Scalable Cloud-Based Deep Learning for Real-Time Risk Analysis and Market Forecasting

Abstract

Cloud-Based Deep Learning for Real-Time Financial Risk Assessment and Market Forecasting Reviews key themes in real-time risk assessment and market forecasting in finance with cloud-based deep learning. Research and development directions for these real-time applications deployed in the cloud are discussed. State-of-the-art deep learning applications in the financial domain and their limitations are reviewed, providing insights for cloud engineering. Real-time risk assessment and market forecasting require cloud-based deep learning that does not reside on edge computing but rather leverages the scalable compute, storage, and orchestration resources of the cloud. Ingestion of structured and unstructured data, as well as the engineering of features for risk and forecasting signals, are foundational components of these cloud solutions. The cloud-based reinforcement-learning-driven risk assessment communications the risk of large losses and assists in strategic decision-making for high-net-worth individuals. Time-series modeling approaches deployed in the cloud achieve accurate predictions of future financial instrument price movements. With further improvements to achieve low-latency predictions, the ensemble forecasting of multiple correlated financial instruments provides information on future price movements and uncertainty quantification.

Real-time risk assessment communications the risk of large losses and assists in supporting decisions for high-net-worth individuals. These communications utilize deep reinforcement learning for the risk assessment of personalized portfolios. Accurate predictions of price movements—a key component of speculative trading—are achieved with cloud-based architectures. State-of-the-art time-series modeling approaches based on recurrent neural networks, Transformers, and their hybrids are real-time solutions with low latency for Time-series modeling. Market forecasting models provide future price movements for correlated financial instruments, and the ensemble prediction framework supports simultaneous forecasts for multiple assets. Abundant information is conveyed by ensemble predictions with a probabilistic representation, yielding quantified uncertainty for prudent trading. Cloud-based computing is increasingly prevalent in diverse domains. Nevertheless, real-time risk assessment and market forecasting in finance with the prevalent cloud-based deep-learning approach remain largely unexplored.