FIN 588 | Session 1 | The Modigliani-Miller Theorems: A Cornerstone of Finance - 2005

Marco Pango

Summary:

The Modigliani-Miller (MM) theorems are regarded as the foundational cornerstone of modern finance for both substantive and methodological reasons. Substantively, they serve as "irrelevance propositions," providing a clear benchmark in which a firm's value is unaffected by its capital structure or dividend policy. This invariance holds under specific idealized conditions: the absence of taxes, no bankruptcy costs, and perfectly competitive, frictionless markets free of informational asymmetry. By establishing this neutral baseline, the theorems have driven the subsequent development of corporate finance toward exploring how relaxing these assumptions—such as considering the tax advantages of debt or the impact of asymmetric information—affects firm performance and value in the real world. Methodologically, the MM theorems introduced arbitrage arguments to financial theory, shifting the field from descriptive methods to formal, deductive reasoning and setting a precedent for subsequent breakthroughs in asset pricing, such as the Black-Scholes formula.

To understand these theorems, it may be helpful to think of them as a map of a frictionless world; while such a world does not exist, having the map allows researchers to identify and measure the specific "frictions"—like taxes and information gaps—that alter a firm's true value.

ENTR 810 | Session 12 | Netflix, IP Strategy, and Entertainment Trends

Introduciton:

Netflix established itself as a global entertainment leader by successfully pivoting from DVD rentals to streaming, notably overcoming Blockbuster, which declined the founders' $50 million sale offer in 2000. Guided by a culture of reinvention and leadership from figures like co-founder Reed Hastings and co-CEO Ted Sarandos, Netflix secured its competitive edge by shifting to an original content strategy—highlighted by the 2013 success of House of Cards—that emphasized diversity, global reach, and substantial investment to attract top talent. Today, Netflix maintains its position as the subscription streaming leader with 260 million paying customers, even as analysts proclaim the "streaming wars" are largely won, evidenced by rivals licensing content to the platform. To ensure continued growth in a market where streaming viewing has now surpassed linear TV for the first time, Netflix has cracked down on password sharing, diversified its offerings into live sports like WWE's Raw, and is pursuing future innovations such as personalized trailers and interactive content using AI technology. Furthermore, the company plans to engage fans in new ways by launching physical retail and experience locations called "Netflix House" starting in 2025.

MBADM 850 | Session 12 | Business Models and Innovation Monetization

Introduction:

A business model is a framework that supports a product or company's viability by showing how it creates, delivers, and captures value and revenue while meeting customer needs. Monetization involves generating income from assets or actions, effectively turning product or service usage into profit. Common strategies include offering products for free while earning from advertising, using the freemium model where basic services are free but premium features require a subscription (e.g., LinkedIn and Spotify), charging a recurring low fee (e.g., Netflix), applying tiered pricing based on volume, or adopting the Razor and Blades model, where a low-cost item requires expensive, disposable supplies. Companies like Amazon innovate mainly in sales and delivery channels, while Spotify disrupted the music industry with new revenue models, showing that success often results from optimizing or radically changing one part of the business. Achieving product-market-pricing fit is crucial, ensuring customers need, see value in, and are willing to pay for the product, often by discussing pricing early in the R&D process. Disruptive innovations, such as how airline loyalty programs evolved into large banking-partnered financial systems after deregulation, demonstrate how new business models can completely transform industries.

MKTG 556 | Session 9 | Augmented Reality in Retail and Its Impact on Sales - 2022

Yong-Chin Tan, Sandeep R. Chandukala, and Srinivas K. Reddy

Introduction:

The rise of augmented reality (AR) technology offers marketers exciting opportunities to engage customers and enhance their brand experience. Although companies are eager to invest in AR, research showing its real-world impact is limited. In this article, the authors identify four main ways the technology is used in retail settings. They focus specifically on AR's role in helping customers evaluate products before buying and analyze its effect on online retail sales. Using data from an international cosmetics retailer, they find that AR use on the retailer's mobile app correlates with higher sales for less popular brands, niche products, and more expensive items. Additionally, the impact of AR is stronger among customers new to the online channel or product category, indicating that sales growth results from increased online channel adoption and category expansion. These findings support the idea that AR is most effective when product-related uncertainty is high, showing the technology's potential to boost sales by reducing uncertainty and building purchase confidence. To promote more meaningful research, the authors outline a research agenda for AR in marketing.

MKTG 556 | Session 9 | AI–Human Hybrids for Marketing Research: Leveraging Large Language Models (LLMs) as Collaborators - 2025

Neeraj Arora, Ishita Chakraborty, and Yohei Nishimura

Introduction:

The authors' main idea is that a hybrid approach combining humans and large language models (LLMs) improves efficiency and effectiveness in marketing research. In qualitative research, they show that LLMs can help with both data generation and analysis; LLMs effectively create sample characteristics, generate synthetic respondents, and conduct and moderate in-depth interviews. The AI–human hybrid produces information-rich, coherent data that exceeds human-only data in depth and insightfulness and matches human performance in tasks like generating themes and summaries. Evidence from expert judges indicates that humans and LLMs have complementary skills; the human–LLM hybrid outperforms either humans or LLMs alone. For quantitative research, the LLM correctly identifies the answer's direction and valence, with the quality of synthetic data greatly improving through few-shot learning and retrieval-augmented generation. The authors highlight the value of the AI–human hybrid by working with a Fortune 500 food company and replicating a 2019 study using GPT-4. For their empirical work, they design system architecture and prompts to create personas, ask questions, and gather responses from synthetic respondents. They provide road maps for integrating LLMs into qualitative and quantitative marketing research and conclude that LLMs are valuable partners in generating insights.

