nep-fmk New Economics Papers
on Financial Markets
Issue of 2020‒04‒06
fourteen papers chosen by

  1. FinTech credit: a critical review of empirical research By Nicola Branzoli; Ilaria Supino
  2. Risk Spillover between Bitcoin and Conventional Financial Markets: An Expectile-Based Approach By Yue-Jun Zhang; Elie Bouri; Rangan Gupta
  3. Low-volatility Anomaly and the Adaptive Multi-Factor Model By Robert A. Jarrow; Rinald Murataj; Martin T. Wells; Liao Zhu
  4. Beta and Coskewness Pricing: Perspective from Probability Weighting By Shi, Yun; Cui, Xiangyu; Zhou, Xunyu
  5. Tail Risk Measurement In Crypto-Asset Markets By Daniel Felix Ahelegbey; Paolo Giudici; Fatemeh Mojtahedi
  6. Let's chat... When communication promotes efficiency in experimental asset markets: A Review By Brice Corgnet; Mark Desantis; David Porter
  7. Feverish Stock Price Reactions to COVID-19 By Stefano Ramelli; Alexander F. Wagner
  8. Market structure dynamics during COVID-19 outbreak By Pier Francesco Procacci; Carolyn E. Phelan; Tomaso Aste
  9. Forecasting Stock Market Recessions in the US: Predictive Modeling using Different Identification Approaches By Felix Haase; Matthias Neuenkirch
  10. Repo market and leverage ratio in the euro area By Luca Baldo; Filippo Pasqualone; Antonio Scalia
  11. Will Stock Rise on Valentine’s Day? By Chong, Terence Tai Leung; Hou, Siqi
  12. An analysis of sovereign credit risk premia in the euro area: are they explained by local or global factors? By Sara Cecchetti
  13. Sentiment, emotions and stock market predictability in developed and emerging markets By Steyn, Dimitri H. W.; Greyling, Talita; Rossouw, Stephanie; Mwamba, John M.
  14. Network-Based Measures of Systemic Risk in Korea By Jaewon Choi; Jieun Lee

  1. By: Nicola Branzoli (Bank of Italy); Ilaria Supino (Bank of Italy)
    Abstract: FinTech credit has attracted significant attention from academics and policymakers in recent years. Given its growing importance, in this paper we provide an overview of the empirical research on FinTech credit to households and non-financial corporations (NFCs). We focus on three broad topics: i) the factors supporting the development of innovative business models for credit intermediation, such as marketplace lending; ii) the benefits of new credit risk assessment data and methods; iii) the implications of these innovations for access to credit. Three main messages emerge from the literature. First, the growth of lenders with innovative business models is mainly driven by the degree of local economic development and of competition in the banking sector. Second, new data and methods can improve traditional credit risk models because they are particularly helpful in screening opaque borrowers, such as those with scant credit history. Third, FinTech borrowers generally lack (or have limited) access to finance and tend to be riskier than traditional bank borrowers.
    Keywords: artificial intelligence, credit, digital technologies, FinTech, marketplace lending
    JEL: G21 G22 G23 G24
    Date: 2020–03
  2. By: Yue-Jun Zhang (Business School, Hunan University, Changsha 410082, China; Center for Resource and Environmental Management, Hunan University, Changsha 410082, China); Elie Bouri (USEK Business School, Holy Spirit University of Kaslik, Jounieh, Lebanon); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, South Africa)
    Abstract: We challenge the existing literature that points to the detachment of Bitcoin from the global financial system. We use daily data from August 17, 2011 - February 14, 2020 and apply a risk spillover approach based on expectiles. Results show reasonable evidence to imply the existence of downside risk spillover between Bitcoin and four assets (equities, bonds, currencies, and commodities), which seems to be time dependent. Our main findings have implications for participants in both the Bitcoin and the traditional financial markets for the sake of asset allocation, and risk management. For policy makers, our findings suggest that Bitcoin should be monitored carefully for the sake of financial stability.
