Article
Version 1
This version is not peer-reviewed
Deep Learning in Finance: Time Series Prediction
Version 1
: Received: 12 August 2024 / Approved: 12 August 2024 / Online: 12 August 2024 (14:50:59 CEST)
How to cite: Muhammed, A.; Olaosun, K. S.; Popoola, S. J.; Byers, J. Deep Learning in Finance: Time Series Prediction. Preprints 2024, 2024080836. https://doi.org/10.20944/preprints202408.0836.v1 Muhammed, A.; Olaosun, K. S.; Popoola, S. J.; Byers, J. Deep Learning in Finance: Time Series Prediction. Preprints 2024, 2024080836. https://doi.org/10.20944/preprints202408.0836.v1
Abstract
The purpose of this project is to explore the potential of Long Short-Term Memory (LSTM), a type of recurrent neural network (RNN), as a tool for making directional predictions for assets. The focus will be on using LSTM for classifying up/down moves in stock indices, stocks, and FX rates (EUR/USD). Various financial ratios, advanced technical indicators, and volatility estimators will be used as features to enhance the predictive power of the model. Incorporating credit spreads and news indicators can also improve the model. The advantage of LSTM is its ability to handle sequences of arbitrary length, which means that longer time frames such as 5-day, 10-day, or 1-week returns can be used for prediction. Before using RNNs, exploratory data analysis (EDA) will be performed to understand the underlying patterns in the data. This may include dimensionality reduction techniques such as autoencoding, self-organizing maps, or decision tree regression. The results of the EDA will be used to guide the selection of features for the LSTM model. It is important to note that long memory and stationarity are common characteristics of time series data, such as interest rates or economic indicators. Power-law autocorrection, Hurst exponent fractional, or Markov processes may be used to model these data. Predicting 5-day, 10-day returns for equities or 1-week, 1-month returns for financial factors presents two challenges. First, a large amount of data is required, typically 7–10 years of data. Second, positive autocorrelation in the 5-day or 10-day returns in equity time series must be isolated.
Keywords
Deep Learning; LSTM; RNN; EDA; Financial Time Series Prediction; Volatility Estimators; Stock Indices; FX Rates; EUR; USD; Python.
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment
