nep-fmk New Economics Papers
on Financial Markets
Issue of 2019‒09‒30
eighteen papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. To Detect Irregular Trade Behaviors In Stock Market By Using Graph Based Ranking Methods By Loc Tran; Linh Tran
  2. Low Risk Anomalies? By Paul Schneider; Christian Wagner; Josef Zechner
  3. Sentiment Risk Premia in the Cross-Section of Global Equity and Currency Returns By Roland Fuess; Massimo Guidolin; Christian Koeppel
  4. Does the leverage effect affect the return distribution? By Dangxing Chen
  5. How to design a derivatives market? By Bastien Baldacci; Paul Jusselin; Mathieu Rosenbaum
  6. The Memory of Beta Factors By Becker, Janis; Hollstein, Fabian; Prokopczuk, Marcel; Sibbertsen, Philipp
  7. FinTech, BigTech, and the Future of Banks By Stulz, Rene M.
  8. Backtesting Marginal Expected Shortfall and Related Systemic Risk Measures By Denisa Banulescu; Christophe Hurlin; Jeremy Leymarie; O. Scaillet
  9. Gradient Boost with Convolution Neural Network for Stock Forecast By Jialin Liu; Chih-Min Lin; Fei Chao
  10. Reinforcement Learning for Portfolio Management By Angelos Filos
  11. A Volatility Smile-Based Uncertainty Index By José Valentim Machado Vicente; Jaqueline Terra Moura Marins
  12. The Economic Value of VIX ETPs By Kim Christensen; Charlotte Christiansen; Anders M. Posselt
  13. Multi-agent reinforcement learning for market microstructure statistical inference By J. Lussange; S. Bourgeois-Gironde; S. Palminteri; B. Gutkin
  14. Synergizing Ventures By Ufuk Akcigit; Emin Dinlersoz; Jeremy Greenwood; Veronika Penciakova
  15. Spillovers of funding dry-ups By Aldasoro, Inaki; Balke, Florian; Barth, Andreas; Eren, Egemen
  16. Financial Regulation and the Federal Budget By Congressional Budget Office
  17. The Valuation of Financial Derivatives Subject to Counterparty Risk and Credit Value Adjustment By Xiao, Tim
  18. Ownership structure and the cost of debt : Evidence from the Chinese corporate bond market By Chatterjee, Sris; Gu, Xian; Hasan, Iftekhar; Lu, Haitian

  1. By: Loc Tran; Linh Tran
    Abstract: To detect the irregular trade behaviors in the stock market is the important problem in machine learning field. These irregular trade behaviors are obviously illegal. To detect these irregular trade behaviors in the stock market, data scientists normally employ the supervised learning techniques. In this paper, we employ the three graph Laplacian based semi-supervised ranking methods to solve the irregular trade behavior detection problem. Experimental results show that that the un-normalized and symmetric normalized graph Laplacian based semi-supervised ranking methods outperform the random walk Laplacian based semi-supervised ranking method.
    Date: 2019–09
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1909.08964&r=all
  2. By: Paul Schneider (University of Lugano - Institute of Finance; Swiss Finance Institute); Christian Wagner (WU Vienna University of Economics and Business; Vienna Graduate School of Finance (VGSF)); Josef Zechner (Vienna University of Economics and Business)
    Abstract: This paper shows that low risk anomalies in the CAPM and in traditional factor models arise when investors require compensation for coskewness risk. Empirically, we find that option-implied ex-ante skewness is strongly related to ex-post residual coskewness, which allows us to construct coskewness factor mimicking portfolios. Controlling for skewness renders the alphas of betting-against-beta and -volatility insignificant. We also show that the returns of beta- and volatility-sorted portfolios are largely driven by a single principal component, which is in turn largely explained by skewness.
    Keywords: low risk anomaly, coskewness, skewness, risk premia, equity options
    JEL: G12
    Date: 2019–09
    URL: http://d.repec.org/n?u=RePEc:chf:rpseri:rp1950&r=all
  3. By: Roland Fuess; Massimo Guidolin; Christian Koeppel
    Abstract: This paper introduces a new sentiment-augmented asset pricing model in order to provide a comprehensive understanding of the role of non-fundamental risk factors. We find that news and social media search-based indicators that measure the aggregate investor sentiment are significantly related to excess returns across different asset classes and markets. Adding sentiment factors to both classical and more recent state-of-the-art pricing models leads to a significant increase in model performance. Following a two-stage Fama-MacBeth procedure, our modified pricing model obtains positive estimates of the risk premium for negative sentiment for global equity markets. We interpret them as measures of additional market uncertainty not captured by standard risk factors. Negative sentiment captures investors' fear, for which they demand an additional risk premium on sentiment-sensitive assets. Consequently, our empirical results contribute to the explanation of the cross-section of average, international excess equity and foreign exchange returns.
