About me

I'm a Finance PhD Student at UT Austin, where my academic and research interests focus on financial asset pricing, market efficiency, environmental finance, and energy finance. I completed my undergraduate studies at the University of Texas at Dallas, graduating Summa Cum Laude with a B.S. in Finance and Economics, and earned recognition as a National Merit Scholar. With a 4.0 GPA and top GRE scores (167 Verbal, 169 Quant), I have built a strong analytical foundation that prepares me for rigorous research. I've also recieved my Master's in Financial Economics from Utah State University, where I got to work as a researcher for the Center for Growth and Opportunity, and as a researcher for the Finance Research Group at USU.

Outside of my studies, I have a passion for technology and AI, having mentored students as part of the AI Society at UT Dallas. In my spare time, I enjoy programming — whether it’s building models in R, automating tasks in Python, or building a personal website with HTML/CSS/JS — and maintain an active lifestyle through weightlifting and swing dancing.

Featured Research

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    Machine Learning and Fama French Factors

    Using a variety of ML models to optimize Fama-French Factors.

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    Financial Risk Tolerance

    Exploring appetite for financial risk, controlling for martial status, gender, and number of kids.

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    Market Efficiency

    Exploring market efficiency during heightened volatility (with a focus on retail investors).

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    RINs' Imperfect Fungibility

    Exploring the impact of imperfect fungibility on the pricing of D6 RINs.

Daniel lewis
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CGO Research Fellow

I am a graduate research fellow with the Center for Growth and Opportunity (CGO) at Utah State University.

Programming Experience

Resume

Education

  1. UT Austin

    August 2025 - May 2030 Expected

    Finance PhD Student

  2. Utah State University

    August 2024 - April 2025

    Master’s of Financial Economics - Center for Growth and Opportunity Graduate Research Fellow — Investment Practicum: Handling Mortgage-Backed Securities for $150,000+ Portfolio

  3. University of Texas at Dallas

    August 2021 — May 2024

    B.S. in Finance and Economics - 4.0 GPA - Summa Cum Laude — National Merit Scholars Program - Honors College

Extracurriculars

  1. Center for Growth and Opportunity Graduate Research Fellow

    August 2024 — April 2024

    I researched energy policy (specifically the trading of environmental credits) and market efficiency during intense volatility.

  2. Team Captain of USU CFA Research Challenge

    August 2024 — April 2024

    As Team Captain for the CFA Research Challenge, I led a team in conducting comprehensive financial analysis and valuation for a publicly traded company (CRCT). This included detailed research on industry trends, financial modeling, and crafting a strategic buy/sell recommendation. I oversaw the preparation of a professional-quality written report and deliverables, including a formal presentation to industry judges. Through this role, I managed team collaboration, delegated responsibilities, and ensured alignment with CFA Institute standards, fostering both technical expertise and professional communication skills within the team.

  3. Mentor for the Artificial Intelligence Society at UTD

    January 2024 — May 2024

    As a mentor for the Artificial Intelligence Society at UTD, I guided a team of students in developing and presenting an AI project, offering technical expertise, project management insights, and fostering effective collaboration. My role included providing direction on project execution, answering technical questions, and ensuring milestones were met, culminating in a successful final presentation.

Finance Work Experience

  1. Kinder Morgan — Rotational Finance Intern

    May 2024 — August 2024

    I interned in the OSG, ESG (Environmental and Social Governance) and ETV (Energy Transition Ventures) departments. In OSG, I was involved in updating and reconfiguring the 2025 Budgets for our departments, as well as formatting data pulls. In ESG, I was responsible for quality assurance checks and automating reports in Excel VBA. In ETV, I was responsible for exploring the creation and monetization of RINs.

  2. Sunoco LP - Finance Intern

    May 2023 — August 2023

    I interned in the FP&A department, collaborating with the data analytics and sales teams, as well as with Energy Transfer’s finance team. I got hands-on experience processing reports and automating summaries using Excel VBA and Python. I also inspected the data system to find reporting discrepancies and double counting.

Notable Coursework

  1. Econometrics II (PhD Course audited)

  2. Markets and Trading (Master’s)

  3. Advanced Financial Economics (Master’s))

  4. Microeconomic Theory (Master’s)

  5. Financial Management (Master’s)

  6. Financial Information and Analysis (Master’s)

  7. Accounting for Managers (Master’s)

  8. Business Analytics with R (Master’s)

  9. Advanced Statistics for Data Science (Master’s)

  10. Multivariable Calculus

  11. Linear Algebra

  12. Econometrics

  13. ...and lots more from my undergrad!

Research

Machine Learning and Finance

  1. Reconstructing Fama-French Factors: Machine Learning-Driven Fabrication for Adaptive Asset Pricing

    WIP. Tucker Strow

    Abstract: This paper investigates the application of machine learning (ML) techniques to enhance the Fama-French (FF) asset pricing framework, addressing limitations in traditional factor models that assume linear, static relationships in a dynamic market environment. Multiple ML approaches, including Quantile Binning, K-Means Clustering, Optimized Regression, Bayesian Optimization, Random Forests, and Support Vector Machines (SVMs), are evaluated across U.S. market industries over an extended period (2007-2023) and a focused post-COVID timeframe (2020-2023). Results demonstrate substantial improvements in explanatory power, with ML-enhanced models achieving higher R-squared values across all sectors compared to traditional FF models. This research highlights the transformative potential of ML in asset pricing, offering a more flexible, data-driven approach to capturing complex, non-linear market relationships and paving the way for future cross-market applications and real-time adaptability.

