Sökning: "the stock market neural"

Visar resultat 1 - 5 av 59 uppsatser innehållade orden the stock market neural.

1. 1. A comparison of forecasting techniques: Predicting the S&P500

   Kandidat-uppsats, Uppsala universitet/Statistiska institutionen

   Författare :Axel Neikter; Nils Sjöberg; [2023]
   Nyckelord :Forecasting; machine learning; random forest; arima;

   Sammanfattning : 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. LÄS MER

3. 2. Swedish Stock and Index Price Prediction Using Machine Learning

   Kandidat-uppsats, Mälardalens universitet/Akademin för utbildning, kultur och kommunikation

   Författare :Henrik Wik; [2023]
   Nyckelord :Stock Price Prediction; Machine Learning; Time Series Analysis; Linear Regression; K-Nearest Neighbors; Random Forest; Support Vector Machines; Neural Networks;

   Sammanfattning : Machine learning is an area of computer science that only grows as time goes on, and there are applications in areas such as finance, biology, and computer vision. Some common applications are stock price prediction, data analysis of DNA expressions, and optical character recognition. LÄS MER

4. 3. An Investigation and Comparison of Machine Learning Methods for Selecting Stressed Value-at-Risk Scenarios

   Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Avdelningen för systemteknik

   Författare :Moa Tennberg; [2023]
   Nyckelord :Value-at-Risk; Total margin; Procyclicality; Machine learning; Binary classification; Supervised learning; Unsupervised learning; Random forest; Multilayer perceptron;

   Sammanfattning : Stressed Value-at-Risk (VaR) is a statistic used to measure an entity's exposure to market risk by evaluating possible extreme portfolio losses. Stressed VaR scenarios can be used as a metric to describe the state of the financial market and can be used to detect and counter procyclicality by allowing central clearing counterparities (CCP) to increase margin requirements. LÄS MER

5. 4. Classifying High-Growth Manufacturing Firms on the Swedish Stock Market:A Comparative Study Between the Logistic Regression, Support Vector Machine and Artificial Neural Network

   Master-uppsats, Lunds universitet/Nationalekonomiska institutionen

   Författare :William Fridström; [2023]
   Nyckelord :Machine Learning; Econometrics; Firm Growth; Binary Classification; Prediction; Model comparision.; Business and Economics;

   Sammanfattning : This is a comparative study between two modern machine learning algorithms, the Support Vector Machine and Artificial neural network, and one traditional econometric model, the Logistic regression. The main objective is to compare their performance by classifying high-growth companies. LÄS MER

7. 5. Stock Price Prediction with Social Media Sentiment

   Kandidat-uppsats, Göteborgs universitet/Företagsekonomiska institutionen

   Författare :Marco Cuskic; Christian Nilsson; Marcus Remgård; [2022-07-06]
   Nyckelord :Sentiment analysis; Machine learning; Financial time series; Stock price; Long Short-Term Memory;

   Sammanfattning : This thesis investigates the correlation effects between social media sentiments and the stock price of AMZN and TSLA, by utilizing pre-trained machine learning models, so-called transformers, and lexicon-based models. The comments were fetched from two sources, Reddit and Twitter. LÄS MER