nep-fmk New Economics Papers
on Financial Markets
Issue of 2021‒03‒08
seventeen papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Investor Confidence and Forecastability of US Stock Market Realized Volatility : Evidence from Machine Learning By Rangan Gupta; Jacobus Nel; Christian Pierdzioch
  2. Forecasting Realized Volatility of International REITs: The Role of Realized Skewness and Realized Kurtosis By Matteo Bonato; Oguzhan Cepni; Rangan Gupta; Christian Pierdzioch
  3. Risk & Returns around Fomc Press Conferences: A Novel Perspective from Computer Vision By Alexis Marchal
  4. On the origin of systemic risk By Montagna, Mattia; Torri, Gabriele; Covi, Giovanni
  5. Can the variance after-effect distort stock returns? By Tony Berrada
  6. The high frequency impact of economic policy narratives on stock market uncertainty By Perico Ortiz, Daniel
  7. CRIX an Index for cryptocurrencies By Trimborn, Simon; Härdle, Wolfgang Karl
  8. Too-big-to-fail Reforms and Systemic Risk By Kakuho Furukawa; Hibiki Ichiue; Yugo Kimura; Noriyuki Shiraki
  9. The optimal spending rate versus the expected real return of a sovereign wealth fund By Aase, Knut K.; Bjerksund, Petter
  10. An introduction to the current debate on central bank digital currency (CBDC) By Juan Ayuso Huertas; Carlos Antonio Conesa Lareo
  11. Inventory management, dealers’ connections, and prices in OTC markets By Colliard, Jean-Edouard; Foucault, Thierry; Hoffmann, Peter
  12. Machine Learning and Credit Risk: Empirical Evidence from SMEs By Alessandro Bitetto; Paola Cerchiello; Stefano Filomeni; Alessandra Tanda; Barbara Tarantino
  13. FRM Financial Risk Meter for Emerging Markets By Ben Amor, Souhir; Althof, Michael; Härdle, Wolfgang Karl
  14. It's not time to make a change: Sovereign fragility and the corporate credit risk By Fornari, Fabio; Zaghini, Andrea
  15. Deep Learning application for fraud detection in financial statements By Craja, Patricia; Kim, Alisa; Lessmann, Stefan
  16. How do secured funding markets behave under stress? Evidence from the gilt repo market By Hüser, Anne-Caroline; Lepore, Caterina; Veraart, Luitgard
  17. Does Private Equity Investment in Healthcare Benefit Patients? Evidence from Nursing Homes By Atul Gupta; Sabrina T. Howell; Constantine Yannelis; Abhinav Gupta

  1. By: Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield, 0028, South Africa); Jacobus Nel (Department of Economics, University of Pretoria, Private Bag X20, Hatfield, 0028, South Africa); Christian Pierdzioch (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008 Hamburg, Germany)
    Abstract: Using a machine-learning technique known as random forests, we analyze the role of investor confidence in forecasting monthly aggregate realized stock-market volatility of the United States (US), over and above a wide-array of macroeconomic and financial variables. We estimate random forests on data for a period from 2001 to 2020, and study horizons up to one year by computing forecasts for recursive and a rolling estimation window. We find that investor confidence, and especially investor confidence uncertainty has out-of-sample predictive value for overall realized volatility, as well as its “good†and “bad†variants. Our results have important implications for investors and policymakers.
    Keywords: Investor Confidence, Realized Volatility, Macroeconomic and Financial Predictors, Forecasting, Machine Learning
    JEL: C22 C53 G10 G17
  2. By: Matteo Bonato (Department of Economics and Econometrics, University of Johannesburg, Auckland Park, South Africa; IPAG Business School, 184 Boulevard Saint-Germain, 75006 Paris, France); Oguzhan Cepni (Copenhagen Business School, Department of Economics, Porcelaenshaven 16A, Frederiksberg DK-2000, Denmark; Central Bank of the Republic of Turkey, Haci Bayram Mah. Istiklal Cad. No:10 06050, Ankara, Turkey); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa); Christian Pierdzioch (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008 Hamburg, Germany)
    Abstract: We use an international dataset on 5-minutes interval intraday data covering nine leading markets and regions to construct measures of realized volatility, realized jumps, realized skewness, and realized kurtosis of returns of international Real Estate Investment Trusts (REITs) over the daily period of September, 2008 to August, 2020. We study out-of-sample the predictive value of realized skewness and realized kurtosis for realized volatility over and above realized jumps, where we also differentiate between measures of ``good" realized volatility and ``bad" realized volatility. We find that realized skewness and realized kurtosis significantly improve forecasting performance at a daily, weekly, and monthly forecast horizon, and that their contribution to forecasting performance outweighs in terms of significance the contribution of realized jumps. Our results have important implications for investors and policymakers.
