nep-fmk New Economics Papers
on Financial Markets
Issue of 2020‒10‒05
ten papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Venture Capitalists and COVID-19 By Paul Gompers; Will Gornall; Steven N. Kaplan; Ilya A. Strebulaev
  2. Gold as a Financial Instrument By Gomis-Porqueras, Pedro; Shi, Shuping; Tan, David
  3. Investor Sentiment and the (Discretionary) Accrual-return Relation By Jiajun Jiang; Qi Liu; Bo Sun
  4. Do Mutual Funds and ETFs Affect the Commonality in Liquidity of Corporate Bonds? By Efe Çötelioğlu
  5. Man vs. Machine Learning: The Term Structure of Earnings Expectations and Conditional Biases By Jules H. van Binsbergen; Xiao Han; Alejandro Lopez-Lira
  6. The Stock Market Response to a "Regulatory Sine Curve" By Bo Sun; Xuan S. Tam; Eric R. Young
  7. Is Downside Risk Priced In Cryptocurrency Market? By Victoria Dobrynskaya
  8. Volatility Forecasting with 1-dimensional CNNs via transfer learning By Bernadett Aradi; G\'abor Petneh\'azi; J\'ozsef G\'all
  9. A Machine Learning Based Regulatory Risk Index for Cryptocurrencies By Xinwen Ni; Wolfgang Karl H\"ardle; Taojun Xe
  10. The determinants of sovereign risk premiums in the UK and the European government bond market: The impact of Brexit By Samir Kadiric

  1. By: Paul Gompers; Will Gornall; Steven N. Kaplan; Ilya A. Strebulaev
    Abstract: We survey over 1,000 institutional and corporate venture capitalists (VCs) at more than 900 different firms to learn how their decisions and investments have been affected by the COVID-19 pandemic. We compare their survey answers to those provided by a large sample of VCs in early 2016 and analyzed in Gompers, Gornall, Kaplan, and Strebulaev (2020). VCs have slowed their investment pace (71% of normal) and expect to invest at 81% of their normal pace over the coming year. Not surprisingly, they have devoted more time to guiding the portfolio companies through the pandemic. VCs report that 52% of their portfolio companies are positively affected or unaffected by the pandemic; 38% are negatively affected; and 10% are severely negatively affected. Overall, they expect the pandemic to have a small negative effect on their fund IRRs (-1.6%) and MOICs (-0.07). Surprisingly, we find little change in the allocation of their time to helping portfolio companies relative to looking for new investments. In general, we find only modest differences between institutional and corporate VCs.
    JEL: G24 G30 G31 L26
    Date: 2020–09
  2. By: Gomis-Porqueras, Pedro; Shi, Shuping; Tan, David
    Abstract: In this paper, we explore the effectiveness of gold as a hedging and safe haven instrument for a variety of market risks. Rather than confining the analysis to specific countries, we treat gold as a global asset and apply the novel Phillips, Shi and Yu (2015a,b) methodology to identify extreme price movements. This method accounts for both the level and speed of changes in price dynamics that better characterises periods of abnormally high risks. We find that gold is a strong safe haven for stock, European sovereign, and oil inflation market risks. We also show that gold is a strong hedge to inflationary and currency risks. We demonstrate that gold had exhibited safe haven properties during the 2020 Covid-19 crisis, and highlight the importance of considering explosive behaviour in identifying periods of risk.
    Keywords: Gold; Hedge; Safe Haven; Sovereign Debt; Equity Markets.
    JEL: E4 E44 G0
    Date: 2020–09–07
  3. By: Jiajun Jiang; Qi Liu; Bo Sun
    Abstract: Discretionary accruals are positively associated with stock returns at the aggregate level but negatively so in the cross section. Using Baker-Wurgler investor sentiment index, we find that a significant presence of sentiment-driven investors is important in accounting for both patterns. We document that the aggregate relation is only prominent during periods of high investor sentiment. Similarly, the cross-section relation is considerably stronger in high-sentiment periods in both economic magnitude and statistical significance. We then embed investor sentiment into a stylized model of earnings management, and illustrate that a positive (negative) relationship between stock returns and earnings management can endogenously emerge in the aggregate (cross section). Our analysis suggests that the (discretionary) accrual-return relation at both the aggregate and firm levels at least partially reflects mispricing that is related to market-wide investor sentiment.
    Keywords: Investor sentiment; Uncertainty; Earnings management; Accrual anomaly
    JEL: D82 D83 G12 G14
    Date: 2020–09–18
  4. By: Efe Çötelioğlu (Swiss Finance Institute; USI Lugano)
    Abstract: The paper studies the effect of demand-side sources on the commonality in liquidity of corporate bonds as the growing mutual fund and ETF ownership in the corporate bond market may give rise to correlated trading across bonds. I document that there is a positive and significant relationship between ETF ownership and liquidity commonality of investment-grade corporate bonds. In contrast, and unlike for equities, I find that mutual fund ownership does not increase commonality in liquidity of corporate bonds. I show that three different channels explain the differential impact of ETFs and mutual funds on liquidity commonality: flow-driven trading, different investor clienteles, and ETF arbitrage mechanism.
