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Predicting the Movement of the S&P 500 Index using Machine Learning

Bah, Bakary LU (2023) DABN01 20231
Department 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:
author
Bah, Bakary LU
supervisor
organization
course
DABN01 20231
year
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}},
}