Unveiling Outperformance: A Portfolio Analysis of Top AI-Related Stocks against IT Indices and Robotics ETFs

Ali Trabelsi Karoui, Sonia Sayari (), Wael Dammak and Ahmed Jeribi
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Ali Trabelsi Karoui: Laboratory BESTMOD, University of Sfax, Sfax 3029, Tunisia
Sonia Sayari: College of Business Admiration and Finance, Saudi Electronic University, Riyadh 11673, Saudi Arabia
Wael Dammak: Institute of Financial and Insurance Sciences, University of Lyon, University Claude Bernard Lyon 1, LSAF-EA2429, F-69007 Lyon, France
Ahmed Jeribi: Faculty of Economics and Management of Mahdia, University of Monastir, Monastir 5000, Tunisia

Risks, 2024, vol. 12, issue 3, 1-21

Abstract: In this study, we delve into the financial market to compare the performance of prominent AI and robotics-related stocks against traditional IT indices, such as the Nasdaq, and specialized AI and robotics ETFs. We evaluate the role of these stocks in diversifying portfolios, analyzing their return potential and risk profiles. Our analysis includes various investment scenarios, focusing on common AI-related stocks in the United States. We explore the influence of risk management strategies, ranging from “buy and hold” to daily rebalancing, on AI stock portfolios. This involves investigating long-term strategies like buy and hold, as well as short-term approaches, such as daily rebalancing. Our findings, covering the period from 30 April 2021, to 15 September 2023, show that AI-related stocks have not only outperformed in recent years but also highlight the growing “AI bubble” and the increasing significance of AI in investment decisions. The study reveals that these stocks have delivered superior performance, as indicated by metrics like Sharpe and Treynor ratios, providing insights into market trends and financial returns in the technology and robotics sectors. The results are particularly relevant for investors and traders in the AI sector, offering a balanced view of potential returns against the risks in this rapidly evolving market. This paper adds to the financial market literature by demonstrating that investing in emerging trends, such as AI, can be more advantageous in the short term compared to traditional markets like the Nasdaq.

Keywords: AI stocks; investment; performance; diversification; return; portfolios (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2024
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