Acceptance Speech at Investiture Ceremony

Professor Marcos López de Prado

International Member of the Royal Academy of Engineering of Spain

Madrid, Spain - October 6, 2026

Excellencies, Mr. President, Members of the Royal Academy of Engineering, distinguished guests, colleagues, family and friends:

It is a profound honor to join the Royal Academy of Engineering of Spain.

I receive this distinction with gratitude, but also with a certain sense of wonder. My professional life has been devoted to computational science and systematic investing—fields that, not so long ago, many would have placed outside the traditional boundaries of engineering.

For that reason, I believe that today’s ceremony has a significance that extends beyond one individual. My election says something about how engineering itself is evolving, and about how investment management is evolving with it.

When I began my professional career nearly three decades ago, investing was still regarded primarily as a branch of economics or finance. Mathematics, econometrics and portfolio theory brought rigor and elegant solutions to problems of allocation and risk. Those contributions remain fundamental. But the nature of the investment problem has changed, a transformation to which I have tried to contribute through my work.

Today, large pools of capital are managed through systems that ingest enormous quantities of data, run on high-performance computing infrastructure, employ machine learning and causal inference, execute complex algorithms, and continuously monitor interacting risks. The modern quantitative investor increasingly confronts problems familiar to engineers.

Our inputs are uncertain. Our systems are dynamic. Our observations are noisy. Controlled experiments are often impossible. The environment changes while we are measuring it. Models that perform well in research may fail in production. In such an environment, mathematical elegance is not enough.

Consider aviation or space exploration. An aircraft is not engineered only for the case in which every sensor is accurate, every component functions perfectly, and the weather behaves as expected. A spacecraft is not designed only for its nominal trajectory. Engineers introduce safety margins, redundancy, fault tolerance, stress testing, and graceful degradation because they know that assumptions will eventually be violated.

Investment systems face a different environment, but the engineering principle is the same. We should ask how a model behaves when data are noisy, relationships change, forecasts are wrong, and markets depart from the conditions under which it was designed. Robustness is not an afterthought to optimization. It is part of the design objective.

Engineering begins where a stylized mathematical solution meets reality.

That distinction has shaped much of my research. In portfolio construction, Hierarchical Risk Parity, Nested Clustered Optimization, and Allocation to Allocators are examples of this engineering approach: redesigning solution architectures for greater robustness.

The same engineering philosophy has guided my work beyond portfolio construction: in market microstructure and high-frequency trading, in backtesting, in the statistical detection of false discoveries, in the identification of structural breaks, and in financial machine learning. Some of that work has produced new algorithms; some has produced mathematical theorems; and some has resulted in patented technology. What unifies these contributions is not a particular technique, but a common objective: making financial decisions more reliable when data are noisy, samples are limited, and models are imperfect.

This is the broader idea I have tried to develop under the concept of Investment Engineering: the development of computational, pragmatic and robust solutions to investment problems for which exact mathematical solutions are either unavailable or too dependent on unrealistic assumptions.

This distinction will become increasingly important as artificial intelligence transforms finance. Large language models and generative AI have expanded the frontier again. They can process vast quantities of unstructured information, generate and critique hypotheses, write and review code, assist researchers, and increasingly coordinate complex workflows through AI agents.

But greater computational power also allows us to make mistakes faster, at larger scale, and with greater confidence. Generative AI makes this challenge especially visible: a model can produce an answer that is fluent, plausible, and wrong. The danger of artificial intelligence is therefore not merely that algorithms may fail. It is that they may fail with great conviction.

In finance, where signal-to-noise ratios are exceptionally low and experimentation is constrained, false positives produced by complex methods can easily masquerade as discoveries. The central challenge will therefore not simply be to build more powerful models, but to engineer more reliable research and decision-making systems—with rigorous validation, reproducibility, verification, transparency about uncertainty, and appropriate human oversight.

In that sense, I see my election to this Academy as recognition not only of work already done, but of an emerging branch of engineering.

Professor Frank Fabozzi recently observed that my election brings into a national academy a practitioner whose primary professional identity was developed in investment management rather than in full-time academia. I find that observation particularly meaningful. It suggests that the boundary between scientific research and investment practice is becoming increasingly porous.

That is a positive development. Practice poses problems that theory has not yet solved. Science gives us methods with which to approach them. Engineering turns those methods into systems capable of operating in the real world.

There is also a broader reason why this matters. Investing allocates scarce resources today in pursuit of benefits tomorrow. The quality of those decisions affects productivity, innovation, employment, infrastructure, retirement security, and ultimately the prosperity of societies. Better-engineered investment decisions therefore have consequences far beyond individual portfolios. When the capital being invested represents the wealth of a nation, this responsibility becomes greater still. Decisions made today may affect the welfare of generations not yet born. In that setting, robustness and reliability are not merely desirable properties of an investment model; they are part of the responsibility entrusted to those who manage that wealth.

I am deeply grateful that the Royal Academy of Engineering has chosen to recognize this field. Its motto, Scientia · Ingenium · Homini—science and ingenuity for humanity—captures what engineering should aspire to achieve.

I hope that my election helps establish that engineering extends beyond bridges, machines, semiconductors or aircraft. It also encompasses the disciplined design of reliable decision systems for complex social and economic environments.

I am indebted to the many colleagues, co-authors, students and institutions that have made my work possible. I am especially grateful to the Abu Dhabi Investment Authority for embracing the deployment of these methods at institutional scale; to Cornell University for providing an academic home within its College of Engineering; and to the many scientists with whom I have had the privilege to collaborate over the decades.

Most importantly, I thank my family, and especially my wife and children. Their support has accompanied every stage of this journey.

To the President and Members of the Royal Academy of Engineering: thank you for the confidence you have placed in me. I look forward to contributing to the Academy’s institutional role in providing the highest level of scientific and engineering advice to governments and public officials.

I accept this medal not as a conclusion, but as an obligation—to continue contributing to engineering science, to strengthen the connection between computation and investment practice, and to help ensure that the extraordinary tools now being created by artificial intelligence are used with rigor, responsibility and purpose.

The central lesson of my work can perhaps be expressed very succinctly:

Data inform algorithms. Algorithms inform decisions. But the objective is not the most sophisticated model. The objective is the most reliable decision under uncertainty.

That, to me, is the essence of Investment Engineering.

Muchas gracias.

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(A recording of the Investiture Ceremony is available at the Academy’s YouTube Channel)