Ekaterina Popovska

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About

Ekaterina Popovska is a pioneering researcher at the intersection of computational finance, energy economics, and complex systems. Her work focuses on developing advanced quantitative models for financial and energy markets, with a particular emphasis on fractal analysis and machine learning. Popovska’s most notable contribution is her 2024 paper, "Fractal-Based Robotic Trading Strategies Using Detrended Fluctuation Analysis and Fractional Derivatives: A Case Study in the Energy Market," which has already garnered 3 citations. In this work, she introduces an integrated robotic trading strategy for day-ahead energy markets, combining Detrended Fluctuation Analysis (DFA), Rescaled Range Analysis, fractional derivatives, and Long Short-Term Memory (LSTM) networks. This innovative approach demonstrates how non-linear dynamics and memory effects in time series can be harnessed for automated trading, offering a robust framework for handling the volatility and complexity of energy markets. Popovska’s research bridges theoretical mathematics with practical algorithmic trading, providing tools that enhance predictive accuracy and risk management. Her work is particularly relevant for researchers and practitioners in quantitative finance, energy economics, and artificial intelligence, highlighting her as a rising voice in the application of fractal geometry to real-world market challenges.