    Keywords: REITs, International data, Realized volatility, Forecasting
    JEL: C22 C53 G15
    Date: 2021–02
  3. By: Alexis Marchal (EPFL; SFI)
    Abstract: I propose a new tool to characterize the resolution of uncertainty around FOMC press conferences. It relies on the construction of a measure capturing the level of discussion complexity between the Fed Chair and reporters during the Q&A sessions. I show that complex discussions are associated with higher equity returns and a drop in realized volatility. The method creates an attention score by quantifying how much the Chair needs to rely on reading internal documents to be able to answer a question. This is accomplished by building a novel dataset of video images of the press conferences and leveraging recent deep learning algorithms from computer vision. This alternative data provides new information on nonverbal communication that cannot be extracted from the widely analyzed FOMC transcripts. This paper can be seen as a proof of concept that certain videos contain valuable information for the study of financial markets.
    Keywords: FOMC, Machine learning, Computer vision, Alternative data, Asset pricing, Equity premium.
    JEL: C45 C55 C80 E58 G12 G14
    Date: 2021–03
  4. By: Montagna, Mattia (European Central Bank); Torri, Gabriele (University of Bergamo); Covi, Giovanni (Bank of England)
    Abstract: Systemic risk in the banking sector is usually associated with long periods of economic downturn and very large social costs. On one hand, shocks coming from correlated exposures towards the real economy may induce correlation in banks’ default probabilities thereby increasing the likelihood for systemic tail events like the 2008 Great Financial Crisis. On the other hand, financial contagion also plays an important role in generating large-scale market failures, amplifying the initial shocks coming from the real economy. To study the sources of these rare phenomena, we propose a new definition of systemic risk (ie the probability of a large number of banks going into distress simultaneously) and thus we develop a multilayer microstructural model to study empirically the determinants of systemic risk. The model is then calibrated on the most comprehensive granular dataset for the euro-area banking sector, capturing roughly 96% or €23.2 trillion of euro-area banks’ total assets over the period 2014–2018. The outputs of the model decompose and quantify the sources of systemic risk showing that correlated economic shocks, financial contagion mechanisms, and their interaction are the main sources of systemic events. The results obtained with the simulation engine resemble common market-based systemic risk indicators and empirically corroborate findings from existing literature. This framework gives regulators and central bankers a tool to study systemic risk and its developments, pointing out that systemic events and banks’ idiosyncratic defaults have different drivers, hence implying different policy responses.
    Keywords: Systemic risk; financial contagion; microstructural models
    JEL: D85 G17 G33 L14
    Date: 2021–01–29
  5. By: Tony Berrada (University of Geneva - Geneva Finance Research Institute (GFRI); Swiss Finance Institute)
    Abstract: Variance after-effect is a perceptual bias in the dynamic assessment of variance. Experimental evidence shows that perceived variance is decreased after prolonged exposure to high variance and increased after exposure to low variance. We introduce this effect in an otherwise standard financial model where information about variance is incomplete and updated sequentially. We introduce a variance after- effect adjustment factor in a bayesian learning model and derive the associated predictive variance. We show theoretically how this adjustment factor affects both average and volatility of excess returns. We construct a proxy of the adjustment factor using the sequence of dispersion of analysts earnings forecast. We provide empirical evidence using US stock data over the sample 1982 - 2019, that fluctuations in this measure are significantly and positively related to excess volatility as predicted by the model. Further confirming the model's implications, we also show how stock returns are positively impacted by the adjustment factor and construct long short strategies that generate significant positive alpha with respect to the Fama-French 5 factor model.
