|
on Financial Markets |
| By: | Rebeca Anguren (BANCO DE ESPAÑA); Gabriel Jiménez (BANCO DE ESPAÑA); José-Luis Peydró (BANCO DE ESPAÑA) |
| Abstract: | We study the short-term effects of both tighter and looser bank capital requirements on bank risk-taking in a crisis period. We exploit credit register data matched with firm and bank level data in conjunction with changes in capital requirements stemming from Basel III, including the introduction of a SME supporting bank capital factor in the European Union. We find that tighter capital requirements reduce the supply of bank credit to firms, while looser capital requirements mitigate the credit supply effects of increasing capital. Importantly, at the loan level (credit supply), banks more affected by capital requirements temporarily change less the supply of credit to riskier than to safer firms, and these asymmetric effects occur for both the tightening and the loosening of bank capital requirements. Finally, these effects are also important at the firm-level for total credit availability and for firm survival. Interestingly, our results suggest that those banks most impacted by the tighter Basel III capital requirements prioritize credit among ex-ante riskier firms to avoid their closure, consistent with loan evergreening. |
| Keywords: | bank capital requirements, credit supply, bank risk-taking, Basel III, loan evergreening |
| JEL: | G21 G28 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:bde:wpaper:2508 |
| By: | Coen, Jamie; Coen, Patrick; Hüser, Anne-Caroline |
| Abstract: | Repo markets are systemically important funding markets, but are also used by firms to obtain the assets provided as collateral. Do these two functions complement each other? We build and estimate a model of repo trade between heterogeneous firms, and f ind that the answer is no: volumes and gains to trade would both be higher absent collateral demand. This is because on average the firms that need funding are also those that value the collateral to speculate or hedge interest rate risk. These results have implications for policies that affect collateral demand in repo markets, including rules on short selling. |
| Keywords: | Repo, collateral demand, intermediation, financial crises. |
| JEL: | G01 G21 G23 G11 L14 |
| Date: | 2024–05 |
| URL: | https://d.repec.org/n?u=RePEc:tse:wpaper:130323 |
| By: | Abhishek Bhardwaj; Abhinav Gupta; Sabrina T. Howell |
| Abstract: | We study the causal effect of a large increase in firm leverage. Our setting is dividend recapitalizations in private equity (PE), where portfolio companies take on new debt to pay investor returns. After accounting for positive selection into more debt, we show that dramatically increasing leverage makes firms much riskier. The debt-bankruptcy relationship is in line with Altman-Z model predictions. Dividend recapitalizations increase deal returns but reduce: (a) wages among surviving firms; (b) pre-existing loan prices; and (c) fund returns, which seems to reflect moral hazard via new fundraising. These results suggest negative implications for employees, pre-existing creditors, and investors. |
| JEL: | G23 G32 G33 G35 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33435 |
| By: | George Fatouros; Kostas Metaxas; John Soldatos; Manos Karathanassis |
| Abstract: | MarketSenseAI is a novel framework for holistic stock analysis which leverages Large Language Models (LLMs) to process financial news, historical prices, company fundamentals and the macroeconomic environment to support decision making in stock analysis and selection. In this paper, we present the latest advancements on MarketSenseAI, driven by rapid technological expansion in LLMs. Through a novel architecture combining Retrieval-Augmented Generation and LLM agents, the framework processes SEC filings and earnings calls, while enriching macroeconomic analysis through systematic processing of diverse institutional reports. We demonstrate a significant improvement in fundamental analysis accuracy over the previous version. Empirical evaluation on S\&P 100 stocks over two years (2023-2024) shows MarketSenseAI achieving cumulative returns of 125.9% compared to the index return of 73.5%, while maintaining comparable risk profiles. Further validation on S\&P 500 stocks during 2024 demonstrates the framework's scalability, delivering a 33.8% higher Sortino ratio than the market. This work marks a significant advancement in applying LLM technology to financial analysis, offering insights into the robustness of LLM-driven investment strategies. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.00415 |
| By: | Daniil Karzanov; Rub\'en Garz\'on; Mikhail Terekhov; Caglar Gulcehre; Thomas Raffinot; Marcin Detyniecki |
| Abstract: | This paper introduces a novel agent-based approach for enhancing existing portfolio strategies using Proximal Policy Optimization (PPO). Rather than focusing solely on traditional portfolio construction, our approach aims to improve an already high-performing strategy through dynamic rebalancing driven by PPO and Oracle agents. Our target is to enhance the traditional 60/40 benchmark (60% stocks, 40% bonds) by employing the Regret-based Sharpe reward function. To address the impact of transaction fee frictions and prevent signal loss, we develop a transaction cost scheduler. We introduce a future-looking reward function and employ synthetic data training through a circular block bootstrap method to facilitate the learning of generalizable allocation strategies. We focus on two key evaluation measures: return and maximum drawdown. Given the high stochasticity of financial markets, we train 20 independent agents each period and evaluate their average performance against the benchmark. Our method not only enhances the performance of the existing portfolio strategy through strategic rebalancing but also demonstrates strong results compared to other baselines. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.02619 |