I worked at Market IQ to help develop a stock trading system that uses natural language analysis of real-time news events broadcast on Twitter, blogs, newswire services, and other sources to guide stock purchase decisions.
By analyzing the event-driven, online machine learning systems and applying various techniques (such as kernel methods) to improve the algorithms, I was able to increase classification accuracy of predicted stock direction by 12.7% despite a noisy input dataset.
Additionally, I conducted due diligence on target North American companies in the technology sector by preparing research reports and constructing discounted cash flow models to determine if the valuations generated by the algorithm are within a realistic range.
I loved the energy that Bill brought to the team at MarketIQ and the enthusiasm he brought to the team.
Bill has a keen willingness to learn, and can pick up knowledge that he didn’t have before and apply them to his work within a short timeline. For example when we gave Bill the task of finding an automated method of performing technical analysis, Bill took the initiative to do the necessary background research in order to understand each pattern, how they are identified, and the broader implications of the emergence of a pattern in the market.
He shows his dedication to his team as he is continuing to help us in our customer acquisition process even after his official work term has ended, by leading product demos with prospective institutional customers.
Bill is a very talented and balanced individual […] he was able to explain advanced mathematical concepts with clarity and passion. These concepts were later helpful in enhancing our statistical models. He was effective at performing statistical analysis on large datasets to uncover correlations that proved extremely helpful in modeling.