P164 Event-Driven Financial Decision Making via Spiking Neural Networks: Neuromorphic-Inspired Approach
Tae-hoon Lee1, Hoon-hee Kim*2
1 Department of Data Engineering, Pukyong National University, Busan, South Korea
2 Department of Computer Engineering and Artificial Intelligence, Pukyong National University, Busan, South Korea
*Email: h2kim@pknu.ac.kr
Introduction
Spiking Neural Networks (SNNs) are well-suited for financial decision-making due to their ability to capture temporal dynamics and process information in an event-driven manner. In volatile markets, price movements can be sudden and irregular, making asynchronous event-based processing critical for timely responses. SNNs naturally handle such inputs, modeling temporal patterns more effectively than traditional neural networks. In this study, we integrate SNNs with a Genetic Algorithm (GA) for feature selection and parameter optimization, and a Support Vector Machine (SVM) for decision-making. This pipeline leverages the adaptive, event-driven processing of SNNs to improve stock market prediction and trading decisions.
Methods
For our experiments, we used historical data from the top 20 S&P 500 stocks, encompassing bull, bear, and volatile market conditions. Price data were transformed into multiple technical indicators (e.g., moving averages, RSI). A GA then optimized the indicator parameters and selected the most predictive features. Next, the time-series features were encoded into spike trains via rate coding with a fixed time window and fed into an SNN composed of Leaky Integrate-and-Fire neurons. The SNN processed the temporal patterns, and its spiking outputs were summarized (e.g., as spike counts over time). These features were then passed to an SVM for final classification of the trading action.
Results
In backtesting, the SNN-based framework surpassed the buy-and-hold strategy across multiple market regimes, demonstrating higher predictive accuracy and stronger trading returns. This performance gap was especially evident during volatile market phases, where passive buy-and-hold approaches often struggled to adapt. By capitalizing on the event-driven nature of spiking neurons, our system reacted swiftly to abrupt price swings, refining its signals in real time and thus helping to mitigate slippage and transaction costs. Overall, these findings highlight the neuromorphic framework’s resilience and effectiveness, suggesting it can outperform simpler investment strategies under diverse market conditions.
Discussion
This work demonstrates the potential of neuromorphic computing in financial decision-making. The SNN-based approach offers adaptive, event-driven processing suited to volatile markets, while its reservoir-like architecture (with only the output classifier trained) reduces computational complexity. In addition, the model exhibits robustness to noisy market data and regime shifts. However, limitations remain: the approach relies on a predefined rate-coding scheme, and the hybrid design combining a spiking network with an external classifier is not end-to-end. Future research can explore improved encoding methods and end-to-end spiking models, as well as deployment on neuromorphic hardware for faster, energy-efficient execution.
Figure 1. Flowchart: Stock data and optimized technical indicators are converted into spike trains for the spiking neural network (SNN), whose outputs feed into a classifier for trading decisions
Acknowledgements
This study was supported by the National Police Agency and the Ministry of Science, ICT & Future Planning (2024-SCPO-B-0130), the National Research Foundation of Korea grant funded by the Korea government (RS-2023-00242528), the National Program for Excellence in SW, supervised by the IITP(Institute of Information & communications Technology Planing & Evaluation) in 2025(2024-0-00018)
References
[1] Maass, W. (1997). Networks of Spiking Neurons: The Third Generation of Neural Network Models.Neural Networks, 10(9), 1659–1671. https://doi.org/10.1016/S0893-6080(97)00011-7
[2] Holland, J. H. (1992). Genetic algorithms.Scientific American, 267(1), 66–73.http://www.jstor.org/stable/24939139
[3] Lin, X., Yang, Z., & Song, Y. (2011). Intelligent stock trading system based on improved technical analysis and Echo State Network.Expert Systems with Applications, 38(9), 11347–11354. https://doi.org/10.1016/j.eswa.2011.03.001