    Keywords: Variance after-effect, learning, turnover, volatility, earnings forecasts
    JEL: G11 G12 G41
    Date: 2021–02
  6. By: Perico Ortiz, Daniel
    Abstract: This paper investigates the causal relationship between economic policy narratives, derived from President Trump's tweets and tweeting behavior, and stock market uncertainty. To this end, I define different event types based on the occurrence probability of identifted narratives or unusual tweet behaviors. High-frequency market uncertainty responses to different events are recovered using time-series regressions. Events regarding foreign policy, trade, monetary policy, and immigration policy exhibit a signiftcant effect on market uncertainty. Impulse responses become signiftcant between one and three hours after the event occurs, for most of the events. Furthermore, behavior events, such as increases in the tweet or retweeted counts above their average, matter for stock market uncertainty.
    Keywords: Twitter,Donald Trump,Economic Narratives,Economic Policy Uncertainty,VIX
    JEL: D83 E71 C54
    Date: 2021
  7. By: Trimborn, Simon; Härdle, Wolfgang Karl
    Abstract: The cryptocurrency market is unique on many levels: Very volatile, frequently changing market structure, emerging and vanishing of cryptocurrencies on a daily level. Following its development became a difficult task with the success of cryptocurrencies (CCs) other than Bitcoin. For fiat currency markets, the IMF offers the index SDR and, prior to the EUR, the ECU existed, which was an index representing the development of European currencies. Index providers decide on a fixed number of index constituents which will represent the market segment. It is a challenge to fix a number and develop rules for the constituents in view of the market changes. In the frequently changing CC market, this challenge is even more severe. A method relying on the AIC is proposed to quickly react to market changes and therefore enable us to create an index, referred to as CRIX, for the cryptocurrency market. CRIX is chosen by model selection such that it represents the market well to enable each interested party studying economic questions in this market and to invest into the market. The diversified nature of the CC market makes the inclusion of altcoins in the index product critical to improve tracking performance. We have shown that assigning optimal weights to altcoins helps to reduce the tracking errors of a CC portfolio, despite the fact that their market cap is much smaller relative to Bitcoin. The codes used here are available via
    Keywords: Index construction,Model selection,Bitcoin,Cryptocurrency,CRIX,Altcoin
    JEL: C51 C52 G10
    Date: 2020
  8. By: Kakuho Furukawa (Bank of Japan); Hibiki Ichiue (Bank of Japan); Yugo Kimura (Bank of Japan); Noriyuki Shiraki (Bank of Japan)
    Abstract: We examine the effects of too-big-to-fail reforms using ĢCoVaR and SRISK. Developments in these market-based systemic risk measures suggest that the reforms have led to a larger decline in the systemic risk contribution of global systemically important banks (G-SIBs) than of other banks. The systemic risk measures also suggest that the larger the systemic risk associated with a G-SIB, the more the reforms have led to a decline in its systemic risk. These findings are consistent with the objectives of the reforms and are validated by statistical analyses, including quantile panel regressions. We also highlight the importance of using data for a subset of financial institutions to adjust for the increase in data coverage when using popular estimates of SRISK. Furthermore, SRISK may overestimate systemic risk in recent years by ignoring the role of total loss absorbing capacity (TLAC)-eligible bonds.
    Keywords: Too Big to Fail, Systemic Risk, Financial Regulations, CoVaR, SRISK
    JEL: G21 G23 G28
  9. By: Aase, Knut K. (Dept. of Business and Management Science, Norwegian School of Economics); Bjerksund, Petter (Dept. of Business and Management Science, Norwegian School of Economics)
    Abstract: We consider a sovereign wealth fund that invests broadly in the international financial markets. The influx to the fund has stopped. We adopt the life cycle model and demonstrate that the optimal spending rate from the fund is significantly less than the fund’s expected real rate of return. The optimal spending rate secures that the fund will last ”forever”. Spending the expected return will deplete the fund with probability one. Moreover, this strategy is inconsistent with optimal portfolio choice. Our results are contrary to the idea that it is sustainable to spend the expected return of a sovereign wealth fund.
    Keywords: Optimal spending rate; endowment funds; expected utility; risk aversion; EIS; recursive utility
    JEL: D51 D53 D90 E21 G10 G12
    Date: 2021–02–04
  10. By: Juan Ayuso Huertas (Banco de España); Carlos Antonio Conesa Lareo (Banco de España)
    Abstract: This paper provides an overview of the concept of central bank digital currency (CBDC) that may serve as a basis for an ordered discussion and an in-depth analysis of the different relevant aspects in the ongoing debate about this digital financial asset. The primary objective is to review the grounds that could warrant issuing CBDC and undertake a preliminary analysis of its main implications, especially those linked to the CBDC models that seem most likely to be adopted, judging by the motivations of the central banks whose issuance plans are currently most advanced.
