Carlos is an FCA-regulated investment professional with an excellent track record of managing equity strategies. He helped raise AUM while the Head of Equities at LCAM from $200 million to over $1 billion. He enjoys working on projects where his investing and machine learning background adds value. He has extensive experience working with institutional investors, family offices, as well as UHNWI investors.
Delivered lectures to university students on coding, data science, and machine learning using Python, VBA, and R programming languages.
Built syllabus content and materials for data science and machine learning lectures using R and Python. The syllabus included advanced coding, classes, multi-processing, threading, and version control, supervised machine learning algorithms, and unsupervised machine learning algorithms.
Conducted investment research and machine learning projects on behalf of the University of London with clients, including investment banks, asset managers, and pension plans. Projects included developing a backtesting engine using Python for the generation of systematic investment strategies, and a blend of corporate finance, fundamental investing, and machine learning methods to enhance a turnaround/self-help deep value investing approach.
Conducted corporate training on behalf of the University of London for hedge funds, investment banks, and asset managers, helping to secure critical contracts with large financial companies by leveraging machine learning and investment background. Topics delivered included data science, machine learning, and finance and investment applications.
Focus areas: Machine Learning, Data Science, Coding, R, Python, Investments, Public Speaking, Lecturer, Investment Research
Portfolio Manager, Equity Analyst
2018 - 2018
Involved with analytical, portfolio management, and execution duties of Jefferies International's proprietary trading desk.
Enhanced stock screening, strategy, and back-testing proprietary tools to shortlist long and short ideas. An illustration is the use of conventional and alternative data to construct systematic screens to filter ideas based on stocks' geographic revenue and asset exposure that allow desk traders to stop relying on old-fashion external feedback from brokers to generate thematic ideas.
Built proprietary tools using Python to enhance the desk workflow. An example was the development of a Python app with a user-friendly GUI to monitor short positions and screen short opportunities in real-time, improving accuracy, transparency, and efficiency.
Performed daily and weekly portfolio management tasks, including position rebalancing, net/gross exposure risk management, derivatives hedging, performance attribution analysis, and order execution via Fidessa and Bloomberg EMSX using both discretionary trading and routing orders via algorithmic trading.
Authored concise market comments daily for distribution to the Head of Sales and Trading that covered asset class moves, top stock movers, and a brief overview of hot topics that drove headlines.
Led a team of two junior analysts responsible for long-only and absolute return long/short equity mandates, delivering compounded annual growth of 15-18% and achieving Sharpe ratios of 1.8x to 2.0x for an institutional client base. These performance metrics were critical in increasing assets under management from less than $200 million to over $1 billion. Attained several industry awards, such as the "Alternative Investment Awards - Best Global Equity Strategy distributed in the UK" in 2015 and 2016.
Built the investment process for multiple in-house equity strategies using a three-pillar approach based on a back-tested stock screen of long/short ideas using machine learning methods, a systematic "quantamental" scoring system, and a disciplined, bottom-up analysis approach to optimize idea selection. This investment process enabled a team of only three professionals to efficiently manage multiple mandates, including US, Europe, UK, and Europe-only portfolios, as well as developed markets, emerging markets, thematic baskets, and long/short strong conviction.
Conducted in-depth analysis and generated thematic investing reports leveraging primary research, including surveys as well as expert networks, and external sources. These reports served as the rationale for investing in thematic baskets that delivered significant absolute and abnormal returns from gaining exposure to secular trends such as cybersecurity, obesity, robotics, IoT, Cloud computing, and electric vehicles.
Involved in the origination, due diligence, negotiation, and execution of private deals on behalf of family offices, UHNWI, and institutional investors. Due diligence conducted was praised by investors as multiple fraudulent corporate cases were successfully detected, including Powa Technologies, where the ground-level analysis was critical in the detection of the firm's overstatement of revenue.
Developed ESG criteria based on back-testing environmental, social, and governance factors to determine which ones were statistically significant when constructing a long-only portfolio. The methodology helped create a brand new strategy in 2013 that allowed the firm to attract substantial new assets and jump ahead of competitors before the inception of the current ESG bull market.
Delivered training to junior staff and seminars to clients' including family offices, pension plans, and UHNWI on multiple topics such as financial statement analysis, forensic accounting, corporate finance, quantitative methods, portfolio management, derivatives, and public speaking.
Focus areas: Fundamental Analysis, Quantitative Analysis, Portfolio Management , Machine Learning, Event Driven & Special Situations, Financial Statement Analysis, Forensic Accounting Analysis, Financial Modelling, Corporate Finance, Private Equity, Equity Strategy, Backtesting, Alternative Data, Risk Management, Mentoring, Public Speaker
2005 - 2010
BNP Paribas IP
Performed in-depth sector and industry analysis examining macroeconomic drivers, supply chain (suppliers and clients), market niches, demand and supply dynamics, degree of competitiveness (HHI Index, competitors, new entrants, and structural threats) as well as interviewing company executives, suppliers, and clients while creating a network of industry experts for companies under coverage.
Conducted intensive financial modeling, including financial statements, adjustments and projections, fundamental ratio analysis, forensic accounting analysis, and valuation. Introduced automated financial statement analysis model using VBA to significantly cut down time spent on financial analysis and forensic accounting analysis that helped the team to reduce manual input and boosted the team's analytical productivity.
Covered companies within the industrials, technology, consumer, and telecommunications sectors in North America, UK, and Europe, maintaining responsibility for over 500 stocks under coverage with the capacity to provide monitoring and regular model updating of approximately 40 to 50 live ideas in portfolios.
Performed idea generation for derivatives strategies for hedging and directional purposes using a proprietary model. The model considered multiple inputs including moneyness, implied volatility cheapness, and open interest, and was backed by scholar research that enhanced the ability of the investment team to carry out portfolio beta hedging, speculative pair trades and a systematic covered call program that added over 300 bps per annum to a long-only income/quality-driven mandate for multiple pension plan clients.
Built pair trading surveillance model for stocks under coverage using a quantitative approach including VAR (Vector Autoregression), Engle-Granger Approach, back-testing, and daily surveillance of the significant pairs with automated signals warning on entry/exit opportunities for more than 100 potential pair trading opportunities.