How Predictive Analytics Can Stop a Global Financial Meltdown

How Predictive Analytics Can Stop a Global Financial Meltdown

As a regular reader of Project Syndicate’s website, I came across Nouriel Roubini’s article (August 27, 2015) in which he upholds the view that the world needs a global early warning system to avert financial crashes.

Roubini says that rating agencies, banks and other risk watchdogs usually fail to gauge correctly in a timely manner the dynamic complexity of financial, social and economic variables that should buttress the assessment of business and sovereign debt ratings.

Roubini states that “an assessment of sovereign risk that is systematic and data-driven could help to spot the risks that changing global (financial) headwinds imply.”  He uses a model based on 200 variables to score 174 countries on a quarterly basis to identify business risks and opportunities.

What Roubini is basically doing is applying data mining and predictive analytics to foresee fluctuations in multiple variables and trends in the global market. Many major vanguard business intelligence and software houses have already been following in the footsteps of predictive analytics.

FICO is a good example. FICO has been investing for quite some time in the development of advanced predictive models and dashboards for rating purposes. FICO boasts top credentials in credit risk forecasting. Gartner, Forrester, CGI and many others are on the same path.

Descriptive and proactive business intelligence is definitely not enough these days.

The volatility of the environment implies constant watch, analysis and forecasting. Business conditions change overnight. Business intelligence thus needs to be predictive in the current muddy environment in order to keep afloat.

The advent of High Frequency Trading (HFT) in the financial markets with constantly developing algorithms for forecasting is boosting the speed of capital movements. Sudden capital fluctuations and consequent rapid market reactions have been blamed for various major runs on assets and flash sales (e.g. the USA “flash crash” of May 2010).

Altogether with the underlying prevailing critical economic conditions in Europe in 2015 and the recent economic and financial plights of the BRICs (China’s August 2015 bear market and BRICs’ currencies rollercoasters), the ingredients for increasing global financial instability are all there.

At the same time, increasing business automation and robotization, amongst other technological advances, are disrupting many parameters beyond expectation such as labour, academic education and the way companies do business.

The aggregate complexity and fluidity of the environment implies the need to further develop predictive analytic methodologies. The basic signal detection principle of predictive analysis, as per the ability of the system to discern between information-bearing patterns and random patterns (noise) that distract from the information, is not enough and is now under a dimensional snowballing effect due to the huge non-stop deluge of data.

The determining factors that a detecting system identifies as a signal and separates it from noise (which is the ability of data mining to extract valuable information) are reaching levels of utter complexity.

As a consequence, the explosion of predictors for forecasts and the need to refine them implies increasing computer processing power. Altogether it also implies not just better data collection and crunching, but also more advanced human skills to complement and monitor the process.

Last but not the least, the final stage of the process requires fine-tuned human talent and intuition to interpret the results in order to formulate visions. Financial forecasting is thus a complex and tricky challenge. Risks of data validation abound in the process together with the risk of the Observer Effect.

If a number of forecasts are generated regarding some specific situation or risk of financial crash, the fact that those forecasts become publicly known may influence the situation itself and lead to unforeseen deviations from expected courses.

The rationale behind a global financial early warning system is smart. Early warning systems are safeguards against something devastating, as long as they stop short of triggering unnecessary panics for whatever reason.

Article written by Manuel Gomes Samuel
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