Trading places: the rise of the DIY hedge fund

@machinelearnbot

Naoki Nagai, a 36-year-old Harvard graduate who grew up in Japan, is a one-man hedge fund. For the past 16 months he has written hundreds of algorithms in much the same manner as quantitative traders in the City of London or Wall Street. But, rather than trade from a Canary Wharf skyscraper or a Manhattan boutique fund, he does so from his home in Honolulu. In August 2006, Nagai left his job as a management consultant in Tokyo to establish a translation company, which over the next few years began to thrive. The success of his organisation, and the fact it wasn't dependent on location, gave Nagai the opportunity to reconsider his lifestyle. He chose to move from Japan to Hawaii. With its appealing climate and laid-back lifestyle, Honolulu seemed a great place to raise a family. Nagai and his wife arrived in the US in January 2014.


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.


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.


Last days of the stock picker as money managers embrace artificial intelligence

#artificialintelligence

Financial technology is disrupting traditional approaches to investing, and BlackRock Inc.'s recent announcement that it is replacing human stock pickers with machine-run algorithms for some of its equity funds, signals that the money management industry is getting the message. The decision by BlackRock, which has more than US$5 trillion in assets under management, follows a similar move by the world's largest hedge fund, Bridgewater Associates (US$160 billion in AUM), to start using software to automate its day-to-day decision making. The popularity of computerized quantitative trading strategies, and the growing use of artificial intelligence (AI) techniques, stems in large part from their impressive returns. AI and machine learning hedge funds outperformed both traditional quantitative and the average global hedge fund, with annualized gains of 10.6 per cent over a two year period, according to Eurekahege. These new machine-based funds also posted better risk-adjusted returns, with considerably lower volatility.


AI firm to use machine-learning programs to decipher corporate earnings announcements

The Japan Times

SYDNEY – After applying his machine-learning programs to central bank policy statements to churn out trading calls, a hedge fund-backed political economy specialist is aiming his sights on corporate earnings announcements.