Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on an exchange. The successful prediction of a stock's future price could yield significant profit. The efficient-market hypothesis suggests that stock prices reflect all currently available information and any price changes that are not based on newly revealed information thus are inherently unpredictable. Others disagree and those with this viewpoint possess myriad methods and technologies which purportedly allow them to gain future price. With the advent of the digital computer, stock market prediction has since moved into the technological realm.
Problem statement was to predict increase or decrease in stock price for next day. I addressed this as classification problem. There are many classification algorithms in neural network (Examples: SVM,Back Propagation Algorithm,LSTm etc).I have LSTM as my algorithm.LSTM was used for many time series problems and the same approach is used to predict trend for stock by memorizing history data.
LSTM stands for Long Short Term memory. It is building block of a neural network (like perceptron). LSTM blocks are used to build a recurrent neural network. An RNN is a type of neural network where the output of a block is fed as input to the next iteration. An LSTM block is composed of four main components: a cell, an input gate, an output gate and a forget gate. The cell is responsible for "remembering" values over arbitrary time intervals; hence the word "memory" in LSTM. Each of the three gates can be thought of as a "conventional" artificial neuron, as in a multi-layer (or feedforward) neural network: that is, they compute an activation (using an activation function) of a weighted sum. Intuitively, they can be thought as regulators of the flow of values that goes through the connections of the LSTM; hence the denotation "gate". There are connections between these gates and the cell. Some of the connections are recurrent, some of them are not.
In this project, I have demonstrated a machine learning approach to predict stock market trend using LSTM. Result shows how I can use the data to predict stock movement with reasonable accuracy. I would like to conclude that if I incorporate all the factors that affect stock performance and feed them to neural network with proper data preprocessing and filtering, after training the network I will be able to have a model which can predict stock momentum very accurately and this can result into better stock forecasting and profit for financial firms.