Module 2: Capital Markets, Trading, and Risk
Module 2 Description
We describe the evolution of the capital markets and trading businesses of Goldman Sachs over a quarter century, from 1994 to 2019. During that time, Goldman Sachs and its clients and competitors responded to the collapse of LTCM, the dot-com bubble and bust, Regulation NMS, the 2008 financial crisis, the Dodd-Frank Act, the oversight of the Federal Reserve, the availability of data and analytics, and the rise of open software and cloud services transformed the business.
The creation and deployment of SecDB, a proprietary platform for risk management, underpinned and accelerated the transformation of the trading business during that 25-year period. More recently, Goldman Sachs pivoted from closely guarding the intellectual property in SecDB, to sharing it broadly with clients -- first as tools used by Goldman Sachs traders and salespeople, then as user experiences available over the web to supplement phone conversations and internet / Bloomberg chat, and finally as APIs produced by Goldman Sachs Marquee and consumed by buy-side clients.
The design goal of SecDB was to gather all the time series, market data, trades, positions, models, and reports in one place. SecDB represents an early instance of a platform vision: After agreeing to put everything in one place, we had a trivial, uniform answer -- it’s in SecDB -- to the perennial question, Where is it? To achieve that design goal, we built custom proprietary software that the rest of the world, mostly in open-source communities, developed 10-15 years later, including NoSQL / DynamoDB (“secserv”), Python (the proprietary Slang programming language), Redis (), and Lamba (the Compute Engine infrastructure).
Over 25 years, we closed the gap between SecDB and the business, so that SecDB became the gold-standard representation of all the processes of the front-office trading desk. By asymptotically converging the gap between the software and the business, we were able to ask and answer in software, with high fidelity, counterfactual questions about the business. It’s much less painful to lose billions of dollars in software simulation than in real life. Effective risk management becomes an exercise in asking and answering the right questions, in software simulation, comprehensively and in near-real time.
SecDB demonstrated its prowess in the depths of the financial crisis. During that period, GS observed and mitigated accumulations of market-price and counterparty credit risk in the mortgages business, particularly in the inventory of Collateralized Debt Obligations (CDOs). GS’s competitors were slow to respond, if they responded at all. In the aftermath of the failure of Lehman Brothers, GS immediately calculated its derivatives close-out claims against 40+ defaulting entities, pivoting the comprehensive SecDB view of risk by Lehman counterparty; in many cases, competitors required weeks and months to perform the same calculation, by painstakingly rolling up and aggregating thousands of disparate spreadsheets. As a result, GS experienced manageable market-price and credit shocks during the 2008 period, emerging as the dominant provider of liquidity in the aftermath of the crisis.
Recorded Lectures
Module 2, Chapter 1
Zoom with Transcript / Vimeo
Module 2, Chapter 3
Zoom with Transcript / Vimeo
Module 2
(All Four Chapters)
Explainer Videos
The Lifecycle of a Trade (YouTube)
The Lifecycle of a Trade (Vimeo)
Study Questions
In the assigned case, Why did GS revenues and income drop significantly in the last 10 years, and what did Blankfein do to manage this decline?
Is it a good idea to open SIMON to competitors? Does it reflect a shift in GS strategy?
Can GS become a platform business? How is a platform business different from a product-based business?
What organization structure did Blankfein put in place (with Marty’s help) to manage this transformation?
What were the consequences of Regulation NMS for the US equities markets?
How would you grade the regulators on Regulation NMS? Did it achieve its stated goals?
Are there alternatives to price-time priority for US equity exchanges? If so, please explain what they are and how they work.
Why aren't there central limit order books for bond trading as there are for equity trading, and what might the future structure of the credit markets look like?
What is the relevance of APIs for the wholesale markets in foreign exchange, futures, and OTC derivatives markets? How might they be employed?
Do you agree or disagree with the thesis of the Alliance Bernstein piece on passive investing (as explained in the Bloomberg article)? Why or why not?
What explains the success of SecDB -- its analytic prowess; its amalgamation of data; the convergence between SecDB and the business, so that the software and data artifact became one and the same with the business; the cultural context of Goldman Sachs; something else?
Reading List
Required
Gupta, Sunil, and Sara Simonds. "Goldman Sachs' Digital Journey." Harvard Business School Case 518-039, September 2017 (Revised May 2019). (Available for Purchase)
Regulation NMS (pgs. 5-21). Securities and Exchange Commision. 2005.
Scott, Gordon. “Matching Orders.” Investopedia, Dotdash, 29 Jan. 2020.
“NYSE Execution Model (Parity / Priority).” NYSE, The Intercontinental Exchange, 2020.
Damodaran, Aswath. The Bid-Ask Spread (pgs. 5-17). New York University, Trading Costs and Taxes.
Clayton, Jay. “Equity Market Structure 2019: Looking Back & Moving Forward.” Gabelli School of Business, Fordham University, New York, NY, 8 March, 2019.
Damodaran, Aswath. “The Price of Risk!” Musings on Markets, Blogspot, 10 Feb. 2020.
“The Next Generation Bond Market (pgs. 6-10),” BlackRock, 3 Jan. 2018.
Kawa, Luke. “Bernstein: Passive Investing Is Worse for Society Than Marxism.” Bloomberg, 23 Aug. 2016. (Available Behind Paywall)
Optional
SIrri, Erik. "Electric Money: Trading in the 21st Century". Financial Times Conference, 14 Nov. 2007, New York, NY.
Aisen, Daniel. “Incentivizing Trading Behavior through Market Design.” IEX Group Inc., Dec. 2017.
Re-Thinking the Lender of Last Resort, Bank for International Settlements, BIS Paper No. 79, Sep. 2014.
Deep Dive
Flash Boys: A Wall Street Revolt (eBook)
Bachelier, L. (1900). "Théorie de la spéculation". Annales Scientifiques de l'École Normale Supérieure. 17: 21–86. doi:10.24033/asens.476. ISSN 0012-9593.
Mandelbrot, Benoit (January 1963). "The Variation of Certain Speculative Prices". The Journal of Business. 36 (4): 394. doi:10.1086/294632. ISSN 0021-9398.
Samuelson, Paul A. (23 August 2015), "Proof that Properly Anticipated Prices Fluctuate Randomly", The World Scientific Handbook of Futures Markets, World Scientific Handbook in Financial Economics Series, 5, WORLD SCIENTIFIC, pp. 25–38, doi:10.1142/9789814566926_0002, ISBN 9789814566919.
Fama, Eugene (2013). "Two Pillars of Asset Pricing" (PDF). Prize Lecture for the Nobel Foundation.
Fama, Eugene F; French, Kenneth R (Summer 2004). "The Capital Asset Pricing Model: Theory and Evidence". Journal of Economic Perspectives. 18 (3): 25–46. doi:10.1257/0895330042162430
Taleb, Nassim N. (2004). "Fooled by Randomness".
Goldman Sachs API Documentation