High-frequency trading through artificial intelligence for financial innovation
Prof. Chien-Feng Huang explores AI and genetic algorithms for high-frequency trading and financial innovation.
What it examines
Prof. Chien-Feng Huang's research focuses on high-frequency trading using AI to develop efficient investment models. By leveraging big data, data mining, and machine learning, the goal is to create innovative strategies for various financial circumstances, enhancing systematic and reason-based investing.
What it concludes
The research suggests that advanced GA-based systems can significantly improve high-frequency trading strategies. Potential applications include more accurate stock price predictions and optimized trading models. Future research could further refine these methodologies and explore additional financial scenarios.
Evidence objects
The research suggests that advanced GA-based systems can significantly improve high-frequency trading strategies. Potential applications include more accurate stock price predictions and optimized trading models. Future research could further refine these methodologies and explore additional financial scenarios.
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Raw abstract and provenance
In the past two decades, Prof. Chien-Feng Huang has been working on several lines of interdisciplinary research across Artificial Intelligence (AI) and Finance, including high-frequency trading. His ultimate goal is to discover efficient as well as effective investment and trading models to create a blue ocean strategy in Finance. Big data technology, data mining and machine learning play key roles in his research because many niches have not been discovered yet or are not even comprehensible by humans currently. Prof. Huang regards this as an outstanding opportunity for AI to assist humans in exploring unknown territories in the investment world. Through AI, Prof. Huang thinks those who trust systematic, effective, reason-based investing strategies shall benefit from the systems he has developed.
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