nep-fmk New Economics Papers
on Financial Markets
Issue of 2019‒04‒08
thirteen papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Policy News and Stock Market Volatility By Scott R. Baker; Nicholas Bloom; Steven J. Davis; Kyle J. Kost
  2. The Credit Rating Agencies and Their Role in the Financial System By Lawrence J. White
  3. Hedge Fund Performance: What Do We Know? By Joenväärä, Juha; Kaupila, Mikko; Kosowski, Robert; Tolonen, Pekka
  4. Using Deep Learning Neural Networks and Candlestick Chart Representation to Predict Stock Market By Rosdyana Mangir Irawan Kusuma; Trang-Thi Ho; Wei-Chun Kao; Yu-Yen Ou; Kai-Lung Hua
  5. Deep Learning in Asset Pricing By Luyang Chen; Markus Pelger; Jason Zhu
  6. The Dynamics of Households' Stock Market Beliefs By Hans-Martin von Gaudecker; Axel Wogrolly
  7. The Drivers of Hedge Fund Interconnectedness By Dilyana Dimova; Sheheryar Malik; Miguel A. Segoviano Basurto
  8. FLIGHTS TO SAFETY By Lieven Baele; Geert Bekaert; Koen Inghelbrecht; Min Wei
  9. Quantile coherency networks of international stock markets By Baumöhl, Eduard; Shahzad, Syed Jawad Hussain
  10. Risk Aversion and Bitcoin Returns in Normal, Bull, and Bear Markets By Elie Bouri; Rangan Gupta; Chi Keung Marco Lau; David Roubaud
  11. Momentum and liquidity in cryptocurrencies By Stjepan Begu\v{s}i\'c; Zvonko Kostanj\v{c}ar
  12. Event studies, the random walk hypothesis and risk spreads: What role for central bank sovereign bond purchases in the Euro area? By Ansgar Belke; Daniel Gros
  13. Fat Tails in Financial Return Distributions Revisited: Evidence from the Korean Stock Market By Cheoljun Eom; Taisei Kaizoji; Enrico Scalas

  1. By: Scott R. Baker; Nicholas Bloom; Steven J. Davis; Kyle J. Kost
    Abstract: We create a newspaper-based Equity Market Volatility (EMV) tracker that moves with the VIX and with the realized volatility of returns on the S&P 500. Parsing the underlying text, we find that 72 percent of EMV articles discuss the Macroeconomic Outlook, and 44 percent discuss Commodity Markets. Policy news is another major source of volatility: 35 percent of EMV articles refer to Fiscal Policy (mostly Tax Policy), 30 percent discuss Monetary Policy, 25 percent refer to one or more forms of Regulation, and 13 percent mention National Security matters. The contribution of particular policy areas fluctuates greatly over time. Trade Policy news, for example, went from a virtual nonfactor in equity market volatility to a leading source after Donald Trump’s election and especially after the intensification of U.S-China trade tensions. The share of EMV articles with attention to government policy rises over time, reaching its peak in 2017-18. We validate our measurement approach in various ways. For example, tailoring our EMV tracker to news about petroleum markets yields a measure that rises and falls with the implied and realized volatility of oil prices.
    JEL: E44 G12 G18
    Date: 2019–03
  2. By: Lawrence J. White
    Date: 2018
  3. By: Joenväärä, Juha; Kaupila, Mikko; Kosowski, Robert; Tolonen, Pekka
    Abstract: This paper proposes a novel database merging approach and re-examines the fundamental questions regarding hedge fund performance. Before drawing conclusions about fund performance, we form an aggregate database by exploiting all available information across and within seven commercial databases so that widest possible data coverage is obtained and the effect of data biases is mitigated. Average performance is significantly lower but more persistent when these conclusions are inferred from aggregate database than from some of the individual commercial databases. Although hedge funds deliver performance persistence, an average fund or industry as a whole do not deliver significant risk-adjusted net-of-fee returns while the gross-of-fee returns remain significantly positive. Consistent with previous literature, we find a significant association between fund-characteristics related to share restrictions as well as compensation structure and risk-adjusted returns.
