, market rumors, interest rates, investor mood and so on (Ghosh et al, 2019). These factors can be handled by Artificial Intelligence which considers the past data, social sentiment analysis and company’s net growth to predict stock prices. For any company to sell its shares or to be a part of trading, their credentials have to be registered in stock exchange. Upon the instructions of investor, broker executes the order and facilitates the trade
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