Alexandre Pimenta
Papers
1
Total Citations
3
H-Index
1
About
Alexandre Pimenta is a researcher in quantitative finance and computational intelligence, whose work bridges artificial neural networks and econometric modeling to address the inherent unpredictability of financial markets. His most-cited paper, "An automated investment strategy using artificial neural networks and econometric predictors" (2016), introduces a hybrid framework that combines machine learning with traditional economic indicators to forecast stock market movements and automate trading decisions. This study directly confronts the challenge of market volatility, where countless dynamic factors influence asset prices, by designing systems that can learn from historical data while adapting to new information. Though his citation count is modest—with this key work garnering 3 citations—Pimenta’s contribution lies in its practical, interdisciplinary approach: integrating econometric predictors with neural networks to reduce risk in automated trading. His research speaks to students and practitioners seeking robust, data-driven strategies for navigating financial time series, highlighting the ongoing need for models that balance predictive power with real-world complexity.
Research Focus
Key Achievements
Top Papers
- 1