Graduation Year

2024

Date of Submission

12-2024

Document Type

Campus Only Senior Thesis

Degree Name

Bachelor of Arts

Department

Economics

Reader 1

Cameron Shelton

Rights Information

© 2024 Kumaran E Selva

Abstract

This study investigates the financial impact of AI-driven consumer innovations in the music technology sector, centering on Spotify’s pivotal role as the market leader. By leveraging event study methodology, this research isolates the effect of specific AI innovation announcements on firm valuation. Event studies are used to analyze the financial market's reaction to time-bound events, as they measure abnormal returns, deviations from the expected returns predicted by a financial model. These deviations provide insights into how investors interpret and value the information contained in announcements. To calculate expected returns, this study employs the Fama-French five-factor model. This model incorporates the market risk premium, the firm size, firm value, firm profitability, and firm investment strategies. By regressing 250 trading days of historical stock data prior to each event, the study estimates the coefficients for each factor, which are then used to calculate normal returns during the event window, the day of the announcement and the subsequent two trading days. Abnormal returns are derived as the difference between actual and predicted returns. The methodology is applied to six selected events, including Spotify’s AI DJ and AI Playlist feature beta launches and expansions, as well as comparative announcements from Warner Music Group and Anghami. The study emphasizes the scalability and strategic deployment of consumer-facing AI features, examining how these innovations influence investor sentiment and firm value. The findings reveal that consumer-facing AI innovations, such as Spotify's AI DJ and AI Playlist features, consistently result in positive cumulative abnormal returns (CARs), showing their potential to enhance user engagement and drive revenue growth and how investors react to this information. For both innovations from Spotify, the AI DJ feature and the AI Playlist feature the expansion of each feature sported a higher increase in CAR, even when normalized for market size, showing that investors place greater confidence in the scalability and revenue potential of AI-driven features following successful beta launches. The study identifies key factors affecting market reactions, including firm size, geographic reach, and the perceived scalability of AI features. Larger firms like Spotify benefit from robust investor confidence, while smaller firms or those operating in niche markets, such as Anghami, face scalability challenges that temper investor enthusiasm. This research contributes to the literature on AI's financial impact by demonstrating the importance of tailored deployment strategies in maximizing firm value. It also highlights the need for future studies to explore long-term market effects, regional variations, and cross-industry comparisons of AI adoption. By bridging the gap between technological innovation and financial outcomes, this study offers insight for stakeholders in the music technology sector to understand how AI music technology may impact firm value, and how firm characteristics and rollout processes may differentiate market success.

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This thesis is restricted to the Claremont Colleges current faculty, students, and staff.

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