    Keywords: central bank digital currency, stablecoins, cryptocurrencies, cryptoassets
    JEL: E42 E52 E58
    Date: 2020–03
  11. By: Colliard, Jean-Edouard; Foucault, Thierry; Hoffmann, Peter
    Abstract: We propose a new model of trading in OTC markets. Dealers accumulate inventories by trading with end-investors and trade among each other to reduce their inventory holding costs. Core dealers use a more efficient trading technology than peripheral dealers, who are heterogeneously connected to core dealers and trade with each other bilaterally. Connectedness affects prices and allocations if and only if the peripheral dealers’ aggregate inventory position differs from zero. Price dispersion increases in the size of this position. The model generates new predictions about the effects of dealers' connectedness and dealers' aggregate inventories on prices. JEL Classification: G10, G12, G19
    Keywords: interdealer trading, inventory management, OTC markets
    Date: 2021–02
  12. By: Alessandro Bitetto (University of Pavia); Paola Cerchiello (University of Pavia); Stefano Filomeni (University of Essex); Alessandra Tanda (University of Pavia); Barbara Tarantino (University of Pavia)
    Abstract: In this paper we assess credit risk of SMEs by testing and comparing a classic parametric approach fitting an ordered probit model with a non-parametric one calibrating a machine learning historical random forest (HRF) model. We do so by exploiting a unique and proprietary dataset comprising granular firm-level quarterly data collected from a large European bank and an international insurance company on a sample of 810 Italian small- and medium-sized enterprises (SMEs) over the time period 2015-2017. Our results provide novel evidence that a dynamic Historical Random Forest (HRF) approach outperforms the traditional ordered probit model, highlighting how advanced estimation methodologies that use machine learning techniques can be successfully implemented to predict SME credit risk. Moreover, by using Shapley values for the first time, we are able to assess the relevance of each variable in predicting SME credit risk. Traditionally, credit risk evaluation of informationally-opaque SMEs has relied on soft information-intensive relationship banking. However, the advent of large banking conglomerates and the limits to successfully "harden" and transmit soft information across large banking organizations, challenge the traditional role of relationship banking, urging the need to evaluate SME credit risk by implementing alternative methodologies mostly based on hard information.
    Keywords: Credit Rating, SME, Historical Random Forest, Machine Learning, Relationship Banking, Soft Information
    JEL: C52 C53 D82 D83 G21 G22
    Date: 2021–02
  13. By: Ben Amor, Souhir; Althof, Michael; Härdle, Wolfgang Karl
    Abstract: The fast-growing Emerging Market (EM) economies and their improved transparency and liquidity have attracted international investors. However, the external price shocks can result in a higher level of volatility as well as domestic policy instability. Therefore, an efficient risk measure and hedging strategies are needed to help investors protect their investments against this risk. In this paper, a daily systemic risk measure, called FRM (Financial Risk Meter) is proposed. The FRM@ EM is applied to capture systemic risk behavior embedded in the returns of the 25 largest EMs' FIs, covering the BRIMST (Brazil, Russia, India, Mexico, South Africa, and Turkey), and thereby reflects the financial linkages between these economies. Concerning the Macro factors, in addition to the Adrian & Brunnermeier (2016) Macro, we include the EM sovereign yield spread over respective US Treasuries and the above-mentioned countries' currencies. The results indicated that the FRM of EMs' FIs reached its maximum during the US financial crisis following by COVID 19 crisis and the Macro factors explain the BRIMST' FIs with various degrees of sensibility. We then study the relationship between those factors and the tail event network behavior to build our policy recommendations to help the investors to choose the suitable market for investment and tail-event optimized portfolios. For that purpose, an overlapping region between portfolio optimization strategies and FRM network centrality is developed. We propose a robust and well-diversified tail-event and cluster risk- sensitive portfolio allocation model and compare it to more classical approaches.
