Essays about: "predicting stocks returns"

Showing result 1 - 5 of 17 essays containing the words predicting stocks returns.

  1. 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 ekonomi

    Author : Tomoya Narita; Povilas Stankevicius; [2023]
    Keywords : Machine Learning; XGBoost; Relative Valuation; Convergence Trade;

    Abstract : 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

  3. 2. LSTM-based Directional Stock Price Forecasting for Intraday Quantitative Trading

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Isabella Mustén Ross; [2023]
    Keywords : Deep Learning; Long-Short-Term-Memory LSTM ; ARIMA; Financial Time Series Forecasting; Algorithmic Trading; Intraday Trading; Stock Prediction; Djupinlärning; LSTM; ARIMA; finansiella tidsserier; algoritmisk aktiehandel; intradagshandel; aktieprediktion;

    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

  4. 3. Predicting stock trading outcomes with deep learning

    University essay from Uppsala universitet/Institutionen för informationsteknologi

    Author : Emil Murman; [2022]
    Keywords : ;

    Abstract : 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

  5. 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 ledning

    Author : Sandra Nordlöw; Einar Ottesen Kalman; [2022]
    Keywords : COVID-19; Insider Trading; NPR; SEC; Market Reactions;

    Abstract : 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

  6. 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)

    Author : Klara Larsson; Freja Ling; [2021]
    Keywords : Machine learning; clean energy; neural networks; stock market; LSTM; time series; multivariate LSTM; correlation.;

    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