This report aims to shed light on some of the challenges facing the buy- and sell-sides, and to identify the key business drivers for popular data analysis and modelling techniques within the financial services industry.
By surveying experts from the buy- and sell-sides and academia we have captured a range of industry insights and opinions to better understand the importance that models and data have in all aspects of the trade lifecycle.
The research comes against the backdrop of the financial crisis of 2008 and the subsequent unprecedented levels of regulation which the industry now faces.
The second largest outlay for the average financial institution is its data – second only to the cost of staff. Data is used across the organisation, most commonly and potentially profitably in trading strategies, but it also supports every other function, including risk management.
The 'data deluge' has captured the interest of the media worldwide, as industries seek to capitalise on the data that exists in their own organisations and in the public domain. However this is not a new phenomenon for financial services, which has sought techniques to monetise data for decades. Today's challenge is dealing with the 'three Vs of data' – volume, velocity and variety.
Unprecedented levels of regulation are facing the industry, post-2008, including MiFID II, UCITS, Basel III and Dodd-Frank. However it is apparent from our research that while firms are adapting to adhere to regulation, this is not a key driver in model production or model use. In fact, businesses commonly hold the view that instead of dedicating resources to risk models to appease regulators, this would be better allocated to bring alpha generating models into production.
Against this backdrop, MathWorks has sought to obtain industry insights and opinions from within the financial services industry to better understand the importance that models and data have in all aspects of the trade lifecycle.
This in-depth study involved questioning 43 experts from within the buy- and sell-sides, as well as academia, including hedge funds, institutional investors, banks (including proprietary trading desks), broker/dealers and university lecturers. On the sell-side, four of the top 10 investment banks1 participated in the study.
In this report, we have tried to shed light on some of the challenges facing the buy- and sell-sides, and understand the key business drivers for popular data analysis and modelling techniques.
1 Financial Times: Investment Banking Review, H1 2012, http://markets.ft.com/investmentBanking/tablesAndTrends.asp