nep-fmk New Economics Papers
on Financial Markets
Issue of 2021‒10‒25
eleven papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Learning about Unprecedented Events: Agent-Based Modelling and the Stock Market Impact of COVID-19 By Bazzana, Davide; Colturato, Michele; Savona, Roberto
  2. The Option Value of Municipal Liquidity: Evidence from Federal Lending Cutoffs during COVID-19 By Andrew F. Haughwout; Benjamin Hyman; Or Shachar
  3. Sizing hedge funds' Treasury market activities and holdings By Ayelen Banegas; Phillip J. Monin; Lubomir Petrasek
  4. Impact of public news sentiment on stock market index return and volatility By Anese, Gianluca; Corazza, Marco; Costola, Michele; Pelizzon, Loriana
  5. Media abnormal tone, earnings announcements, and the stock market By David Ardia; Keven Bluteau; Kris Boudt
  6. Robustifying Markowitz By Härdle, Wolfgang; Klochkov, Yegor; Petukhina, Alla; Zhivotovskiy, Nikita
  7. Is Stock Index Membership for Sale? By Kun Li; Xin (Kelly) Liu; Shang-Jin Wei
  8. How Robust are Limit Order Book Representations under Data Perturbation? By Yufei Wu; Mahmoud Mahfouz; Daniele Magazzeni; Manuela Veloso
  9. Systemic Risk and Portfolio Diversification: Evidence from the Futures Market By Radoslav Raykov
  10. Sector Volatility Prediction Performance Using GARCH Models and Artificial Neural Networks By Curtis Nybo
  11. An empirical characterization of volatility dynamics in the DAX By Virla, Leonardo Quero

  1. By: Bazzana, Davide; Colturato, Michele; Savona, Roberto
    Abstract: We model the learning process of market traders during the unprecedented COVID-19 event. We introduce a behavioral heterogeneous agents’ model with bounded rationality by including a correction mechanism through representativeness (Gennaioli et al., 2015). To inspect the market crash induced by the pandemic, we calibrate the STOXX Europe 600 Index, when stock markets suffered from the greatest single-day percentage drop ever. Once the extreme event materializes, agents tend to be more sensitive to all positive and negative news, subsequently moving on to close-to-rational. We find that the deflation mechanism of less representative news seems to disappear after the extreme event.
    Keywords: Farm Management, Risk and Uncertainty
    Date: 2021–10–20
  2. By: Andrew F. Haughwout; Benjamin Hyman; Or Shachar
    Abstract: We estimate the option value of municipal liquidity by studying bond market activity and public sector hiring decisions when government budgets are severely distressed. Using a regression discontinuity (RD) design, we exploit lending eligibility population cutoffs introduced by the federal sector’s Municipal Liquidity Facility (MLF) to study the effects of an emergency liquidity option on yields, primary debt issuance, and public sector employment. We find that while the announcement of the liquidity option improved overall municipal bond market functioning, lower-rated issuers additionally benefited from direct access to the facility—their bonds traded at higher prices and were issued more frequently, suggesting a potential credit-risk sharing channel on top of the Fed’s role as liquidity-provider of last resort. Local governments, by contrast, responded to emergency liquidity measures by recalling a greater share of service-providing government employees (mostly educational institution workers), but recalls were only sustained for higher-rated municipalities. This hiring responsiveness is consistent with the hypothesis that large government furloughs might have over-weighted the worst possible outcomes based on past experience. Together, the results suggest that municipalities would likely have been more distressed absent the emergency liquidity.
    Keywords: municipal debt; state and local governments; COVID-19; Federal Reserve lending facilities
    JEL: G14 G18 H74
    Date: 2021–10–01
  3. By: Ayelen Banegas; Phillip J. Monin; Lubomir Petrasek
    Abstract: Hedge funds play an increasingly important role in U.S. Treasury (UST) cash and futures markets, a role that has been widely discussed following the March 2020 U.S. Treasury sell-off. In this note, we analyze hedge funds' holdings of UST securities and their UST market activities in normal times and in times of financial market stress using regulatory data from the SEC Form PF.
    Date: 2021–10–06
  4. By: Anese, Gianluca; Corazza, Marco; Costola, Michele; Pelizzon, Loriana
    Abstract: Recent advances in natural language processing have contributed to the development of market sentiment measures through text content analysis in news providers and social media. The effectiveness of these sentiment variables depends on the implemented techniques and the type of source on which they are based. In this paper, we investigate the impact of the release of public financial news on the S&P 500. Using automatic labeling techniques based on either stock index returns or dictionaries, we apply a classification problem based on long short-term memory neural networks to extract alternative proxies of investor sentiment. Our findings provide evidence that there exists an impact of those sentiments in the market on a 20-minute time frame. We find that dictionary-based sentiment provides meaningful results with respect to those based on stock index returns, which partly fails in the mapping process between news and financial returns.