    Keywords: Hedge Fund Performance; Managerial Skill; Persistence; Sample selection bias
    JEL: G11 G12 G23
    Date: 2019–03
  4. By: Rosdyana Mangir Irawan Kusuma; Trang-Thi Ho; Wei-Chun Kao; Yu-Yen Ou; Kai-Lung Hua
    Abstract: Stock market prediction is still a challenging problem because there are many factors effect to the stock market price such as company news and performance, industry performance, investor sentiment, social media sentiment and economic factors. This work explores the predictability in the stock market using Deep Convolutional Network and candlestick charts. The outcome is utilized to design a decision support framework that can be used by traders to provide suggested indications of future stock price direction. We perform this work using various types of neural networks like convolutional neural network, residual network and visual geometry group network. From stock market historical data, we converted it to candlestick charts. Finally, these candlestick charts will be feed as input for training a Convolutional Neural Network model. This Convolutional Neural Network model will help us to analyze the patterns inside the candlestick chart and predict the future movements of stock market. The effectiveness of our method is evaluated in stock market prediction with a promising results 92.2% and 92.1% accuracy for Taiwan and Indonesian stock market dataset respectively. The constructed model have been implemented as a web-based system freely available at for predicting stock market using candlestick chart and deep learning neural networks.
    Date: 2019–02
  5. By: Luyang Chen; Markus Pelger; Jason Zhu
    Abstract: We estimate a general non-linear asset pricing model with deep neural networks applied to all U.S. equity data combined with a substantial set of macroeconomic and firm-specific information. Our crucial innovation is the use of the no-arbitrage condition as part of the neural network algorithm. We estimate the stochastic discount factor (SDF or pricing kernel) that explains all asset prices from the conditional moment constraints implied by no-arbitrage. For this purpose, we combine three different deep neural network structures in a novel way: A feedforward network to capture non-linearities, a recurrent Long-Short-Term-Memory network to find a small set of economic state processes, and a generative adversarial network to identify the portfolio strategies with the most unexplained pricing information. Our model allows us to understand what are the key factors that drive asset prices, identify mis-pricing of stocks and generate the mean-variance efficient portfolio. Empirically, our approach outperforms out-of-sample all other benchmark approaches: Our optimal portfolio has an annual Sharpe Ratio of 2.1, we explain 8% of the variation in individual stock returns and explain over 90% of average returns for all anomaly sorted portfolios.
    Date: 2019–03
  6. By: Hans-Martin von Gaudecker; Axel Wogrolly
    Abstract: We analyse a long panel of households’ stock market beliefs to gain insights into the nature of their expectations formation processes. We classify respondents into one of five groups based on their data and estimate group-wise models of expectations formation. Two of the groups are at opposite extremes in terms of optimism: Pessimists who expect substantially negative returns and financially sophisticated individuals whose expectations are close to the historical average. Two groups expect returns around zero and differ only in how they respond to information: Extrapolators who become more optimistic following positive information and mean-reverters for whom the opposite is the case. The final group is characterised by poor probability numeracy; its individuals are not willing or able to quantify their beliefs about future returns. None of the estimated belief formation processes passes a rational expectations test.
    Keywords: Stock market expectations, household finance, heterogeneity, clustering methods
    JEL: D83 D84 D14 C38
    Date: 2019–04
  7. By: Dilyana Dimova; Sheheryar Malik; Miguel A. Segoviano Basurto
    Abstract: We study the drivers of interconnectedness among hedge fund strategies. Using the comovement box approach, we find evidence of interconnectedness across all hedge fund strategies and especially compelling evidence for returns falling in the lower end of the distribution. We then identify factors which are likely drivers of interconnectedness: the Multinomial Logit and Multiple Indicators Multiple Causes (MIMIC) model. A higher repo spread and a higher credit spread contribute to a greater interconnectedness in period of low returns; while a higher Treasury-Fed spread, and a prime broker index appears to be associated with interconnectedness in periods of high returns.
    Date: 2019–03–08
  8. By: Lieven Baele; Geert Bekaert; Koen Inghelbrecht; Min Wei (-)
    Abstract: We identify flight-to-safety (FTS) days for 23 countries using only stock and bond returns and a model averaging approach. FTS days comprise less than 2% of the sample, and are associated with a 2.7% average bond-equity return differential and significant flows out of equity funds and into government bond and money market funds. FTS represents flights to both quality and liquidity in international equity markets, but mainly a flight-to-quality in the US corporate bond market. Emerging markets, endowment funds, and hedge funds all perform poorly during FTS, while hedge funds appear to vary their systematic exposures prior to a FTS
    Keywords: Flight-to-Safety, Flight-to-Quality, Stock-Bond Return Correlation, Liquidity, Hedge Funds
    JEL: G11 G12 G14 E43 E44
    Date: 2019–03
  9. By: Baumöhl, Eduard; Shahzad, Syed Jawad Hussain
    Abstract: This paper uses the novel quantile coherency approach to examine the tail dependence network of 49 international stock markets in the frequency domain. We find that geographical proximity and state of market development are important factors in stock markets networks. Both the short- and long-run connectedness significantly increased after the global financial crisis and spillover is higher during bearish market states, highlighting the possibility of contagion effect mainly among developed markets. Frontier and emerging markets are relatively less connected. These findings have implications for international equity market diversification and risk management.
