Predicting the Movement of the S&P 500 Index using Machine Learning
(2023) DABN01 20231Department of Economics
Department of Statistics
- Abstract
- Predicting the stock market has been a longstanding topic of interest in financial research. It is regarded as a highly challenging but important task given the vital role the financial markets play in shaping the global economies. In this thesis, the goal is to predict the movement of the S&P 500 Index using machine learning methods. To this end, we apply two machine learning algorithms, random forest, and logistic regression, to financial data in a quest to try and predict if the S&P 500 Index will move in a positive or negative direction the following day. To further test the validity of the best performing machine learning model in our study, we develop a dynamic trading strategy where the predictions of the model act as an investment... (More)
- Predicting the stock market has been a longstanding topic of interest in financial research. It is regarded as a highly challenging but important task given the vital role the financial markets play in shaping the global economies. In this thesis, the goal is to predict the movement of the S&P 500 Index using machine learning methods. To this end, we apply two machine learning algorithms, random forest, and logistic regression, to financial data in a quest to try and predict if the S&P 500 Index will move in a positive or negative direction the following day. To further test the validity of the best performing machine learning model in our study, we develop a dynamic trading strategy where the predictions of the model act as an investment signal. If the model predicts that the S&P 500 Index will move in a positive direction the following day, we invest in equities (SPDR S&P 500 ETF Trust). Conversely, if the model predicts a negative movement, we instead invest in fixed income (Vanguard Total Bond Market ETF). We assess the performance of the trading strategy by comparing its Sharpe ratio to a second strategy, a traditional portfolio that holds 60% equities and 40% fixed income. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9137039
- author
- Bah, Bakary LU
- supervisor
- organization
- course
- DABN01 20231
- year
- 2023
- type
- H1 - Master's Degree (One Year)
- subject
- keywords
- Machine Learning, S&P 500 Index, Random Forest, Logistic Regression
- language
- English
- id
- 9137039
- date added to LUP
- 2023-11-21 12:53:31
- date last changed
- 2023-11-21 12:53:31
@misc{9137039, abstract = {{Predicting the stock market has been a longstanding topic of interest in financial research. It is regarded as a highly challenging but important task given the vital role the financial markets play in shaping the global economies. In this thesis, the goal is to predict the movement of the S&P 500 Index using machine learning methods. To this end, we apply two machine learning algorithms, random forest, and logistic regression, to financial data in a quest to try and predict if the S&P 500 Index will move in a positive or negative direction the following day. To further test the validity of the best performing machine learning model in our study, we develop a dynamic trading strategy where the predictions of the model act as an investment signal. If the model predicts that the S&P 500 Index will move in a positive direction the following day, we invest in equities (SPDR S&P 500 ETF Trust). Conversely, if the model predicts a negative movement, we instead invest in fixed income (Vanguard Total Bond Market ETF). We assess the performance of the trading strategy by comparing its Sharpe ratio to a second strategy, a traditional portfolio that holds 60% equities and 40% fixed income.}}, author = {{Bah, Bakary}}, language = {{eng}}, note = {{Student Paper}}, title = {{Predicting the Movement of the S&P 500 Index using Machine Learning}}, year = {{2023}}, }