Trading, AI and augmenting behaviour
Traders in financial markets perform tasks that require them to absorb large amounts of data in limited time and make rapid decisions. It is relatively easy to show that their performance is sub-optimal and costs firms hundreds of millions of dollars a year. Our company’s product augments the human expert with a variety of technologies, from anomaly detection pattern recognition algorithms to natural language generation and visualizations. In this talk I will review the challenges we experienced in adoption and adherence of new technologies by human experts, deconstructing the methods that were effective in overcoming the obstacles, and review similarities across other industries.
As Head of Trader Intelligence and Alpha Generation Strategies, Tom is responsible for creating new strategies and systems to help generate ”Trader Alpha” for Liquidnet’s Member community. Tom joined the company following Liquidnet’s acquisition of OTAS Technologies in 2017. OTAS provides next-generation market analytics and trader intelligence to the world’s leading banks and institutions. It uses artificial intelligence and big data analysis to alert clients to exceptions, allowing them to make faster, more informed trading decisions and ultimately more control over achieving best execution. Formerly CEO of OTAS, Tom remains Head of OTAS at Liquidnet and is responsible for the integration and product development of both OTAS, the market data analytics platform, and OTAS TradeShaper, the real-time microstructure analysis and trading optimization platform.
Prior to his time at OTAS, Tom worked on optimized execution and microstructure analytics in the quant trading group at Marshall Wace, and led the exotic equity derivatives quant development team at Bear Stearns/JP Morgan. Tom also spent 5 years as a research and design engineer at Intel, where he received a patent for his work on compiler optimizations targeting non-uniform memory architecture multicore processors. He holds a Ph.D. in computer science for work in computational neuroscience.