nep-fmk New Economics Papers
on Financial Markets
Issue of 2021‒04‒19
twelve papers chosen by

  1. The Covid-19 Pandemic and the Degree of Persistence of US Stock Prices and Bond Yields By Guglielmo Maria Caporale; Luis A. Gil-Alana; Carlos Poza
  2. High-yield bond markets during the COVID-19 crisis: the role of monetary policy By Dmitry Khametshin
  3. Oil-US Stock Market Nexus: Some insights about the New Coronavirus Crisis By Claudiu Albulescu; Michel Mina; Cornel Oros
  4. The Efficient Hedging Frontier with Deep Neural Networks By Zheng Gong; Carmine Ventre; John O'Hara
  5. Black-box model risk in finance By Samuel N. Cohen; Derek Snow; Lukasz Szpruch
  6. Loss of structural balance in stock markets By E. Ferreira; S. Orbe; J. Ascorbebeitia; B. \'Alvarez Pereira; E. Estrada
  7. Financial Markets Prediction with Deep Learning By Jia Wang; Tong Sun; Benyuan Liu; Yu Cao; Degang Wang
  8. How to explain the cross-section of equity returns through Common Principal Components By Cascos Fernández, Ignacio; Grané Chávez, Aurea; Cueto, José Manuel
  9. Political Insider Trading: A narrow versus comprehensive approach By Jan Hanousek; Christos Pantzalis; Jung Chul Park
  10. Euro area equity risk premia and monetary policy: a longer-term perspective By Kapp, Daniel; Kristiansen, Kristian
  11. Uncertainty and Stock Returns in Energy Markets: A Quantile Regression Approach By Samir Cedic; Alwan Mahmoud; Matteo Manera; Gazi Salah Uddin
  12. Do nonfinancial firms hold risky financial assets? Evidence from Germany By Hoang, Daniel; Silbereis, Fabian; Stengel, Raphael

  1. By: Guglielmo Maria Caporale; Luis A. Gil-Alana; Carlos Poza
    Abstract: This paper analyses the possible effects of the Covid-19 pandemic on the degree of persistence of US monthly stock prices and bond yields using fractional integration techniques. The model is estimated first over the period January 1966-December 2020 and then a recursive approach is taken to examine whether or not persistence has changed during the following pandemic period. We find that the unit root hypothesis cannot be rejected for stock prices while for bond yields the results differ depending on the maturity date and the specification of the error term. In general, bond yields appear to be more persistent, although there is evidence of mean reversion in case of 1-year yields under the assumption of autocorrelated errors. The recursive analysis shows no impact of the Covid-19 pandemic on the persistence of stock prices, whilst there is an increase in the case of both 10- and 1- year bond yields but not of their spread.
    Keywords: stock market prices, US bonds, persistence, fractional integration, Covid-19
    JEL: C22 G10
    Date: 2021
  2. By: Dmitry Khametshin (Banco de España)
    Abstract: This article documents the difference in corporate bond issuance between the euro area (EA) and the United States (US) in 2020, especially in the high-yield (HY) segment, and discusses the role that the monetary policy measures undertaken by the US Federal Reserve (Fed) and the ECB in response to the Covid-19 crisis may have played in explaining such difference. We document that the issuance of HY bonds since February 2020 has been lower by historical standards in the EA than in the US. The Fed’s measures aimed at the HY segment, mainly the purchase of HY bond exchange traded funds (ETFs), could have reduced credit spreads and improved market liquidity, which in turn could have stimulated debt issuance. Alternatively, HY issuers in the EA may have faced better bank funding conditions due to the ECB’s targeted longer term refinancing operations (TLTRO) and to other measures by national fiscal authorities, leading such issuers to substitute bank credit for bond finance. The article discusses these possibilities and argues that they all may have played a role to a certain extent.
    Keywords: corporate bond purchase programs, monetary policy, COVID-19
    JEL: E58 E43 G12
    Date: 2021–03
  3. By: Claudiu Albulescu (CRIEF); Michel Mina; Cornel Oros
    Abstract: We provide a new investigation of the relationship between oil and stock prices in the context of the outbreak of the new coronavirus crisis. Specifically, we assess to what extent the uncertainty induced by COVID-19 affects the interaction between oil and the United States (US) stock markets. To this end, we use a wavelet approach and daily data from February 18, 2020 to August 15, 2020. We identify the lead-lag relationship between oil and stock prices, and the intensity of this relationship at different frequency cycles and moments in time. Our unique findings show that co-movements between oil and stock prices manifest at 3-5-day cycle and are stronger in the first part of March and the second part of April 2020, when oil prices are leading stock prices. The partial wavelet coherence analysis, controlling for the effect of COVID-19 and US economic policy-induced uncertainty, reveals that the coronavirus crisis amplifies the shock propagation between oil and stock prices.
