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John Garcia

Associate Professor of Finance & Analytics · Cal Lutheran
(effective Fall 2026)

From immigrant field worker roots to finance researcher — bridging industry and academia.

Available for speaking, collaboration, and workshops on finance, analytics, and AI in education.

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My Story

My story begins in a family shaped by sacrifice, hard work, and the belief that education could open doors. I was born in Los Angeles to immigrant parents and grew up between California and Mexico, becoming the first in my family to graduate from high school and college. Those roots still shape how I see the world. Before entering academia, I spent more than two decades in finance and analytics leadership roles at Ernst & Young and Toyota, where I learned how data, strategy, and judgment drive real decisions. But at a certain point, I felt called to build something different with the second half of my career. I earned a doctorate and moved into higher education because I wanted to invest in people, not just performance. Today, as a finance and analytics professor, my work brings together research, teaching, and real-world experience in behavioral finance, machine learning, and AI. At the center of all of it is a simple idea: where you start should never define where you can go.

Areas of Expertise

Finance & Strategy

  • Corporate Finance
  • Behavioral Finance
  • Financial Analysis & Reporting
  • Financial Distress
  • Pricing Strategy

Analytics & Technology

  • Business Analytics
  • Machine Learning
  • Data Mining
  • FinTech
  • Generative AI Literacy

Interdisciplinary Focus

  • AI in Education
  • Diversity, Equity & Inclusion in the Workplace

Education & Credentials

Certifications & Programs

Teaching

I teach across undergraduate, MBA, and EMBA levels, integrating behavioral finance, machine learning, and generative AI into a practitioner-informed, technology-enhanced curriculum. Since joining Cal Lutheran in Fall 2021, I have earned an average instructor rating of 4.72 / 5.0 (median 4.80). I have developed eight new courses and co-created CLU's first undergraduate Data Science program (launched Fall 2024). Course-specific AI tutors I developed are accessed over 1,000 times monthly by students across my courses.

EMBA

EMBA 502 · Strategic Decision Making for High Performance ★

Single continuous case (RegalTel churn crisis) runs all 8 weeks through a full descriptive → predictive → prescriptive pipeline (Tableau, JMP Pro, board capstone); builds executives as analytics consumers, not practitioners.

Graduate

ECON 562 / MBA 580 · Advanced Analytics ★

The most technically demanding course I teach — covering neural networks, ensemble methods, and model deployment — yet consistently earns 5.0 / 5.0 instructor ratings.

Graduate

MBA 526 · Foundations of Analytics ★

Students replicate analysis from my published research (PCA in Applied Finance Letters), bridging academic rigor with live business data using JMP Pro.

Graduate

MBA 541 · Strategic Financial Analysis

Connects advanced valuation and financial modeling to strategic decision-making; draws directly on industry experience from Toyota and Ernst & Young.

Graduate

MBA 521 · Corporate Finance

Grounds corporate finance theory in real-world capital markets, with emphasis on analytical frameworks for complex organizational decisions.

Graduate

MBA 503 · Foundations of Business Statistics ★

Features AI-generated podcast-style chapter reviews and avatar-delivered intro lectures to extend learning beyond the classroom for working professionals.

Graduate

MBA5STI · Innovations in FinTech ★

One of the first FinTech courses in the region; integrates blockchain, robo-advising, and AI in financial services with guest practitioners.

Undergraduate

BUS 391 · Principles of Finance

Consistently maintains a waitlist; AI Finance Tutor handles 600+ student queries per month, and multimodal resources include Excel toolkits, AI-generated podcasts, and recorded lectures.

Undergraduate

BUS 356 · Business Analytics II ★

Advances students into predictive modeling (regression through neural networks) using R and Python; paired with a course-specific AI tutor that students rate more helpful than the textbook.

Undergraduate

BUS 256 · Business Analytics I ★

Gateway analytics course now required for all business majors; builds the statistical mindset and data fluency that BUS 356 builds upon.

Undergraduate

UNIV-3ST · Generative AI Literacy ★

Cross-disciplinary course — one of the first at Cal Lutheran — equipping students to use, evaluate, and critically assess AI tools in academic and professional contexts.

★ Course developed or significantly redesigned by John Garcia

Research & Publications

My research examines how alternative information — social media sentiment, employee reviews, and crowdsourced signals — shapes financial market behavior, and how machine learning can extract actionable insight from this data. An emerging third stream uses large language model agents as experimental subjects to test whether AI systems replicate, or distort, the behavioral biases documented in human traders. Together, these streams sit at the intersection of behavioral finance, computational methods, and the economics of artificial intelligence.

Research Streams

Stream 1 — Alternative Data & Financial Markets

How do non-traditional signals — Twitter sentiment, Glassdoor reviews, crowdsourced earnings forecasts — predict liquidity, financial distress, and investor behavior before conventional indicators do? This stream establishes that "soft" information carries hard economic value, often preceding traditional signals by months, with implications for retail investors, regulators, and corporate managers.

Stream 2 — Computational Methods for Finance

Developing machine learning and NLP tools to extract insight from high-dimensional financial data. Key contributions include a PCA-based valuation uncertainty index that outperforms eleven traditional proxies and novel synthetic sampling techniques for imbalanced bankruptcy prediction data.

