Can neural networks and genetic algorithm predict the future value of stock market?
This module employs Neural Networks and Genetic Algorithm to predict the future values of stock market. The test data used for simulation is from the Bombay Stock Exchange (BSE) for the past 40 years.
What is stock market prediction?
Stock market prediction is the “act of determining” 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.
Do stock prices reflect all available information?
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. In this project, we have proposed a stock market prediction model using Genetic Algorithm and Neural Networks.
Which data is used to train the neural network?
The data used to train the neural network is the securities exchange on the Bombay Stock Exchange (BSE) for the time period Jan 1, 1996 to Jan 1 2016. The data used to test the neural network is from Jan 2016 to 31 July 2017.
Is it possible to predict stock prices with a neural network?
Neural networks do not make any forecasts. Instead, they analyze price data and uncover opportunities. Using a neural network, you can make a trade decision based on thoroughly examined data, which is not necessarily the case when using traditional technical analysis methods.
Which neural network is best for stock prediction?
Recurrent Neural Networks may provide better predictions than the neural networks used in this study, e.g., LSTM (Long Short-Term Memory). Since statements and opinions of renowned personalities are known to affect stock prices, some Sentiment Analysis can help in getting an extra edge in stock price prediction.
Can stock prices be predicted with machine learning?
Stock Price Prediction using machine learning helps you discover the future value of company stock and other financial assets traded on an exchange. The entire idea of predicting stock prices is to gain significant profits. Predicting how the stock market will perform is a hard task to do.
Can we use RNN for stock price prediction?
The main Advantage is that since the model uses RNN, LSTM, Machine Learning and Deep Learning models the prediction of stock prices will be more accurate. And also in the model it can predict the future 30 days Stock Prices and it can show it in a graph.
Which algorithm is best for stock prediction?
LSTM, short for Long Short-term Memory, is an extremely powerful algorithm for time series. It can capture historical trend patterns, and predict future values with high accuracy.
How do you predict a stock price?
Price to Earnings ratio is one of the traditional methods to analyse the company performance and predict the prices of the stock of the company. This ratio considers the market price of the shares of the company and the earnings per share (EPS) of the company.
Can LSTM predict stock market?
Utilizing a Keras LSTM model to forecast stock trends At the same time, these models don't need to reach high levels of accuracy because even 60% accuracy can deliver solid returns. One method for predicting stock prices is using a long short-term memory neural network (LSTM) for times series forecasting.
What type of neural network is used by stock market indices?
Kuo, Chen, and Hwang (2001) developed a decision support system through combining a genetic algorithm based fuzzy neural network (GFNN) and ANN for stock market.
Is RNN and LSTM same?
LSTM networks are a type of RNN that uses special units in addition to standard units. LSTM units include a 'memory cell' that can maintain information in memory for long periods of time.
Which type of neural network is used by stock market indices?
They reported that PNN has higher performance in stock index than generalized methods of moments-Kalman filter and random walk forecasting models. Kuo, Chen, and Hwang (2001) developed a decision support system through combining a genetic algorithm based fuzzy neural network (GFNN) and ANN for stock market.
Does Arima work on stocks?
One of the most widely used models for predicting linear time series data is this one. The ARIMA model has been widely utilized in banking and economics since it is recognized to be reliable, efficient, and capable of predicting short-term share market movements.
How does RNN predict?
A simplified sequence of the RNN's process steps goes as follows: It does a forward pass and computes the prediction errors to obtain the loss values on the training dataset and on the validation set. It calculates the gradients at each layer, and backpropagates the errors, back across t timesteps.
How accurate is LSTM?
Accuracy in this sense is fairly subjective. RMSE means that on average your LSTM is off by 0.12, which is a lot better than random guessing. Usually accuracies are compared to a baseline accuracy of another (simple) algorithm, so that you can see whether the task is just very easy or your LSTM is very good.
What is a feature in machine learning?
In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon being observed. Choosing informative, discriminating and independent features is a crucial step for effective algorithms in pattern recognition, classification and regression. The features used in this project are as follows:
What is the efficient market hypothesis?
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.