    Keywords: FRM (Financial Risk Meter),Lasso Quantile Regression,Network Dynamics,Emerging Markets,Hierarchical Risk Parity
    JEL: C30 C58 G11 G15 G21
    Date: 2021
  14. By: Fornari, Fabio; Zaghini, Andrea
    Abstract: Relying on a perspective borrowed from monetary policy announcements and introducing an econometric twist in the traditional event study analysis, we doc- ument the existence of an "event risk transfer", namely a significant credit risk transmission from the sovereign to the corporate sector after a sovereign rating downgrade. We find that after the delivery of the downgrade, corporate CDS spreads rise by 36% per annum and there is a widespread contagion across coun- tries, in particular among those which were most exposed to the sovereign debt crisis. This effect exists on top of the standard relation between sovereign and corporate credit risk.
    Keywords: sovereign rating,corporate credit risk,CDS spreads
    JEL: G15 G32 G38
    Date: 2021
  15. By: Craja, Patricia; Kim, Alisa; Lessmann, Stefan
    Abstract: Financial statement fraud is an area of significant consternation for potential investors, auditing companies, and state regulators. Intelligent systems facilitate detecting financial statement fraud and assist the decision-making of relevant stakeholders. Previous research detected instances in which financial statements have been fraudulently misrepresented in managerial comments. The paper aims to investigate whether it is possible to develop an enhanced system for detecting financial fraud through the combination of information sourced from financial ratios and managerial comments within corporate annual reports. We employ a hierarchical attention network (HAN) with a long short-term memory (LSTM) encoder to extract text features from the Management Discussion and Analysis (MD&A) section of annual reports. The model is designed to offer two distinct features. First, it reflects the structured hierarchy of documents, which previous models were unable to capture. Second, the model embodies two different attention mechanisms at the word and sentence level, which allows content to be differentiated in terms of its importance in the process of constructing the document representation. As a result of its architecture, the model captures both content and context of managerial comments, which serve as supplementary predictors to financial ratios in the detection of fraudulent reporting. Additionally, the model provides interpretable indicators denoted as “red-flag” sentences, which assist stakeholders in their process of determining whether further investigation of a specific annual report is required. Empirical results demonstrate that textual features of MD&A sections extracted by HAN yield promising classification results and substantially reinforce financial ratios.
    Keywords: fraud detection,financial statements,deep learning,text analytics
    JEL: C00
    Date: 2020
  16. By: Hüser, Anne-Caroline (Bank of England); Lepore, Caterina (International Monetary Fund); Veraart, Luitgard (London School of Economics and Political Science)
    Abstract: We examine how the overnight gilt repo market operates during three episodes of liquidity stress, using novel transaction-level data on repurchase agreements on gilts. Using network analysis we document that the structure of the repo market significantly changes during stress relative to normal times, with a focus on how sectors adjust volumes, spreads and haircuts in their repo transactions. We find several common patterns in the two most recent stress episodes (the US repo turmoil in 2019 and the Covid-19 crisis in 2020): a preference for dealers and banks to transact in the cleared rather than the bilateral segment of the market, increased usage of the market by hedge funds and central counterparties increasing their reinvestment of cash margin into reverse repo.
    Keywords: Repo market; liquidity risk; financial networks; market microstructure; Brexit referendum; US repo turmoil; Covid-19 crisis
    JEL: D85 G01 G21 G23
    Date: 2021–02–26
  17. By: Atul Gupta; Sabrina T. Howell; Constantine Yannelis; Abhinav Gupta
    Abstract: The past two decades have seen a rapid increase in Private Equity (PE) investment in healthcare, a sector in which intensive government subsidy and market frictions could lead high-powered for-profit incentives to be misaligned with the social goal of affordable, quality care. This paper studies the effects of PE ownership on patient welfare at nursing homes. With administrative patient-level data, we use a within-facility differences-in-differences design to address non-random targeting of facilities. We use an instrumental variables strategy to control for the selection of patients into nursing homes. Our estimates show that PE ownership increases the short-term mortality of Medicare patients by 10%, implying 20,150 lives lost due to PE ownership over our twelve-year sample period. This is accompanied by declines in other measures of patient well-being, such as lower mobility, while taxpayer spending per patient episode increases by 11%. We observe operational changes that help to explain these effects, including declines in nursing staff and compliance with standards. Finally, we document a systematic shift in operating costs post-acquisition toward non-patient care items such as monitoring fees, interest, and lease payments.
    JEL: G3 G32 G34 G38 I1 I18
    Date: 2021–02

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