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Can ‘quantamental’ apply in corporate treasury too?

The past year has seen the rising use of a new non-word in investment circles: 'quantamental', a cross between quantitative and fundamental. What does it mean and should corporate treasurers care?

So what does quantamental mean?

It's used to describe an investment methodology that combines the quantitative approach to analysing big data using algorithms, with the traditional analysis of a company's basic economic indicators (its 'fundamentals'). Quantavista has the following useful definition: “Quantamental combines two types of investment strategies, quantitative and fundamental. Quantitative investing generally uses Ph.Ds with complex algorithms, large computer systems and lots of data to invest in many securities. Fundamental investing uses analysis based on the bottoms up fundamentals of a company a la Warren Buffett.”

In other words, quantamental investing uses a traditional approach, based on the analysis of corporate financial data, combined with complex algorithms and analysis of far larger, historical sets of data – and often data from non-traditional sources is used. It's an approach that combines both active (human-led) and passive (computer-led) investing techniques. The theory is that this method helps investment analysts to search for patterns in current securities, compare them with historical or wider contemporary patterns, therefore enabling them to better understand how the investment could perform. It's really the rise of big data and the development of machines with the computing power to crunch data from a wide range of 'alternative' sources, from the number of customers in shops and car numbers in car parks, to data gleaned from social media and the Internet.

Democratisation of data

The largest investment fund company in the world, BlackRock, announced six months ago that it would consolidate a large number of actively managed mutual funds with peers that rely more on algorithms and models to pick stocks. Laurence D. Fink, a founder and chief executive of BlackRock, told the New York Times in March: “The democratization of information has made it much harder for active management. We have to change the ecosystem – that means relying more on big data, artificial intelligence, factors and models within quant and traditional investment strategies.”

More stable results

And an article in last week's Euromoney also ponders on the use of quantamental analytics in the world of FX trading. Most FX trading firms are already using both the quantitative and the fundamental approaches to guide their trading decisions. And it seems there is no longer an 'either/or' argument but a recognition that both approaches, if used in conjunction, are necessary to reaching a better investment or trade decision.

The Euromoney article argues that quantitative models start to underperform if they disregard fundamental factors. Likewise, fundamental analysis techniques, for trading or for investing, are enhanced by complex algorithmic analysis or big data sets. In short, the two techniques are complementary. One of the problems is that many fundamental considerations aren't easily quantifiable, so combining the two techniques requires both quantitative and qualitative evaluations. The article quotes Alex Krishtop, founder of Edgesense Solutions, saying: “In addition, the quantamental approach should produce more stable results with quantitative models compared to those designed using traditional technical analysis or machine learning.”

Should treasurers go quantamental?

More complex data analytics, combined with qualitative evaluations of 'fundamentals' could drive more precise analysis and better investing or FX trading results, which corporate treasurers could benefit from. Understanding developments used by investment or FX trading professionals certainly helps those in treasury in their discussions with service providers. There has been much talk of using big data analysis within corporate treasury, for example in cash flow forecasts and in managing global cash and liquidity positions.

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