nep-fmk New Economics Papers
on Financial Markets
Issue of 2020‒03‒30
twelve papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Market states: A new understanding By Hirdesh K. Pharasi; Eduard Seligman; Thomas H. Seligman
  2. Optimal market making with persistent order flow By Paul Jusselin
  3. Joint extreme events in equity returns and liquidity and their cross-sectional pricing implications By Ruenzi, Stefan; Ungeheuer, Michael; Weigert, Florian
  4. Multidimensional Analysis of Monthly Stock Market Returns By Osman Gulseven
  5. Industrial firms and systemic risk By Thomas J.Flavin; Mardi Dungey; Thomas O'Connor; Michael Wosser
  6. Application of Deep Q-Network in Portfolio Management By Ziming Gao; Yuan Gao; Yi Hu; Zhengyong Jiang; Jionglong Su
  7. Deep Deterministic Portfolio Optimization By Ayman Chaouki; Stephen Hardiman; Christian Schmidt; Emmanuel S\'eri\'e; Joachim de Lataillade
  8. The Dynamic Impact of FX Interventions on Financial Markets By Lukas Menkhoff; Malte Rieth; Tobias Stöhr
  9. Do investors care about tax disclosure? By Flagmeier, Vanessa; Gawehn, Vanessa
  10. Coronavirus and financial volatility: 40 days of fasting and fear By Claudiu Albulescu
  11. The Corona Virus Stock Exchange Crash By Daube, Carl Heinz
  12. Overpricing in China’s Corporate Bond Market By Yi Ding; Wei Xiong; Jinfan Zhang

  1. By: Hirdesh K. Pharasi; Eduard Seligman; Thomas H. Seligman
    Abstract: We present the clustering analysis of the financial markets of S&P 500 (USA) and Nikkei 225 (JPN) markets over a period of 2006-2019 as an example of a complex system. We investigate the statistical properties of correlation matrices constructed from the sliding epochs. The correlation matrices can be classified into different clusters, named as market states based on the similarity of correlation structures. We cluster the S&P 500 market into four and Nikkei 225 into six market states by optimizing the value of intracluster distances. The market shows transitions between these market states and the statistical properties of the transitions to critical market states can indicate likely precursors to the catastrophic events. We also analyze the same clustering technique on surrogate data constructed from average correlations of market states and the fluctuations arise due to the white noise of short time series. We use the correlated Wishart orthogonal ensemble for the construction of surrogate data whose average correlation equals the average of the real data.
    Date: 2020–03
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2003.07058&r=all
  2. By: Paul Jusselin
    Abstract: We address the issue of market making on electronic markets when taking into account the self exciting property of market order flow. We consider a market with order flows driven by Hawkes processes where one market maker operates, aiming at optimizing its profit. We characterize an optimal control solving this problem by proving existence and uniqueness of a viscosity solution to the associated Hamilton Jacobi Bellman equation. Finally we propose a methodology to approximate the optimal strategy.
    Date: 2020–03
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2003.05958&r=all
  3. By: Ruenzi, Stefan; Ungeheuer, Michael; Weigert, Florian
    Abstract: We merge the literature on downside return risk and liquidity risk and introduce the concept of extreme downside liquidity (EDL) risks. The cross-section of stock returns reflects a premium if a stock's return (liquidity) is lowest at the same time when the market liquidity (return) is lowest. This effect is not driven by linear or downside liquidity risk or extreme downside return risk and is mainly driven by more recent years. There is no premium for stocks whose liquidity is lowest when market liquidity is lowest.
    Keywords: Asset Pricing,Crash Aversion,Downside Risk,Liquidity Risk,Tail Risk
    JEL: C12 C13 G01 G11 G12 G17
    Date: 2020
    URL: http://d.repec.org/n?u=RePEc:zbw:cfrwps:2001&r=all
  4. By: Osman Gulseven
    Abstract: This study examines the monthly returns in Turkish and American stock market indices to investigate whether these markets experience abnormal returns during some months of the calendar year. The data used in this research includes 212 observations between January 1996 and August 2014. I apply statistical summary analysis, decomposition technique, dummy variable estimation, and binary logistic regression to check for the monthly market anomalies. The multidimensional methods used in this article suggest weak evidence against the efficient market hypothesis on monthly returns. While some months tend to show abnormal returns, there is no absolute unanimity in the applied approaches. Nevertheless, there is a strikingly negative May effect on the Turkish stocks following a positive return in April. Stocks tend to be bullish in December in both markets, yet we do not observe anya significant January effect is not observed.
    Date: 2020–03
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2003.05750&r=all
  5. By: Thomas J.Flavin (Department of Economics Finance and Accounting, National University of Ireland, Maynooth); Mardi Dungey (Tasmanian School of Business and Economics, University of Tasmania, Hobart, TAS 7001, Australia); Thomas O'Connor (Department of Economics Finance and Accounting, National University of Ireland, Maynooth); Michael Wosser (Financial Stability Division, Central Bank of Ireland, Dublin, Ireland.)
    Abstract: We investigate the systemic importance of U.S. industrial firms and analyse the firm-specific characteristics that identify systemically important industrials. We compute two firm-specific measures of systemic risk for 367 non-financial corporations and confirm that industrial firms are both vulnerable to systemic shocks and contribute to system-wide risk. Systemic risk measures exhibit substantial variation across firms and over time. Debt and trade credit are related to both dimensions of systemic risk, while a range of other firm characteristics are associated with systemic risk in at least one direction. The differences between the dimensions of risk and their associated characteristics underline the importance of analysing both measures of risk. Finally, we report some striking differences vis-Ã -vis the extant literature on banks and non-bank financials.
