nep-fmk New Economics Papers
on Financial Markets
Issue of 2019‒05‒20
ten papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Using the Tools of Industrial Organization to Illuminate the Credit Rating Industry By Lawrence J. White
  2. Premium for Heightened Uncertainty: Solving the FOMC Puzzle By Grace Xing Hu; Jun Pan; Jiang Wang; Haoxiang Zhu
  3. Impact is not just volatility By Fr\'ed\'eric Bucci; Iacopo Mastromatteo; Michael Benzaquen; Jean-Philippe Bouchaud
  4. A Stock Selection Method Based on Earning Yield Forecast Using Sequence Prediction Models By Jessie Sun
  5. What is the Minimal Systemic Risk in Financial Exposure Networks? By Christian Diem; Anton Pichler; Stefan Thurner
  6. A Note on Bayesian Long-Term S&P 500 Factor Investing By Taran Grove; Akram Reshad; Andrey Sarantsev
  7. The Microfinance Alphabet By Marek Hudon; Marc Labie; Ariane Szafarz
  8. A Three-state Opinion Formation Model for Financial Markets By Bernardo J. Zubillaga; Andr\'e L. M. Vilela; Chao Wang; Kenric P. Nelson; H. Eugene Stanley
  9. Banks as Patient Lenders: Evidence from a Tax Reform By Carletti, Elena; De Marco, Filippo; Ioannidou, Vasso; Sette, Enrico
  10. Do Fundamentals Drive Cryptocurrency Prices? By Bhambhwani, Siddharth; Delikouras, Stefanos; Korniotis, George

  1. By: Lawrence J. White
    Abstract: Until slightly more than a decade ago, the credit rating industry was largely a little-recognized and little-understood part of the financial system “plumbing†. This obscurity changed with the financial crisis of 2008 and its aftermath. After a few years of intensive attention, however, the CRAs have retreated back to semi-obscurity and attract little media or political attention. The tools of industrial organization (IO) can help us understand this industry: its structure; its behavior; and its outcomes; and the public policies that are likely to improve its functioning.
    Keywords: credit rating agency (CRA); prudential regulation; barriers to entry; asymmetric information
    JEL: G14 L59
    Date: 2019
  2. By: Grace Xing Hu; Jun Pan; Jiang Wang; Haoxiang Zhu
    Abstract: Lucca and Moench (2015) document that prior to the announcement from FOMC meetings, the stock market yields substantial returns without major increase in conventional measures of risk. This presents a “puzzle” to the simple risk-return connection in most (static) asset pricing models. We hypothesize that the arrival of macroeconomic news, with FOMC announcements at the top of the list, brings heightened uncertainty to the market, as investors cautiously await and assess the outcome. While this heightened uncertainty may not be accurately captured by conventional risk measures, its dissolution occurs during a short time window, mostly prior to the announcement, bringing a significant price appreciation. This hypothesis leads to two testable implications: First, we should see similar return patterns for other pre-scheduled macroeconomic announcements. Second, to the extent that we can find other proxies for heightened uncertainty, we should also observe abnormal returns accompanying its dissolution. Indeed, we find large pre-announcement returns prior to the releases of Nonfarm Payroll, GDP and ISM index. Using CBOE VIX index as a primitive gauge for market uncertainty, we find disproportionately large returns on days following large spike-ups in VIX. Akin to the FOMC result, such heightened-uncertainty days occur on average only eight times per year, but account for more than 30% of the average annual return on the S&P 500 index. Inspired by the VIX result, we search for direct evidence of heightened uncertainty using VIX as a proxy and find a gradual but significant build-up in VIX over a window of up to six business days prior to the FOMC announcements.
    JEL: G12
    Date: 2019–05
  3. By: Fr\'ed\'eric Bucci; Iacopo Mastromatteo; Michael Benzaquen; Jean-Philippe Bouchaud
    Abstract: The notion of market impact is subtle and sometimes misinterpreted. Here we argue that impact should not be misconstrued as volatility. In particular, the so-called ``square-root impact law'', which states that impact grows as the square-root of traded volume, has nothing to do with price diffusion, i.e. that typical price changes grow as the square-root of time. We rationalise empirical findings on impact and volatility by introducing a simple scaling argument and confronting it to data.
    Date: 2019–05
  4. By: Jessie Sun
    Abstract: Long-term investors, different from short-term traders, focus on examining the underlying forces that affect the well-being of a company. They rely on fundamental analysis which attempts to measure the intrinsic value an equity. Quantitative investment researchers have identified some value factors to determine the cost of investment for a stock and compare different stocks. This paper proposes using sequence prediction models to forecast a value factor-the earning yield (EBIT/EV) of a company for stock selection. Two advanced sequence prediction models-Long Short-term Memory (LSTM) and Gated Recurrent Unit (GRU) networks are studied. These two models can overcome the inherent problems of a standard Recurrent Neural Network, i.e., vanishing and exploding gradients. This paper firstly introduces the theories of the networks. And then elaborates the workflow of stock pool creation, feature selection, data structuring, model setup and model evaluation. The LSTM and GRU models demonstrate superior performance of forecast accuracy over a traditional Feedforward Neural Network model. The GRU model slightly outperformed the LSTM model.
