KEYWORDS: Machine learning, Data modeling, Artificial intelligence, Performance modeling, Signal to noise ratio, Visual process modeling, Neural networks, Modeling, Data processing, Robots
Artificial intelligence (AI) appears more and more in people's life. Many academic articles point out that there are signal to noises in the financial market itself. The paper aims to find whether or not we can avoid the emergence of signal to noises by using different factors or models. We will select several machine learning models, including linear regression model, XGBoost in recent 5 years, and long short-term memory (LSTM) in time series. Using exponential moving average as a factor, this paper compares the performance of different machine learning models in the stock market. Finally, we find the results simulated by the regressor models are similar.
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