AI-Powered Investment Decision Support Systems: Building Smart Data Products with Embedded Governance Controls
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Abstract
This paper explores some of the possible investment decision support systems powered by artificial intelligence, what components they include and how they operate. Progressing further, we analyze how different artificial intelligence and machine learning bases algorithm types, using different initial data and solving different tasks from simple classification of predefined assets to high-level algorithmic decision generation and implementing task can be combined together and layered to obtain a hierarchical multi-module architecture of the investment decision support system, which would maximize the advantages and minimize the disadvantages of utilizing artificial intelligence methods in the context of generating synthetic market predictions by the investment decision support system. Another critical aspect of investment decision support systems is the aggregation and optimization of the raw signals received from the prediction modules into trading signals, actionable within the high-frequency trading framework and deployable by algorithmic trading systems. We muse upon the possible trading signals aggregation function types and optimization traffic routing from the aggregated trading signals up to the algorithmic trading systems.
Within the next decade or so, investment decision support systems, generating synthetic market predictions and supporting traders dealing with tradeable assets, financial markets and instruments, will be heavily augmented and empowered with Artificial Intelligence and Machine Learning innovative algorithms and techniques, much the same way as classical industrial production architectures operated and supervised within the boundaries of the predetermined parameters are augmented and supported by Industrial AI and Machine Learning algorithms nowadays. Some of the main stages of decision making on the part of such systems follow the stages of cognitive vision and cognitive speech to some extent, observing the abstraction level ontology from raw primary inputs, such as images, sounds and other sensory data information for cognitive vision and cognitive speech systems to more complicated systems patterns formed on the system cognitive level.
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