lf0

Research Desk

About

LF0 is an editorial research desk focused on quantitative markets, systematic strategy design, backtesting, portfolio construction, and applied AI for research workflows.

The site prioritizes transparent assumptions, reproducible thinking, and clear separation between evidence, interpretation, and limitations.

Profiles

Matthew Smith

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Matthew holds a PhD in Machine Learning Applications to Economics and Finance and has spent his career applying machine learning to asset pricing, quantitative trading, and public-policy research. His current focus is agentic modelling with large language models and their application to financial markets.

Machine Learning LLMs & Agentic AI Asset Pricing Quantitative & Algorithmic Trading Python R

Barcelona skyline at sunset

Matthew Smith

Matthew holds a PhD in Machine Learning Applications to Economics and Finance from Universidad Complutense de Madrid (2017-2022). His research and applied work has spanned asset pricing, quantitative and algorithmic trading, public-policy modelling, and graduate-level teaching in investments and corporate finance.

His current research focuses on agentic modelling with large language models and their application to financial markets.

Education

  • PhD, Economics (Machine Learning Applications to Economics & Finance) – Universidad Complutense de Madrid, 2017-2022
  • Master’s, Economics – Universitat de Barcelona, 2013-2016
  • BSc, Business Management – Swansea University, 2007-2010

Experience

  • Knowledge Graph & GenAI Platform Lead (Global Corporate & Investment Banking client) – 2026-Present: Leading delivery of a production knowledge-graph and GenAI chatbot platform for querying thousands of contracts and loan agreements; managing a team of AI engineers within a 20+ person delivery group.
  • Post-Doctoral Researcher, NLP & LLMs in Quantitative Finance (Spanish business school) – 2024-Present: Researching agentic modelling with large language models (GPT, Claude, Llama) and their application to financial markets, across fine-tuning, alignment, retrieval-augmented generation, and evaluation.
  • Adjunct Professor (Spanish business school) – 2022-Present: Teaching MSc/MBA courses in Asset Pricing, Financial Markets, Investments, and Corporate Finance (CAPM, Fama-French, derivatives, Black-Scholes-Merton); supervising 8-10 MBA/MSc theses per year.
  • Senior Consultant, Data Scientist (Global professional services firm, Advanced Analytics) – 2022-2024: Led machine-learning projects in financial forensics, real estate, and M&A; built econometric and macro-forecasting models for banking and insurance clients; managed interns and junior data scientists.
  • Senior Post-Doctoral Machine Learning Researcher (National supercomputing research center) – 2020-2022: Built FlowMaps, a geographic information system tracking COVID-19 mobility across Spain, and researched public-policy decision-making during the pandemic; published in two Nature journals.
  • Researcher, Department of Finance – IESE Business School, 2017-2019: Empirical research in corporate finance, M&A, and dynamic modelling; authored IESE-published case studies on NVIDIA, Xiaomi, and eBay taught in MBA programs.
  • M&A Analyst – ARS Corporate, 2015: Buy-side fundamental analysis, DCF valuation, and comparable-company analysis of Spanish firms.

Selected Publications

  • Machine Learning for Applied Economic Analysis: Gaining Practical Insights

    Smith, M., Álvarez, F. (2025) · FEDEA Working Paper No. 2025-03 · Read paper ↗

  • Evaluating the policy of closing bars and restaurants in Cataluña and its effects on mobility and COVID-19 incidence

    Smith, M., Ponce, M., Valencia, A. (2021) · Scientific Reports · Read paper ↗

  • COVID-19 Flow-Maps: An open geographic information system on COVID-19 and human mobility for Spain

    Ponce, M., Valle, J., Fernandez, J., Bernardo, M., Sanches-Valle, J., Smith, M., Capella-Gutierrez, S., Gullón, T., Valencia, A. (2021) · Scientific Data · Read paper ↗

  • A Machine Learning Research Template for Binary Classification Problems and Shapley Values Integration

    Smith, M., Alvarez, F. (2021) · Software Impacts · Read paper ↗

  • Identifying mortality factors from Machine Learning using Shapley values – a case of COVID-19

    Smith, M., Alvarez, F. (2021) · Expert Systems with Applications · Read paper ↗

  • Using Shapley values to assess the impact of temporary traffic restrictions on NO2 levels in Madrid urban area

    Alvarez, F., Smith, M. (2021) · International Journal of Environmental Science and Technology · Read paper ↗

  • Predicting Firm-Level Bankruptcy in the Spanish Economy Using Extreme Gradient Boosting

    Smith, M., Alvarez, F. (2021) · Computational Economics · Read paper ↗

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