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on Financial Markets |
| By: | Mirza, Harun; Salleo, Carmelo; Trachana, Zoe |
| Abstract: | This study assesses euro area banks’ profitability using granular stress test data from three EU-wide exercises, coordinated by the European Banking Authority, that took place in 2016, 2018, and 2021. We propose a credit portfolio-level risk-adjusted return on assets for the euro area as a whole and for individual countries to assess the profitability of lending activities among euro area banks. Using banks’ own projections under the adverse scenarios of the stress test exercises for a consistent sample of euro area banks, we aim to uncover the effect of severe macroeconomic and financial conditions on the profitability of the various portfolios. We investigate how many country portfolios switch from profitable to loss-making under adverse conditions and show that this number peaks in the 2018 stress test exercise, while the 2021 exercise yields the lowest overall profitability. Overall, around 30% of exposures become unprofitable under stress conditions across the latest two exercises (compared to 20% for the 2016 exercise), mostly concentrated in the non-financial corporations (NFC) segment and, to a lesser extent, in the financial and mortgage portfolios. We also show in a regression analysis that the yield curve is an important determinant of portfolio-level profitability in a stress test setting, while the unemployment rate seems to be relevant in determining portfolio switches and GDP growth seems to influence the change in profitability. The results also point to some portfolio heterogeneity. |
| Keywords: | Bank profitability, cost of risk, net interest income, portfolio analysis, scenario analysis, stress testing |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbops:2024356 |
| By: | Marianne Andries (USC - University of Southern California); Milo Bianchi (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Karen Huynh (AMUNDI Asset Management); Sébastien Pouget (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement) |
| Abstract: | In an investment experiment, we show variations in information affect belief and decision behaviors within the information-beliefs-decisions chain. Subjects observe the time series of a risky asset and a signal that, in random rounds, helps predict returns. When they perceive the signal as useless, subjects form extrapolative forecasts, and their investment decisions underreact to their beliefs. When they perceive the signal as predictive, the same subjects rationally use it in their forecasts, they no longer extrapolate, and they rely significantly more on their forecasts when making risk allocations. Analyzing investments without observing forecasts and information sets leads to erroneous interpretations. |
| Keywords: | Return Predictability, Expectations, Long-Term Investment, Extrapolation, Model Uncertainty. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-04680777 |
| By: | Roberto Frota Decourt (UNISINOS); Heitor Almeida (UIUC); Philippe Protin (UGA INP IAE); Matheus R. C. Gonzalez (UNISINOS) |
| Abstract: | The purpose of the research was to build an index of informational asymmetry with market and firm proxies that reflect the analysts' perception of the level of informational asymmetry of companies. The proposed method consists of the construction of an algorithm based on the Elo rating and captures the perception of the analyst that choose, between two firms, the one they consider to have better information. After we have the informational asymmetry index, we run a regression model with our rating as dependent variable and proxies used by the literature as the independent variable to have a model that can be used for other researches that need to measure the level of informational asymmetry of a company. Our model presented a good fit between our index and the proxies used to measure informational asymmetry and we find four significant variables: coverage, volatility, Tobin q, and size. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.06272 |
| By: | Prudence Djagba; Callixte Ndizihiwe |
| Abstract: | This study investigates the application of machine learning algorithms, particularly in the context of pricing American options using Monte Carlo simulations. Traditional models, such as the Black-Scholes-Merton framework, often fail to adequately address the complexities of American options, which include the ability for early exercise and non-linear payoff structures. By leveraging Monte Carlo methods in conjunction Least Square Method machine learning was used. This research aims to improve the accuracy and efficiency of option pricing. The study evaluates several machine learning models, including neural networks and decision trees, highlighting their potential to outperform traditional approaches. The results from applying machine learning algorithm in LSM indicate that integrating machine learning with Monte Carlo simulations can enhance pricing accuracy and provide more robust predictions, offering significant insights into quantitative finance by merging classical financial theories with modern computational techniques. The dataset was split into features and the target variable representing bid prices, with an 80-20 train-validation split. LSTM and GRU models were constructed using TensorFlow's Keras API, each with four hidden layers of 200 neurons and an output layer for bid price prediction, optimized with the Adam optimizer and MSE loss function. The GRU model outperformed the LSTM model across all evaluated metrics, demonstrating lower mean absolute error, mean squared error, and root mean squared error, along with greater stability and efficiency in training. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.03204 |