    Keywords: quantile coherency,networks,stock markets,extreme negative returns,financial crisis
    JEL: C32 C40 G01 G15
    Date: 2019
  10. By: Elie Bouri (USEK Business School, Holy Spirit University of Kaslik, Jounieh, Lebanon); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, South Africa); Chi Keung Marco Lau (Department of Accountancy, Finance and Economics, Huddersfield Business School, University of Huddersfield, Queensgate, Huddersfield, HD1 3DH, UK); David Roubaud (Montpellier Business School, Montpellier, France)
    Abstract: We study whether level of risk aversion can be used to predict Bitcoin returns. Using a copula-quantile approach, we find evidence of predictability for the lower and upper quantiles of the conditional distribution of returns (i.e., in bull and bear markets). To reveal the sign of the predictability, we apply the cross-quantilogram approach and find that the cross-quantilogram is similar when risk aversion is at the low or medium level for various quantiles of Bitcoin returns. In particular, we find positive predictability when the risk aversion is very low and at the medium level. However, the predictability becomes negative when both the risk aversion and Bitcoin returns are very high, suggesting that very high levels of risk aversion are likely to drive down Bitcoin returns in a bull market.
    Keywords: Risk-aversion, Bitcoin returns, price predictability, copulas, quantiles
    JEL: C22 G10
    Date: 2019–03
  11. By: Stjepan Begu\v{s}i\'c; Zvonko Kostanj\v{c}ar
    Abstract: The goal of this paper is to explore the relationship between momentum effects and liquidity in cryptocurrency markets. Portfolios based on momentum-liquidity bivariate sorts are formed and rebalanced on a varying number of cryptocurrencies through time. We find a strong momentum effect in the most liquid cryptocurrencies, which supports the theories of investor herding behavior. Moreover, we propose two profitable long-only strategies: the illiquid losers and liquid winners, which exhibit improved risk adjusted performance over the market capitalization weighted portfolio.
    Date: 2019–04
  12. By: Ansgar Belke; Daniel Gros
    Abstract: The asset purchase program of the Euro area, active between 2015 and 2018, constitutes an interesting special case of Quantitative Easing (QE) because the ECB’s (Public Sector Purchase Program) PSPP program involved the purchase of the bonds of peripheral Euro area governments, which were clearly not riskless. Moreover, these purchases were undertaken by national central banks at their own risk. Intuition suggests, and a simple model confirms, that, ceteris paribus, large purchases of the bonds of the own sovereign by the national central bank should increase the risk for the remaining private bond holders. This might seem incompatible with the observation that risk spreads on peripheral bonds fell when the Euro area’s QE was announced. However, the initial fall in risk premia might have been due to the expectation of the bond being effective in lowering risk free rates. When these expectations were disappointed risk premia went back to their initial level. Formal statistical test confirm that indeed risk premia on peripheral bonds did not follow a random walk (contrary to what is assumed in event studies). Nor did the announcements of bond buying change the stochastics of these premia. One should thus not expect the impact effect to have been permanent.
    Keywords: European Central Bank, Quantitative Easing, unconventional monetary policies, spreads, structural breaks, time series econometrics
    JEL: E43 E58 G12 G15
    Date: 2019–01
  13. By: Cheoljun Eom; Taisei Kaizoji; Enrico Scalas
    Abstract: This study empirically re-examines fat tails in stock return distributions by applying statistical methods to an extensive dataset taken from the Korean stock market. The tails of the return distributions are shown to be much fatter in recent periods than in past periods and much fatter for small-capitalization stocks than for large-capitalization stocks. After controlling for the 1997 Korean foreign currency crisis and using the GARCH filter models to control for volatility clustering in the returns, the fat tails in the distribution of residuals are found to persist. We show that market crashes and volatility clustering may not sufficiently account for the existence of fat tails in return distributions. These findings are robust regardless of period or type of stock group.
    Date: 2019–04

This nep-fmk issue is ©2019 by Kwang Soo Cheong. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
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