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Entrepreneurship and Start-up’s in the AI Era
M Ramesh Naik Associate Professor, Farhana Begum Associate Professor, Department of Commerce, Karishma Chhabria Associate Professor, Department of Language, Avinash College of Commerce, Secunderabad, Telangana.
Pages: 1-15 | First Published: 05 Nov 2025
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Abstract
Global entrepreneurship is being reconfigured by artificial intelligence (AI), presenting a complex landscape of unprecedented opportunity and profound responsibility. This qualitative study investigates the transformative intersection of AI and startup ventures, positing that success in this new era relies on integrating ethical principles into core business strategy. Moving beyond technical implementation, the research explores the concomitant cultural evolution within startups, where a mandate for "responsible scaling" is beginning to supplement the traditional "move fast" ethos. The analysis is structured around five critical dimensions: the role of AI in enabling sustainable and equitable business models; the unique challenges of scaling ethical AI ventures across diverse global markets; shifting investment trends that increasingly weigh ethical considerations alongside financial returns; the incubation of AI-driven solutions for positive social impact; and the intricacies of cross-border digital trade in an AI-driven economy. Preliminary findings suggest that while AI lowers barriers to innovation and data-driven insight, it simultaneously raises pivotal challenges pertaining to data sovereignty, algorithmic transparency, and the global competition for specialized talent. The study concludes that fostering a sustainable AI-enabled entrepreneurial ecosystem requires a collaborative commitment from founders, investors, and regulators to align technological advancement with steadfast ethical governance, ensuring that the future of global innovation is both revolutionary and responsible.

Keywords: Artificial Intelligence, Startup Ecosystem, Ethical Governance, Qualitative Research, Global Scaling, Sustainable Innovation, Venture Capital, Entrepreneurial Culture.

References

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  3. Cai, L., Yang, Y., & Lu, S. (2019). Review and Prospect of the research on the impact of digital technology on entrepreneurial activities. Studies in Science of Science, 37(10), 1816–1824.

  4. Dutta, D. K., & Crossan, M. M. (2010). The nature of entrepreneurial opportunities: Understanding the process using the 4i organizational learning framework. Entrepreneurship: Theory and Practice, 29(4), 425–449.

  5. Al-Fraihat, Dimah, Mike Joy, and Jane Sinclair. 2020. Evaluating E-learning systems success: An empirical study. Computers in Human Behavior 102: 67–86. ]

  6. Blank, Steve, and Bob Dorf. 2020. The Startup Owner’s Manual: The Step-by-Step Guide for Building a Great Company. London: John Wiley & Sons and Springer Science & Business Media

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  9. Kitsios, Fotis, and Maria Kamariotou. 2021. Artificial intelligence and business strategy towards digital transformation: A research agenda. Sustainability 13: 2025

  10. Khalid, N. (2020). AI learning and entrepreneurial performance among university students: Evidence from Malaysian higher educational institutions. Journal of Intelligent Fuzzy Systems, 39(4), 5417–5435

  11. Luo, Y., & Bai, Y. (2021). Business model innovation of technical start-ups in emerging markets. Journal of Industrial Integration and Management, 6(03), 319–332

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Machine Learning in Mutual Fund Performance Prediction: An Empirical Study on Unlocking Opportunities for Smarter Investments
V. Sujata, Research Scholar, Dr. M. Kamaladevi Associate Professor and Research Supervisor, Department of Commerce, St. Peter’s Institute of Higher Education and Research, Avadi.
Pages: 16-24 | First Published: 05 Nov 2025
Full text | Abstract | PDF | References | Request permissions

Abstract

Mutual funds are one of the most popular investment avenues in India, yet predicting their performance remains a challenge for both investors and fund managers. Traditional evaluation methods such as regression models, risk-adjusted ratios, and historical NAV analysis often fail to capture the dynamic and non-linear nature of financial markets. This study investigates the application of machine learning (ML) models—specifically Random Forest, XGBoost, and Neural Networks—in forecasting mutual fund returns, using a dataset of 100 equity mutual funds over the period 2015 to 2024. The performance of these models is compared with traditional approaches such as OLS regression and ARIMA. The results reveal that ML models significantly improve predictive accuracy, reducing forecast errors by more than 20 percent relative to conventional benchmarks. Portfolio simulations further demonstrate that ML-driven fund selection strategies deliver higher cumulative returns and superior Sharpe ratios, offering practical value for investors. Directional accuracy analysis shows that ML models correctly predict fund performance trends in more than 80 percent of cases, underscoring their robustness. The findings highlight the potential of machine learning to transform investment decision-making, enhance investor confidence, and unlock smarter investment opportunities in the digital era.

Keywords: Mutual Funds, Machine Learning, Predictive Analytics, Investment Decisions, Portfolio Management

References

  1. Chen, Y., & He, J. (2021). Machine learning in mutual fund performance prediction: Evidence from neural networks. Journal of Financial Data Science, 3(2), 45–61. https://doi.org/10.3905/jfds.2021.1.067

  2. Gupta, R., & Sharma, A. (2022). Artificial intelligence and predictive analytics in Indian mutual funds: An empirical review. International Journal of Finance & Economics, 27(4), 5672–5688. https://doi.org/10.1002/ijfe.2351

  3. Narayan, P. K., & Singh, B. (2020). Predicting stock returns and mutual fund performance using machine learning methods. Economic Modelling, 86, 198–212. https://doi.org/10.1016/j.econmod.2019.11.001

  4. Wermers, R. (2011). Performance evaluation of mutual funds, hedge funds, and institutional accounts. Annual Review of Financial Economics, 3(1), 537–574. https://doi.org/10.1146/annurev-financial-102710-144908

  5. Choudhury, M., & Tripathy, N. (2021). Machine learning in financial markets: A review of applications in mutual funds and portfolio management. Asian Journal of Economics and Finance, 3(1), 23–35.

  6. Jain, A., & Singhal, R. (2020). Predictive analytics in Indian mutual fund industry: Opportunities and challenges. Journal of Emerging Market Finance, 19(2), 189–210. https://doi.org/10.1177/0972652720907062

  7. Yao, S., Wang, J., & Luo, X. (2022). Deep learning models for portfolio optimization and mutual fund forecasting. Finance Research Letters, 47, 102689. https://doi.org/10.1016/j.frl.2021.102689