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The Code-Free Movement Reaches Capital Markets

Graphical user interfaces can help financial professionals master their data and make better decisions.

The code-free movement was started by Bill Gates and perfected by Steve Jobs. It’s about to reach capital markets.

When Jobs visited Xerox PARC in 1979 and saw the first-ever prototype for a point-and-click replacement for a command line interface, the age of user-friendly computers was arguably born: “I was so blinded by the first thing they showed me, which was the graphical user interface. . . . Within ten minutes it was obvious to me that all computers would work like this someday.”

We now take for granted the ability to zoom into a virtual map containing millions of data points with our fingertips and to have a system reveal to us relevant information in stages and layers, as we tap and swipe phone numbers, photos, addresses and GPS coordinates. Voice-command real-time computation of dozens of alternative traffic routes, factoring in live satellite data on millions of vehicle movements? Just another day in Silicon Valley.

By contrast, working with financial data is still too often a choice between the rock of limitation and the hard place of specialized tools and training.

Spreadsheets are not programming languages. They have neither the interface granularity nor the processing speed needed to properly model and compute financial big data. This limitation spawned the era of the quant and the data scientist, who use complex programming languages for statistical modeling. But this was no panacea: Quants are hard to recruit, expensive to compensate and often require days to produce static, individual reports — few of which are integrated with one another. Scrubbing and standardizing data is tedious and a suboptimal application of talent.

Moreover, dependencies emerged. Within global financial firms, a large number of professionals became ever more dependent on a very small number of programmers and statisticians for risk management, alpha generation and complex modeling. The world of talent in finance and investing is thus increasingly viewed in binary terms: There are those who can write code, and there are those who can’t. Across institutions, large pools of buy-side money have become reliant on a handful of outside funds, often led by academically trained data wizards wielding black boxes and environments capable of a systematic approach to markets.

But even if Wall Streeters accept these pathways of dependency — and the corresponding compensation and fee structures — that come with not being able to do complex financial computing themselves, these lopsided relationships are not sustainable.

Data is becoming bigger and finance is becoming hyper-quantitative at a rate faster than universities can mint advanced degrees in engineering, math and computer programming. Wall Street has had an especially hard time competing with cool companies like Google and Apple for premier talent. This means that a steadily mounting number of financial professionals who cannot program compete within a firm for the bandwidth of ever fewer people who can, soon resulting in a breaking point in risk management and the decline of alpha.

Similarly, the handful of elite funds to which one can truly outsource a systematic computational approach to alpha and risk management can manage only so much money while still maintaining their edge. This can result in a system of adverse algorithm selection, where buy-side capital can only get into the computational club that will have it as a member. Simply put, even if you are willing to pay the steep cost, you can’t eternally rely on someone else for your digital alpha.

Computational finance needs an accessibility revolution, where high-end computational capabilities are put in the hands of nonprogrammers, exactly in the same way leading consumer tech companies have put formerly military-grade navigation systems at the fingertips — and voice commands — of nontechnicians.

Hope once again comes from the consumer Internet, where the code-free movement is making previously unimaginable advances by breaking down the barriers of complexity around one of the most iconic citadels of programming: website building. Adobe, maker of the PDF and Photoshop, has recently released a product called Muse, which enables companies to design and publish professional websites “without writing a single line of code.” Surely, if technology has advanced to the point where graphical user interfaces (GUIs) are being used by nonprogrammers to build enterprise-grade interactive websites, we can bring financial professionals a lot closer to asking complex questions of big data without writing code.

Many of us working in the new generation of financial technology believe we’re entering an era where the code-free movement will finally reach financial computing. Encouragingly, work is already under way to develop GUIs, coupled with cloud-based, massively parallel computing technologies, to allow nonprogrammers on Wall Street to make complex if-then statistical queries of big data in near-real time and to visualize large data sets in new ways that drive an immediate, intuitive understanding of the results.

What was previously the work of quants and programmers will be opened up and made accessible to all financial professionals. The latter will be empowered to design and test quantitative financial research and investment strategies without writing code (or depending on the people and institutions that do).

Research cycles will be shortened from days to minutes. Massive sets of heterogeneous data will be integrated with market data and opened to near-real-time visual analysis. Millions of wasted hours and high-priced human capital that are now deployed toward spreadsheet manipulations will be saved, and the professionals currently mired in those tasks will be freed to ask important questions and find needed answers — with their voices, fingertips and eyes.

Look out for the code-free movement. When it finally reaches capital markets, it will change everything. New code-free platforms will foster accessibility and meritocracy and enable better, faster decision making and more-informed risk-taking.

Daniel Nadler is a Ph.D. candidate at Harvard University and co-founder of Kenshō, a provider of user-friendly analytics software for capital markets.

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