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Visar resultat 1 - 5 av 56 uppsatser som matchar ovanstående sökkriterier.
1. Riskperception och kundupplevelse: Potentiella kunders syn på automatiserade finansiella robotrådgivare : En kvantitativ studie om unga småsparares möjliga övergång till robotrådgivning och dess påverkan av beslutet
Kandidat-uppsats, Södertörns högskola/FöretagsekonomiSammanfattning : Bakgrund: Bakgrunden presenterar digitaliseringens utveckling och jämför det med media som tidigare varit analogt. Därefter redogörs för utvecklingen av automatiserade finansiella rådgivare och de utvecklingsstadier det genomgått. LÄS MER
2. Statistical Modelling of Price Difference Durations Between Limit Order Books: Applications in Smart Order Routing
Master-uppsats, KTH/Matematisk statistikSammanfattning : The modern electronic financial market is composed of a large amount of actors. With the surge in algorithmic trading some of these actors collectively behave in increasingly complex ways. Historically, academic research related to financial markets has been focused on areas such as asset pricing, portfolio management and financial econometrics. LÄS MER
3. A Study on Algorithmic Trading
Kandidat-uppsats, KTH/Hälsoinformatik och logistikSammanfattning : Algorithms have been used in finance since the early 2000s and accounted for 25% of the market around 2005. In this research, algorithms account for approximately 85% of the market. The challenge faced by many investors and fund managers is beating the Swedish market index OMXS30. LÄS MER
4. An Artificial Neural Network Approach to Algorithmic Trading
Master-uppsats, Lunds universitet/Matematisk statistikSammanfattning : The field of machine learning has advanced significantly in recent decades, and, at the same time, computational power has improved to the point where training large machine learning models, such as artificial neural networks, is now accessible. Consequently, there has been a rise in the use of these models within the financial sector, with some firms leveraging them to assist with investment decisions. LÄS MER
5. LSTM-based Directional Stock Price Forecasting for Intraday Quantitative Trading
Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : 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. LÄS MER