nep-fmk New Economics Papers
on Financial Markets
Issue of 2021‒08‒09
ten papers chosen by

  1. Dealer Inventory Constraints in the Corporate Bond Market during the COVID Crisis By Craig A. Chikis; Jonathan Goldberg
  2. Economic Value of Modeling the Joint Distribution of Returns and Volatility: Leverage Timing By Cem Cakmakli; Verda Ozturk
  3. Stock Movement Prediction with Financial News using Contextualized Embedding from BERT By Qinkai Chen
  4. International Evidence on Extending Sovereign Debt Maturities By Jens H. E. Christensen; Jose A. Lopez; Paul Mussche
  5. The impact of machine learning and big data on credit markets By Eccles, Peter; Grout, Paul; Siciliani, Paolo; Zalewska, Anna
  6. Is to Forgive to Forget? Sovereign Risk in the Aftermath of a Default By Silvia Marchesi; Tania Masi; Pietro Bomprezzi
  7. A data-science-driven short-term analysis of Amazon, Apple, Google, and Microsoft stocks By Shubham Ekapure; Nuruddin Jiruwala; Sohan Patnaik; Indranil SenGupta
  8. Oil and US stock market shocks: implications for Canadian equities By Reinhold Heinlein; Scott M. R. Mahadeo
  9. Impact of EU-wide Insurance Stress Tests on Equity Prices and Systemic Risk By Petr Jakubik; Saida Teleu
  10. Spillovers among Energy Commodities and the Russian Stock Market By Costola, Michele; Lorusso, Marco