    Keywords: Sentiment; Cross-section of international equity indices; Currency returns; Fama-MacBeth risk premia estimation
    Date: 2019
    URL: http://d.repec.org/n?u=RePEc:baf:cbafwp:cbafwp19116&r=all
  4. By: Dangxing Chen
    Abstract: The leverage effect refers to the generally negative correlation between the return of an asset and the changes in its volatility. There is broad agreement in the literature that the effect should be present for theoretical reasons, and it has been consistently found in empirical work. However, a few papers have pointed out a puzzle: the return distributions of many assets do not appear to be affected by the leverage effect. We analyze the determinants of the return distribution and find that the impact of the leverage effect comes primarily from an interaction between the leverage effect and the mean-reversion effect. When the leverage effect is large and the mean-reversion effect is small, then the interaction exerts a strong effect on the return distribution. However, if the mean-reversion effect is large, even a large leverage effect has little effect on the return distribution. To better understand the impact of the interaction effect, we propose an indirect method to measure it. We apply our methodology to empirical data and find that the S&P 500 data exhibits a weak interaction effect, and consequently its returns distribution is little impacted by the leverage effect. Furthermore, the interaction effect is closely related to the size factor: small firms tend to have a strong interaction effect and large firms tend to have a weak interaction effect.
    Date: 2019–09
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1909.08662&r=all
  5. By: Bastien Baldacci; Paul Jusselin; Mathieu Rosenbaum
    Abstract: We consider the problem of designing a derivatives exchange aiming at addressing clients needs in terms of listed options and providing suitable liquidity. We proceed into two steps. First we use a quantization method to select the options that should be displayed by the exchange. Then, using a principal-agent approach, we design a make take fees contract between the exchange and the market maker. The role of this contract is to provide incentives to the market maker so that he offers small spreads for the whole range of listed options, hence attracting transactions and meeting the commercial requirements of the exchange.
    Date: 2019–09
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1909.09257&r=all
  6. By: Becker, Janis; Hollstein, Fabian; Prokopczuk, Marcel; Sibbertsen, Philipp
    Abstract: Researchers and practitioners employ a variety of time-series processes to forecast betas, using either short-memory models or implicitly imposing infinite memory. We find that both approaches are inadequate: beta factors show consistent long-memory properties. For the vast majority of stocks, we reject both the short-memory and difference-stationary (random walk) alternatives. A pure long-memory model reliably provides superior beta forecasts compared to all alternatives. Finally, we document the relation of firm characteristics with the forecast error differentials that result from inadequately imposing short-memory or random walk instead of long-memory processes.
    Keywords: Long memory; beta; persistence; forecasting; predictability
    JEL: C58 G15 G12 G11
    Date: 2019–09
    URL: http://d.repec.org/n?u=RePEc:han:dpaper:dp-661&r=all
  7. By: Stulz, Rene M. (Ohio State University (OSU) - Department of Finance; National Bureau of Economic Research (NBER); European Corporate Governance Institute (ECGI))
    Abstract: Banks are unique in that they combine the production of liquid claims with loans. They can replicate most of what FinTech firms can do, but FinTech firms benefit from an uneven playing field in that they are less regulated than banks. The uneven playing field enables non-bank FinTech firms to challenge banks for specific products whose success is not tied to what makes banks unique, but they cannot replace banks as such. In contrast, BigTech firms have unique advantages that banks cannot easily replicate and therefore present a much stronger challenge to established banks in consumer finance and loans to small firms. Both Fintech and BigTech are contributing to a secular trend of banks losing their comparative advantage as they have less access to unique information about parties seeking credit.
    JEL: G21 G23 G24 G28
    Date: 2019–09
    URL: http://d.repec.org/n?u=RePEc:ecl:ohidic:2019-20&r=all
  8. By: Denisa Banulescu (University of Orleans; Maastricht School of Business and Economics); Christophe Hurlin (University of Orleans); Jeremy Leymarie (University of Orleans); O. Scaillet (University of Geneva GSEM and GFRI; Swiss Finance Institute; University of Geneva - Research Center for Statistics)
    Abstract: This paper proposes an original approach for backtesting systemic risk measures. This backtesting approach makes it possible to assess the systemic risk measure forecasts used to identify the financial institutions that contribute the most to the overall risk in the financial system. Our procedure is based on simple tests similar to those generally used to backtest the standard market risk measures such as value-at-risk or expected shortfall. We introduce a concept of violation associated with the marginal expected shortfall (MES), and we define unconditional coverage and independence tests for these violations. We can generalize these tests to any MES-based systemic risk measures such as SES, SRISK, or ∆CoVaR. We study their asymptotic properties in the presence of estimation risk and investigate their finite sample performance via Monte Carlo simulations. An empirical application is then carried out to check the validity of the MES, SRISK, and ∆CoVaR forecasts issued from a GARCH-DCC model for a panel of U.S. financial institutions. Our results show that this model is able to produce valid forecasts for the MES and SRISK when considering a medium-term horizon. Finally, we propose an original early warning system indicator for future systemic crises deduced from these backtests. We then define an adjusted systemic risk measure that takes into account the potential misspecification of the risk model.
