Stock Price Prediction on Daily Stock Data using Deep Neural Networks

Asmita A. Moghe

50 citations0/0 files storedInternational Conference on Advanced Computation and Telecommunication (ICACAT)

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Stock Market Forecasting Methods Hydrological Forecasting Using AI Time Series Analysis and Forecasting

Abstract

The price of a stock is volatile and complex in nature which makes its prediction a difficult task. This paper plans to predict the prices of Tata Consultancy Services (TCS) and Madras Rubber Factory Limited (MRF) stocks on a short-term basis. In this paper, a comparative analysis of various Deep Neural Network techniques applied for a stock price prediction application is done. The networks used are pertinent to the problem include Convolutional Neural Networks, Long Short-Term Memory Networks and Conv1D-LSTM. The different neural network models are trained on daily stock price data which includes Open High, Low, and Close price values. These are used to predict the next day closing price. From the last 5 days of data, the prediction is made. Results of different models a recompared with each other. In this paper, a deep neural network Conv1D-LSTM is proposed which is based on the combining of layers of two different techniques - CNN and LSTM top redict the price of a stock. The performance of the models is evaluated using RMSE, MAE and MAPE. These errors in Conv1D-LSTM model are found to be very low compared to CNN & LSTM. For stock price prediction, Conv1DLSTMnetworkisfoundtobeeffective,depending on the nature of stock hyper-parameters may require some variations.

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References

2004Forecasting stock market movement direction with support vector machine 2017Stock price prediction using LSTM, RNN and CNN-sliding window model 2016Artificial Neural Networks architectures for stock price prediction: comparisons and applications 2017Predicting Stock Market Behavior using Data Mining Technique and News Sentiment Analysis 2013Performance analysis of Indian stock market index using neural network time series model

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