    Date: 2019–05
  5. By: Christian Diem; Anton Pichler; Stefan Thurner
    Abstract: Management of systemic risk in financial markets is traditionally associated with setting (higher) capital requirements for market participants. There are indications that while equity ratios have been increased massively since the financial crisis, systemic risk levels might not have lowered, but even increased. It has been shown that systemic risk is to a large extent related to the underlying network topology of financial exposures. A natural question arising is how much systemic risk can be eliminated by optimally rearranging these networks and without increasing capital requirements. Overlapping portfolios with minimized systemic risk which provide the same market functionality as empirical ones have been studied by [pichler2018]. Here we propose a similar method for direct exposure networks, and apply it to cross-sectional interbank loan networks, consisting of 10 quarterly observations of the Austrian interbank market. We show that the suggested framework rearranges the network topology, such that systemic risk is reduced by a factor of approximately 3.5, and leaves the relevant economic features of the optimized network and its agents unchanged. The presented optimization procedure is not intended to actually re-configure interbank markets, but to demonstrate the huge potential for systemic risk management through rearranging exposure networks, in contrast to increasing capital requirements that were shown to have only marginal effects on systemic risk [poledna2017]. Ways to actually incentivize a self-organized formation toward optimal network configurations were introduced in [thurner2013] and [poledna2016]. For regulatory policies concerning financial market stability the knowledge of minimal systemic risk for a given economic environment can serve as a benchmark for monitoring actual systemic risk in markets.
    Date: 2019–05
  6. By: Taran Grove; Akram Reshad; Andrey Sarantsev
    Abstract: We fit a dynamic factor model: monthly inflation-adjusted S\&P 500 returns vs 10-year trailing earnings yield (the inverse of Shiller price-to-earnings ratio), 10-year trailing dividend yield, and 10-year real interest rate. We model these three factors as AR(1) in three dimensions. We use long-term data from 1881 compiled by Robert Shiller, available at However, in the short run, fluctuations have heavy tails and do not significantly depend on previous values. We use Bayesian regression with normal residuals. We show significant dependence of long-term returns on the initial factor values.
    Date: 2019–05
  7. By: Marek Hudon; Marc Labie; Ariane Szafarz
    Abstract: Much has been learnt in microfinance over the last ten years. But there is yet so much to discover on how to improve financial inclusion and development. This paper offers an—evidently subjective—microfinance alphabet, hoping to so provide the microfinance scientific community with an opportunity to “read together” both where we stand and where we are heading.
    Keywords: Microfinance; Microcredit; Financial inclusion; Social finance; Hybrid organizations; Portsmouth
    JEL: G21 G23 O16 G32 O19
    Date: 2019–05–10
  8. By: Bernardo J. Zubillaga; Andr\'e L. M. Vilela; Chao Wang; Kenric P. Nelson; H. Eugene Stanley
    Abstract: We propose a three-state microscopic opinion formation model for the purpose of simulating the dynamics of financial markets. In order to mimic the heterogeneous composition of the mass of investors in a market, the agent-based model considers two different types of traders: noise traders and contrarians. Agents are represented as nodes in a network of interactions and they can assume any of three distinct possible states (e.g. buy, sell or remain inactive). The time evolution of the state of an agent is dictated by probabilistic dynamics that include both local and global influences. A noise trader is subject to local interactions, tending to assume the majority state of its nearest neighbors, whilst a contrarian is subject to a global interaction with the behavior of the market as a whole, tending to assume the state of the global minority of the market. The model exhibits the typical qualitative and quantitative features of real financial time series, including distributions of returns with heavy tails, volatility clustering and long-time memory for the absolute values of the returns. The distributions of returns are fitted by means of coupled Gaussian distributions, quantitatively revealing transitions between leptokurtic, mesokurtic and platykurtic regimes in terms of a non-linear statistical coupling which describes the complexity of the system.
    Date: 2019–05
  9. By: Carletti, Elena; De Marco, Filippo; Ioannidou, Vasso; Sette, Enrico
    Abstract: We study how a greater reliance on deposits affects bank lending policies. For identification, we exploit a tax reform in Italy that induced households to substitute bank bonds with deposits. We show that the reform led to larger increases (decreases) in term deposits (bonds) in areas where households held more bonds before the reform. We then find that banks with larger increases in deposits did not change their overall credit supply, but increased credit-lines and the maturity of term-loans. These results are consistent with key theories on the role of deposits as a discipline device and of banks as liquidity providers.
    Keywords: banks; deposits; government guarantee; Maturity; risk-taking
    JEL: G01 G21 G28
    Date: 2019–05
  10. By: Bhambhwani, Siddharth; Delikouras, Stefanos; Korniotis, George
    Abstract: We test the theoretical prediction that blockchain trustworthiness and transaction benefits determine cryptocurrency prices. Measuring these fundamentals with computing power and adoption levels, we find a significant long-run relationship between them and the prices of five prominent cryptocurrencies. Conducting factor analysis, we find that the returns of the five cryptocurrencies are exposed to aggregate fundamental-based factors related to computing power and adoption levels, even after accounting for Bitcoin returns and cryptocurrency momentum. These factors have positive risk premia and Sharpe ratios comparable to those of the U.S. equity market. They further explain return variation in an out-of-sample set of cryptocurrencies.
    Keywords: Asset Pricing Factors; Bitcoin; cointegration; Computing Power; Dash; ethereum; Hashrate; Litecoin; Monero; network
    JEL: E4 G12 G14
    Date: 2019–05

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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