| SEMINAR ŞTIINŢIFIC MONEDA, FINANŢE, BĂNCI – ARHIVĂ |
Democracy, Pandemic Crisis and Dividend Policy
Marți, 17 decembrie 2024 – ora 16:40 – sala 3M4 (etaj 1, clădirea Moxa)
Victor Dragotă
Bucharest University of Economic Studies; victor.dragota@fin.ase.ro
Abstract:
Using as data 120,690 firm-year observations from 12,069 companies from 59 countries, we tested whether different indicators for democracy, pandemic crisis, and their interaction explain dividend policy. Our results are mixed. Propensity to pay dividends is positively influenced by democracy, but only for the entire sample and for the developing countries; for developed countries, democracy has a negative impact on this indicator. On the other hand, for dividend / assets, the impact of democracy is reversed. This result can be explained by the different informational content of these two indicators. Dividend policy was negatively affected by the COVID-19 pandemic. Finally, we have interacted democracy with epidemic crisis and analyzed its impact on dividend policy.
Zoom:
https://ase.zoom.us/j/88975840175?pwd=Q0ZnbHl0TjhqcEtzVnhMUWRmdWdPQT09
Activitate de socializare: Old Kitchen
Comparative Analysis of Base Line, Deep Learning, and Large Language Models in Credit Scoring Using Synthetic Datasets
Marți, 10 decembrie 2024 – ora 16:40 – sala 3M4 (etaj 1, clădirea Moxa)
Andreea Bozagiu
Bucharest University of Economic Studies; andreea.bozagiu@fin.ase.ro
Abstract:
This study compares traditional credit scoring methods, deep learning models, and large language models (LLMs), using synthetic data to protect privacy and ensure consistency. Credit scoring has traditionally used methods like logistic regression and new AI models which may improve prediction accuracy. In this paper it was tested and evaluated these models baseline methods (logistic regression), deep learning (Gradient Boosting Machine and Neural Networks), and LLM-based models for feature extraction and prediction looking at performance in areas like accuracy, precision, and recall. The results show that deep learning and LLM-based models perform better with complex data, while traditional models still work well with lower computational demands. This paper provides valuable insights into balancing accuracy, interpretability, and computational efficiency when developing credit scoring models.
Zoom:
https://ase.zoom.us/j/88975840175?pwd=Q0ZnbHl0TjhqcEtzVnhMUWRmdWdPQT09
Activitate de socializare: Zăganu
Cerc vicios sau virtuos? Analiza interacțiunii dintre riscurile bancare asociate deținerii titlurilor de stat și riscul suveran
Marti, 3 decembrie 2024 – ora 16:40 – sala 3M4 (etaj 1, clădirea Moxa)
Leonard Uzum
Bucharest University of Economic Studies and National Bank of Romania; uzumleonard@yahoo.com
Abstract:
We propose a rotated dynamic Nelson-Siegel model for modeling the Romanian sovereign yield curve and a tailored Lasso VAR model with macroeconomic and financial predictors for forecasting it. As expected, inflation and the key interest rate emerge as primary drivers of the yield curve. Nonetheless, our results also show that other factors significantly impact the yield curve, including credit growth, the current account balance, the market operations balance, interbank interest rates, international financial stress (proxied by the VIX), and local banking sector risk (measured by a stock volatility index). Furthermore, our results suggest that domestic bank risk is partially responsive to the spread component of the yield curve but remains unaffected by other yield curve characteristics. This study offers a valuable tool for fiscal policymakers aiming to comprehend yield curve dynamics in an emerging economy and for institutional investors considering borderline investment-grade sovereign bonds in their portfolios.
Zoom:
https://ase.zoom.us/j/88975840175?pwd=Q0ZnbHl0TjhqcEtzVnhMUWRmdWdPQT09
Activitate de socializare: Coolinart
Balancing an unbalanced word: can deep learning models predict credit defaults without bias?
