PUBLISHED: 04 March 2020 AUTHOR: Alejandra Litterio In a new investing era defined by automation, algorithms and Big Data, where evolution is more like a (r)evolution, where man and machine seem to melt embracing the so- called route toward full artificial intelligence integration, where modelling is a must, and where finding the right approach seems to be crucial to build a unique differentiated road map to succeed in the capital markets, the truth is that in the realm of investment all these “pieces” move strategically changing the value dramatically depending on their position on the board, permeating the company culture no matter if they are small firms or asset management empires. These days, we might say that the “checkboard” is not just for two players. Though traditionally there were two separate modes, belonging to different realms: the pure human instinct spotting the best places to invest (fundamentalist) and the computer- driven prospect (quantitative analysts), the tendency have changed combining the best of both approaches to extract the power of big data. Like in any competitive game, moves are compulsory. But, who is willing to make the next move without compromising one’s position risking a “checkmate”? Admittedly, mixing fundamental and quantitative investing is easier said than done. The newly coined term “Quantamental” (the portmanteau of quantitative and fundamental), apparently does not solve the entire problem. Behind the scenes, one may find different positions: the early adopters, the sceptics, the conservatives, the naysayer. Though the acceptance of this symbiotic relationship makes sense, there might be room for disaster. According to experts, the perfect storm is crystallized when firms are tempted to hire a team of quants to build their own models precisely due to the underlying clash of cultures. For one part, there might be resistance since fundamentalist might see the advances in artificial intelligence techniques as a menace feeling that they are competing with a machine and struggling for survival in a tough environment. For the other, quantitative firms with their greediness for growth create robust analytics strategies in a maneuver to achieve long-term positioning advantages during the game. It might be true that the hybridization of human expertise and computer power will augment the possibilities to gain an edge and in the best of the cases reduce costs. Still an essential component is missing, a new holistic vision, a new player: the MetaQuant (as I define it: a profile which merges the linguist, the sociologist, the philosopher) who will develop a synergetic environment reconciling the two modes. Automation is not enough. Human judgment as added value is still needed to detect the hidden meaning behind the words. It is not a matter of having more computers or fewer humans. It is a matter of a new insight. And that is exactly what the MetaQuant means for those who want to win the game, finding and engineering opportunities to trade advantageously in the race for a better position. Of course, in the end there is no miraculous recipe.