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on Financial Markets |
| By: | Pieter Nel (Department of Economics, University of Pretoria); Renee van Eyden (Department of Economics, University of Pretoria) |
| Abstract: | Does media sentiment create artificial volatility, or do stock markets efficiently filter media sentiment as noise? This study tests these hypotheses using daily data (1994-2024) across the S&P 500, Dow Jones, and NASDAQ. Principal Component Analysis decomposes four uncertainty measures into fundamental uncertainty (PC1) and media-amplified supply sentiment (PC2). EGARCH modeling reveals that media sentiment mutes rather than amplifies volatility contradicting behavioral finance predictions. Time Varying Granger causality tests suggests no causality from uncertainty variables to volatility, but volatility has a causal relationship with fundamental uncertainty. The asymmetric relationship demonstrates that information flows from stock markets to uncertainty sentiment, not uncertainty sentiment to stock markets. These findings support rational updating hypothesis where investors observe volatility and correctly infer elevated uncertainty, rather than being misled by media sentiment. |
| Keywords: | Media sentiment, EGARCH modeling, Principal component analysis, Time-varying causality |
| JEL: | G41 C58 E44 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202605 |
| By: | Alexander Dyck; Freda Fang; Camille Hebert; Ting Xu |
| Abstract: | We assemble the first comprehensive sample of venture fraud cases involving 614 U.S. venture capital (VC)-backed startups founded since 2000. We find that VC-backed firms are 54% more likely to face fraud charges than comparable non-VC-backed firms within a subsample of newly public firms where detection likelihood is high and homogeneous. We then examine the role of governance in explaining venture fraud, focusing on two features that have risen in recent years—founder-friendly structures and cap table complexity. In a panel prediction model examining all venture fraud cases, we find that fraud is more likely in startups with stronger founder control rights, more convex founder cash flow rights, more investors, and greater participation of non-traditional investors. Founder-controlled boards are 88% more likely to commit fraud than VC-controlled or shared-control boards, even within the same firm. Governance variables matter much more than founder characteristics in predicting fraud. Hot funding conditions at the initial round, which weaken governance incentives, predict future fraud. Fraudulent entrepreneurs continue to found new VC-backed startups unharmed relative to non-fraudulent entrepreneurs, suggesting a lack of market discipline. Overall, our results highlight rising agency costs in VC-backed firms that could lead to misallocation and broader social costs. |
| JEL: | G24 G3 G38 K22 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34868 |
| By: | Yijie Wang; Hao Gao; Campbell R. Harvey; Yan Liu; Xinyuan Tao |
| Abstract: | The standard approach to portfolio selection involves two stages: forecast the asset returns and then plug them into an optimizer. We argue that this separation is deeply problematic. The first stage treats cross-sectional prediction errors as equally important across all securities. However, given that final portfolios might differ given distinct risk preferences and investment restrictions, the standard approach fails to recognize that the investor is not just concerned with the average forecast error - but the precision of the forecasts for the specific assets that are most important for their portfolio. Hence, it is crucial to integrate the two stages. We propose a novel implementation utilizing machine learning tools that unifies the expected return generation process and the final optimized portfolio. Our empirical example provides convincing evidence that our end-to-end method outperforms the traditional two-stage approach. In our framework, each investor has their own, endogenously determined, efficient frontier that depends on risk preferences, investor-specific constraints, as well as exposure to market frictions. |
| JEL: | C45 C55 G11 G12 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34861 |
| By: | Krishna Neupane; Prem Sapkota; Ujjwal Prajapati |
| Abstract: | This study establishes the causal effects of market sentiment on firm profitability, moving beyond traditional correlational analyses. It leverages a causal forest machine learning methodology to control for numerous confounding variables, enabling systematic analysis of heterogeneity and non-linearities often overlooked. A key innovation is the use of a pre-trained FinancialBERT to generate sentiment scores from quarterly reports, which are then treated as causal interventions impacting profitability dynamics like returns and volatilities. Utilizing a comprehensive dataset from NEPSE, NRB, and individual financial institutions, the research employs SHAP analysis to identify influential profit predictors. A two-pronged causal analysis further explores how sentiment's impact is conditioned by Loan Portfolio/Asset Composition and Balance Sheet Strength/Leverage. Average Treatment Effect analyses, combined with SHAP insights, reveal statistically significant causal associations between certain balance sheet and expense management variables and profitability. This advanced causal machine learning framework significantly extends existing literature, providing a more robust understanding of how financial sentiment truly impacts firm performance. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.17851 |