Marti, 26 noiembrie 2024 – ora 16:40 – sala 3M4 (etaj 1, clădirea Moxa)
Alexie Alupoaiei
National Bank of Romania; alexie.alupoaiei@bnro.ro
Abstract:
We investigate potential biases related to using AI/ML models for default predictions or credit scoring, choosing gender (female) as protective attribute. We propose a new methodology based on the principle of „equal risks, equal rights”, using Deep Learning Networks. Specifically, we investigate the probability of AI/ML models generating Type I errors (false positives) depending on the borrowers’ risk levels. We use a database containing all consumer and mortgage loans with value higher than EUR 4000 (nearly 900,000 debtors) granted by a European Union banking sector. The results show that proper use of ML model feeded with unbias dataset do not generate bias in outcome.
Zoom:
https://ase.zoom.us/j/88975840175?pwd=Q0ZnbHl0TjhqcEtzVnhMUWRmdWdPQT09
Activitate de socializare: La Radu
AI in portfolio optimization: challenges and opportunities
Joi, 21 noiembrie 2024 – ora 16:40 – sala 3207
Walter Farkas
University of Zürich and ETH Zürich; farkas@math.ethz.ch
Abstract:
Artificial intelligence is the automation of statistical analysis – aiming to produce highly accurate outcome distributions. In order to be reliable, statistical predictions crucially depend on whether the future observations are similar to the past. For AI to be effective, identified patterns must be reoccurring but on efficient financial markets identified performance patterns are unlikely to persist. In this talk, drawing on an ongoing research project with Andreas Zimmermann, we will show the new opportunities AI brings to portfolio optimization. Specifically, we will discuss how AI enables to quantify complex dependencies of financial instruments performance and the integration of their diverse outcome distributions in different financial market states to enhance portfolio optimisation.
Zoom:
https://ase.zoom.us/j/88975840175?pwd=Q0ZnbHl0TjhqcEtzVnhMUWRmdWdPQT09
Activitate de socializare: N/A
The psychobiological predictability of experimental asset bubbles
Marți, 5 noiembrie 2024 – ora 16:40 – sala 3M4 (etaj 1, clădirea Moxa)
Mihai Toma
Bucharest University of Economic Studies, mihai.toma@fabiz.ase.ro
Abstract:
Asset bubble crashes often result in severe negative consequences for financial markets, extending repercussions to the real economy. Understanding the dynamics of these crashes is critical for developing mechanisms to mitigate their impact. This study employs machine learning techniques alongside experimental tools and biometric data to forecast bubble crashes. It incorporates a three-legged task involving asset bubble formation, asset price forecasting, and risk elicitation to explore fluctuations in individual risk preferences at different bubble stages. Preliminary results indicate biometric activity can be used to forecast stock returns and bubble crashes. We add to the literature on neuroforecasting and bubble crash prediction by using innovative biometric data, while also providing insights into the dynamics of boom-and-bust cycles.x
Zoom:
https://ase.zoom.us/j/88975840175?pwd=Q0ZnbHl0TjhqcEtzVnhMUWRmdWdPQT09
Activitate de socializare: Zăganu
Impact of ESG-related government efforts on economic growth
Marți, 29 octombrie 2024 – ora 16:40 – sala 3M4 (etaj 1, clădirea Moxa)
Iulian Norocel
Bucharest University of Economic Studies, iulian.norocel@fin.ase.ro
Abstract:
The sustainability-linked discussion has gained international importance in the recent years, as the concept of ESG seems to be on everyone’s lips nowadays. Debates at the highest levels are, however, still ongoing as to whether sustainability matters should be treated as a priority or little to no added economic value is added by transitioning to a green economy. What is without doubt in this equation is the position of the public sector, as the north star that will guide and drive the global economies towards a sustainable future or not. This paper aims at shedding some light over this very topical subject by presenting the link between ESG-related government efforts and economic development. Based on an extensive set of econometric techniques, the results indicate mixed impacts of various ESG-related forms of public spending and revenue on economic growth. The results can provide public policy advice as to how authorities should make use of their available resources to promote sustainability while retaining wealth creation.