    Date: 2019–09
    URL: http://d.repec.org/n?u=RePEc:chf:rpseri:rp1948&r=all
  9. By: Jialin Liu; Chih-Min Lin; Fei Chao
    Abstract: Market economy closely connects aspects to all walks of life. The stock forecast is one of task among studies on the market economy. However, information on markets economy contains a lot of noise and uncertainties, which lead economy forecasting to become a challenging task. Ensemble learning and deep learning are the most methods to solve the stock forecast task. In this paper, we present a model combining the advantages of two methods to forecast the change of stock price. The proposed method combines CNN and GBoost. The experimental results on six market indexes show that the proposed method has better performance against current popular methods.
    Date: 2019–09
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1909.09563&r=all
  10. By: Angelos Filos
    Abstract: In this thesis, we develop a comprehensive account of the expressive power, modelling efficiency, and performance advantages of so-called trading agents (i.e., Deep Soft Recurrent Q-Network (DSRQN) and Mixture of Score Machines (MSM)), based on both traditional system identification (model-based approach) as well as on context-independent agents (model-free approach). The analysis provides conclusive support for the ability of model-free reinforcement learning methods to act as universal trading agents, which are not only capable of reducing the computational and memory complexity (owing to their linear scaling with the size of the universe), but also serve as generalizing strategies across assets and markets, regardless of the trading universe on which they have been trained. The relatively low volume of daily returns in financial market data is addressed via data augmentation (a generative approach) and a choice of pre-training strategies, both of which are validated against current state-of-the-art models. For rigour, a risk-sensitive framework which includes transaction costs is considered, and its performance advantages are demonstrated in a variety of scenarios, from synthetic time-series (sinusoidal, sawtooth and chirp waves), simulated market series (surrogate data based), through to real market data (S\&P 500 and EURO STOXX 50). The analysis and simulations confirm the superiority of universal model-free reinforcement learning agents over current portfolio management model in asset allocation strategies, with the achieved performance advantage of as much as 9.2\% in annualized cumulative returns and 13.4\% in annualized Sharpe Ratio.
    Date: 2019–09
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1909.09571&r=all
  11. By: José Valentim Machado Vicente; Jaqueline Terra Moura Marins
    Abstract: We propose a new uncertainty index based on the discrepancy of the smile of FX options. We show that our index spikes near turbulent periods, forecasts economic activity and its innovations hold a significant and negative equity premium. Unlike other uncertainty indexes, our index is supported by equilibrium models, which relate the difference of options prices across moneyness to uncertainty. Moreover, our index is based on investment decisions, can be easily and continuously updated and is comparable across countries.
    Date: 2019–09
    URL: http://d.repec.org/n?u=RePEc:bcb:wpaper:502&r=all
  12. By: Kim Christensen (Aarhus University and CREATES); Charlotte Christiansen (Aarhus University and CREATES, Lund University); Anders M. Posselt (Aarhus University and CREATES)
    Abstract: The fairly new VIX ETPs have been promoted for providing effective and easily accessible diversification. We examine the economic value of using VIX ETPs for diversification of stock-bond portfolios. We consider seven different investment strategies based on short-sales constrained and unconstrained investors who use four different investment styles for their optimization strategy. Our analysis begins in 2009, when the first VIX ETPs are introduced, and therefore only considers the period after the recent financial crisis. For investors prohibited from short selling, the diversification benefits of the VIX ETPs do not offset the negative returns on the VIX ETPs. Hence there is a negative economic value of including VIX ETPs in stock-bond portfolios. This applies to all investment styles. It even applies when adjusting for a simulated market crash. For investors who are not constrained from selling assets short, the results are mixed as the economic value of VIX ETPs vary with respect to investment style and product.
