William Voisine

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Email
wv20590@essex.ac.uk -
Location
Colchester Campus
Profile
- Financial Forecasting
- Portfolio Selection
- Large Language Models
- Machine Learning
Biography
I am a PhD researcher in Financial Forecasting and AI-Driven Portfolio Optimization at the University of Essex. Based near Washington, DC, my research leverages advanced machine learning techniques, including Large Language Models (LLMs), to engineer forward-looking methods in market forecasting and adaptive portfolio strategies. With over 20 years of professional experience, I've developed econometric forecasting models to guide resource allocation in public sector programs, performed statistical analyses on massive datasets to enhance operational performance, and designed financial systems supporting oversight and governance in acquisitions. Currently, my research explores leveraging AI-driven insights from macroeconomic trends, derivatives markets, market sentiment, and alternative data to craft smarter, data-powered portfolio decisions.
Qualifications
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BSc Computer Information Systems (Husson University) (2002)
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MSc Systems Engineering (The George Washington University) (2014)
Research and professional activities
Research interests
Financial Forecasting
Developing novel methods of utilizing various data sources, ranging from macro economic indicators to news headlines, filtering noise, and creating algorithms to best project performance of a security or index over multiple time periods.
Portfolio Selection
Using multiple data sources and the application of algorithmic techniques to determine the most profitable combinations and proportions of securities to hold while dynamically predicting necessary changes over time.
Sentiment Analysis on Financial Market News
Using Transformers for natural language processing with focus on sentiment calculation and analysis of financial market news. Analyzing how market sentiment affects market prices and how this can be used to shape a portfolio.
Contact
Location:
Colchester Campus
Working pattern:
8:00am to 4:30pm Eastern Standard Time