Energy-Efficient Neuromorphic Spiking Neural Networksfor Real-Time Event-Driven Stock Market Prediction: A Systematic Review
Keywords:
Neuromorphic Computing, Stock Market Prediction, Spiking Neural Networks, LIF Neurons, STDP, Surrogate gradient learning, Energy-efficient AI, Event-driven processing, Intel Loihi, Financial Time Series.Abstract
I believe that neuromorphic computing and financial prediction is now a genuinely exciting, yet under-researched, area of applied machine learning study. The paper presents a systematic literature review of the literature based on energy efficient Spiking Neural Networks (SNNs) in forecasting events-based real-time stock market data. Event based in character Financial time-series data is not continuous in nature, but consists of major discrete events like significant price changes, volume surges, indicator shifts, etc. This is a structural factor that makes SNNs, the only neuron model that grows the input events, a theoretically optimum architecture to process financial signals, the Leaky Integrateandfire (LIF) neuron model. We survey work in the fields of deep learning to financial forecasting, SNN training algorithms (including STDP, surrogate gradient methods and ANN-to-SNN conversion), neuromorphic hardware problems, and the small but growing number of literature applying SNNs to financial data. Significant works in IIT Kharagpur, IIT Bombay and IIT Delhi are discussed too as well as the standard foreign works. The review identifies four gaps in research, including standardised SNN benchmarks on financial data, the analysis of the real implementation of hardware to financial SNNs, the analysis of multimodal SNN architecture to financial inputs, and the near-complete absence of online learning protocols which are suitable on non-stationary market environments. These areas of gaps are visible and concrete and in which future research can be done on the frontier of neuromorphic financial intelligence.
References
[1] AbouHassan, I., Kasabov, N. K., Jagtap, V., and Kulkarni, P. (2023). Spiking neural networks for predictive and explainable modelling of multimodal streaming data with a case study on financial time series and online news. Scientific Reports, 13, Article 18367. https://doi.org/10.1038/s41598-023-42605-0 [2] Mohan, A.,et al. (2025). Spiking Neural Network for Cross-Market Portfolio Optimization in Financial Markets: A Neuromorphic Computing Approach. IIT Kharagpur. https://arxiv.org/abs/2510.15921
[3] Anwani, N. and Rajendran, B. (2019). Training multi-layer spiking neural networks using NormAD based spatio-temporal error backpropagation. Neurocomputing, Elsevier. IIT Bombay. https://doi.org/10.1016/j.neucom.2019.01.104
[4] Suri, M., et al. (2021). DEXAT: Double Exponential Adaptive Threshold Neuron for accurate, fast and energy- efficient neuromorphic AI. Nature Communications. IIT Delhi. https://doi.org/10.1038/s41467-021-26558-4
[5] Muduli, P. K. and Bhowmik, D. (2023). Spintronics-based neuromorphic synaptic device for STDP learning. ACS Applied Electronic Materials. IIT Delhi and IIT Bombay. https://doi.org/10.1021/acsaelm.2c01536
[6] IOP Conference Series: Journal of Physics. (2021). Spiking neural networks for financial time-series classification. Journal of Physics: Conference Series, 1828(1), 012050. https://doi.org/10.1088/1742- 6596/1828/1/012050
[7] IEEE. (2020). Spike-timing dependent plasticity for temporal pattern recognition in financial data streams. IEEE International Conference on Neural Networks. https://doi.org/10.1109/ICNN.2020.9290393
[8] IEEE. (2024). Neuromorphic energy efficient stress detection using SNN on Intel Loihi-2. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/TNNLS.2025.11331306
[9] arXiv Preprint. (2021). Spiking neural networks for financial time-series modelling: energy-efficient approaches to market prediction. https://arxiv.org/abs/2106.02568
[10] 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
[11] Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
[12] Hazan, A., Saunders, D., Khan, H., Sanghavi, D., Siegelmann, H. T., and Kozma, R. (2018). BindsNET: A machine learning-oriented spiking neural networks library in Python. Frontiers in Neuroinformatics, 12, 89. https://doi.org/10.3389/fninf.2018.00089
[13] Preprints.org. (2025). Neuromorphic Computing with Large Scale Spiking Neural Networks. https://www.preprints.org/manuscript/202503.1505
[14] Chen, X., Tang, G., Ren, Y., Lin, X., and Li, T. (2024). Accurate and efficient stock market index prediction: an integrated approach based on VMD-SNNs. Journal of Applied Statistics, 52(4), 841-867. https://doi.org/10.1080/02664763.2024.2395961
[15] Intel Corporation. (2021). Taking Neuromorphic Computing with Loihi 2 to the Next Level. Intel Technology Brief. https://download.intel.com/newsroom/2021/new-technologies/neuromorphic-computing-loihi-2-brief.pdf
[16] 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
[17] Fang, W., Yu, Z., Chen, Y., Masquelier, T., Huang, T., and Tian, Y. (2021). Incorporating learnable membrane time constant to enhance learning of spiking neural networks. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). https://doi.org/10.1109/ICCV48922.2021.00344
[18] Intel Corporation. (2021). Intel Advances Neuromorphic with Loihi 2, New Lava Software Framework and New Partners. Press Release. https://www.intc.com/news-events/press-releases/detail/1502
[19] Nature npj Unconventional Computing. (2025). Integrated algorithm and hardware design for hybrid neuromorphic systems. https://doi.org/10.1038/s44335-025-00036-2
[20] Open Neuromorphic. (2023). A Look at Loihi — Intel Neuromorphic Chip. https://open- neuromorphic.org/neuromorphic-computing/hardware/loihi-intel/
[21] Uplatz Blog. (2024). The Convergence of Spiking Neural Networks and Neuromorphic Hardware: An In- Depth Analysis of Intel's Loihi 2 Ecosystem. https://uplatz.com/blog/the-convergence-of-spiking-neural-networks- and-neuromorphic-hardware
[22] Engineering.com. (2021). Is Neuromorphic Computing the Future of AI? A Look at Intel's New Loihi 2 Chip. https://www.engineering.com/is-neuromorphic-computing-the-future-of-ai
[23] Quick Market Pitch. (2025). What's new in neuromorphic computing? July 2025 market update. https://quickmarketpitch.com/blogs/news/neuromorphic-computing-news [24] ArXiv. (2025). Stock Price Prediction Using a Hybrid LSTM-GNN Model. arXiv:2502.15813. https://arxiv.org/abs/2502.15813
[25] Frontiers in Neuroscience. (2024). A survey of training methods for spiking neural networks. Frontiers in Neuroscience, 18:1383844. https://doi.org/10.3389/fnins.2024.1383844
[26] Aribe Jr., Sales G. (2025). Comparative analysis of SNN training approaches for energy-efficient inference. International Journal of Engineering Trends and Technology, 73(10), 32-48. https://ijettjournal.org/Volume- 73/Issue-10/IJETT-V73I10P104.pdf
[27] Frontiers in Neuroscience. (2026). Adaptive and lightweight surrogate gradients: enhancing training efficiency of spiking neural networks. https://doi.org/10.3389/fnins.2026.1795946
