Abstract
The project proposes a type of Artificial Neural Network (ANN) known as Long Short Term Memory (LSTM) to predict the prices for a portfolio of best stocks from five different sectors since election. The current price, technical and fundamental indicators will be learned to predict the next day’s price. The LSTM model will modulate and internally optimize the prices based on certain rules. By comparing the actual and predicted prices, the network provides trading signals which will then be used to back-test the data on a reliable platform. Later, we compare the back-testing results to measure the performance and risk metrics for two types of investors – risk-averse and risk-seeking. The main purpose of this project is to develop a system which either performs on par or outperforms the market.
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