|
on Financial Markets |
| By: | Junyi Ye; Bhaskar Goswami; Jingyi Gu; Ajim Uddin; Guiling Wang |
| Abstract: | This paper comprehensively reviews the application of machine learning (ML) and AI in finance, specifically in the context of asset pricing. It starts by summarizing the traditional asset pricing models and examining their limitations in capturing the complexities of financial markets. It explores how 1) ML models, including supervised, unsupervised, semi-supervised, and reinforcement learning, provide versatile frameworks to address these complexities, and 2) the incorporation of advanced ML algorithms into traditional financial models enhances return prediction and portfolio optimization. These methods can adapt to changing market dynamics by modeling structural changes and incorporating heterogeneous data sources, such as text and images. In addition, this paper explores challenges in applying ML in asset pricing, addressing the growing demand for explainability in decision-making and mitigating overfitting in complex models. This paper aims to provide insights into novel methodologies showcasing the potential of ML to reshape the future of quantitative finance. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.06779&r=fmk |
| By: | Di Tommaso, Caterina; Perdichizzi, Salvatore; Vigne, Samuel; Zaghini, Andrea |
| Abstract: | This research focuses on the cost of financing green projects on the primary bond market and tests for a potential price differential between green bonds issued by government entities and those issued by supranational and private sector issuers. Our findings indicate that government entities benefit from more favorable pricing conditions worldwide. This advantage is growing over time and particularly pronounced for sovereigns and municipal authorities. Our analysis also reveals that country-specific factors, such as strong political commitment to address climate change, low income level and high degree of indebtedness are significant predictors of the pricing spread across bonds. |
| Keywords: | Green bonds, Sovereign debt, Yield spread, Greenium |
| JEL: | G15 G32 H63 C21 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:cfswop:285366&r=fmk |
| By: | Masoud Taherinia; Mehrdad Matin; Jamal Valipour; Kavian Abdolahi; Peyman Shouryabi; Mohammad Mahdi Barzegar |
| Abstract: | Shareholders' expectations of stock returns and fluctuations are constantly changing due to restrictions in financial status and undesirable capital structure, which constrain managers to limit the changes in price trends in order to cover the risk instigated and infused by the unfavorable situation. The present research examines the moderating impact of information asymmetry on the relationship between capital structure adjustment and expected returns. The data from 120 companies approved in the Tehran Stock Exchange were extracted, and a hybrid data regression model was used to test the research hypotheses. Findings indicate that the capital structure adjustment speed correlates with the expected returns. Moreover, the information asymmetry positively affects the relationship between capital structure adjustment speed and expected returns. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.06035&r=fmk |