ACCTG 502 | Session 9 | The Timeliness of Bad Earnings News and Litigation Risk - 2012

Dain C. Donelson, John M. Mclnnis, Richard D. Mergenthaler, Yong Yu

Introduction:

This study examines whether the prompt disclosure of bad earnings news is linked to a lower rate of litigation. The promptness of earnings news is measured by a new metric based on the progression of the consensus analyst earnings forecast. After controlling for total bad earnings news and other factors influencing litigation, we find that earlier disclosure of bad earnings news decreases the likelihood of litigation. This finding applies to both settled and dismissed lawsuits. Additionally, we compare our results with previous research that measures timeliness through managerial warnings issued via press releases. These tests indicate that our findings are due to the ability of our timeliness measure to detect bad earnings news disclosed through channels other than press releases.

ACCTG 502 | Session 9 | Rules-Based Accounting Standards and Litigation - 2012

Dain C. Donelson, John M. Mclnnis, Richard D. Mergenthaler

Introudction:

Some claim that rules-based accounting standards shield firms from litigation, while others argue that violations of detailed rules give plaintiffs a "roadmap" to successful litigation. We inform this debate by investigating whether rules-based standards are associated with the incidence and outcome of securities class action litigation. Overall, our results suggest that rules-based standards are associated with a lower incidence of litigation but are not associated with litigation outcomes. These results are of interest in the debate regarding the switch from a more rules-based U.S. GAAP a more principles-based IFRS.

ACCTG 502 | Session 7 | Fair value accounting standards and securities litigation - 2025

Musaib Ashraf, Dain C. Donelson, John McInnis, Richard D. Mergenthaler

Introduction:

We examine the effect of fair value standards on firms' litigation risk. The discretion required by fair value allows plaintiffs to "second guess" managers' judgments, potentially increasing litigation risk. Alternatively, the complexity of fair value may decrease litigation risk if it's more difficult to demonstrate scienter. Our evidence suggests firms that rely more on fair value standards are relatively less likely to be sued. We find no evidence of a relation between fair value and the risk of misstatements or fraud, but do find evidence of a slight increase in firms' litigation risk via an increase in volatility. However, the primary effect of fair value standards in reducing litigation risk dominates the volatility effect. Finally, we find average litigation rates increase after the passage of new standards, but less so for fair value standards. On balance, our evidence suggests fair value is a relatively low litigation risk area in GAAP.

MBADM 850 | Session 11 | Benchmarking and Competitive Analysis for Innovation

Introduction:

The essential practices of competitor analysis and benchmarking are important for both startups and established companies seeking innovation and a competitive edge. Competitor analysis for a startup involves identifying rivals, understanding their products and market segments, evaluating their strengths and weaknesses, and using this information to anticipate reactions or attract customers. For new markets, the traditional X/Y axis competitive graph is often inadequate, so the recommended "Petal Diagram" is used to visualize potential customer segments from neighboring markets. Benchmarking is a systematic process, separate from competitor research, that measures performance against best practices or world-class standards, often outside one's own industry, to identify performance gaps and promote continuous improvement. Various types include process, strategy, and performance benchmarking, with Net Promoter Score (NPS) frequently serving as a key comparative metric. In large organizations, recent data shows a shift, with less time and resources allocated to transformational innovation (Horizon 3) and more focus on incremental and adjacent innovations. The success of innovation initiatives and the ability to secure additional resources depend heavily on gaining leadership support and demonstrating impact on revenue, although innovators often cite politics, turf wars, and cultural issues as their main challenges.

ENTR 810 | Session 11 | AI: Von Neumann to Altman Hardware Evolution

Introduction:

The history of Artificial Intelligence (AI) ranges from its foundational theories to its present role as a transformative technology, illustrating the close connection between software models and the hardware that supports them. Early pioneers like Alan Turing, who helped crack the Enigma code and introduced the Turing Test for machine intelligence, and John McCarthy, who coined the term "Artificial Intelligence" at the 1956 Dartmouth Workshop, laid the groundwork for the field. John von Neumann also made significant contributions by imagining adaptive systems and defining the "von Neumann Architecture" for computer design, which remained the standard into the 21st century. After challenging periods such as the "AI Winter," the field saw a resurgence fueled by deep learning—a method that uses deep neural networks and improves predictably as more compute power and data become available. Today's AI boom relies heavily on massive computational infrastructure, with GPUs (Graphics Processing Units) serving as the "heartbeat of AI." Nvidia capitalized on this shift, transitioning from a gaming company to the key provider of AI acceleration hardware, offering GPUs and CUDA libraries. Breakthroughs in software, especially the 2017 introduction of Transformers by Google Brain, enabled models to learn context and track relationships in sequential data, leading to the development of the Generative Pre-Trained Transformer (GPT) and the landmark release of ChatGPT in 2022. Although this progress is expected to usher in an "Age of Intelligence" characterized by widespread prosperity, personalized assistants, and solutions to complex problems, experts also raise serious concerns about ethics, potential disruptions to labor markets, and the fundamental risk that AI goals could diverge from human values, or that humans might weaponize the technology.