    Keywords: Systemic risk; MES; ∆CoVaR; industrial firms; financial crises.
    JEL: G32
    Date: 2020
    URL: http://d.repec.org/n?u=RePEc:may:mayecw:n298-20.pdf&r=all
  6. By: Ziming Gao; Yuan Gao; Yi Hu; Zhengyong Jiang; Jionglong Su
    Abstract: Machine Learning algorithms and Neural Networks are widely applied to many different areas such as stock market prediction, face recognition and population analysis. This paper will introduce a strategy based on the classic Deep Reinforcement Learning algorithm, Deep Q-Network, for portfolio management in stock market. It is a type of deep neural network which is optimized by Q Learning. To make the DQN adapt to financial market, we first discretize the action space which is defined as the weight of portfolio in different assets so that portfolio management becomes a problem that Deep Q-Network can solve. Next, we combine the Convolutional Neural Network and dueling Q-net to enhance the recognition ability of the algorithm. Experimentally, we chose five lowrelevant American stocks to test the model. The result demonstrates that the DQN based strategy outperforms the ten other traditional strategies. The profit of DQN algorithm is 30% more than the profit of other strategies. Moreover, the Sharpe ratio associated with Max Drawdown demonstrates that the risk of policy made with DQN is the lowest.
    Date: 2020–03
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2003.06365&r=all
  7. By: Ayman Chaouki; Stephen Hardiman; Christian Schmidt; Emmanuel S\'eri\'e; Joachim de Lataillade
    Abstract: Can deep reinforcement learning algorithms be exploited as solvers for optimal trading strategies? The aim of this work is to test reinforcement learning algorithms on conceptually simple, but mathematically non-trivial, trading environments. The environments are chosen such that an optimal or close-to-optimal trading strategy is known. We study the deep deterministic policy gradient algorithm and show that such a reinforcement learning agent can successfully recover the essential features of the optimal trading strategies and achieve close-to-optimal rewards.
    Date: 2020–03
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2003.06497&r=all
  8. By: Lukas Menkhoff; Malte Rieth; Tobias Stöhr
    Abstract: Evidence on the effectiveness of FX interventions is either limited to short horizons or hampered by debatable identification. We address these limitations by identifying a structural vector autoregressive model for the daily frequency with an external instrument. Generally, we find, for freely floating currencies, that FX intervention shocks significantly affect exchange rates and that this impact persists for months. The signaling channel dominates the portfolio channel. Moreover, interest rates tend to fall in response to sales of the domestic currency, whereas stock prices of large (exporting) firms increase after devaluation of the domestic currency.
    Keywords: Foreign exchange intervention, structural VAR, exchange rates, interest rates, stock prices
    JEL: F31 F33 E58
    Date: 2020
    URL: http://d.repec.org/n?u=RePEc:diw:diwwpp:dp1854&r=all
  9. By: Flagmeier, Vanessa; Gawehn, Vanessa
    Abstract: We assess the investor reaction to a potential introduction of public country-by-country reporting (CbCR) into the European Capital Requirements Directive IV. Estimating cumulative abnormal returns with the help of a multivariate regression model, we find weak significant evidence around our event date (February 20th, 2013) that investors perceive the introduction of CbCR as beneficial. In additional tests, we assess investor perceptions relative to different control groups (domestic institutions and non-EU institutions) and in the cross-section (splitting across size, systemically relevant, pre-event level of GAAP ETR and pre-event level of geographic disclosure). The only significant outcome is a negative reaction for large international EU institutions.
    Keywords: Country-by-country reporting,CbCR,financial institutions,investor reactions,eventstudy,multivariate regression model
    JEL: H25 H26 G21 G28
    Date: 2020
    URL: http://d.repec.org/n?u=RePEc:zbw:arqudp:254&r=all
  10. By: Claudiu Albulescu (CRIEF - Centre de Recherche sur l'Intégration Economique et Financière - Université de Poitiers)
    Abstract: 40 days after the start of the international monitoring of COVID-19, we search for the effect of official announcements regarding new cases of infection and death ratio on the financial markets volatility index (VIX). Whereas the new cases reported in China and outside China have a mixed effect on financial volatility, the death ratio positively influences VIX, that outside China triggering a more important impact. In addition, the higher the number of affected countries, the higher the financial volatility is.
    Keywords: coronavirus,financial volatility,VIX,announcement effect
    Date: 2020–03–08
    URL: http://d.repec.org/n?u=RePEc:hal:wpaper:hal-02501814&r=all
  11. By: Daube, Carl Heinz
    Abstract: The aim of this working paper is to provide a first analysis of the massive price decrease in the international financial markets since early March 2020. This is done on the basis of an economic view, but sociological and psychological approaches are also used. The initial thesis is that the economic parameters were already "toxic" at the beginning of 2020.
    Keywords: Corona Virus,Stock Exchange Crash
    JEL: G15
    Date: 2020
    URL: http://d.repec.org/n?u=RePEc:zbw:esprep:214881&r=all
  12. By: Yi Ding; Wei Xiong; Jinfan Zhang
    Abstract: Using a comprehensive dataset of Chinese corporate bond issuances, we uncover substantial evidence of issuance overpricing: the yield spread of newly issued bonds at their first secondary-market trading day is on average 5.35 bps higher than the issuance spread. This overpricing is robust across subsamples of bond issuances with different credit ratings, maturities, issuance types, and issuer status. We further provide extensive evidence to support a hypothesis that competition among underwriters drives this overpricing through two specific channels—either through rebates to participants in issuance auctions or through direct auction bidding by the underwriters for themselves or their clients.
    JEL: G15 G23
    Date: 2020–03
    URL: http://d.repec.org/n?u=RePEc:nbr:nberwo:26815&r=all

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