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Different Techniques used in Stock Market Prediction

The stock market has been one of the primary
revenue streams for many for years. The stock market is often
incalculable and uncertain; therefore predicting the ups and
downs of the stock market is an uphill task even for the financial
experts, which they been trying to tackle without any little success.
But it is now possible to predict stock markets due to rapid
improvement in technology which led to better processing speed
and more accurate algorithms. It is necessary to forswear the
misconception that prediction of stock market is only meant for
people who have expertise in finance; hence an application can be
developed to guide the user about the tempo of the stock market
and risk associated with it.The prediction of prices in stock market
is a complicated task, and there are various techniques that are
used to solve the problem, this paper investigates some of these
techniques and compares the accuracy of each of the methods.
Forecasting the time series data is important topic in many
economics, statistics, finance and business. Of the many
techniques in forecasting time series data such as the
Autoregressive, Moving Average, and the Autoregressive
Integrated Moving Average, it is the Autoregressive Integrated
Moving Average that has higher accuracy and higher precision
than other methods. And with recent advancement in
computational power of processors and advancement in
knowledge of machine learning techniques and deep learning,
new algorithms could be made to tackle the problem of predicting
the stock market. This paper investigates one of such machine
learning algorithms to forecast time series data such as Long
Short Term Memory. It is compared with traditional algorithms
such as the ARIMA method, to determine how superior the LSTM
is compared to the traditional methods for predicting the stock
market.