Zoom:
https://ase.zoom.us/j/88975840175?pwd=Q0ZnbHl0TjhqcEtzVnhMUWRmdWdPQT09
Activitate de socializare: La Radu
The US Banks Dividend Policy During COVID-19 Pandemic: The Response to Government Containment and Economic Support Measures
Marți, 22 octombrie 2024 – ora 16:40 – sala 3M4 (etaj 1, clădirea Moxa)
Mihaela Dragotă
Bucharest University of Economic Studies, mihaela.dragota@fin.ase.ro
Abstract:
This study examines the determinants of dividend policy in the US commercial banking sector during the COVID-19 pandemic, using a comprehensive sample of 3,770 commercial banks. The analysis reveals a significant pandemic-induced decline in the dividend-to-equity ratio among these banks, indicating a significant shift in dividend policy. Interestingly, this reduction in dividend payouts was not accompanied by a deterioration in loan portfolio quality, as evidenced by a substantial 43% decrease in the non-performing loan rates in the second quarter of 2022 compared to the fourth quarter of 2019. Using a fixed-effect Tobit regression we show that under normal market conditions, dividend policy was influenced by various bank-specific factors, including size, profitability, equity to assets, loans-to-deposits, not performing loans, cash to central banks, and goodwill-to-assets. However, during the COVID-19 pandemic, only profitability, equity-to-assets, goodwill-to-assets, and size consistently and significantly affected dividend-to-equity ratio. Additionally, the study shows that the dividend payments were influenced by different COVID-19 support measures across US states. Furthermore, analyzing only dividend payers, during the COVID-19 pandemic, higher non-performing loan rate levels negatively and significantly influenced dividend payments. This relationship strengthened with increasing dividend-to-equity ratio levels, indicating a cautious approach by US commercial banks during the pandemic in terms of dividend policy. Despite receiving financial support, banks maintained conservative dividend payouts policies possible due to managerial accountability and regulatory oversight. This research provides valuable insights into how US commercial banks adjusted their dividend policies during the pandemic, emphasizing the roles of government support measures and bank fundamentals in these decisions.
Zoom:
https://ase.zoom.us/j/88975840175?pwd=Q0ZnbHl0TjhqcEtzVnhMUWRmdWdPQT09
Activitate de socializare: Old KItchen
Reviewing the origin and development of the Irrational fractional Brownian Motion model
Marți, 15 octombrie 2024 – ora 16:40 – sala 3M4 (etaj 1, clădirea Moxa)
Gurjeet Dhesi
London South Bank University, dhesig@lsbu.ac.uk
Abstract:
This paper reports a new methodology and results on the forecast of the numerical value of the fat tail(s) in asset returns distributions using the irrational fractional Brownian motion model. Optimal model parameter values are obtained from fits to consecutive daily 2-year period returns of S&P500 index over [1950–2016], generating 33-time series estimations. Through an econometric model, the kurtosis of returns distributions is modelled as a function of these parameters. Subsequently an auto-regressive analysis on these parameters advances the modelling and forecasting of kurtosis and returns distributions, providing the accurate shape of returns distributions and measurement of Value at Risk.
Zoom:
https://ase.zoom.us/j/88975840175?pwd=Q0ZnbHl0TjhqcEtzVnhMUWRmdWdPQT09
Activitate de socializare: Coolinart
Investor Sentiment and Systemic Risk: A Spillover in Spillovers Analysis
Marți, 8 octombrie 2024 – ora 16:40 – sala 3M4 (etaj 1, clădirea Moxa)
Dan Gabriel Anghel
Bucharest University of Economic Studies, dan.anghel@fin.ase.ro
Abstract:
We analyze if and how daily tail risk transmission between Financial stocks is influenced by investor sentiment (spillovers). On the one hand, we find that systemic risk and investor sentiment levels are negatively correlated. On the other hand, we show that investor sentiment spillovers are directly responsible for tail risk spillovers, most prominently for the month of January. The latter result suggests a sentiment contagion explanation for the January effect.
Zoom:
https://ase.zoom.us/j/88975840175?pwd=Q0ZnbHl0TjhqcEtzVnhMUWRmdWdPQT09
Activitate de socializare: J’ai Bistrot