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Evaluering av LASSO och ARIMA algoritmerna för prognostisering i den finansiella marknaden
KTH, School of Engineering Sciences (SCI).
KTH, School of Engineering Sciences (SCI).
2019 (Swedish)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Evaluating LASSO and ARIMA Algorithms in Financial Forecasting (English)
Abstract [sv]

Att förutspå händelser i aktiemarknaden anses vara en särskilt utmanande uppgift på grund av dess komplexitet och volatilitet. I detta projekt utvärderar vi befintliga maskininlärningsalgoritmer som metoder för modellering och prognostisering i finansmarknaden. I vårt försök att förutspå stängningsvärdet på Nestlés aktiekurs, implementerades linjära LASSO- och ARIMA-modeller baserat på antagandet att datat har ett linjärt beroende. Metoderna utvärderades sedan genom att beräkna tre stycken feltermer baserat på metodernas prestanda gällande kortsiktiga och långsiktiga förutsägelser. Våra resultat tyder på att LASSO-algoritmen fungerar bättre med avseende på kortsiktiga förutsägelser medan ARIMA ger mer exakta långsiktiga förutsägelser. När det gäller förutsägelse av framtida trender visar båda metoderna god övergripande prestanda. Slutligen föreslår vi intressanta områden att överväga för att kunna göra mer precisa förutsägelser när data av hög volatilitet används.

Abstract [en]

Stock market forecasting is considered to be a particularly challenging task due to the complexity and volatility of the stock market. In this project we evaluate the performance of existing machine learning techniques as methods for modeling and predicting patterns in the financial market. In our attempt to predict the Nestl\'e stock closing price point, linear LASSO and ARIMA models were implemented based on the assumption that the volatile data has some type of linear dependency. The methods was evaluated by calculating the Mean Absolute Deviation, Mean Squared Error and Mean Absolute Percentage Error values based on their performance in making short and long-term predictions. Our results suggest that the LASSO algorithm performs better in regards to short-term predictions whereas the ARIMA provides more accurate long-term predictions. In terms of prediction of future trends, both methods show good overall performance. Finally, we propose interesting areas to consider in order to make more precise predictions on volatile data.

Place, publisher, year, edition, pages
2019.
Series
TRITA-SCI-GRU ; 2019:233
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-255711OAI: oai:DiVA.org:kth-255711DiVA, id: diva2:1341413
Supervisors
Examiners
Available from: 2019-08-08 Created: 2019-08-08 Last updated: 2019-08-08Bibliographically approved

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