| By: | Fernando Broner (CREI); Juan J. Cortina (World Bank); Sergio L. Schmukler (World Bank); Tomas Williams (George Washington University) |
| Abstract: | This paper examines how shifts in investor demand influence firm financing and investment decisions. For identification, the paper exploits a large-scale MSCI methodo logical reform that mechanically redefined the stock weights in major international equity benchmark indexes, changing the portfolio allocation of 2, 508 firms across 49 countries. Because benchmark-tracking investors closely follow these indexes, the rebalancing constituted a clean shock to equity demand. The results show that portfolio rebalancing by benchmark-tracking investors generated significant capital inflows and outflows at the firm level. Firms experiencing larger inflows increased equity issuance, even more so debt financing, and real investment. The paper complements the empirical analysis with a simple model of firm financing in which a decline in the cost of equity increases the value of equity and relaxes borrowing constraints. Higher equity valuations allow firms to expand borrowing even without issuing substantial new equity, so debt financing responds more strongly than equity issuance. |
| Keywords: | asset managers; benchmark indexes; corporate debt; equity; investment; institutional investors; issuance activity |
| JEL: | F33 G00 G01 G15 G21 G23 G31 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:anc:wmofir:196 |
| By: | Abeeb Olaniran (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Xin Sheng (Lord Ashcroft International Business School, Anglia Ruskin University, Chelmsford, United Kingdom.); Oguzhan Cepni (Ostim Technical University, Ankara, Turkiye; University of Edinburgh Business School, Centre for Business, Climate Change, and Sustainability; Department of Economics, Copenhagen Business School, Denmark); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa) |
| Abstract: | Climate-related risks have become an increasingly important source of market disruption, with potential implications for credit markets. As such, this study examines whether and how climate risks influence credit risk, as measured by credit default swap (CDS) spreads. Using a U.S. state-level climate risk measure and a local projections framework, we analyze both linear and asymmetric effects of climate shocks on CDS spreads. The linear results provide strong evidence that climate shocks exert a positive and statistically significant impact on credit risk, with findings robust across multiple CDS tenors. Within a nonlinear framework, where climate risk serves as a regime-switching variable, we uncover asymmetric responses of CDS spreads across different maturities and term structures. Overall, the findings offer valuable insights for market participants, including investors and financial institutions. |
| Keywords: | Climate Shocks, Credit Risk, Credit Default Swaps, Linear and Non-Linear Panel Frameworks |
| JEL: | C23 C33 G32 Q54 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202604 |
| By: | Jiaqi Liu (Australian National University); Chen Tang (Australian National University) |
| Abstract: | This paper investigates the impact of the Shanghai-Hong Kong Stock Connect (SHHK Stock Connect) on the A-H share price premium and examines whether the policy effect is contingent on market efficiency. Using monthly data for 67 Shanghai-listed A-H dual-listed firms from January 2011 to May 2019, we employ a dynamic panel model estimated via two-step system generalized method of moment (GMM) to account for the persistence of the premium and potential endogeneity. Market efficiency is proxied by trading-friction measures derived from daily high-low price ranges. Our findings indicate that the implementation of SHHK Stock Connect is associated with an average 18.4% increase in the A-H premium. However, this effect is heterogeneous: the marginal impact of the policy is more pronounced for firms operating in less efficient markets and weaker for those with higher efficiency, suggesting that pre-existing trading frictions shape the policy outcome. No significant response is observed at the announcement stage. Placebo tests and alternative efficiency measures confirm the robustness of the efficiency-dependent effect. Overall, the results underscore the importance of the information environment in shaping the outcomes of financial liberalization. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.14754 |