Essays about: "Stock Market Learning"
Showing result 21 - 25 of 86 essays containing the words Stock Market Learning.
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21. Predicting Stock Market Movement Using Machine Learning : Through r/wallstreetbets sentiment & Google Trends, Herding versus Wisdom of Crowds
University essay from Uppsala universitet/Företagsekonomiska institutionenAbstract : Stock market analysis is a hot-button topic, especially with the growth of online communities surrounding trading and investment. The goal of this paper is to examine the sentiment of r/wallstreetbets and the Google Trends score for a number of stocks – and then understanding whether the herding nature of investors on r/wallstreetbets is better at predicting the movement of the stock market than the WOC nature of Google Trends scores. READ MORE
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22. Deep Reinforcement Learning Approach to Portfolio Optimization
University essay from Lunds universitet/Nationalekonomiska institutionenAbstract : This paper evaluates whether a deep reinforcement learning (DRL) approach can be implemented, on the Swedish stock market, to optimize a portfolio. The objective is to create and train two DRL algorithms that can construct portfolios that will be benchmarked against the market portfolio, tracking OMXS30, and the two conventional methods, the naive portfolio, and minimum variance portfolio. READ MORE
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23. Stock Price Prediction Using Machine Learning
University essay from Södertörns högskola/NationalekonomiAbstract : Accurate prediction of stock prices plays an increasingly prominent role in the stock market where returns and risks fluctuate wildly, and both financial institutions and regulatory authorities have paid sufficient attention to it. As a method of asset allocation, stocks have always been favored by investors because of their high returns. READ MORE
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24. Learning From Investor Attention: Examining the Predictive Power of Investor Attention on Market Returns with Machine Learning
University essay from Handelshögskolan i Stockholm/Institutionen för finansiell ekonomiAbstract : We study the predictive properties of investor attention on time series market returns. Extending an earlier proposed index of investor attention aggregated from twelve popularly studied attention proxies, we show that it strongly predicts excess returns on the stock market. READ MORE
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25. Predicting stock trading outcomes with deep learning
University essay from Uppsala universitet/Institutionen för informationsteknologiAbstract : Multiple deep learning approaches are applied on price data of Swedish stocks, including traditional artificial neural networks as well as recurrent neural networks. The trained models are evaluated using average returns from backtesting rather than training and/or validation error in order to approximate real-world trading performance. READ MORE