    Keywords: Bitcoin, financial markets, asset classes, downside risk spillover, expectile VaR, CAR-ARCHE
    Date: 2020–03
  3. By: Robert A. Jarrow; Rinald Murataj; Martin T. Wells; Liao Zhu
    Abstract: The paper explains the low-volatility anomaly from a new perspective. We use the Adaptive Multi-Factor (AMF) model estimated by the Groupwise Interpretable Basis Selection (GIBS) algorithm to find the basis assets significantly related to each of the portfolios. The AMF results show that the two portfolios load on very different factors, which indicates that the volatility is not an independent measure of risk, but are related to the basis assets and risk factors in the related industries. It is the performance of the loaded factors that results in the low-volatility anomaly. The out-performance of the low-volatility portfolio may not because of its low-risk (which contradicts the risk-premium theory), but because of the out-performance of the risk factors the low-volatility portfolio is loaded on. Also, we compare the AMF model with the traditional Fama-French 5-factor (FF5) model in various aspects, which shows the superior performance of the AMF model over FF5 in many perspectives.
    Date: 2020–03
  4. By: Shi, Yun; Cui, Xiangyu; Zhou, Xunyu
    Abstract: The security market line is often flat or downward-sloping. We hypothesize that probability weighting plays a role and that one ought to differentiate between periods in which agents overweight extreme events and those in which they underweight them. Overweighting inflates the probability of extremely bad events and demands greater compensation for beta risk. Underweighting has the opposite effect. Overall, these two effects offset each other, resulting in a flat or slightly negative return--beta relationship. Similarly, overweighting the tails enhances the negative relationship between return and coskewness, whereas underweighting reduces it. We support our theory through an extensive empirical study.
    Date: 2020–03–27
  5. By: Daniel Felix Ahelegbey (Università di Pavia); Paolo Giudici (Università di Pavia); Fatemeh Mojtahedi (Sari Agricultural Sciences and Natural Resources University)
    Abstract: The paper examines the relationships among market assets during stressful times, using two recently proposed econometric modeling techniques for tail risk measurement: the extreme downside hedge (EDH) and the extreme downside correlation (EDC). We extend both measures taking into account the sensitivity of asset’s return to innovations not only from the overall market index, but also from its components, by means of network modelling. Applying our proposal to the cryptocurrencies market, we find that crypto-assets can be clustered in two groups: speculative assets, such as Bitcoin, which are mainly “givers” of tail contagion; and technical assets, such as Ethereum, which are mainly “receivers” of contagion.
    Keywords: Crypto-assets, Extreme downside hedge, Extreme downside correlation, Network Models, Systematic risk, Systemic risk.
    JEL: C31 C58 G01 G12
    Date: 2020–03
  6. By: Brice Corgnet (emlyon business school, GATE Lyon Saint-Étienne - Groupe d'analyse et de théorie économique - ENS Lyon - École normale supérieure - Lyon - UL2 - Université Lumière - Lyon 2 - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon - UJM - Université Jean Monnet [Saint-Étienne] - Université de Lyon - CNRS - Centre National de la Recherche Scientifique); Mark Desantis (Chapman University); David Porter (Chapman University)
    Abstract: The growing prevalence of stock market chat rooms and social media suggests communication between traders may affect market outcomes. Using data from a series of laboratory experiments, we study the causal effect of trader communication on the price efficiency of markets. We show that communication allows markets to convey private information more effectively. This effect is most pronounced when the communication platform publicizes a reputation score that might identify a person as not being truthful. This illustrates the need for market designers to consider social interactions when designing market institutions to leverage the social motives that foster information aggregation. Abstract The growing prevalence of stock market chat rooms and social media suggests communication
    Keywords: Information aggregation,market efficiency,communication,experimental asset markets,social market design
    Date: 2020
  7. By: Stefano Ramelli (University of Zurich - Department of Banking and Finance); Alexander F. Wagner (University of Zurich - Department of Banking and Finance; Centre for Economic Policy Research (CEPR); European Corporate Governance Institute (ECGI); Swiss Finance Institute)
    Abstract: This paper studies how markets adjust to the sudden emergence of previously neglected risks. It does so by analyzing the stock price effects of the 2019 novel Coronavirus disease (COVID-19) pandemic. The telecom and health care industries did relatively well, while transportation and energy plummeted. Within industries, US firms reliant on Chinese inputs and those with a strong export orientation towards China suffered. Sophisticated investors appear to have started pricing in the effects of the virus already in the first part of January (the "Incubation" phase), that is, before managers or analysts started paying attention; the first earnings conference call that contained a discussion of "Coronavirus" took place on January 22. The "Outbreak" phase followed, during which China-oriented stocks and internationally oriented stocks more generally strongly underperformed. In the last week of February and early March (the "Fever" phase), the aggregate market first fell strongly and then entered a whipsaw pattern. But behind these feverish and seemingly behaviorally-driven price moves, some patterns emerge. In particular, investors became increasingly worried about corporate debt and liquidity, indicating widespread concerns that the health crisis may evolve into a financial crisis.
