Essays about: "stock using neural network"
Showing result 1 - 5 of 39 essays containing the words stock using neural network.
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1. An evaluation study of 3D imaging technology as a tool to estimate body weight and growth in dairy heifers
University essay from SLU/Dept. of Animal Nutrition and ManagementAbstract : The aim of this thesis was to evaluate the use of a 3D camera as a tool to estimate body weight and growth in dairy heifers. Data collection lasted from October 2022 to January 2023 and was performed at the Swedish Livestock Research Centre in Uppsala, Sweden. READ MORE
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2. A comparison of forecasting techniques: Predicting the S&P500
University essay from Uppsala universitet/Statistiska institutionenAbstract : Accurately predicting the S\&P 500 index means knowing where the US economy is heading. If there was a model that could predict the S\&P 500 with even some accuracy, this would be extremely valuable. Machine learning techniques such as neural network and Random forest have become more popular in forecasting. READ MORE
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3. Empirical investigation on the performance of a feed-forward artificial neural network on the Nordic stock markets
University essay from Göteborgs universitet/Graduate SchoolAbstract : In this paper, the authors have made an empirical investigation on the performance of a feed-forward artificial neural network (ANN) on the four main Nordic stock markets, Sweden, Norway, Denmark, and Finland. First, a benchmark OLS regression model is compared against an ANN model to see which model performs best in terms of predictive accuracy and has the least amount of error. READ MORE
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4. 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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5. Uncertainty quantification for neural network predictions
University essay from Umeå universitet/StatistikAbstract : Since their inception, machine learning methods have proven useful, and their usability continues to grow as new methods are introduced. However, as these methods are used for decision-making in most fields, such as weather forecasting,medicine, and stock market prediction, their reliability must be appropriately evaluated before the models are deployed. READ MORE