Efficient Market Hypothesis

  1. Retail Trading and Price Efficiency

    WIP. Tucker Strow and Mark Bell

    Abstract: This paper explores the influence of retail trading on market efficiency, particularly during periods of financial stress, using the GameStop (GME) short squeeze as a case study. It examines the role of retail investors in driving market volatility, price clustering, and autocorrelation in returns. The study applies event-based analysis and statistical models to assess whether retail-driven phenomena challenge the Efficient Market Hypothesis (EMH), revealing how retail participation may enhance or hinder market stability. Findings suggest that retail investors can both contribute to price discovery and exacerbate inefficiencies, particularly in high-volatility environments.

Behavioral Economics

  1. Risk Tolerance

    WIP. Tucker Strow and Dr. Claudia Strow

    Abstract: Previous research has consistently shown that women lag behind men in financial literacy. Women also face reduced access to credit markets, even in more developed countries, and are less likely to hold an account at a financial institution. When they do have joint accounts, women often have limited control if their husband is listed as the primary account holder. Studies have further demonstrated that women are less likely to have pensions or make high-risk, high-reward investments (e.g., Almenberg and Dreber 2015; Lusardi and Mitchell 2008). However, the connection between parenthood and risk-taking in financial investments remains underexplored. This paper aims to examine whether mothers are more risk-averse in their financial investments compared to women without children, and whether this risk aversion is related to the age at which a woman marries or gives birth.

Renewable Energy Finance

  1. Pricing D6 RINs Under Imperfect Fungibility

    WIP. Tucker Strow and Dr. Todd Griffith

    Abstract: This paper examines the pricing dynamics of D6 Renewable Identification Numbers (RINs), highlighting the complexities that arise due to imperfect fungibility. While existing literature often focuses on the price spread between ethanol and gasoline as a primary determinant of RIN prices, this study introduces a more nuanced model that accounts for additional factors such as RIN generation year, Quality Assurance Program (QAP) verification status, volatility in ethanol and gasoline prices, and seasonal trading patterns. The results suggest that past RIN prices, energy price volatility, verification status, RIN age, and transaction month all play significant roles in determining current RIN values, offering important insights for traders and policymakers alike. This research challenges traditional pricing models and provides a framework for better understanding the dynamics of the D6 RIN market.

  2. Pricing Cellulosic Biofuels and D3 RINs

    WIP. Tucker Strow

    Abstract: This paper explores the core value of D3 Renewable Identification Numbers (RINs), which are issued under the Renewable Fuel Standard (RFS) for cellulosic biofuel production. D3 RIN prices are predicted to be influenced by a combination of market forces, production costs, regulatory mandates, and environmental incentives. In this paper, we develop an econometric model that relates D3 RIN prices to key drivers, including cellulosic biofuel production costs, Cellulosic Waiver Credit (CWC) prices, the gap between mandated and actual biofuel production, carbon intensity reductions (for interplay with other environmental incentives), and natural gas prices. The model incorporates interaction terms to capture the dynamic relationships between production costs and the scarcity of cellulosic biofuels relative to mandated volumes. Using historical data on RIN prices, production costs, and regulatory factors, we estimate the model to assess the relative contributions of these factors to D3 RIN price movements. Our findings provide insights into the pricing dynamics of D3 RINs, offering a framework for market participants and policymakers to better understand the valuation of cellulosic biofuel credits under the RFS program.

  3. Seasonality in RIN Production and Trading

    WIP. Tucker Strow

    Abstract: This paper examines the seasonality in Renewable Identification Numbers (RINs) trading and production, challenging the assumption that RIN markets are non-seasonal. Using data from the Environmental Protection Agency (EPA) on multiple RIN types (D3, D4, D5, D6, D7), we analyze variations in generation and transaction volumes across months and quarters. Employing statistical tests, we find significant seasonal patterns in both RIN production and trading activity, with notable deviations during specific periods. These findings suggest that RIN markets are influenced by business cycles, presenting opportunities for market participants to exploit seasonal fluctuations and enhance market stability.

  4. Cognitive Bias in Perceptions of Ethanol-Imbued Fuels

    WIP. Tucker Strow

    Abstract: This paper explores the potential impact of cognitive biases on the perception of ethanol-imbued fuels. Ethanol-blended fuels have become a significant component of energy markets, driven by environmental and regulatory considerations. However, public perception and acceptance of these fuels may be influenced by cognitive biases, potentially affecting their broader adoption and market dynamics. This study reviews relevant literature, examines theoretical frameworks of bias in decision-making, and analyzes consumer and market responses to ethanol-based fuels. By identifying key factors that shape these perceptions, the paper seeks to provide a comprehensive understanding of how biases may influence views on ethanol-imbued fuels.

  5. Are Energy Prices Leading Indicators of D6 RIN prices?

    WIP. Tucker Strow

    Abstract: This paper investigates the relationship between energy prices and D6 Renewable Identification Number (RIN) prices, focusing on whether energy prices serve as leading indicators for RIN price movements. Using a dataset of RIN prices and corresponding energy prices, including ethanol, gasoline, crude oil, and natural gas, we employ time-series econometric techniques to analyze the predictive power of energy price fluctuations on RIN prices. The analysis incorporates lagged price variables, energy spreads, and market volatility to assess the temporal dynamics between these markets. Our findings indicate that energy prices, particularly gasoline and ethanol, exhibit significant predictive power for RIN price trends. These results contribute to the broader understanding of how energy markets influence environmental policy instruments and provide insights for stakeholders in biofuel markets and regulatory agencies monitoring RIN trading dynamics.

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