Stream 3 — LLM Agents & Experimental Finance (emerging)

Using large language model agents — calibrated to empirical distributions from federal consumer finance surveys — as synthetic market participants in controlled behavioral experiments. This program tests whether AI agents reproduce documented anomalies (herding, momentum, overconfidence) or generate novel market dynamics. Working papers include Homo Silicus is Hyper-Rational and Algorithmic Anchoring.

Keywords

  • Investor Sentiment
  • Behavioral Finance
  • Liquidity & Financial Distress
  • Machine Learning in Finance
  • AI in Business Analytics

Current Projects & Working Papers

  1. 2026
    SSRN “Algorithmic Anchoring: How Prompt-Embedded Reference Points Bias LLM Financial Estimates”
  2. 2025
    SSRN “Homo Silicus is Hyper-Rational: Why LLM Agents Fail to Replicate Attention-Driven Trading” Presented at SWFA 2026
  3. 2025
    SSRN “The Attention Economy of Retail Trading: Evidence from Robinhood and Social Media” Under review at Journal of Behavioral and Experimental Finance

Peer-Reviewed Journal Articles

  1. 2026
    DOI “A Stabilizing Force? How Religiosity Moderates the Effect of Sentiment on Stock Liquidity” Review of Behavioral Finance
  2. 2025
    DOI “The Power of Attention: Examining The Roles of Institutional Investor and Macroeconomic News Attention in Shaping Share Liquidity” Global Finance Journal, 67, 101160
  3. 2025
    DOI “Beyond the Headlines: Sentiment Divergence and Financial Distress” Global Finance Journal, 66, 101126
  4. 2025
    DOI “Sentiment Divergence and its Impact on Share Liquidity” Multinational Finance Journal
  5. 2024
    DOI “Herding the Crowds: Effect of Sentiment on Crowdsourced Earnings Forecasts” Financial Markets and Portfolio Management, 38(3), 331–370
  6. 2023
    DOI “Do Employees Wave Financial Red Flags Through the Glassdoor?” Journal of Forensic Accounting Research, 8(1), 160–187
  7. 2023
    DOI “Measuring Valuation Uncertainty: A PCA Approach” Applied Finance Letters, 12(1)
  8. 2022
    DOI “Analysts’ Stock Ratings and the Predictive Value of News and Twitter Sentiment” Investment Analysts Journal, 51(4), 236–252
  9. 2022
    DOI “An Evaluation of Bankruptcy Prediction Algorithms Using Synthetic Samples” Machine Learning with Applications, 9, 100343
  10. 2021
    DOI “Analyst Herding and Firm-Level Investor Sentiment” Financial Markets and Portfolio Management, 35(4), 461–494
  11. 2021
    DOI “Measuring the Effect of Investor Sentiment on Financial Distress” Managerial Finance, 47(12), 1834–1852
  12. 2021
    DOI “Measuring the Effect of Investor Sentiment on Liquidity” Managerial Finance, 47(1), 59–85

Professional Experience

Academic Positions

Industry Experience

Consulting

I selectively take on consulting engagements where my research and industry background can deliver measurable impact. My work sits at the intersection of advanced analytics, behavioral science, and strategic decision-making — the same space that drives my academic research and that I navigated for two decades in industry.

What I bring to engagements:

My consulting is grounded in 20+ years of executive and professional experience at Ernst & Young and Toyota North America, a doctorate in finance and analytics, active peer-reviewed research in behavioral finance and AI, and hands-on fluency with the full analytics stack — from experimental design and survey methodology through machine learning, large-scale LLM engineering, and deployed simulation tools. I hold the Certified Analytics Professional (CAP) designation and am a CPA, giving me the rare ability to move fluidly between rigorous quantitative analysis and the business and financial context that makes it actionable.

Areas where I've delivered results:
  • Voter & market segmentation — Clustering, persuadability modeling, and message-response experimentation using advanced statistical methods (K-medoids, PCA, gradient-boosted trees) and LLM-based voter simulation agents calibrated against real survey data
  • Behavioral analytics — Applying sentiment analysis, attention modeling, anchoring research, and experimental economics to real-world decision problems in both financial markets and public policy
  • AI strategy, integration & literacy — Helping organizations understand where generative AI creates genuine value, designing responsible AI workflows, and building internal capability through training and tool development
  • Data science for financial services — Pricing, risk modeling, forecasting, portfolio analytics, and financial distress prediction, informed by both academic research and years of hands-on corporate finance leadership
  • LLM experimentation & agent design — Designing and running large-scale LLM experiments, building theory-grounded AI agent architectures, and deploying production-ready simulation tools for ongoing client use
How I work:

I engage on a project basis with clearly scoped deliverables, transparent pricing, and a bias toward work that generates lasting strategic assets, not just reports that sit on a shelf. I'm equally comfortable as the sole analyst on a focused research study or as a senior advisor embedded within a larger team. Recent engagements have included statewide voter segmentation and message-response modeling for political strategy, AI-in-education advisory for university programs, and experimental design for behavioral finance research.

I bring an academic's rigor and a practitioner's pragmatism. If you have a problem that lives at the intersection of data, behavior, and strategy, I'd welcome a conversation.

Technical Skills

Statistical & ML
Data & Visualization
LLM & AI

Fluent in Spanish

Awards & Service

Recognition

University & Professional Service