    Keywords: Behavioral finance, Corporate debt, Coronavirus, COVID-19, Earnings conference calls, Event study, Global Value Chains, Neglected risks, Pandemic, SARS-CoV-2, Supply Chains
    JEL: G01 G02 G14 G15 F15 F23 F36
    Date: 2020–03
  8. By: Pier Francesco Procacci; Carolyn E. Phelan; Tomaso Aste
    Abstract: In this note, we discuss the impact of the COVID-19 outbreak from the perspective of the market-structure. We observe that the US market-structure has dramatically changed during the past four weeks and that the level of change has followed the number of infected cases reported in the USA. Presently, market-structure resembles most closely the structure during the middle of the 2008 crisis but there are signs that it may be starting to evolve into a new structure altogether. This is the first article of a series where we will be analyzing and discussing market-structure as it evolves to a state of further instability or, more optimistically, stabilization and recovery.
    Date: 2020–03
  9. By: Felix Haase; Matthias Neuenkirch
    Abstract: Stock market recessions are often early warning signals for financial or economic crises. Hence, forecasting bear markets is important for investors, policymakers, and economic agents in general. In our two-step procedure, we first identify stock market regimes in the US using three different techniques (Markov-switching models, dating rules, and a naïve moving average). Second, we predict recessions in the S&P 500 with the help of several modeling approaches, utilizing the information of 92 macro-financial variables. Our results suggest that several variables are suitable for forecasting recessions in stock markets in-sample and out-of-sample. Our early warning models for the US equity market, in particular those using principal components to aggregate the information in the macro-financial variables, provide a statistical improvement over several benchmarks. In addition, these generate economic value by boosting returns, improving the sharp ratio and the omega, and substantially reducing drawdowns.
    Keywords: Dating Algorithms, Markov-Switching Models, Predictions, Principal Component Analysis, Specific-to-General Approach, Stock Market Recessions
    JEL: C53 G11 G17
    Date: 2020
  10. By: Luca Baldo (Bank of Italy); Filippo Pasqualone (Bank of Italy); Antonio Scalia (Bank of Italy)
    Abstract: This paper provides new evidence on the effect of the leverage ratio (LR) on repo market activity in the euro area. The share of trades with central counterparties has increased in recent years as a result of greater regulatory efficiency. After controlling for factors that may affect participation in the repo market, banks are found to exert market power towards non-bank financial institutions by applying lower rates and larger bid-ask spreads. While there is a permanent rate differential between transactions conducted via CCPs – which can easily be netted for LR purposes - and those with non-banks, on average this differential and the bid-ask spread do not increase at quarter-end. The widening of the bid-ask spread at year-end is sizeable, but this is not necessarily due to the LR, since other important factors enter into play. This evidence lessens the concern that the additional LR reporting and disclosure requirements based on daily averages, which will take effect on June 2021, might cause a contraction in repo volume and greater rate dispersion.
    Keywords: repo market, leverage ratio, monetary policy transmission
    JEL: E4 E5 G2
    Date: 2020–03
  11. By: Chong, Terence Tai Leung; Hou, Siqi
    Abstract: This study is a pioneer in academic literature to investigate the relationship between Valentine’s Day and stock market returns of major economies around the world. The findings indicate that stock returns are higher on the days when Valentine’s Day is approaching than on other days for most cases, showing “the Valentine Effect” in the stock market. Specific control variables for Valentine’s Day are also introduced to eliminate the potential influence of other effects. Unlike other holiday effects in previous literature, the Valentine’s Day Effect cannot be explained by many conventional theories, such as tax-loss selling and the inventory adjustment hypothesis.
