Technology has and continues to provide financial services firms, including Goldman Sachs, a significant edge in the marketplace. Some experts believe that the capital markets industry stands to gain the most from the applications of AI: both supervised and unsupervised machine learning.1 Firms generate massive amounts of data and “how that data gets harnessed, analyzed, and used across the value chain will increasingly not be up to humans but rather machines.”2
Machine learning will be an invaluable tool in tackling process improvement across Goldman’s trading business. Currently, traders utilize various trading applications to manually execute a number of critical tasks, including: processing trades, assessing and calculating risk metrics, and modeling optimal portfolios. The immense capability of machine learning to analyze millions of data points simultaneously and at high speeds and apply these learnings in real time will have significant implications for Goldman and its clients. Some of these benefits include higher returns, cost savings, and increased capacity for trading professionals. 1,2 The various applications of machine learning across trading are detailed below.
Machine learning will predict the level of systematic and idiosyncratic risk at a much faster pace than humans. Additionally, these tools will be able to quickly react to and optimize portfolios for various market events.3,4 According to experts, “deeper” machine learning applications will be able to utilize historical data sets to predict, with the highest degree of accuracy, the range of outcomes for any given market security. Machine learning will also be able to aggregate data from informal sources such as social media and blogs to gain “a previously unattainable level of insight into a stock’s trading ability.” These insights will then be utilized to model out highly sophisticated “what-if” scenarios to construct the most optimal portfolio for any given set of constraints.4
Machine learning will also harness the power of speech recognition. Experts conclude that, “in the future, speech recognition may be able to tell the intent, sentiment or even urgency of speech, not just the words themselves.”4 This “intent” will be utilized to execute trades “in a fraction of the time it takes to do an electronic transaction.”4
Over the past several years, Goldman has been intensely building and internal AI Team to improve performance and cut costs.2 But in the absence of these sophisticated machine learning tools, trading professionals will continue to utilize an in-house application called Securities DataBase or “SecDB” in the short- and medium-term. Known as Goldman’s “secret sauce,” SecDB is used to (i) track securities and their historical performance under various scenarios; (ii) model performance of these securities under future scenarios; and (iii) determine aggregate risk these securities introduce to various portfolios.5
While SecDB is a powerful tool in computing and analyzing data, the speed at which inputs to the system are provided and the speed at which outputs of the system are analyzed and applied are constrained by human capacity and are subject to a high-degree of human error.5,6 In a fast-paced capital markets environment where pricing inefficiencies exist for very short periods of time, speed is critical in gaining opportunities to generate alpha or outsized returns. Though Goldman’s quant team is continuously focused on improving SecDB, introducing new variables or new sources of information is a highly iterative process and undergoes significant testing to ensure outputs are accurate and useable.
Technology remains a key competitive advantage for financial services firms and a “rat race” has commenced as notable competitors are also focused on rapidly recruiting experts to develop a viable proof of concept. Speed to market will be key in determining “winners and losers,” as banks with well built and implemented machine learning applications will be in a position to win over a large number of clients, while laggards may struggle to survive. AI has seen a burgeoning in FinTech with start-up companies developing a gamut of solutions across financial services. Goldman would be well served in outright acquiring IP or structuring exclusive licensing agreements with start-ups to potentially box out competitors.
Goldman must evaluate the consequences of utilizing legacy systems to build its machine learning platform. It must ensure that inherent biases across SecDB are identified, as machine learning “algorithms are not natively intelligent,”7 and learn from data that is being inputted. Additionally, the Firm must also think through the extent to which a trader’s “gut feeling” or “intuition” relative to the movement of certain securities impacts performance and how an environment of evolving regulation must be accounted for in the development of these tools.
As we continue to follow this evolution of machine learning, what role will humans play in a scenario of complete trading automation? If proven machine learning solutions become available to the masses, what role will banks play? Will the banking industry experience consolidation in the long-term as firms lose their competitive advantages?
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1Butcher Dan, “Goldman Sachs has created an elite tech team to tackle AI, big projects,” efinancialcareers, November 16, 2017, https://news.efinancialcareers.com/us-en/301350/goldman-building-new-rd-engineering-group-hiring-ai-team, accessed November 2018.
2Dickinson Claire, “AI the ‘big winner’ as banks and fund managers dig deep on tech,” Financial News London, August 16, 2017, https://www.fnlondon.com/articles/ai-the-big-winner-as-banks-and-fund-managers-dig-deep-on-tech-20170816?mod=article_inline, accessed November 2018.
3Bharadwaj Raghav, “Artificial Intelligence at Investment Banks – 5 Current Applications, techemergence, November 7, 2018, https://www.techemergence.com/artificial-intelligence-at-investment-banks-5-current-applications/, accessed November 2018.
4Coles Terri, “How Trading Systems will Shake up Wall Street,” IT Pro Today, January 12, 2018, https://www.itprotoday.com/machine-learning/how-ai-trading-systems-will-shake-wall-street, accessed November 2018.
5Baer Justin, “Understanding SecDB: Goldman Sach’s Most Valued Trading Weapon,” The Wall Street Journal, September 7, 2016, https://www.wsj.com/articles/understanding-secdb-goldman-sachss-most-valued-trading-weapon-1473242401, accessed November 2018.
6Baer Justin, “Goldman Sachs Has Started Giving Away Its Most Valuable Software,” The Wall Street Journal, September 7, 2016, https://www.wsj.com/articles/goldman-sachs-has-started-giving-away-its-most-valuable-software-1473242401, accessed November 2018.
7Press Gill, “These Banks are Using AI to Help Their Customers Manage Their Finances,” Forbes, September 12, 2018, https://www.forbes.com/sites/gilpress/2018/09/12/these-banks-are-using-ai-to-help-their-customers-manage-their-finances/, accessed November 2018.