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on Financial Markets |
| By: | Chunxiao Lu (University of Canterbury) |
| Abstract: | I examine how firm-level political connections impact stock returns through institutional demand. I find that demand shifts by mutual funds significantly contribute to the decline in concurrent stock returns: mutual funds generally reduce holdings of stocks with higher political connections, especially those headquartered in provinces with elevated corruption indices, following the 2012 Chinese anti-corruption campaign announcement. Overall, while political connections have an insignificant direct effect on stock returns as an additional pricing factor, they significantly and negatively impact stock returns through the fund demand channel. Under a characteristic-based demand asset pricing framework, I further investigate the aggregate price impact for stocks with different political connections. I show that stocks with stronger political connections experience greater price impacts, particularly among the least liquid stocks and around the announcement of the 2012 anti-corruption campaign. This demand-based approach offers a novel perspective to understand the decrease in stock returns, isolates the effects of political connections from those of corruption, and emphasizes the demand-side effects on increased volatility during periods marked by unexpected political events. |
| Keywords: | Demand shifts, political connection, corruption, stock returns, mutual fund, institutional investor, demand elasticity |
| JEL: | G11 G12 G15 G18 |
| Date: | 2025–09–01 |
| URL: | https://d.repec.org/n?u=RePEc:cbt:econwp:25/15 |
| By: | Chunxiao Lu (University of Canterbury); Linxiang Ma; Yuyang Zhang |
| Abstract: | This paper investigates whether institutional investors incorporate firm-level environmental regulatory risk into their portfolio decisions. We document substantial heterogeneity across investor types in their responses to changes in firm-level environmental regulatory risk. Long-horizon investors, such as banks, insurance companies, and pension funds, tend to tilt their portfolios toward stocks with higher environmental regulatory risk. In contrast, short-horizon investors, including investment advisors and mutual funds, reduce their holdings of these firms. These opposing portfolio adjustments offset each other, attenuating the aggregate impact on stock returns. We further find that these risk-induced demand shifts vary systematically around federal elections. Following Democratic victories, the resolution of regulatory uncertainty induces long-horizon investors to decrease their exposure to environmentally risky firms, while short-horizon investors increase their holdings. By comparison, portfolio adjustments are substantially less pronounced after Republican victories. Overall, our findings highlight the role of investment horizons and heterogeneous environmental preferences in driving institutional portfolio allocation. |
| Keywords: | Environmental regulatory risk, Institutional investors, Asset demand, Investor horizon, Environmental commitment |
| JEL: | G11 G12 G20 G28 Q50 |
| Date: | 2025–12–01 |
| URL: | https://d.repec.org/n?u=RePEc:cbt:econwp:25/14 |
| By: | Hui, Xitong |
| Abstract: | Can rising asset prices reduce wealth inequality? This paper builds a continuous-time heterogeneous-agent general equilibrium in which entrepreneurs hold risky private capital and traditional savers hold safe assets. Safe-asset expansions—via financial innovation, public debt, or a stable equity bubble—operate through a single pass-through: they lower entrepreneurs’ undiversified risk exposure, compress risk premia, and raise the interest rate. This slows entrepreneurial wealth accumulation and redistributes wealth toward traditional savers, so inequality falls even as risky asset valuations rise. Savers gain unambiguously. Entrepreneurs’ welfare is state-dependent: when their wealth share is low, they prefer a higher risk premium and lose from safe-asset expansions; once sufficiently wealthy, they prefer a higher interest rate that protects a larger wealth base and gain. JEL Classification: D31, G12, E21, E44 |
| Keywords: | asset prices, interest rates, safe assets, wealth inequality, welfare |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20253162 |
| By: | Ting-Jung Lee; W. Brent Lindquist; Svetlozar T. Rachev; Abootaleb Shirvani |
| Abstract: | This paper addresses a critical inconsistency in models of the term structure of interest rates (TSIR), where zero-coupon bonds are priced under risk-neutral measures distinct from those used in equity markets. We propose a unified TSIR framework that treats zero-coupon bonds as European options with deterministic payoffs ensuring that they are priced under the same risk-neutral measure that governs equity derivatives. Using put-call parity, we extract zero-coupon bond implied yield curves from S&P 500 index options and compare them with the US daily treasury par yield curves. As the implied yield curves contain maturity time T and strike price K as independent variables, we investigate the K-dependence of the implied yield curve. Our findings, that at-the-money, option-implied yield curves provide the closest match to treasury par yield curves, support the view that the equity options market contains information that is highly relevant for the TSIR. By insisting that the risk-neutral measure used for bond valuation is the same as that revealed by equity derivatives, we offer a new organizing principle for future TSIR research. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.10823 |
| By: | Jun Kevin; Pujianto Yugopuspito |
| Abstract: | This paper introduces a hybrid framework for portfolio optimization that fuses Long Short-Term Memory (LSTM) forecasting with a Proximal Policy Optimization (PPO) reinforcement learning strategy. The proposed system leverages the predictive power of deep recurrent networks to capture temporal dependencies, while the PPO agent adaptively refines portfolio allocations in continuous action spaces, allowing the system to anticipate trends while adjusting dynamically to market shifts. Using multi-asset datasets covering U.S. and Indonesian equities, U.S. Treasuries, and major cryptocurrencies from January 2018 to December 2024, the model is evaluated against several baselines, including equal-weight, index-style, and single-model variants (LSTM-only and PPO-only). The framework's performance is benchmarked against equal-weighted, index-based, and single-model approaches (LSTM-only and PPO-only) using annualized return, volatility, Sharpe ratio, and maximum drawdown metrics, each adjusted for transaction costs. The results indicate that the hybrid architecture delivers higher returns and stronger resilience under non-stationary market regimes, suggesting its promise as a robust, AI-driven framework for dynamic portfolio optimization. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.17963 |
| By: | Frederik Rech (School of Economics, Beijing Institute of Technology, Beijing, China); Fanchen Meng (Faculty of Economics, Shenzhen MSU-BIT University, Shenzhen, China); Hussam Musa (Faculty of Economics, Matej Bel University, Bansk\'a Bystrica, Slovakia); Martin \v{S}ebe\v{n}a (Faculty of Arts and Social Sciences, Hong Kong Baptist University, Hong Kong, China); Siele Jean Tuo (Business School, Liaoning University, Shenyang, China) |
| Abstract: | This study investigates whether firm-level artificial intelligence (AI) adoption improves the out-of-sample prediction of corporate financial distress models beyond traditional financial ratios. Using a sample of Chinese listed firms (2008-2023), we address sparse AI data with a novel pruned training window method, testing multiple machine learning models. We find that AI adoption consistently increases predictive accuracy, with the largest gains in recall rates for identifying distressed firms. Tree-based models and AI density metrics proved most effective. Crucially, models using longer histories outperformed those relying solely on recent "AI-rich" data. The analysis also identifies divergent adoption patterns, with healthy firms exhibiting earlier and higher AI uptake than distressed peers. These findings, while based on Chinese data, provide a framework for early-warning signals and demonstrate the broader potential of AI metrics as a stable, complementary risk indicator distinct from traditional accounting measures. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.02510 |