Portfolios formed on stock-split predictions for the next year using the GBM technique can generate abnormal returns, especially among smaller stocks. Further tests show that the abnormal returns are indeed due to GMBs' ability to successfully predict stock splits in the next year: true positive ...
In this chapter, we compared the predictive performance of financial econometrics and machine learning and deep learning methods for the returns of the stocks of the S&P 100 index. The analysis is enriched by using COVID-19-related news sentiments data collected for a period of 10 months. We ...
Bot.Bet predicts the future of stocks, commodities and currencies based on the Machine Learning of a huge amount of historic data, financial reports and the current news trend. According to their 'How it works' page, they use the Microsoft Azure Machine Learning technology to build...
Machine learning algorithmsMarket participants use a wide set of information before they decide to invest in risk assets, such as stocks. Investors often follow the news to collect the information that will help them decide which strategy to follow. In this study, we analyze how public news and...
Disclaimer(before we move on): There have been attempts to predict stock prices using time series analysis algorithms, though they still cannot be used to place bets in the real market. This is just a tutorial article that does not intent in any way to “direct” people into ...
The LSTM-based model, which we call dynamic LSTM, is initially built and continuously retrained using newly augmented data to predict future stock prices. We evaluate the proposed method using data sets of four stocks. The results show that the proposed method outperforms others in predicting ...
3) Deep Learning Model 3.1) Training and Validation Data Now that we have the data that we want to use, we need to define what defines our traning and validation data. As stocks could vary depending on the dates, the function I have created requires 3 basic arguments: Ticker Symbol: GOOG...
As a first endeavor, changes in Microsoft's stocks were predicted using the average sentiment of technology news headlines over a period of one day. A support vector machine was trained using 70% of the dataset. The average accuracies on the test set using five-fold cross-validation were 65...
Also, stocks market represents a continuous space that depends on millions parameters. Note: The purpose of the whole reinforcement learning part of this notebook is more research oriented. We will explore different RL approaches using the GAN as an environment. There are many ways in which we ...
Also, stocks market represents a continuous space that depends on millions parameters. Note: The purpose of the whole reinforcement learning part of this notebook is more research oriented. We will explore different RL approaches using the GAN as an environment. There are many ways in which we ...