A data-science-driven short-term analysis of Amazon, Apple, Google, and Microsoft stocks
This work addresses stock market forecasting for investors, but it appears incremental as it applies existing methods to new data without major breakthroughs.
The paper tackles short-term stock market movement prediction by combining technical analysis with machine/deep learning to classify trends into buy, hold, or sell signals for Amazon, Apple, Google, and Microsoft stocks, resulting in classification outputs and statistical analysis.
In this paper, we implement a combination of technical analysis and machine/deep learning-based analysis to build a trend classification model. The goal of the paper is to apprehend short-term market movement, and incorporate it to improve the underlying stochastic model. Also, the analysis presented in this paper can be implemented in a \emph{model-independent} fashion. We execute a data-science-driven technique that makes short-term forecasts dependent on the price trends of current stock market data. Based on the analysis, three different labels are generated for a data set: $+1$ (buy signal), $0$ (hold signal), or $-1$ (sell signal). We propose a detailed analysis of four major stocks- Amazon, Apple, Google, and Microsoft. We implement various technical indicators to label the data set according to the trend and train various models for trend estimation. Statistical analysis of the outputs and classification results are obtained.