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Enhancing mean-reverting strategies in financial forecasting: integrating Ornstein-Uhlenbeck models with neural networks
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Resumo(s)
This thesis proposes a hybrid financial forecasting model integrating the Ornstein-Uhlenbeck
process with neural networks, particularly Long Short-Term Memory (LSTM) models. The
model aims to enhance the accuracy of mean-reverting strategies by capturing both long memory properties and the mean-reverting behavior of stock prices. The use of fractional
differencing as a preprocessing step improves data stationarity, further increasing the model's
predictive performance. This study evaluates the hybrid model's profitability using the Excess
Profitability (EP) test, while assessing the suitability of the Ornstein-Uhlenbeck process for
stock price prediction. Results show improvements in forecasting accuracy and financial
viability compared to traditional methods.
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Palavras-chave
Ornstein-Uhlenbeck process Neural networks Financial forecasting Time series analysis Machine fearning in finance Stochastic processes Financial time series Quantitative finance Predictive modeling Stationarity in time series