Estimating Investor’s Risk for Companies Going for IPOs – Correlations Between Altman Z Scores, Price Action, Valuations & Financial Risks - Akella, Sandeep (Digital Repository of Theses, 2025)
Background In the ever-evolving financial markets, Initial Public Offerings (IPOs) remain one of the most significant yet uncertain investment opportunities. While they offer the potential for strong returns, IPOs are also known for their volatility and unpredictability, especially in the short term. Investors, particularly in emerging markets like India, often struggle to assess the true risk associated with newly listed companies due to limited trading history and market hype. This thesis aims to address that gap by developing a dual-framework approach to evaluate IPO risk—combining traditional financial assessment through the Altman Z-Score with forward-looking market modeling via Monte Carlo simulations. Methods The study uses a quantitative, correlational, and predictive research design, selecting a sample of ten Indian companies that went public in recent years. Each company‘s financial data was used to calculate the Altman Z-Score, a widely recognized metric for evaluating bankruptcy risk and financial health. Simultaneously, historical stock price data from the first 30 trading days post-IPO was analyzed using Monte Carlo simulations to model potential price behaviour and identify volatility patterns. Microsoft Excel served as the primary tool for data analysis, offering accessible yet effective methods to carry out the simulation and financial calculations. Results The results reveal that while most companies in the sample had strong Z-Scores, indicating sound financial health, their post-IPO stock performance did not always reflect this stability. Some firms with high Z-Scores experienced early price declines, while others with moderate financials performed unexpectedly well in the short term. Monte Carlo simulations provided a nuanced view of this behaviour, highlighting the potential price paths and capturing the uncertainty investors face during the early phase of public trading. In several cases, the simulation exposed a wide range between upper and lower confidence intervals, emphasizing market sensitivity and the influence of external factors beyond financial fundamentals. Discussion and Conclusion The discussion highlights a crucial insight: financial metrics like the Altman Z-Score are helpful but not sufficient when used alone to predict short-term IPO performance. Market behaviour is shaped not only by a company‘s balance sheet but also by investor sentiment, timing, sector trends, and broader economic conditions. By combining Z-Score analysis with Monte Carlo simulations, this research presents a more holistic framework for evaluating risk, enabling investors to make more informed decisions. The dual-method approach fills a critical gap in IPO analysis, offering a balanced view that incorporates both structural and behavioral dimensions of market risk. In conclusion, this thesis provides a valuable contribution to investment research by proposing a practical, accessible, and insightful model for IPO risk assessment. It encourages investors, analysts, and financial advisors to adopt a more layered approach to evaluating IPOs—one that respects both the numbers and the market dynamics that influence them. The study also opens avenues for future research in expanding this model across different sectors, timeframes, and market conditions.