Portfolio management with data mining techniques in time series analysis

Lazarenko, Iryna; Krykun, Yevhen

Portfolio management with data mining techniques in time series analysis

Файли

99-106.pdf (635.35 KB)

Дата

2025

Автори

Lazarenko, Iryna

Krykun, Yevhen

Видавець

КПІ ім. Ігоря Сікорського

Анотація

This study explores the application of Data Mining techniques, specifically deep neural networks (DNN) and recurrent neural networks (RNN), for optimizing stock portfolios. Using time series data, we compare the performance of DNN and RNN models in predicting stock prices and constructing optimal portfolios. Key evaluation metrics demonstrate that the RNN model's forecasts yield a portfolio with income and risk metrics that closely match actual values, outperforming the DNN model. Furthermore, the RNN model's portfolio weights show a stronger alignment with actual distributions, indicating superior predictive accuracy in asset allocation. This study concludes that RNN, with their inherent capability for processing sequential data, are particularly well-suited for time series forecasting in financial applications.

Ключові слова

portfolio management, Data Mining techniques, Deep Neural Networks, Recurrent Neural Networks, investment, управління інвестиційним портфелем, методи інтелектуального аналізу даних, глибокі нейронні мережі, рекурентні нейронні мережі, інвестування

Бібліографічний опис

Lazarenko, I. Portfolio management with data mining techniques in time series analysis / Lazarenko Iryna, Krykun Yevhen // Економічний вісник НТУУ «КПІ» : збірник наукових праць. – 2025. – № 34. – C. 99-106. – Бібліогр.: 8 назв.