# A Novel Neuromorphic Processors Realization of Spiking Deep Reinforcement Learning for Portfolio Management

arXiv: https://arxiv.org/abs/2203.14159 · HTML: https://mlsanity.com/paper/2203.14159/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 34 references that are in the map.
- First posted on arXiv 26 Mar 2022.

## Builds on (most cited first, 14 of 14)

- [Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks](https://mlsanity.com/paper/1706.02609/) (2017)
- [SLAYER: Spike Layer Error Reassignment in Time](https://mlsanity.com/paper/1810.08646/) (2018)
- [Long short-term memory and learning-to-learn in networks of spiking neurons](https://mlsanity.com/paper/1803.09574/) (2018)
- [A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem](https://mlsanity.com/paper/1706.10059/) (2017)
- [Deep Reinforcement Learning for Trading](https://mlsanity.com/paper/1911.10107/) (2019)
- [More is Less: A More Complicated Network with Less Inference Complexity](https://mlsanity.com/paper/1703.08651/) (2017)
- [Deep Reinforcement Learning with Population-Coded Spiking Neural Network for Continuous Control](https://mlsanity.com/paper/2010.09635/) (2020)
- [Reinforcement co-Learning of Deep and Spiking Neural Networks for Energy-Efficient Mapless Navigation with Neuromorphic Hardware](https://mlsanity.com/paper/2003.01157/) (2020)
- [Spiking Neural Network on Neuromorphic Hardware for Energy-Efficient Unidimensional SLAM](https://mlsanity.com/paper/1903.02504/) (2019)
- [Application of Deep Q-Network in Portfolio Management](https://mlsanity.com/paper/2003.06365/) (2020)
- [Neural Population Coding for Effective Temporal Classification](https://mlsanity.com/paper/1909.08018/) (2019)
- [Learning First-to-Spike Policies for Neuromorphic Control Using Policy Gradients](https://mlsanity.com/paper/1810.09977/) (2018)
- [A Spiking Neural Network Emulating the Structure of the Oculomotor System Requires No Learning to Control a Biomimetic Robotic Head](https://mlsanity.com/paper/2002.07534/) (2020)
- [Integrated Fault Diagnosis and Control Design for DER Inverters using Machine Learning Methods](https://mlsanity.com/paper/2202.09996/) (2022)

## Abstract (arXiv)

The process of continuously reallocating funds into financial assets, aiming to increase the expected return of investment and minimizing the risk, is known as portfolio management. Processing speed and energy consumption of portfolio management have become crucial as the complexity of their real-world applications increasingly involves high-dimensional observation and action spaces and environment uncertainty, which their limited onboard resources cannot offset. Emerging neuromorphic chips inspired by the human brain increase processing speed by up to 1000 times and reduce power consumption by several orders of magnitude. This paper proposes a spiking deep reinforcement learning (SDRL) algorithm that can predict financial markets based on unpredictable environments and achieve the defined portfolio management goal of profitability and risk reduction. This algorithm is optimized forIntel's Loihi neuromorphic processor and provides 186x and 516x energy consumption reduction is observed compared to the competitors, respectively. In addition, a 1.3x and 2.0x speed-up over the high-end processors and GPUs, respectively. The evaluations are performed on cryptocurrency market between 2016 and 2021 the benchmark.