    Date: 2021–04
  4. By: Zheng Gong; Carmine Ventre; John O'Hara
    Abstract: The trade off between risks and returns gives rise to multi-criteria optimisation problems that are well understood in finance, efficient frontiers being the tool to navigate their set of optimal solutions. Motivated by the recent advances in the use of deep neural networks in the context of hedging vanilla options when markets have frictions, we introduce the Efficient Hedging Frontier (EHF) by enriching the pipeline with a filtering step that allows to trade off costs and risks. This way, a trader's risk preference is matched with an expected hedging cost on the frontier, and the corresponding hedging strategy can be computed with a deep neural network. We further develop our framework to improve the EHF and find better hedging strategies. By adding a random forest classifier to the pipeline to forecast market movements, we show how the frontier shifts towards lower costs and reduced risks, which indicates that the overall hedging performances have improved. In addition, by designing a new recurrent neural network, we also find strategies on the frontier where hedging costs are even lower.
    Date: 2021–04
  5. By: Samuel N. Cohen; Derek Snow; Lukasz Szpruch
    Abstract: Machine learning models are increasingly used in a wide variety of financial settings. The difficulty of understanding the inner workings of these systems, combined with their wide applicability, has the potential to lead to significant new risks for users; these risks need to be understood and quantified. In this sub-chapter, we will focus on a well studied application of machine learning techniques, to pricing and hedging of financial options. Our aim will be to highlight the various sources of risk that the introduction of machine learning emphasises or de-emphasises, and the possible risk mitigation and management strategies that are available.
    Date: 2021–02
  6. By: E. Ferreira (Department of Quantitative Methods, University of the Basque Country UPV/EHU); S. Orbe (Department of Quantitative Methods, University of the Basque Country UPV/EHU); J. Ascorbebeitia (Department of Economic Analysis, University of the Basque Country UPV/EHU); B. \'Alvarez Pereira (Nova School of Business and Economics); E. Estrada (Institute of Mathematics and Applications, University of Zaragoza, ARAID Foundation. Institute for Cross-Disciplinary Physics and Complex Systems)
    Abstract: We use rank correlations as distance functions to establish the interconnectivity between stock returns, building weighted signed networks for the stocks of seven European countries, the US and Japan. We establish the theoretical relationship between the level of balance in a network and stock predictability, studying its evolution from 2005 to the third quarter of 2020. We find a clear balance-unbalance transition for six of the nine countries, following the August 2011 Black Monday in the US, when the Economic Policy Uncertainty index for this country reached its highest monthly level before the COVID-19 crisis. This sudden loss of balance is mainly caused by a reorganization of the market networks triggered by a group of low capitalization stocks belonging to the non-financial sector. After the transition, the stocks of companies in these groups become all negatively correlated between them and with most of the rest of the stocks in the market. The implied change in the network topology is directly related to a decrease in stocks predictability, a finding with novel important implications for asset allocation and portfolio hedging strategies.
    Date: 2021–04
  7. By: Jia Wang; Tong Sun; Benyuan Liu; Yu Cao; Degang Wang
    Abstract: Financial markets are difficult to predict due to its complex systems dynamics. Although there have been some recent studies that use machine learning techniques for financial markets prediction, they do not offer satisfactory performance on financial returns. We propose a novel one-dimensional convolutional neural networks (CNN) model to predict financial market movement. The customized one-dimensional convolutional layers scan financial trading data through time, while different types of data, such as prices and volume, share parameters (kernels) with each other. Our model automatically extracts features instead of using traditional technical indicators and thus can avoid biases caused by selection of technical indicators and pre-defined coefficients in technical indicators. We evaluate the performance of our prediction model with strictly backtesting on historical trading data of six futures from January 2010 to October 2017. The experiment results show that our CNN model can effectively extract more generalized and informative features than traditional technical indicators, and achieves more robust and profitable financial performance than previous machine learning approaches.
    Date: 2021–04
  8. By: Cascos Fernández, Ignacio; Grané Chávez, Aurea; Cueto, José Manuel
    Abstract: In this paper we propose a procedure to obtain and test multifactor models based on statistical and financial factors. In order to select the factors included in the model,as well as the construction of the portfolios, we use a multivariate technique called Common Principal Components. A block-bootstrap methodology is developed to assess the validity of the model and the significance of the parameters involved. Data come from Reuters, correspond to nearly 1250 EU companies, and span from October 2009 to October 2019. We also compare our bootstrap-based inferential results with those obtained via classical testing proposals. Methods under assessment are time-series regression and cross-sectional regression. The main findings indicate that the multifactor model proposed improves the Capital Asset Pricing Model with regard to the adjusted-R2 in the time-series regressions. Cross-section regression results reveal that Market and a factor related to Momentum and mean of stocks' returns have positive risk premia for the analysed period. Finally, we also observe that tests based onblock-bootstrap statistics are more conservative with the none than classical procedures.