| By: | Onur Polat (Department of Public Finance, Bilecik Seyh Edebali University, Bilecik, Turkiye); Juncal Cunado (University of Navarra, School of Economics, Edificio Amigos, E-31080, Pamplona, Spain); 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: | This paper investigates the impact of oil supply, demand, and risk shocks on U.S. state-level stock and bond returns, utilizing daily data from February 1994 to March 2024. It examines the individual effects of oil price shocks on each state’s stock and bond returns and explores how fluctuations in oil prices influence the interdependence between state-level stock and bond markets. The findings reveal that oil demand shocks have a significant positive impact, while oil supply shocks have a significant negative impact on state-level stock returns. Although state-level bond returns also react to these supply and demand shocks, their response is statistically less significant than that of stock returns, indicating that cross-asset diversification is possible during periods of oil supply and demand shocks. However, both stock and bond returns are significantly and negatively affected by oil risk shocks, which implies limited opportunities for cross-asset diversification when oil price fluctuations are driven by risk factors. Additionally, the interdependence between U.S. equity and bond markets is more significantly influenced by oil risk shocks than by supply or demand shocks, suggesting an increase in the interconnectedness of stock and bond returns following an oil risk shock. Further analysis, using a reverse-MIDAS model to relate high-frequency connectedness measures to monthly oil price shocks, indicates that oil supply shocks positively and significantly impact stock market connectedness, while oil inventory demand shocks negatively affect bond market connectedness. Implications of our findings are discussed. |
| Keywords: | Oil price shocks, state-level stock market returns, state-level municipal bond returns, connectedness. |
| JEL: | C22 C32 G10 Q41 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202438 |
| By: | Simone Boccaletti; Paolo Maranzano; Caterina Morelli; Elisa Ossola |
| Abstract: | Since the Paris Agreement of 2015, firms have been asked to enhance their commitment to ethical, environmental, and social responsibility by many different stakeholders. This movement seeks, alongside minimum required financial returns, positive contributions to the planet and society as a whole. However, these types of practices and investments are threatened by increased geopolitical risks, such as the invasion of Ukraine by Russia, given the interconnectedness between political events and responsible investing. In this paper, we analyze a large worldwide cross-section of stock price reactions to the Ukraine-Russia conflict, specifically differentiating companies by country, industry, and ESG characteristics. By employing an event study methodology approach on more than 17 thousand firms, the empirical analysis unveils, on average, a negative stock market reaction in the days around the event. Nonetheless, different patterns of stock market response are identified, most of which are country-sector specific. We also demonstrate that ESG performance seems to be a moderating factor, as firms with higher industry-adjusted ESG scores obtain less negative CARs. |
| Keywords: | Event Study, ESG, Russia-Ukraine Conflict, Stock Market Performance |
| JEL: | F51 G14 G15 G32 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:mib:wpaper:544 |
| By: | Susanta, Datta |
| Abstract: | The Indian sustainable debt market has grown significantly, dominated by green bonds, but green finance is still at a nascent stage in India. This paper tries to assess India’s relative position in green bond market with reference to world market on the basis of listing and trading information of global sustainable bond markets. Disaggregate level data retrieve from Luxembourg Green Exchange from 1999 to 2024 and SEBI, NSE and Climate Bond Initiatives for Indian Sustainable Bond Market used for this research. The empirical evidence suggest green bond has capture maximum market share in the global market. Luxembourg Green Exchange has maximum variability of bond coupon as well as duration (in months) of bond maturity. EUR, USD widely used currency used for bond trading, while INR has limited presence in the global market. World Bank is the highest issuing institute for both for Green Bonds and Sustainable Bonds in global market. Indian private sector has 84% market share in Indian sustainable bond market. Indian Companies have been involved in issuing green, social, and sustainability bonds, as well as innovative debt instruments such as Sustainability-Linked Bonds and skill impact bonds. However, there are several challenges faced by the green bond market in India, including the lack of a standardized framework for green bonds, limited investor awareness, and the need for more robust disclosure and reporting standards. Increased standardization, openness, and alignment with national and international best practices in policy formulation are essential for the development of India's sustainable finance sector. |
| Keywords: | Green Finance, Sustainable Finance, Bond, Debt, Securities, Green, Sustainable |
| JEL: | G12 G14 Q54 |
| Date: | 2024–01–20 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:119925 |