|
on Financial Markets |
|
Issue of 2022‒03‒14
eight papers chosen by |
| By: | Markus Brueckner; Wensheng Kang; Joaquin Vespignani |
| Abstract: | This paper studies the role of capitalization on firms’ stock price growth in response to new cases of Covid-19 infections in the United States. Controlling for firm and time fixed effects, our panel model estimates show that the effect of new cases of Covid-19 infections on firms’ stock price growth is significantly increasing in capitalization: For each one standard deviation increase in capitalization, a one standard deviation increase in new cases of Covid-19 infections increases the weekly growth rate of firms’ stock prices by about 0.7 percentage points. Effects of capitalization on the impact that Covid-19 infections have on firms’ stock price growth are largest in the travel, tourism, and hospitality sector. Smaller but still positive effects of capitalization are present in the pharmaceutical products, high-tech, and banking and finance sectors. |
| Keywords: | Covid-19, performance of firms, stock market capitalization, US stock market |
| JEL: | G10 E30 |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:een:camaaa:2021-100&r= |
| By: | Alam, Md. Mahmudul (Universiti Utara Malaysia); Wei, Haitian; , Abu N. M. Wahid |
| Abstract: | The outbreak of COVID-19 has weakened the economy of Australia and its capital market since early 2020. The overall stock market has declined. However, some sectors become highly vulnerable while others continue to perform well even in the crisis period. Given this new reality, we seek to investigate the initial volatility and the sectoral return. In this study, we analyse data for eight sectors such as, transportation, pharmaceuticals, healthcare, energy, food, real estate, telecommunications and technology of the Australian stock market. In doing so, we obtain data from Australian Securities Exchange (ASX) and analysed them based on `Event Study' method. Here, we use the 10-days window for the event of official announcement of the COVID-19 outbreak in Australia on 27 February, 2020. The findings of the study show that on the day of announcement, the indices for food, pharmaceuticals and healthcare exhibit impressive positive returns. Following the announcement, the telecommunications, pharmaceuticals and healthcare sectors exhibit good performance, while poor performance is demonstrated by the transportation industry. The findings are vital for investors, market participants, companies, private and public policymakers and governments to develop recovery action plans for vulnerable sectors and enable investors to regain their confidence to make better investment decisions. |
| Date: | 2021–11–30 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:gt4wm&r= |
| By: | Ogulcan E. Orsel; Sasha S. Yamada |
| Abstract: | In this work, we apply machine learning techniques to historical stock prices to forecast future prices. To achieve this, we use recursive approaches that are appropriate for handling time series data. In particular, we apply a linear Kalman filter and different varieties of long short-term memory (LSTM) architectures to historical stock prices over a 10-year range (1/1/2011 - 1/1/2021). We quantify the results of these models by computing the error of the predicted values versus the historical values of each stock. We find that of the algorithms we investigated, a simple linear Kalman filter can predict the next-day value of stocks with low-volatility (e.g., Microsoft) surprisingly well. However, in the case of high-volatility stocks (e.g., Tesla) the more complex LSTM algorithms significantly outperform the Kalman filter. Our results show that we can classify different types of stocks and then train an LSTM for each stock type. This method could be used to automate portfolio generation for a target return rate. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2202.03156&r= |
| By: | Sergio Mayordomo; Maria Rodriguez-Moreno; Juan Ignacio Pe\~na |
| Abstract: | Foreign exchange and credit derivatives increase the bank's contributions to systemic risk. Interest rate derivatives decrease it. The proportion of non-performing loans over total loans and the leverage ratio have stronger impact on systemic risk than derivatives holdings. |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2202.02254&r= |
| By: | Li, Xiang |
| Abstract: | This paper investigates whether and how economic policy uncertainty affects corporate debt maturity. Using a large firm-level dataset for four European countries, we find that an increase in economic policy uncertainty is significantly associated with a shortened debt maturity. Moreover, the impacts are stronger for innovation-intensive firms. We use firms' flexibility in changing debt maturity and the deviation to leverage target to gauge the causal relationship, and identify the reduced investment and steepened term structure as the transmission mechanisms. |
| Keywords: | capital structure,corporate investment,debt maturity,economic policy uncertainty |
| JEL: | D81 G32 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:iwhdps:52022&r= |
| By: | Karen Croxson; Jon Frost; Leonardo Gambacorta; Tommaso Valletti |
| Abstract: | Three types of digital platforms are expanding in financial services: (i) fintech entrants; (ii) big tech firms; and (iii) increasingly, incumbent financial institutions with platformbased business models. These platforms can dramatically lower costs and thereby aid financial inclusion – but these same features can give rise to digital monopolies and oligopolies. Digital platforms operate in multi-sided markets, and rely crucially on big data. This leads to specific network effects, returns to scale and scope, and policy trade-offs. To reap the benefits of platforms while mitigating risks, policy makers can: (i) apply existing financial, antitrust and privacy regulations, (ii) adapt old and adopt new regulations, combining an activity and entity-based approach, and/or (iii) provide new public infrastructures. The latter include digital identity, retail fast payment systems and central bank digital currencies (CBDCs). These public infrastructures, as well as ex ante competition rules and data portability, are particularly promising. Yet to achieve their policy goals, central banks and financial regulators need to coordinate with competition and data protection authorities. |
| Keywords: | financial inclusion, fintech, big tech, platforms |
| JEL: | E51 G23 O31 |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:bis:biswps:986&r= |
| By: | Stefan Pasch; Daniel Ehnes |
| Abstract: | To answer this question, we fine-tune transformer-based language models, including BERT, on different sources of company-related text data for a classification task to predict the one-year stock price performance. We use three different types of text data: News articles, blogs, and annual reports. This allows us to analyze to what extent the performance of language models is dependent on the type of the underlying document. StonkBERT, our transformer-based stock performance classifier, shows substantial improvement in predictive accuracy compared to traditional language models. The highest performance was achieved with news articles as text source. Performance simulations indicate that these improvements in classification accuracy also translate into above-average stock market returns. |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2202.02268&r= |
| By: | Ameni Ben Salem (Fseg Sousse, University of Sousse); Imene Safer; Islem Khefacha (LaREMFiQ, IHEC of Sousse) |
| Date: | 2021–12–17 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-03502428&r= |