nep-fmk New Economics Papers
on Financial Markets
Issue of 2022‒01‒17
ten papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. The impact of COVID-19 on health sector stock returns By Oncu, Erdem
  2. Central bank digital currency research around the World: a review of literature By Ozili, Peterson K
  3. Networks of news and cross-sectional returns By Hu, Junjie; Härdle, Wolfgang
  4. Model Risk in Credit Portfolio Models By Christian Meyer
  5. Corporate Finance and the Transmission of Shocks to the Real Economy By Falk Bräuning; José Fillat; Gustavo Joaquim
  6. Mesoscopic Structure of the Stock Market and Portfolio Optimization By Sebastiano Michele Zema; Giorgio Fagiolo; Tiziano Squartini; Diego Garlaschelli
  7. Why Do Firms Issue Green Bonds? By Julien Xavier Daubanes; Shema Frédéric Mitali; Jean-Charles Rochet
  8. Machine Learning for Predicting Stock Return Volatility By Damir Filipović; Amir Khalilzadeh
  9. Portfolio optimization under mean-CVaR simulation with copulas on the Vietnamese stock exchange By Le, Tuan Anh; Dao, Thi Thanh Binh
  10. Multivariate Realized Volatility Forecasting with Graph Neural Network By Qinkai Chen; Christian-Yann Robert

  1. By: Oncu, Erdem
    Abstract: Abnormal returns were analyzed using event analysis during the pandemic to investigate whether the impact of COVID-19 on biotech stocks had significant effects. The results show that stocks have positive returns based on cumulative and abnormal average return findings. The devastating effect of the COVID-19 epidemic on the financial sector is observed in the literature. While investors avoid investing in many industries, they still see the healthcare industry profitable. Investors direct their investments towards the profitable sectors of the period. With this crisis, companies in the health sector increased their returns by taking advantage of the pandemic.
    Keywords: COVID-19, Health Sector Stock Returns, Abnormal Returns.
    JEL: G00
    Date: 2021
  2. By: Ozili, Peterson K
    Abstract: This paper reviews the recent advances in central bank digital currency research in a way that would help researchers, policy makers and practitioners to take a closer look at central bank digital currency (CBDC). The review shows a general consensus that a central bank digital currency is a liability of the issuing central bank and it has cash-like attributes. The review also presents the motivation and benefits of issuing a central bank digital currency such as the need to improve the conduct of monetary policy, the need to enhance the efficiency of digital payments and the need to increase financial inclusion. The review also shows that many central banks are researching the potential to issue CBDCs due to its many benefits. However, a number of studies have called for caution against over-optimism about the potential benefits of CBDC due to the limiting nature of CBDC design and its inability to meet multiple competing goals. Suggested areas for future research are identified such as the need to find the optimal CBDC design that meets all competing objectives, the need for empirical evidence on the effect of CBDC on the cost of credit and financial stability, the need to undertake country-specific and regional case studies of CBDC design, and the need to find a balance between limiting the CBDC holdings of users and allowing users to hold as much CBDC as they want. The implication of the findings of this review is that central bankers need to pay more attention to the design features of CBDC. Central bankers need to first identify the goals they want to achieve with CBDC, and design the CBDC to have those features. Where possible, there should be opportunities to re-design and re-invent the CBDC to meet changing central bank objectives.
    Keywords: Keywords: Digital currency, Money, Central bank digital currency, CBDC, Digital finance, Cryptocurrency, Financial inclusion, CBDC design, Blockchain, Distributed ledger technology.
    JEL: E42 E51 E52 E58 E59 G21 G28
    Date: 2022–01
  3. By: Hu, Junjie; Härdle, Wolfgang
    Abstract: We uncover networks from news articles to study cross-sectional stock returns. By analyzing a huge dataset of more than 1 million news articles collected from the internet, we construct time-varying directed networks of the S&P500 stocks. The well-defined directed news networks are formed based on a modest assumption about firm-specific news structure, and we propose an algorithm to tackle type-I errors in identifying the stock tickers. We find strong evidence for the comovement effect between the news-linked stocks returns and reversal effect from the lead stock return on the 1-day ahead follower stock return, after controlling for many known effects. Furthermore, a series of portfolio tests reveal that the news network attention proxy, network degree, provides a robust and significant cross-sectional predictability of the monthly stock returns. Among different types of news linkages, the linkages of within-sector stocks, large size lead firms, and lead firms with lower stock liquidity are crucial for cross-sectional predictability.
    Keywords: Networks,Textual News,Cross-Sectional Returns,Comovement,Network Degree
    JEL: G11 G41 C21
    Date: 2021
  4. By: Christian Meyer
    Abstract: Model risk in credit portfolio models is a serious issue for banks but has so far not been tackled comprehensively. We will demonstrate how to deal with uncertainty in all model parameters in an all-embracing, yet easy-to-implement way.
    Date: 2021–11
  5. By: Falk Bräuning; José Fillat; Gustavo Joaquim
    Abstract: Credit availability from different sources varies greatly across firms and has firm-level effects on investment decisions and aggregate effects on output. We develop a theoretical framework in which firms decide endogenously at the extensive and intensive margins of different funding sources to study the role of firm choices on the transmission of credit supply shocks to the real economy. As in the data, firms can borrow from different banks, issue bonds, or raise equity through retained earnings to fund productive investment. Our model is calibrated to detailed firm- and loan-level data and reproduces stylized empirical facts: Larger, more productive firms rely on more banks and more sources of funding; smaller firms mostly rely on a small number of banks and internal funding. Our quantitative analysis shows that bank credit supply shocks lead to a sizable reduction in aggregate output, with substantial heterogeneity across firms, due to the lack of substitutability among alternative credit sources. Finally, we show that our insights have important implications for the validity of standard empirical methods used to identify credit supply effects (Khwaja and Mian 2008).