    Keywords: Public financial news,Stock market,NLP,Dictionary,LSTM neural networks,Investor sentiment,S&P 500
    JEL: G14 G17 C45 C63
    Date: 2021
  5. By: David Ardia; Keven Bluteau; Kris Boudt
    Abstract: We conduct a tone-based event study to examine the aggregate abnormal tone dynamics in media articles around earnings announcements. We test whether they convey incremental information that is useful for price discovery for nonfinancial S&P 500 firms. The relation we find between the abnormal tone and abnormal returns suggests that media articles provide incremental information relative to the information contained in earnings press releases and earnings calls.
    Date: 2021–10
  6. By: Härdle, Wolfgang; Klochkov, Yegor; Petukhina, Alla; Zhivotovskiy, Nikita
    Abstract: Markowitz mean-variance portfolios with sample mean and covariance as input parameters feature numerous issues in practice. They perform poorly out of sample due to estimation error, they experience extreme weights together with high sen- sitivity to change in input parameters. The heavy-tail characteristics of financial time series are in fact the cause for these erratic fluctuations of weights that conse- quently create substantial transaction costs. In robustifying the weights we present a toolbox for stabilizing costs and weights for global minimum Markowitz portfolios. Utilizing a projected gradient descent (PGD) technique, we avoid the estimation and inversion of the covariance operator as a whole and concentrate on robust estimation of the gradient descent increment. Using modern tools of robust statistics we con- struct a computationally efficient estimator with almost Gaussian properties based on median-of-means uniformly over weights. This robustified Markowitz approach is confirmed by empirical studies on equity markets. We demonstrate that robustified portfolios reach higher risk-adjusted performance and the lowest turnover compared to shrinkage based and constrained portfolios.
    Date: 2021
  7. By: Kun Li; Xin (Kelly) Liu; Shang-Jin Wei
    Abstract: While major stock market indices are followed by large monetary investments, we document that membership decisions for the S&P 500 index have a nontrivial amount of discretion. We show that firms’ purchases of S&P ratings appear to improve their chance of entering the index (but purchases of Moody’s ratings do not). Furthermore, firms tend to purchase more S&P ratings when there are openings in the index membership. Such a pattern is also confirmed by an event study that explores a rule change on index membership in 2002. Finally, discretionary additions exhibit subsequent deterioration in financial performance relative to rule-based additions.
    JEL: F30 G10 G2
    Date: 2021–10
  8. By: Yufei Wu; Mahmoud Mahfouz; Daniele Magazzeni; Manuela Veloso
    Abstract: The success of machine learning models in the financial domain is highly reliant on the quality of the data representation. In this paper, we focus on the representation of limit order book data and discuss the opportunities and challenges for learning representations of such data. We also experimentally analyse the issues associated with existing representations and present a guideline for future research in this area.
    Date: 2021–10
  9. By: Radoslav Raykov
    Abstract: This paper explores the extent to which correlated investments in the futures market concentrated systemic risk on large Canadian banks around the 2008 crisis. We find that core banks took positions against the periphery, increasing their systemic risk as a group. On the portfolio level, position similarity was the main systemic risk driver for core banks, while cross-price correlations drove the systemic risk of noncore banks. Core banks were more diversified, but their portfolios also overlapped more. By contrast, non-core banks were less diversified, but also overlapped less. This significantly nuances the debate on concentration versus diversification as systemic risk sources.
    Keywords: Financial institutions; Financial markets
    JEL: G10 G20
    Date: 2021–10
  10. By: Curtis Nybo
    Abstract: Recently artificial neural networks (ANNs) have seen success in volatility prediction, but the literature is divided on where an ANN should be used rather than the common GARCH model. The purpose of this study is to compare the volatility prediction performance of ANN and GARCH models when applied to stocks with low, medium, and high volatility profiles. This approach intends to identify which model should be used for each case. The volatility profiles comprise of five sectors that cover all stocks in the U.S stock market from 2005 to 2020. Three GARCH specifications and three ANN architectures are examined for each sector, where the most adequate model is chosen to move on to forecasting. The results indicate that the ANN model should be used for predicting volatility of assets with low volatility profiles, and GARCH models should be used when predicting volatility of medium and high volatility assets.
    Date: 2021–10
  11. By: Virla, Leonardo Quero
    Abstract: This paper addresses stock market volatility in Germany between 1991 and 2018. Through a GARCH model with leverage term, an estimation of volatility in the DAX is provided. Such estimation is then plugged into a quantile regression model where potential economic determinants are analyzed. The results suggest that stock market volatility in Germany reached its historical peak between 2000 and 2004. Moreover, animal spirits play an important role across different quantiles of the volatility distribution, whereas the relevance of established risk factors proposed in the literature is limited to specific cases. Overall, the findings stress the importance of appropriate distributional assumptions when analyzing extreme financial events.
    Keywords: Asset prices,volatility,GARCH,quantile regression,DAX
    JEL: G12 G17
    Date: 2021

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