Comparison of Different Machine Learning Prediction about Stock based on Multi-Factor Input-Taking the Nasdaq Index as an Example

Abstract

Stock investment has always been widely concerned, and the prediction of future stock trends is what many investors look forward to. There are numerous techniques that can be used to predict stocks as machine learning advances. The commonly used method is support vector machine, random forest, linear regression, etc. Recurrent neural networks, multi-layer perceptron, single-layer LSTM networks, naive Bayes networks, convolutional neural networks, back propagation networks, etc. are examples of deep learning techniques. In historical studies, researchers have tended to predict directly from stock prices or used time series as an independent variable to build a forecasting model for stock prices. In this study, we propose input training parameters based on stock indicators and build a sliding window of it to predict the future price. Based on LSTM, ANN, support vector machine regression, Linear Regression to make predictions on price, and analyze the differences between them. Our research is based on the Nasdaq and the evaluation values show that the neural network approach is effective for stock return forecasting.