  1. By: Craig A. Chikis; Jonathan Goldberg
    Abstract: Beginning in late February 2020, market liquidity for corporate bonds dried up and corporate bond credit spreads soared amid broad financial market dislocations related to the COVID-19 pandemic. The causes of this liquidity dry-up and the spike in corporate bond spreads remain subjects of debate.
    Date: 2021–07–15
  2. By: Cem Cakmakli (Koc University); Verda Ozturk (Duke University)
    Abstract: We propose a joint modeling strategy for timing the joint distribution of the returns and their volatility. We do this by incorporating the potentially asymmetric links into the system of ‘independent’ predictive regressions of returns and volatility, allowing for asymmetric cross-correlations, denoted as instantaneous leverage effects, in addition to cross-autocorrelations between returns and volatility, denoted as intertemporal leverage effects. We show that while the conventional intertemporal leverage effects bear little economic value, our results point to the sizeable value of exploiting the contemporaneous asymmetric link between returns and volatility. Specifically, a mean-variance investor would be willing to pay several hundred basis points to switch from the strategies based on conventional predictive regressions of mean and volatility in isolation of each other to the joint models of returns and its volatility, taking the link between these two moments into account. Moreover, our findings are robust to various effects documented in the literature.
    Keywords: Economic value, system of equations, leverage timing, market timing, volatility timing.
    JEL: C30 C52 C53 C58 G11
    Date: 2021–07
  3. By: Qinkai Chen
    Abstract: News events can greatly influence equity markets. In this paper, we are interested in predicting the short-term movement of stock prices after financial news events using only the headlines of the news. To achieve this goal, we introduce a new text mining method called Fine-Tuned Contextualized-Embedding Recurrent Neural Network (FT-CE-RNN). Compared with previous approaches which use static vector representations of the news (static embedding), our model uses contextualized vector representations of the headlines (contextualized embeddings) generated from Bidirectional Encoder Representations from Transformers (BERT). Our model obtains the state-of-the-art result on this stock movement prediction task. It shows significant improvement compared with other baseline models, in both accuracy and trading simulations. Through various trading simulations based on millions of headlines from Bloomberg News, we demonstrate the ability of this model in real scenarios.
    Date: 2021–07
  4. By: Jens H. E. Christensen; Jose A. Lopez; Paul Mussche
    Abstract: Portfolio diversification is as important to debt management as it is to asset management. In this paper, we focus on diversification of sovereign debt issuance by examining the extension of the maximum maturity of issued debt. In particular, we examine the potential costs to the U.S. Treasury of introducing 50-year bonds as a financing option. Based on evidence from foreign government bond markets with such long-term debt, our results suggest that a 50-year Treasury bond would likely trade at an average yield that is at most 20 basis points above that of a 30-year bond. Our results based on extrapolations from a dynamic yield curve model using just U.S. Treasury yields are similar.
    Keywords: term structure modeling; yield extrapolation; debt management
    JEL: E43 E47 G12 G13
    Date: 2021–07–22
  5. By: Eccles, Peter (Bank of England); Grout, Paul (Bank of England); Siciliani, Paolo (Bank of England); Zalewska, Anna (University of Bath)
    Abstract: There is evidence that machine learning (ML) can improve the screening of risky borrowers, but the empirical literature gives diverse answers as to the impact of ML on credit markets. We provide a model in which traditional banks compete with fintech (innovative) banks that screen borrowers using ML technology and show that the impact of the adoption of the ML technology on credit markets depends on the characteristics of the market (eg borrower mix, cost of innovation, the intensity of competition, precision of the innovative technology, etc.). We provide a series of scenarios. For example, we show that if implementing ML technology is relatively expensive and lower-risk borrowers are a significant proportion of all risky borrowers, then all risky borrowers will be worse off following the introduction of ML, even when the lower-risk borrowers can be separated perfectly from others. At the other extreme, we show that if costs of implementing ML are low and there are few lower-risk borrowers, then lower-risk borrowers gain from the introduction of ML, at the expense of higher-risk and safe borrowers. Implications for policy, including the potential for tension between micro and macroprudential policies, are explored.
    Keywords: Adverse selection; banking; big data; capital requirements; credit markets; fintech; machine learning; prudential regulation
    JEL: G21 G28 G32
    Date: 2021–07–09
  6. By: Silvia Marchesi (University of Milano Bicocca, CefES and Centro Studi Luca D’Agliano); Tania Masi; Pietro Bomprezzi (University of Milano Bicocca and CefES)
    Abstract: We examine the link between sovereign defaults and credit risk, by taking into account the depth of a debt restructuring and by distinguishing between commercial and official debt. The focus is on debt restructuring events, which take place at the end of a default spell. We use a novel methodology (Jordà and Taylor 2016) to estimate the average treatment effect of a default episode on our outcome variables, agency ratings and bond yield spreads, accounting for the endogeneity of the default. Our results show that the average treatment effect on ratings is negative (and positive for bond spreads) up to seven years following a default, while the opposite holds for a default with official creditors. Our results are robust to using a panel analysis, which allows us to investigate on the importance of the (final) haircut size. Specifically, we and that the rating (spread) variation (increase) is larger for cases with deeper haircuts. Therefore, we and evidence that official and private defaults may have different costs and then induce selective defaults.
    Keywords: Sovereign defaults, Haircut, Credit Rating Agencies, bond yield spreads, local projection
    JEL: F34 G15 G24 H63
    Date: 2021–07–16
  7. By: Shubham Ekapure; Nuruddin Jiruwala; Sohan Patnaik; Indranil SenGupta
    Abstract: In this paper, we implement a combination of technical analysis and machine/deep learning-based analysis to build a trend classification model. The goal of the paper is to apprehend short-term market movement, and incorporate it to improve the underlying stochastic model. Also, the analysis presented in this paper can be implemented in a \emph{model-independent} fashion. We execute a data-science-driven technique that makes short-term forecasts dependent on the price trends of current stock market data. Based on the analysis, three different labels are generated for a data set: $+1$ (buy signal), $0$ (hold signal), or $-1$ (sell signal). We propose a detailed analysis of four major stocks- Amazon, Apple, Google, and Microsoft. We implement various technical indicators to label the data set according to the trend and train various models for trend estimation. Statistical analysis of the outputs and classification results are obtained.
    Date: 2021–07
  8. By: Reinhold Heinlein (University of the West of England); Scott M. R. Mahadeo (University of Portsmouth)
    Abstract: Oil and US stock market shocks are expected to be relevant for Canadian equities, as Canada is an oil-exporter adjacent to the US. We evaluate how the relationship between Canadian stock market indices and such external shocks change under extraordinary events. To do this, we subject statistically identified oil and S&P 500 market shocks to a surprise filter, which detects shocks with the greatest magnitude occurring over a given lookback period; and an outlier filter, which detects extrema shocks that exceed a normal range. Then, we examine how the dependence structure between shocks and Canadian equities change under the extreme surprise and outlier episodes through various co-moment spillover tests. Our results show that co-moments beyond correlation are important in reflecting the changes occurring in the relationships between external shocks and Canadian equities in extreme events. Additionally, the differences in findings under extreme positive and negative shocks provide evidence for asymmetric spillover effects from the oil and US stock markets to Canadian equities. Moreover, the observed heterogeneity in the relationships between disaggregated Canadian equities and shocks in the crude oil and S&P 500 markets are useful to policymakers for revealing sector-specific vulnerabilities, and provide portfolio diversification opportunities for investors to exploit.
    Keywords: Canada; oil market; spillover; stock market
    JEL: C32 G15 Q43
    Date: 2021–07–10
  9. By: Petr Jakubik (European Insurance and Occupational Pensions Authority (EIOPA), Germany; Charles University in Prague, Faculty of Social Sciences, Institute of Economic Studies, Czech Republic); Saida Teleu (Maltese Financial Services Authority, Malta; Charles University in Prague, Faculty of Social Sciences, Institute of Economic Studies, Czech Republic)
    Abstract: Since the global financial crisis in 2007, stress tests have become standard tools for regulators and supervisors to assess the risks and vulnerabilities of financial sectors. To this end, the Insurance and Occupational Pensions Authority (EIOPA) regularly performs EU-wide insurance stress tests. This paper analyses the impact of the conducted exercises in 2014, 2016 and 2018 on the equity prices of insurance companies. Using an event study framework, we find a statistically significant impact only for the publication of the 2018 exercise results. Our empirical analysis further suggests that the final version of technical specifications for the 2014 exercise, the initiation of public consultation, and the published stress test scenario of the 2018 exercise contributed to the decline in systemic risk. To our best knowledge, this is the first paper that investigates this topic for the European insurance sector. Our empirical results could help improve the communication and design of future stress test exercises.
    Keywords: European insurance sector; EU-wide insurance stress test, systemic risk, event study, equity prices
    JEL: G23 G12 G14 G18
    Date: 2021–07
  10. By: Costola, Michele; Lorusso, Marco
    Abstract: We examine the connectedness in the energy commodities sector and the Russian stock market over the period 2005-2020 using the variance decomposition approach. Our analysis identifies the booms and busts in the correspondence of political and war episodes that are related to spillover effects in the Russian economy, as well as the energy commodities markets. Our findings show that the Russian Oil & Gas and Metals & Mining sectors are net shock contributors of crude oil and have the highest spillovers to other Russian sectors. Furthermore, we disentangle the sources of spillovers that originated from the financial and energy commodity markets and find that a positive change in the energy commodity volatility spillover is associated with an increase in Russian geopolitical uncertainty. Finally, we show that the spread of COVID-19 increases the stock market volatility spillover, whereas it lowers the energy commodity volatility spillover.
    Keywords: Spillover Effects, Russian Stock Market, Russian Sectoral Indices, Commodity Markets, International Financial Markets
    JEL: C3 C58 E44 G1
    Date: 2021–07–31

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