This post has been motivated by a session of the WIRED UK June-14 issue.
The UnRisk team always looks a little into the future. What about new mathematical schemes, programming techniques, computing platforms and tools for better development and project management tools …. and media and communication platforms …. socio-economic and financial systems. In short, new environments for innovation and marketing?
Look a little further ahead?
The future as it was? It's hard to believe, but back in 2014 people propagated fragile, tightly coupled complex systems for front-to-back investment and risk management and not so few developers believed in one-technology-fits-all approaches.
It was not so difficult to predict that the financial security systems did not make the system safer - regulatory bodies forced banks do "go nuke" (do as the nuclear power plants needed to do, because of the technology)
Why the atifragility argument led to re-decentralization
For some it may be surprising, but ….
Big players providing information technologies did voluntarily split themselves into independent units. Buffers, diffusion and agility will fix what did not work: hierarchies, strategic plans and industrialized work in an economy of scales - flexibility, innovation and enthusiasm drive better offerings and consequently returns - and are safer for the economies.
I finance this led to a retraction of the strict central collateral management and clearing system. Market systems are too complicated to be centrally managed.
In combination with the extreme xVA treatment it broke to a marginal cost regime. Large banks have identified collateral transformation and central clearing as lucrative service, but that created the old problems of market dominance through another doorway.
How to program the money system
A system is universal, if it is solid enough to store and liquid enough to transform. A universal system is programmable. Our money can store values and it is a media for economic transactions, consequently, the money system is programmable. We now understand that this needed an operating system and development tools.
System architects, computer scientists and quants are now indispensable in new jobs dealing with money policy and creation as well as providing the operational semantics of the programs. Symbolic, declarative programs that needed a lot of clever algorithms to implement them.
Following the decentralization logic money creation became even more decentralized and for better programming we invented new money types, like derivative money - futures, options, …
Technologies Do It Yourself investors should use
Structure me this is not longer in disrepute. There are technologies that help to make complex
financial concepts to non experts. Decision support system will use new media and dynamic visualizations techniques. Systems will adapt to a financial "aura" of a market participant describing objectives, risk appetites, ….
High level, simple, interactive, knowledge-based, domain-specific programming will allow them to simulate deals with portfolios of various deal types.
The internet of finance is reality
We all, dealing with financial applications, knew that it was a joke to worry about big data, when still struggling with getting informative small (market)data with the possibility to turn this data into something that supports decisions.
Informations like debt and profit webs came into play to let agents understand feedback loop better ….
Those data are now available and widely accessible. They allow us technology providers to intelligently combine modeling with more data driven adaptability - making data meaningful.
Reasons, why quant work is now more polarized
10 years ago quants whee so overwhelmed by the operational requirements meeting the regulatory system. If they did xVA they did the same thing (valuations) 100 million times an hour instead of 5 million times an hour when doing portfolio-across.scenario simulation. This needed a lot of plumbing, especial if their banks suffered from the was-not-invented-here syndrome.
Now, the new technologies and tools give them time to optimize their business. They can design new financial systems. How they do it will not make a big difference, but what they do will.
Adding value still means: doing the hard work.
Programming is knowledge-based
Programming is symbolic, multi-paradigm and uses algorithmic knowledge-bases. There are development environments that support high level programming in multi-languages, that are platform agnostic. We do not longer care about versions and releases and underlying programming languages. All algorithms for high performance computing are inherently parallel and support all architectures of the new computing muscles.
The most complex applications go where the users go
Write once, deploy anywhere.
Clouds went private and crunch and store all the (risk) numbers blazingly fast.
Even smart devices will have enough power and display-quality to do massive pre-processing creating insight about incoming data flows and prepare them for processing.
Financial objects and their risk spectra can be post-processed and insightfully arranged on the smart mobile devices.
The blog posts are interactive, some stories are created by and act like programs. 70% of the news are created by computers. Ask a question and you will receive a little program that answers all questions of that type.
More quants do more meaningful things
and turned them into businesses
it is late June, the sky has scattered clouds, the birds are singing, …..