Cloud Computing Architectures Enabled by AI for Predictive Financial Market Insights
DOI:
https://doi.org/10.5281/zenodo.20443239Keywords:
Cloud computing paradigms; Predictive analytics; Time series forecasting; Deep learning methods; Infrastructure, platform, and software as a service; Scalability; Latency; Real-time processing; Risk management; Model risk; Model interpretability; Data privacy; Privacy-preserving analytics; Compliance; AI-enabled systems; Equity markets; Fixed income and derivatives; Data availability and quality issues; Computational costs; Energy efficiency.Abstract
The abstract summarizes the research background, methodology, and results. Given the stochastic nature of financial markets, AI models, particularly recurrent neural networks (RNNs) and LSTM networks, have gained traction. Cloud architectures and services can facilitate consumption and offer new paradigms for predictive financial analytics. Past literature has primarily focused on the predictive accuracy and robustness of AI models with economic considerations having a peripheral role. Past voting models suffer from overfitting. The curvilinear relationship between portfolio risk and concentration has not been exploited in signal creation or return prediction. Time series forecasting models, K-Nearest Neighbour classifiers, and supervised regression approaches have offered mixed results. AI-driven investment strategies can be economically valuable, generating excess returns after accounting for transaction costs. Major capabilities include Infrastructure as a Service, Platform as a Service, and Software as a Service. Performance, scalability, latency, and potential for real-time processing are the key factors shaping industry adoption and determining system architecture. The study proposes a distributed framework for signal generation and portfolio strategy backtesting to assess the economic value of AI-driven investment strategies.
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