    Keywords: Valentine Effect; Tax-loss Selling Hypothesis; Inventory Adjustment Hypothesis.
    JEL: G1 G14
    Date: 2020–02–14
  12. By: Sara Cecchetti (Bank of Italy)
    Abstract: We study the determinants of sovereign credit risk in the euro area in a time period that includes the financial and sovereign debt crisis, as well as the unconventional monetary policy adopted by the European Central Bank. First, we detect the presence of commonality in sovereign credit spreads of different countries, justifying the search for the common factors that drive CDS prices. Building on the work of Longstaff et al. (2011), we employ the econometric model used in Cecchetti (2017) to decompose sovereign credit default swap spreads into expected default losses and risk premia, finding evidence of a significant contribution of the latter component. We use the model to understand to what extent the variations in CDS spreads and in the two embedded components of selected euro-area countries are more linked to local or euro area economic variables. The results point to the importance of both global and local factors, which have a greater impact on the risk premium component. Finally, we estimate the contribution of the objective probability and risk premium components of redenomination risk (as measured by the ISDA basis) to the related CDS spread components, detecting some differences between countries.
    Keywords: bond excess return, credit default swap, distress risk premium, credit losses
    JEL: B26 C02 F30 G12 G15
    Date: 2020–03
  13. By: Steyn, Dimitri H. W.; Greyling, Talita; Rossouw, Stephanie; Mwamba, John M.
    Abstract: This paper investigates the predictability of stock market movements using text data extracted from the social media platform, Twitter. We analyse text data to determine the sentiment and the emotion embedded in the Tweets and use them as explanatory variables to predict stock market movements. The study contributes to the literature by analysing high-frequency data and comparing the results obtained from analysing emerging and developed markets, respectively. To this end, the study uses three different Machine Learning Classification Algorithms, the Naïve Bayes, K-Nearest Neighbours and the Support Vector Machine algorithm. Furthermore, we use several evaluation metrics such as the Precision, Recall, Specificity and the F-1 score to test and compare the performance of these algorithms. Lastly, we use the K-Fold Cross-Validation technique to validate the results of our machine learning models and the Variable Importance Analysis to show which variables play an important role in the prediction of our models. The predictability of the market movements is estimated by first including sentiment only and then sentiment with emotions. Our results indicate that investor sentiment and emotions derived from stock market-related Tweets are significant predictors of stock market movements, not only in developed markets but also in emerging markets.
    Keywords: Sentiment Analysis,Classification,Stock Prediction,Machine Learning
    JEL: C6 C8 G0
    Date: 2020
  14. By: Jaewon Choi (Gies College of Business, University of Illinois/College of Business, Yonsei University); Jieun Lee (Economic Research Institute, Bank of Korea)
    Abstract: We estimate systemic risk in the Korean economy using the econometric measures of commonality and connectedness applied to stock returns. To assess potential systemic risk concerns arising from the high concentration of the economy in large business groups and a few export-oriented sectors, we perform three levels of estimation using individual stocks, business groups, and industry returns. Our results show that the measures perform well over our sample period by indicating heightened levels of commonality and interconnectedness during crisis periods. In out-of-sample tests, we show that the measures can predict future losses in the stock market during the crises. We also provide the recent readings of our measures, both at the market, chaebol, and industry levels. The measures indicate systemic risk is currently not a major concern in Korea, as they tend to be at the lowest level since 1998. Systemic risk within-chaebols or within-industries overall has not significantly increased in the recent sub-period. In contrast, commonality within the finance industry has not subsided, which we interpret as capturing the interconnectedness endemic to the finance industry, rather than indicating a heightened systemic risk within the banking sector.
    Keywords: Systemic risk, Network analysis, Korean economy
    JEL: G11 G14 G23
    Date: 2020–03–26

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