A machine learning algorithm is only as good as the data you use to train it with. That’s why any company that has a large proprietary data set can now start to extract exclusive insights from it, something that in the olden days we used to call “data mining.”
Nowadays, it’s all about trying to predict the future using something called “predictive analytics.” Nowhere is the need to predict the future more lucrative than in the world of finance. That’s because there are financial products – like options – that can be used to place highly leveraged bets on particular outcomes. This has led to an increasing availability of obscure data sets that are commonly referred to as “alternative data.”
Alternative Data for Geopolitical Risk
Consumer footfall and transaction data, app downloads and usage, national job listings, vessel tracking, and satellite images of metal ingots are all alternative data sets that can contain signals that help predict the future. Hedge funds are even tracking the movement of private jets in order to try and predict what the rainmakers of the world are going to do next. The demand for alternative data hasn’t gone unnoticed by large firms like Bloomberg which announced they’ll move into the alternative data space as a distributor, or Nasdaq which acquired Quandl – an aggregator of alternative data – late last year. Today, we’re going to look at some firms that are producing alternative data for “geopolitical risk.”
Also known simply as political risk, geopolitical risk is pretty much what it says on the tin – risk that stems from change resulting from the government. Per Investopedia, this could be “taxes, spending, regulation, currency valuation, trade tariffs, labor laws such as the minimum wage, and environmental regulations.” The word “geopolitical” implies a broader focus on international relations as opposed to just what the Americans happen to be squabbling about at the moment.
Up until now, political risk consultancies like Eurasia Group and Oxford Analytica have provided guidance at a strategic level for firms looking to manage political risk. Today, there’s a move towards using machine learning techniques like “natural language processing” to scour the world’s information and quantify geopolitical risk at a much more tactical level. One such firm is Geoquant.
Geoquant
Founded in 2016, San Francisco startup Geoquant raised $4 million in funding to develop “an AI-driven political risk intelligence platform that measures, analyzes, and forecasts political risks in real time.” Geoquant’s top line Political Risk Scores and data streams for G20 countries were made available on the Bloomberg Terminal back in 2017. The company has produced an excellent white paper which talks about how emerging markets have been historically more prone to geopolitical risks because they have less predictable governments, but in today’s world, “developed markets no longer offer investors the political stability and political predictability they once did.”
The paper goes on to talk about how political risk used to be too idiosyncratic to define until Geoquant developed the world’s first benchmark measures for political risk using both structured and unstructured data. Structured data comes in the form of 250 variables drawn from “credible country-level databases maintained by multilateral institutions, NGOs, governments, social scientists, and the like,” with historical data going back to 2009. Since these data points are updated infrequently – annually or quarterly – Geoquant uses high-frequency unstructured data to give their models some responsiveness. This data is “drawn from high-quality traditional and social media sources” using natural language processing algorithms.
Since going live in June 2016, the system has been used “to forecast most major political events in the G20 with remarkable accuracy,” says the company. For example, here’s a chart which shows how the Turkish coup was anticipated by the model.
Source: GeoQuant white paper
There are plenty of other examples given in the white paper which also does a good job of defining the limitations of geopolitical risk models. In other words, this stuff is really complicated and until we figure out how to use quantum computers to time travel, we’re never going to truly predict the future. What might help the predictive power of geopolitical risk – if not make it a bit more confusing – is to throw another magic eight ball into the mix. A firm called Predata is also predicting geopolitical risk, they’re just doing it a bit differently.
Predata
Founded in 2015, New Yawk startup Predata has taken in $3.3 million in funding to develop a platform that uses machine learning algorithms to “condense data sources from around the web into clear and unified signals for geopolitical risk.” Similar to Geoquant, Predata attempts to quantify political risk in an increasingly connected world with many moving parts. The difference is explained in a recent article by the Global Association of Risk Professionals:
“We don’t employ natural language processing or key word analysis,” Dawani explains. “We purely look at sources curated by our expert analysts on serious topics and employ machine learning technologies to identify patterns of behavior.” The software performs in any language – it is language-agnostic; can customize queries, posing a broad range of questions; and covers 180 countries.The end result is a tool that allows you to track geopolitical risk over time and across various countries and regions....MUCH MORE
