Date of Award
12-1-2023
Language
English
Document Type
Master's Thesis
Degree Name
Master of Arts (MA)
College/School/Department
Department of Mathematics and Statistics
Dissertation/Thesis Chair
Karin Reinhold
Committee Members
Joshua Isralowitz, Boris Goldfarb, Yunlong Feng
Keywords
Forecasting, LSTM, Machine Learning, Neural Network, Stock Market, Time Series
Subject Categories
Statistics and Probability
Abstract
This thesis investigates applying a Long Short-Term Memory (LSTM) Neural Network for forecasting optimal times to enter and exit the stock market. Given the inherent imbalance in the dataset, where most days are not opportune for market actions, and the challenge of predicting such infrequent occurrences, we will employ a five-day window before and after the identified optimal market entry or exit time. A prediction is considered correct if it is within this five-day interval. The model was trained on historical S&P 500 data, using features such as exponential and simple moving averages of the closing price, the ten-day percentage change, the application of a smoothing function, and the calculation of the slope between each day and the preceding day, derived from the smoothing function. This approach aims to enhance the model's ability to capture patterns and improve the prediction accuracy for optimal market timing.
Recommended Citation
Hall, Jerome Chinua, "Enhanced Market Timing: Long Short-Term Memory Neural Network For Optimal Entry And Exit In Stock Market" (2023). Legacy Theses & Dissertations (2009 - 2024). 3142.
https://scholarsarchive.library.albany.edu/legacy-etd/3142
