Deep Learning Techniques: A New Trend in the Stock Market Prediction
Stock prediction is highly uncertain and money making approach. There has been a long history and record of stock market prediction. It started with the statistical techniques, flourished with machine learning techniques. Now the new trend is the deep learning techniques for stock market prediction.
What is the Stock Prediction?
Stock prediction is referred to a task of predicting or forecasting the future prices of a stock using any specific technique. There are various statistical and machine learning techniques which are used for this task. Generally, these techniques use historical data of stocks and predict their future values. There are many techniques which often give more than 90% accurate result in prediction.
Stock Analysis and Stock Prediction are Different
Analysis of a stock is different from the prediction of a stock. Analysis means analyzing the past and current performance of the stock and an estimate can be given as a report. People who do this task are called analyst. There are two types of analysts- Fundamental Analysts and Technical Analysts. Fundamental Analysts analyze the fundamentals of a company like its business model, balance sheet, profit/loss, debt, orders etc and give a detailed report indicating its future performance. Warren Buffet and Rakesh Jhunjhunwala are the world’s best known fundamental analysts. Technical analysts do not go behind the fundamentals of a company. They watch their chart and market performance. They give a technical report, mostly in form of predictions of its future prices.
Techniques used in Stock Prediction
There are various techniques used in stock prediction. Mainly these all techniques are either statistical techniques or machine Learning Techniques. These techniques use the historical data of a stock for which prediction is to be done. Larger the number of observations, between 5-10 years data, more accurate will be the prediction.
Regression Analysis: The most popular statistical techniques are Regression Analysis and Time Series Analysis. In regression analysis mainly Multiple Linear Regression technique is used. It fits a model based on several independent variables and one dependent variable. In the case of stock prediction, independent variables are the factors which affect the price of a stock and dependent variable is the price of the stock.
Time Series Analysis: In time series analysis, a chart is analyzed which is the plot of the price of a stock over a specific time period. The time series may have years or, months in a year or, dates in a month or, hours in a day on the X-axis of the chart. On the Y-axis of the chart, the price of that stock is represented. On the basis of this chart, future performance of that stock may be plotted by scaling the time series up to that future time.
There are various machine learning techniques used in this task. One of the most popular machine learning technique used for this task is the Artificial Neural Network.
Artificial Neural Network: Artificial Neural Network is the simulation of the biological nervous system. It takes several input parameters and gives an output value. First, this model requires training which is called the learning by the neural network. In stock market prediction, the historical data of a stock along with the historical values of factors that affect the stock price is used for training this neural network. Once the network is trained well, it can predict the price of a stock as its output. There are various types of neural networks available for this task.
Use of LSTM Recurrent Neural Networks for time series analysis is the biggest achievement in the field of stock market prediction. It has the capability to make predictions with much accuracy.
Deep Learning and Its Future in Stock Prediction
With the increase in the size of data available for analysis, there is always a requirement of techniques which can process this data and find adequate insights into it. Artificial Neural Networks have proven their capability in giving accurate results in predictions. But the increase in the size of data and its increased complexity and structure require better techniques for processing. Deep learning techniques, with an increased number of processing layers, have the capability to process these data. There are many deep learning algorithms which are being used in stock market prediction. They are giving better results than other existing conventional techniques. Deep Neural Network is a variant of the artificial neural network with extra layers of processing. There many stock prediction tasks where deep neural networks are being applied.
Seeing their capability and accuracy in results, it is sure that there is a lot to happen in the field of stock market prediction using these deep leaning techniques.