Essays about: "predicting stocks returns"
Showing result 1 - 5 of 17 essays containing the words predicting stocks returns.
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1. Can Machine Be a Good Stock Picker?: Bridging the Gap between Fundamental Data and Machine Learning
University essay from Handelshögskolan i Stockholm/Institutionen för finansiell ekonomiAbstract : We investigate the efficacy of historical accounting data and consensus forecasts for relative valuation of stocks, employing tree-based machine learning methods. We run an XGBoost model for monthly cross-sections of financial and pricing data of US equities from 1984 to 2021. READ MORE
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2. LSTM-based Directional Stock Price Forecasting for Intraday Quantitative Trading
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Deep learning techniques have exhibited remarkable capabilities in capturing nonlinear patterns and dependencies in time series data. Therefore, this study investigates the application of the Long-Short-Term-Memory (LSTM) algorithm for stock price prediction in intraday quantitative trading using Swedish stocks in the OMXS30 index from February 28, 2013, to March 1, 2023. READ MORE
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3. 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
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4. Profiting from uncertainty: A study on the informativeness of insider trades during the COVID-19 pandemic
University essay from Handelshögskolan i Stockholm/Institutionen för finansiell ekonomi; Handelshögskolan i Stockholm/Institutionen för företagande och ledningAbstract : Using publicly available data on insider trades made in constituent companies of the S&P 500, this paper examines the return of- and market reactions to insider trades made during the first six months of the COVID-19 pandemic. Our results show that insider trading activity peaked in the early months of the pandemic, that insiders did possess predictive ability concerning future stock development and that they hence were able to beat the market during our sample period. READ MORE
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5. Time Series forecasting of the SP Global Clean Energy Index using a Multivariate LSTM
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Clean energy and machine learning are subjects that play significant roles in shaping our future. The current climate crisis has forced the world to take action towards more sustainable solutions. Arrangements such as the UN’s Sustainable Development Goals and the Paris Agreement are causing an increased interest in renewable energy solutions. READ MORE