This paper introduces the Three-Layer Company approach. It is a lean way to understand the distribution of the technology across a project (people vs platform) for senior managers to be able to understand and challenge better their Algorithmization process. It brings to the surface the fact that, oftentimes, the more people there is in tech in a company the less tech there is in place - which, even though it makes sense, given the fact that technology brings massive efficiencies, it is the opposite to the KPIs that have become trendy during the last decade. Once that part is mastered, being able to interconnect departments within a company (unlocking of new business approaches), merge companies of a similar sector (private equity's M&A) or simply take a company to its NextGen level becomes a transparent, neat process

** Reached SSRN Top 10 List **

This essay discloses how to move portfolio management from aggregations to strategies. Your shares in Apple are not all the same - they are not really fungible from a risk perspective. Which shares belong to which strategies? How are those performing? What are the alternative names to consider instead of Apple? If at some point you have too much exposure to US and you want to reduce it, which shares of Apple shall you let go? Artificially moving your portfolio management from particular views on a number of strategies deployed to aggregated, cross-portfolio KPIs is, indeed, bold. And to avoid it, all dimensions of an strategy shall be considered - Dimension-Driven Portfolios (DDP)

** Reached SSRN Top 10 List **

This essay proposes a new way to manage portfolios so that the investors can neatly understand their basket based on a reference (beta); an enhanced version of such reference (alpha 1 - statistical arbitrage ecosystem) and advanced set of free strategies (up to algorithmic market making). This allows asset management companies to have a better control of the overall risk taking as well as to better define performance attribution and to better attract and retain talent (keener on algorithmics than traditional finance)

This essay explains how to leverage the expertise of portfolio managers while releasing them from operational tasks so that they can concentrate more on research. It is part of the Augmented Machines approach we coined in 2012, advocate for ever since and nurture everyday. Our WallStreetLand.xyz community is based on this sole idea

** reached SSRN Top 10 List ** (Didn't see this coming!)

This paper was written for NATO and demanded by the Spanish army. It covers how to take over a country without needing to set out a war but by hacking into the board through the equity markets. Once in the board of the most prolific companies of a country you can influence it towards your own benefits. How? Leveraging a combination of algorithmic trading technology assets along with digital agents that underpin the former hack through cognitive games

This essay explains why it is so difficult for shareholders to find senior managers that seek to go through the process of algorithmization towards benefiting from Maximal Efficiency. It has happened in blue collar companies but not yet in white collar ones. The barrier-to-entry to create such type of technology is very large and, if they decided to create it on their own, there are no low-hanging-fruits along the way. As an equilibrium, then, it seems an area that needs to be fully covered by external, algorithmic-native ERP software. However, there is a Pareto Superior: if that software is federated (FedTech) the evolution is hybrid hence, ad-hoc - i.e. again a new use case where to apply Data MAPs

This essay gives rise to a major discussion: can top-end academics have a significant say on Applied Science? The answer is no unless they have relevant experience as well on the domain (at least, Microeconomics) in order to understand science as a tool, not a target. They also need to understand the software architecture, regulation and usability in order to be able to have a sound judgement. Last, they are used to create a solution upon a sole model. Real world solutions require the combination of experts' heuristics and a myriad of models

** Reached SSRN Top 10 List **

This essay argues why FinTech is rapidly converging into incumbents. Their technology is based on a database (data-driven) which is not enough to take the algorithmic step towards maximal efficiency. Further, the solution in this sector is fairly easy: the retail banking technology shall be started from algorithmic trading technology. If a company can hedge using different dynamics across depths of the order book and different baskets dependent on the context then, it can surely transfer money from A to B. In between, it can also advice for wealth management, manage portfolios, become an insurer (half of whom are asset managers) and democratise trading intelligence to retail as brokers. This is called vertical integration and, we believe, the only way to create a banking champion

** Reached SSRN Top 10 List **

The natural alignment between business and architecture within big techs has boosted their transformation (crucially, upon API-fication and synergies exploitation) compared to that in the rest of organisations. The efficiency gap is so large that even the latter fear the irruption of big techs in their own arenas. Nevertheless, organisations have lately lost control of their architectures. They have become a mix of services offered by big techs and orchestrated by external consultants. Such a dynamic has naturally led to a large convergence between architectures across industries in spite of their idiosyncratic differences. Hence, there is room for improvement through a transformation governance that optimally weighs both microeconomics and microservices. As neither of the fields is easy to master, such an improvement remains a greenfield. This paper proposes a novel data architecture paradigm, Data MAPs, that helps organisations take control of their transformation journey by becoming platforms - i.e. unlocking convergence with big techs’ efficiency levels. Further, it surpasses the theory by having evolved Data MAPs' first instance for the last 7 years. Along that time, the authors gathered real examples that filled out a cube defined by a series of dimensions significant enough to assert the universal validity of their approach