    Keywords: Time Series; Factor Models; Cross-Sectional Regression; Common Principal Component Analysis; Bootstrap; Asset Pricing
    Date: 2021–04–05
  9. By: Jan Hanousek (Department of Finance, University of South Florida, Tampa, FL 33620, Faculty of Business and Economics, Department of Finance, Mendel University in Brno, Czech Republic); Christos Pantzalis (Kate Tiedemann School of Business and Finance, Muma College of Business, BSN3403, University of South Florida, Tampa, FL 33620); Jung Chul Park (Kate Tiedemann School of Business and Finance, Muma College of Business, BSN3403, University of South Florida, Tampa, FL 33620)
    Abstract: We examine senators’ electronically filed stock transactions between 2012 and 2019 to assess the extent of politician’s insider trading. Our results suggest that senators use inside political information when investing and earn significant market-adjusted returns. To extend traditional return-based methods, we propose a new comprehensive approach based on abnormal idiosyncratic volatility (AIV), which captures the degree of information asymmetry around their trading dates. We document that senator trades are associated with substantially high levels of AIV, suggesting that they represent only a tip of the iceberg, since the mass of unfiled transactions using the same inside information remains undetected.
    Keywords: Abnormal idiosyncratic volatility, legislator’s trading, politician’s insider trading, STOCK Act
    JEL: C58 G12 G14 G28
    Date: 2021–04
  10. By: Kapp, Daniel; Kristiansen, Kristian
    Abstract: This study analyses the effects of euro area monetary policy on equity risk premia (ERP). We find that changes in equity prices during periods of accommodative monetary policy mainly reflected adjustments in the discount factor and economic activity – rather than fluctuations in investors’ required risk compensation. Furthermore, the ERP appears to not have declined much since the introduction of unconventional monetary policy and stands higher than prior to the GFC. Use of identified monetary policy shocks points to insignificant effects of monetary policy on the ERP. Further breakdown of these shocks reveals that monetary policy has a significant upwards impact on the ERP if it is perceived as a negative information surprise, while the opposite prevails in the case of a genuine accommodative monetary policy surprise. Accumulating these effects over time suggests that the two might have largely offset each other since the introduction of unconventional monetary policy. JEL Classification: E22, E52, G12
    Keywords: equity risk premia, monetary policy shocks, monetary policy transmission
    Date: 2021–04
  11. By: Samir Cedic (Linköping University); Alwan Mahmoud (Linköping University); Matteo Manera (University of Milano-Bicocca, Fondazione Eni Enrico Mattei); Gazi Salah Uddin (Linköping University)
    Abstract: The aim of this paper is to analyze the relationship between different types of uncertainty and stock returns of the renewable energy and the oil & gas sectors. We use the quantile regression approach developed by Koenker and d’Orey (1987; 1994) to assess which uncertainties are the potential drivers of stock returns under different market conditions. We find that the bioenergy and the oil & gas sectors are most sensitive to uncertainties. Both sectors are affected by financial, euro currency, geopolitical and economic policy uncertainties. Our results have several policy implications. Climate policy makers can prioritize policies that support bioenergy in order to reduce the potentially negative effects of uncertainties on bioenergy investment. Investors aiming to diversify their portfolio should be aware that many uncertainties are common drivers of bioenergy and oil & gas returns, the connectedness between assets of these energy types could therefore increase when uncertainty increases.
    Keywords: Uncertainty, Macroeconomic Conditions, Renewable Energy, Stock Returns, Quantile Regression
    JEL: C1 G15 Q2 Q3 Q43
    Date: 2021–04
  12. By: Hoang, Daniel; Silbereis, Fabian; Stengel, Raphael
    Abstract: Recent empirical evidence suggests that US industrial firms invest heavily in noncash, risky financial assets. Using hand-collected data on financial portfolios of German firms, we show that risky asset holdings are not an anomaly unique to the US. We find that industrial firms in Germany invest 11.6% of their financial assets in noncash and risky assets. Value-weighted, this percentage increases to 25.4%. While the equally-weighted average is substantial, it is clearly lower (5 percentage points or 30% in relative terms) than that in the US. After accounting for cross-country compositional differences (especially the dominance of large firms in the US technology sector), this difference in risky financial asset holdings decreases but remains at 3 percentage points. The remaining difference is driven by institutional differences that affect the relationship between firm characteristics and risky financial asset holdings in the two countries. In contrast to the US, German firms largely follow the precautionary savings motive and do not seem to misappropriate their funds when shifting them towards riskier asset allocations. Our results have implications for how asset management by nonfinancial firms should be regulated.
    Keywords: Cash Policy,Financial Portfolio,Precautionary Savings,Liquidity Management
    JEL: G31 G32 G34 G38 G11
    Date: 2021

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