The investing landscape has dramatically changed over the last thirty years as innovative asset classes and strategies have dispassionately displaced older asset classes and strategies. This is in no small part attributable to technological advances. Technological advances can have an enormous impact on investing because the margin between success and failure in the industry is razor thin. A small improvement, whether in technology or otherwise, can result in significant differences in the success of a firm. As a result, staying at the forefront of the investing strategy that differentiates an investment firm is critical to its long-term viability.<\/p>\n
The hedge fund segment in particular has been grappling with this changing landscape. When D.E. Shaw, one of the world\u2019s largest hedge funds, entered the investing scene in the late 80s, it was an innovative upstart pioneering strategies using computers and quantitative models.[1]<\/a> Since then, quantitative investing has exploded with competition and new assets flowing to it. It currently represents an estimated $500bn to $1tn of the roughly $3tn in assets across all hedge fund strategies.[2]<\/a>,<\/sup> [3]<\/a> This growth is attributable to its success. Of the total gains generated by the top 20 hedge funds in history, 25%, representing more than $110bn, has come from hedge funds that utilize computers and quantitative models.[4]<\/a><\/p>\n While D.E. Shaw has been a pioneer in quantitative trading, the landscape has been evolving rapidly as machine learning takes center stage. Machine learning is the latest technology that naturally progresses from the quantitative models these firms have used since inception. To stay ahead of the competition, the firm needs to continue investing in its technical talent because coders build the firm\u2019s investing strategies. Investing in this is not easy as there is a significant talent shortage in this new field.[5]<\/a> D.E. Shaw\u2019s leadership has been working to climb this hill by hiring one of the field\u2019s leading computer science professors and building a machine learning research organization beneath him.[6]<\/a> When announcing this development, the firm highlighted that \u201c[t]his new, independent venture reflects the importance we attach to gaining fresh perspectives and insights as technology evolves.\u201d[7]<\/a><\/p>\n D.E. Shaw knows, and can\u2019t forget, that there are plenty of others on its heels.[8]<\/a> Both existing funds and new funds are focused on applying machine learning to investing. A recent survey indicated that 17% of hedge funds currently use AI, but 56% plan to make it an integral part of their investment process going forward.[9]<\/a> Additionally, of all the funds launched last year, 40% relied on computer models as a key driver of their strategy, according to Preqin.[10]<\/a><\/p>\n In light of this competitive landscape, a key question I\u2019ve been wrestling with is the durability of a business that relies so heavily on machine learning technology. I wonder how hard it is for the next computer science PhD student or competing fund to build a better mousetrap. If one succeeds, it could threaten even the current titans. Because of this potential instability, should D.E. Shaw diversify its business by building out alternative strategies? On one hand, if they stick to their core competency, they likely maximize their odds of being the long-term leader of the strategy. But if I was a principal of the business, I\u2019d want to diversify across additional strategies. It appears this is the route they are pursuing by building out their fundamental long\/short and activist businesses.[11]<\/a> Is this diversification wise?<\/p>\n (800 words)<\/p>\n [1]<\/a> Michelle Celarier, \u201cHow a Misfit Group of Computer Geeks and English Majors Transformed Wall Street,\u201d New York Magazine<\/em>, January 2018, http:\/\/nymag.com\/intelligencer\/2018\/01\/d-e-shaw-the-first-great-quant-hedge-fund.html, accessed November 2018.<\/p>\n [2]<\/a> Michelle Celarier, \u201cHow a Misfit Group of Computer Geeks and English Majors Transformed Wall Street,\u201d New York Magazine<\/em>, January 2018, http:\/\/nymag.com\/intelligencer\/2018\/01\/d-e-shaw-the-first-great-quant-hedge-fund.html, accessed November 2018.<\/p>\n [3]<\/a> Robin Wigglesworth & Lindsay Fortado, \u201cQuant hedge funds lose their allure as performance sags,\u201d Financial Times<\/em>, July 28, 2018, https:\/\/www.ft.com\/content\/e33e117e-8f7a-11e8-b639-7680cedcc421, accessed November 2018.<\/p>\n [4]<\/a> Nathan Vardi, \u201cComputers Start To Take Over List Of Most Successful Hedge Funds,\u201d Forbes<\/em>, February 1, 2017, https:\/\/www.forbes.com\/sites\/nathanvardi\/2017\/02\/01\/quants-start-to-take-over-list-of-most-successful-hedge-funds\/#6b3256135e00, accessed November 2018.<\/p>\n [5]<\/a> Disruption in Global Financial Services, 2017\u2014Machine Learning is Imperative, Frost & Sullivan, accessed November 2018.<\/p>\n [6]<\/a> \u201cD. E. Shaw Group Forms New Machine Learning Research Group,\u201d D.E. Shaw press release (New York, NY, August 16, 2018).<\/p>\n [7]<\/a> Robin Wigglesworth, \u201cDE Shaw taps academic to set up new machine learning group,\u201d Financial Times<\/em>, August 16, 2018, https:\/\/www.ft.com\/content\/1553b0a2-a177-11e8-85da-eeb7a9ce36e4, accessed November 2018.<\/p>\n [8]<\/a> Nathan Vardi, \u201cComputers Start To Take Over List Of Most Successful Hedge Funds,\u201d Forbes<\/em>, February 1, 2017, https:\/\/www.forbes.com\/sites\/nathanvardi\/2017\/02\/01\/quants-start-to-take-over-list-of-most-successful-hedge-funds\/#6b3256135e00, accessed November 2018.<\/p>\n [9]<\/a> Robin Wigglesworth, \u201cDE Shaw taps academic to set up new machine learning group,\u201d Financial Times<\/em>, August 16, 2018, https:\/\/www.ft.com\/content\/1553b0a2-a177-11e8-85da-eeb7a9ce36e4, accessed November 2018.<\/p>\n