Decoding complexity and navigating climate cascades in firms
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Authors
Palepu, Sai, Radhakrishna Manikant Sarma Palepu
Issue Date
2025-05
Type
Electronic thesis
Thesis
Thesis
Language
en_US
Keywords
Management
Alternative Title
Abstract
The three-essay dissertation delves into two critical areas in finance: (1) Impact of climate change on production networks, and (2) Information processing costs in financial markets. The first two essays are in the domain of climate finance addressing the adverse spillovers caused in production networks of firms by the billion-dollar hurricanes. The third essay delves into the realm of capturing information processing costs that drive the information asymmetry in financial markets by utilizing machine learning methods. Essay 1. Leveraging major, or `billion-dollar', hurricanes as natural exogenous shocks to production networks, we analyze how supply-chain linkages propagate climate-induced economic spillovers. We find that customers that utilize suppliers in hurricane affected zones experience a 2.5% decline in their sales growth relative to customers who do not have suppliers in the same zone. Firms with higher network centrality are, however, insulated to a significant extent against spillovers from disaster-stricken suppliers. We find that a one-standard-deviation increase in a firm's centrality in its network mitigates this adverse impact on sales growth by approximately 37%. Our findings also reveal that a supplier’s proximity to disaster zones increases the likelihood that contract terminations occur by approximately 3.1%, prompting firms to realign strategically with suppliers that they perceive as facing lower climate change exposure. Furthermore, network effects are key to supply-chain adaptations, with more central customers better positioned to manage relationship terminations and form new ones in response to climate change exposure. Essay 2. Using billion-dollar hurricanes between 2011 and 2019, we examine the immediate effects of these disasters on the option-derived implied volatility of firms. Our findings indicate that firms headquartered within the disaster zones experience increases in implied volatility ranging from approximately 3% to 4.5% relative to firms outside the disaster zones. Similarly, firms with plants located within disaster zones exhibit increases in implied volatility of approximately 1.1%. When exposure is measured as the percentage of plants or revenue affected, the increases in implied volatility are even more pronounced, ranging between approximately 3.6% to 11%. We further investigate the firm-level behavior as a response to the ex-ante uncertainty induced by the hurricanes. We find that firms with greater operational exposure and higher implied volatility shift revenue to unaffected locations. Higher implied volatility also raises the likelihood of supplier contract termination by customers, but supplier firms that relocate production are less likely to face terminations and more likely to form new ties. Additionally, we investigate implied volatility spillovers through supply-chain connections between disaster-affected firms and those in unaffected areas. Our analysis reveals no significant evidence of such spillovers within the production networks of these firms. Essay 3. We investigate the role of machine learning (ML) model complexity in capturing the information processing costs that lead to information asymmetry in financial markets. The basic idea is that informed traders are better suited to process complex, non-linear relations between observable characteristics and future returns. As such, we propose and compute an ML-derived complexity metric to capture the magnitude of the relative advantage informed traders have over noise traders. We hypothesize that increased model complexity leads to increased information asymmetry. To this end, we show that our model complexity metric is positively associated with several well-known proxies of information asymmetry. Specifically, we find positive relations between firm complexity and future return volatility, wider bid-ask spreads and elevated probabilities of informed trading (PIN).
Description
May2025
School of Management
School of Management
Full Citation
Publisher
Rensselaer Polytechnic Institute, Troy, NY