Essays about: "neural network stocks"
Showing result 1 - 5 of 9 essays containing the words neural network stocks.
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1. On modelling OMXS30 stocks - comparison between ARMA models and neural networks
University essay from Uppsala universitet/Matematiska institutionenAbstract : This thesis compares the results of the performance of the statistical Autoregressive integrated moving average (ARIMA) model and the neural network Long short-term model (LSTM) on a data set, which represents a market index. Both models are used to predict monthly, daily, and minute close prices of the OMX Stockholm 30 Index. READ MORE
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2. Volatility Forecasting with Artificial Neural Networks: Can we trust them?
University essay from Stockholms universitet/FinansieringAbstract : This thesis investigates how two types of artificial neural network models (ANN), feedforwardneural networks (FNN) and long short-term memory (LSTM), used for realized volatility (RV) forecasting, perform during high and low volatility regimes in comparison to the heterogeneousautoregressive (HAR) model. This is done for 23 stocks, constituents of the Swedish index OMXS30, between the 8th of February 2010 and the 31st of January 2022 using ten exogenous and three endogenous input variables. READ MORE
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3. 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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4. Comparing machine learning models for predicting stock market volatility using social media sentiment : A comparison of the predictive power of the Artificial Neural Network, Support Vector Machine and Decision Trees models on price volatility using social media sentiment
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : We aimed to explore how the machine learning models Artificial Neural Network (ANN), Support Vector Machine (SVM) and Decision tree (DT) compared in analyzing the effects of investor sentiment (from the forum www.reddit.com/r/wallstreetbets) in conjunction with other key parameters, to predict asset price volatility of major US corporations. READ MORE
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5. Evaluation of established and new deep learning models for time series equity securities forecasting
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : In this bachelor thesis we investigate the importance of feature selection when making predictions on time series data. We compare how well different deep neural network models perform within equity securities time series prediction, namely the models RNN (Recurrent Neural Network), LSTM (Long Short Term Memory), LSTM with a peephole connection and last but not least GRU (Gated Recurrent Unit). READ MORE