    Keywords: shock transmission; bank-firm matching; firm financing; credit supply shocks
    JEL: E32 E43 E50 G21 G32
    Date: 2021–11–01
  6. By: Sebastiano Michele Zema; Giorgio Fagiolo; Tiziano Squartini; Diego Garlaschelli
    Abstract: The idiosyncratic (microscopic) and systemic (macroscopic) components of market structure have been shown to be responsible for the departure of the optimal mean-variance allocation from the heuristic `equally-weighted' portfolio. In this paper, we exploit clustering techniques derived from Random Matrix Theory (RMT) to study a third, intermediate (mesoscopic) market structure that turns out to be the most stable over time and provides important practical insights from a portfolio management perspective. First, we illustrate the benefits, in terms of predicted and realized risk profiles, of constructing portfolios by filtering out both random and systemic co-movements from the correlation matrix. Second, we redefine the portfolio optimization problem in terms of stock clusters that emerge after filtering. Finally, we propose a new wealth allocation scheme that attaches equal importance to stocks belonging to the same community and show that it further increases the reliability of the constructed portfolios. Results are robust across different time spans, cross-sectional dimensions and set of constraints defining the optimization problem
    Date: 2021–12
  7. By: Julien Xavier Daubanes (University of Geneva); Shema Frédéric Mitali (Ecole Polytechnique Fédérale de Lausanne); Jean-Charles Rochet (Swiss Finance Institute; University of Geneva - Geneva Finance Research Institute (GFRI); University of Zurich - Swiss Banking Institute (ISB))
    Abstract: Green bonds allow firms to commit to climate-friendly projects. Equity investors react positively to their announcement. Based on prior empirical studies, we suggest that green bond commitments help managers signal the profitability of their green projects and that they do so because they are sensitive to their rm's stock price. We present a signaling model in which firms undertake green projects not only because of carbon penalties but, additionally, because of managerial incentives, predicting that the role of the former is augmented by the latter. We test this prediction by exploiting both cross-industry differences in the stock-price sensitivity of managers' pay and in stock share turnover, and cross-country variations in effective carbon prices. Our results not only support the role that our theory ascribes to managerial incentives, but also show that this role mainly depends on carbon pricing. Green bonds are not substitutes to carbon pricing. On the contrary, the latter is essential to the effectiveness of the former.
    Keywords: Green bonds; Green finance; Climate policy; Carbon pricing; Managerial incentives; Short-termism
    JEL: D53 H23 G14 Q54
    Date: 2021–12
  8. By: Damir Filipović (Ecole Polytechnique Fédérale de Lausanne; Swiss Finance Institute); Amir Khalilzadeh (Ecole Polytechnique Fédérale de Lausanne)
    Abstract: We use machine learning methods to predict stock return volatility. Our out-of-sample prediction of realised volatility for a large cross-section of US stocks over the sample period from 1992 to 2016 is on average 44.1% against the actual realised volatility of 43.8% with an R2 being as high as double the ones reported in the literature. We further show that machine learning methods can capture the stylized facts about volatility without relying on any assumption about the distribution of stock returns. Finally, we show that our long short-term memory model outperforms other models by properly carrying information from the past predictor values.
    Keywords: Volatility Prediction, Volatility Clustering, LSTM, Neural Networks, Regression Trees.
    JEL: C51 C52 C53 C58 G17
    Date: 2021–12
  9. By: Le, Tuan Anh; Dao, Thi Thanh Binh
    Abstract: This paper studies how to construct and compare various optimal portfolio frame-works for investors in the context of the Vietnamese stock market. The aim of the study is to help investors to find solutions for constructing an optimal portfolio strategy using modern investment frameworks in the Vietnamese stock market. The study contains a census of the top 43 companies listed on the Ho Chi Minh stock exchange (HOSE) over the ten-year period from July 2010 to January 2021. Optimal portfolios are constructed using Mean-Variance Framework, Mean-CVaR Framework under different copula simulations. Two-thirds of the data from 26/03/2014 to 27/1/2021 consists of the data of Vietnamese stocks during the COVID-19 recession, which caused depression globally; however, the results obtained during this period still provide a consistent outcome with the results for other periods. Furthermore, by randomly attempting different stocks in the research sample, the results also perform the same outcome as previous analyses. At about the same CvaR level of about 2.1%, for example, the Gaussian copula portfolio has daily Mean Return of 0.121%, the t copula portfolio has 0.12% Mean Return, while Mean-CvaR with the Raw Return portfolio has a lower Return at 0.103%, and the last portfolio of Mean-Variance with Raw Return has 0.102% Mean Return. Empirical results for all 10 portfolio levels showed that CVaR copula simulations significantly outperform the historical Mean-CVaR framework and Mean-Variance framework in the context of the Vietnamese stock exchange.
    Keywords: Gaussian copula, t copula, simulation, Mean-CVaR, Mean-Variance, portfolio optimization, Vietnam
    JEL: C61 G11 G17
    Date: 2021
  10. By: Qinkai Chen; Christian-Yann Robert
    Abstract: The existing publications demonstrate that the limit order book data is useful in predicting short-term volatility in stock markets. Since stocks are not independent, changes on one stock can also impact other related stocks. In this paper, we are interested in forecasting short-term realized volatility in a multivariate approach based on limit order book data and relational data. To achieve this goal, we introduce Graph Transformer Network for Volatility Forecasting. The model allows to combine limit order book features and an unlimited number of temporal and cross-sectional relations from different sources. Through experiments based on about 500 stocks from S&P 500 index, we find a better performance for our model than for other benchmarks.
    Date: 2021–12

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