    Keywords: Corporate Bonds, Liquidity, Commonality, Arbitrage, Exchange-Traded Funds (ETFs), Mutual Funds
    JEL: G12 G14 G20
    Date: 2020–09
  5. By: Jules H. van Binsbergen; Xiao Han; Alejandro Lopez-Lira
    Abstract: We use machine learning to construct a statistically optimal and unbiased benchmark for firms' earnings expectations. We show that analyst expectations are on average biased upwards, and that this bias exhibits substantial time-series and cross-sectional variation. On average, the bias increases in the forecast horizon, and analysts revise their expectations downwards as earnings announcement dates approach. We find that analysts' biases are associated with negative cross-sectional return predictability, and the short legs of many anomalies consist of firms for which the analysts' forecasts are excessively optimistic relative to our benchmark. Managers of companies with the greatest upward biased earnings forecasts are more likely to issue stocks.
    JEL: D22 D83 D84 G11 G12 G14 G31
    Date: 2020–09
  6. By: Bo Sun; Xuan S. Tam; Eric R. Young
    Abstract: We construct new indicators of financial regulatory intensity and find evidence that a "regulatory sine curve" generally exists: regulatory oversight increases following a recession and wanes as the economy returns to normalcy. We then build an asset pricing model, based on the idea that regulatory oversight both deters incentives to commit fraud ex ante and reveals hidden negative information ex post. Our calibration suggests that these mechanisms can be quantitatively important for stock price dynamics.
    Keywords: Cyclical financial regulation; Stock crash risk; Gradual boom and sudden crash
    JEL: G12 G30 K20
    Date: 2020–09–18
  7. By: Victoria Dobrynskaya (National Research University Higher School of Economics)
    Abstract: I look at the cryptocurrency market through the prism of standard multifactor asset-pricing models with particular attention to the downside market risk. The analysis for 1,700 coins reveals that there is a significant heterogeneity in the exposure to the downside market risk, and that a higher downside risk exposure is associated with higher average returns. The extra downside risk is priced with a statistically significant premium in cross-sectional regressions. Adding the downside risk component to the CAPM and the 3-factor model for cryptocurrencies improves the explanatory power of the models significantly. The downside risk is orthogonal to the size and momentum risks and constitutes an important forth component in the multifactor cryptocurrency pricing model.
    Keywords: cryptocurrency, coins, cryptofinance, alternative investments, downside risk, DR-CAPM
    JEL: D14 G12 G15
    Date: 2020
  8. By: Bernadett Aradi; G\'abor Petneh\'azi; J\'ozsef G\'all
    Abstract: Volatility is a natural risk measure in finance as it quantifies the variation of stock prices. A frequently considered problem in mathematical finance is to forecast different estimates of volatility. What makes it promising to use deep learning methods for the prediction of volatility is the fact, that stock price returns satisfy some common properties, referred to as `stylized facts'. Also, the amount of data used can be high, favoring the application of neural networks. We used 10 years of daily prices for hundreds of frequently traded stocks, and compared different CNN architectures: some networks use only the considered stock, but we tried out a construction which, for training, uses much more series, but not the considered stocks. Essentially, this is an application of transfer learning, and its performance turns out to be much better in terms of prediction error. We also compare our dilated causal CNNs to the classical ARIMA method using an automatic model selection procedure.
    Date: 2020–09
  9. By: Xinwen Ni; Wolfgang Karl H\"ardle; Taojun Xe
    Abstract: Cryptocurrencies' values often respond aggressively to major policy changes, but none of the existing indices informs on the market risks associated with regulatory changes. In this paper, we quantify the risks originating from new regulations on FinTech and cryptocurrencies (CCs), and analyse their impact on market dynamics. Specifically, a Cryptocurrency Regulatory Risk IndeX (CRRIX) is constructed based on policy-related news coverage frequency. The unlabeled news data are collected from the top online CC news platforms and further classified using a Latent Dirichlet Allocation model and Hellinger distance. Our results show that the machine-learning-based CRRIX successfully captures major policy-changing moments. The movements for both the VCRIX, a market volatility index, and the CRRIX are synchronous, meaning that the CRRIX could be helpful for all participants in the cryptocurrency market. The algorithms and Python code are available for research purposes on
    Date: 2020–09
  10. By: Samir Kadiric (Europäisches Institut für Internationale Wirtschaftsbeziehungen (EIIW))
    Abstract: This paper analyzes recent developments in the British and European government bond markets with reference to the United Kingdom´s decision to leave the European Union. The two main goals of the study are, firstly, to examine whether the Brexit referendum result has affected the risk premium and, secondly, whether there are any changes in risk pricing following the referendum. The paper finds a significant impact of the Brexit referendum on the risk premium in selected economies. Furthermore, the results suggest that there is a considerable change in risk pricing after the announcement of the referendum result. Credit default risk and the risk aversion play a much important role in the post-referendum period than they did prior to the vote, particularly in the United Kingdom.
    Keywords: asset pricing, government bond yield spreads, risk premium, UK, Europe, Brexit
    JEL: E43 E44 F36 G12 G15
    Date: 2020–03

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