    Keywords: VIX, VIX ETPs, Portfolio diversification, Realized volatility, Mean-variance analysis
    JEL: G11 G15 G23
    Date: 2019–09–18
    URL: http://d.repec.org/n?u=RePEc:aah:create:2019-14&r=all
  13. By: J. Lussange; S. Bourgeois-Gironde; S. Palminteri; B. Gutkin
    Abstract: Quantitative finance has had a long tradition of a bottom-up approach to complex systems inference via multi-agent systems (MAS). These statistical tools are based on modelling agents, which trade via a centralised order book to emulate complex and diverse market phenomena. Nevertheless, the issue of agent learning in MAS, which is crucial to price formation and hence to all market activity, has not yet fully benefited from the recent progress of artificial intelligence, and namely reinforcement learning. In order to address this, we present here a next-generation MAS stock market simulator, in which each agent learns to trade autonomously via reinforcement learning. We calibrate it to real market data from the London Stock Exchange over the years 2007 to 2018, and use it to highlight the beneficial impact of agent suboptimal learning on market stability.
    Date: 2019–09
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1909.07748&r=all
  14. By: Ufuk Akcigit (University of Chicago); Emin Dinlersoz (U.S. Census Bureau); Jeremy Greenwood (University of Pennsylvania); Veronika Penciakova (University of Maryland)
    Abstract: Venture capital and growth are examined both empirically and theoretically. Empirically, VC-backed startups have higher early growth rates and patenting levels than non-VC-backed ones. Venture capitalists increase a startup's likelihood of reaching the right tails of firm size and innovation distributions. Furthermore, there is positive assortative matching: better venture capitalists match with better startups, creating a synergistic effect. An endogenous growth model, where venture capitalists provide both expertise and financing to business startups, is constructed to match these facts. The degree of assortative matching and the taxation of VC-backed startups are important for growth.
    Date: 2019
    URL: http://d.repec.org/n?u=RePEc:red:sed019:36&r=all
  15. By: Aldasoro, Inaki; Balke, Florian; Barth, Andreas; Eren, Egemen
    Abstract: We uncover a new channel for spillovers of funding dry-ups. The 2016 US money market fund (MMF) reform exogenously reduced unsecured MMF funding for some banks. We use novel data to trace those banks to a platform for corporate deposit funding. We show that intensified competition for corporate deposits spilled the funding squeeze over to other banks with no MMF exposure. These banks paid more for deposits, and their pool of funding providers deteriorated. Moreover, their lending volumes and margins declined, and their stocks underperformed. Our results suggest that banks' competitiveness in funding markets affect their competitiveness in lending markets.
    Keywords: funding dry-ups,competition,spillovers,money market funds,corporate deposits,dollarfunding
    JEL: G21 G28
    Date: 2019
    URL: http://d.repec.org/n?u=RePEc:zbw:safewp:259&r=all
  16. By: Congressional Budget Office
    Abstract: Financial regulation affects the federal budget directly through spending for programs that support the stability of financial institutions and through the taxes and fees that those institutions pay. Regulation also affects the budget indirectly through its effects on the economy. Those effects generate a trade-off: Increased financial regulation may lower the likelihood of a financial crisis and mitigate the severity of any crisis that occurred, but it may also raise the cost of financing for investments.
    JEL: G01 G18 G28 H50 H60 H68
    Date: 2019–09–19
    URL: http://d.repec.org/n?u=RePEc:cbo:report:55586&r=all
  17. By: Xiao, Tim
    Abstract: This article presents a generic model for pricing financial derivatives subject to counterparty credit risk. Both unilateral and bilateral types of credit risks are considered. Our study shows that credit risk should be modeled as American style options in most cases, which require a backward induction valuation. To correct a common mistake in the literature, we emphasize that the market value of a defaultable derivative is actually a risky value rather than a risk-free value. Credit value adjustment (CVA) is also elaborated. A practical framework is developed for pricing defaultable derivatives and calculating their CVAs at a portfolio level.
    Keywords: credit value adjustment (CVA),credit risk modeling,financial derivative valuation,collateralization,margin and netting
    JEL: E44 G21 G12 G24 G32 G33 G18 G28
    Date: 2019
    URL: http://d.repec.org/n?u=RePEc:zbw:esprep:204279&r=all
  18. By: Chatterjee, Sris; Gu, Xian; Hasan, Iftekhar; Lu, Haitian
    Abstract: Drawing upon evidence from the Chinese corporate bond market, we study how ownership structure affects the cost of debt for firms. Our results show that state, institutional and foreign ownership formats reduce the cost of debt for firms. The benefits of state ownership are accentuated when the issuer is headquartered in a province with highly developed market institutions, operates in an industry less dominated by the state or during the period after the 2012 anti-corruption reforms. Institutional ownership provides the most benefits in environments with lower levels of marketization, especially for firms with low credit quality. Our evidence sheds light on the nexus of ownership and debt cost in a political economy where state and private firms face productivity and credit frictions. It is also illustrative of how the market environment interacts with corporate ownership in affecting the cost of bond issuance.
    JEL: G12 G18 G32 G34
    Date: 2019–09–19
    URL: http://d.repec.org/n?u=RePEc:bof:bofitp:2019_018&r=all

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