Hedge Funds Look to Machine Learning, Crowdsourcing for Competitive Advantage

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Every day, financial markets and global economies produce a flood of data. As a result, stock traders now have more information about more industries and sectors than ever before. That deluge, combined with the rise of cloud technology, has inspired hedge funds to develop new quantitative strategies that they hope can generate greater returns than the experience and judgement of their own staff. At the Future of Fintech conference hosted by research company CB Insights in New York City, three hedge fund insiders discussed the latest developments in quantitative trading. A session on Tuesday featured Christina Qi, the co-founder of a high-frequency trading firm called Domeyard LP; Jonathan Larkin, an executive from Quantopian, a hedge fund taking a data-driven systematic approach; and Andy Weissman of Union Square Ventures, a venture capital firm that has invested in an autonomous hedge fund.


Hedge Funds Look to Machine Learning, Crowdsourcing for Competitive Advantage

#artificialintelligence

Every day, financial markets and global economies produce a flood of data. As a result, stock traders now have more information about more industries and sectors than ever before. That deluge, combined with the rise of cloud technology, has inspired hedge funds to develop new quantitative strategies that they hope can generate greater returns than the experience and judgement of their own staff. At the Future of Fintech conference hosted by research company CB Insights in New York City, three hedge fund insiders discussed the latest developments in quantitative trading. A session on Tuesday featured Christina Qi, the co-founder of a high-frequency trading firm called Domeyard LP; Jonathan Larkin, an executive from Quantopian, a hedge fund taking a data-driven systematic approach; and Andy Weissman of Union Square Ventures, a venture capital firm that has invested in an autonomous hedge fund.


4 Crowdsourced Hedge Funds for Algorithmic Trading - Nanalyze

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A meritocracy is all about rewarding people on the basis of their abilities. This almost always turns out to be those people who work harder and take bigger risks. It's what has made our present day society so great. Everything you see around you today is the result of a meritocracy that rewards those people who work the hardest and accomplish the most. The social media platforms people use to spew forth outrage about the "myth of meritocracy" and how "unfair the system is" fail to realize that it's a meritocracy that has motivated the founders of said platforms to forgo evenings in front of the television in favor of 70-hour work weeks to build something that made them quite wealthy. The harder you work, the luckier you get. That's one of the reasons why China is on track to eat America's lunch. It is only by rewarding those people who work the hardest, that you can achieve great things as a society.


4 Crowdsourced Hedge Funds for Algorithmic Trading - Nanalyze

#artificialintelligence

A meritocracy is all about rewarding people on the basis of their abilities. This almost always turns out to be those people who work harder and take bigger risks. It's what has made our present day society so great. Everything you see around you today is the result of a meritocracy that rewards those people who work the hardest and accomplish the most. The social media platforms people use to spew forth outrage about the "myth of meritocracy" and how "unfair the system is" fail to realize that it's a meritocracy that has motivated the founders of said platforms to forgo evenings in front of the television in favor of 70-hour work weeks to build something that made them quite wealthy. The harder you work, the luckier you get. That's one of the reasons why China is on track to eat America's lunch. It is only by rewarding those people who work the hardest, that you can achieve great things as a society.


Million Dollar Salaries for AI Researchers? Well, we Quants Have Seen this Movie Before

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Researchers Are Making More than $1 Million at a Nonprofit" declares the NYT [1]. It's certainly not the first article where commentators have opined upon how much top AI researchers make and how it reflects the rosy economics of young people going into a career in machine learning or data science. While the 800k that the AI superstar pulled in is nothing to scoff at, especially for the average thirty-something year-old, hundreds of relatively unknown quants in finance are paid this much every year. In his heyday, a big name like Emanuel Derman would have been paid much more than 800k (perhaps an order of magnitude more). So, rather than be a cause for bullishness, the numbers suggest to me that AI researchers and data scientists are underpaid relative to their quant brethren.