nep-fmk New Economics Papers
on Financial Markets
Issue of 2017‒05‒28
six papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Belief Dispersion in the Stock Market By Atmaz, Adem; Basak, Suleyman
  2. Compressing Over-the-Counter Markets By Marco D'Errico; Tarik Roukny
  3. The robustness of mispricing results in experimental asset markets By Owen Powell; Natalia Shestakova
  4. Are Stock Returns an Inflation Hedge for the UK? Evidence from a Wavelet Analysis Using Over Three Centuries of Data By Aviral Kumar Tiwari; Juncal Cunado; Rangan Gupta; Mark E. Wohar
  5. Herding boosts too-connected-to-fail risk in stock market of China By Shan Lu; Jichang Zhao; Huiwen Wang; Ruoen Ren
  6. Changing Tax Capacity and Tax Effort of Indian States in the Era of High Economic Growth, 2001-2014. By Mukherjee, Sacchidananda

  1. By: Atmaz, Adem; Basak, Suleyman
    Abstract: We develop a dynamic model of belief dispersion with a continuum of investors differing in beliefs. The model is tractable and qualitatively matches many of the empirical regularities in a stock price, its mean return, volatility, and trading volume. We find that the stock price is convex in cash-flow news and increases in belief dispersion, while its mean return decreases when the view on the stock is optimistic, and vice versa when pessimistic. Moreover, belief dispersion leads to a higher stock volatility and trading volume. We demonstrate that otherwise identical two-investor heterogeneous-beliefs economies do not necessarily generate our main results.
    Keywords: Asset Pricing; Bayesian learning.; belief dispersion; heterogeneous beliefs; mean return; stock price; trading volume; volatility
    JEL: D53 G12
    Date: 2017–05
  2. By: Marco D'Errico; Tarik Roukny
    Abstract: In this paper, we show both theoretically and empirically that the size of over-the-counter (OTC) markets can be reduced without affecting individual net positions. First, we find that the networked nature of these markets generates an excess of notional obligations between the aggregate gross amount and the minimum amount required to satisfy each individual net position. Second, we show conditions under which such excess can be removed. We refer to this netting operation as compression and identify feasibility and efficiency criteria, highlighting intermediation as the key element for excess levels. We show that a trade-off exists between the amount of notional that can be eliminated from the system and the conservation of original trading relationships. Third, we apply our framework to a unique and comprehensive transaction-level dataset on OTC derivatives including all firms based in the European Union. On average, we find that around 75% of market gross notional relates to excess. While around 50% can in general be removed via bilateral compression, more sophisticated multilateral compression approaches are substantially more efficient. In particular, we find that even the most conservative multilateral approach which satisfies relationship constraints can eliminate up to 98% of excess in the markets.
    Date: 2017–05
  3. By: Owen Powell; Natalia Shestakova
    Abstract: Many experiments have been conducted on market mispricing, however there is a distinct lack of guidance over how mispricing should be measured. This raises concerns about the sensitivity of mispricing results to variations in the measurement procedure. In this paper, we investigate the robustness of previous results with respect to four variations: the choice of interval length, the use of the bid-ask spread as a price proxy, the choice of aggregation function, and controlling for observable market characteristics. While a majority of previous results are unaffected, roughly 30% do change significance.
    JEL: C43 C90 D49 D84 G14
    Date: 2017–05
  4. By: Aviral Kumar Tiwari (Center for Energy and Sustainable Development (CESD), Montpellier Business School, Montpellier, France); Juncal Cunado (University of Navarra, School of Economics, Edificio Amigos, Spain); Rangan Gupta (University of Pretoria, Department of Economics, Pretoria, South Africa); Mark E. Wohar (College of Business Administration, University of Nebraska at Omaha, Omaha, USA and School of Business and Economics, Loughborough University, Leicestershire, UK)
    Abstract: This paper analyzes the relationship between stock returns and the inflation rates for the UK over a long time period (February 1790 to February 2017) and at different frequencies, by employing a wavelet analysis. We also compare the results for the UK economy with those for the US and two developing countries (India and South Africa). Overall, our results tend to suggest that, while the relationship between stock returns and inflation rates varies across frequencies and time periods, there is no evidence of stock returns acting as an inflation hedge, irrespective of whether we look at the two developed or the two developing markets in our sample.
    Keywords: nominal and real stock returns, inflation, frequency-domain, wavelet analysis
    JEL: C49 E31 G12
    Date: 2017–05
  5. By: Shan Lu; Jichang Zhao; Huiwen Wang; Ruoen Ren
    Abstract: The crowd panic and its contagion play non-negligible roles at the time of the stock crash, especially for China where inexperienced investors dominate the market. However, existing models rarely consider investors in networking stocks and accordingly miss the exact knowledge of how panic contagion leads to abrupt crash. In this paper, by networking stocks of sharing common mutual funds, a new methodology of investigating the the market crash from the perspective of investor behavior is presented. It is surprisingly revealed that the herding, which origins in the mimic of seeking for high diversity across investment strategies to lower individual risk, will produce too-connected-to-fail stocks and reluctantly boosts the systemic risk of the entire market. Though too-connected stocks might be relatively stable during the crisis, they are so influential that a small downward fluctuation will cascade to trigger severe drops of massive successor stocks, implying that their disturbances might be unexpectedly amplified by the collective panic and result in the market crash. Our findings suggest that the whole picture of portfolio strategy has to be carefully supervised to reshape the stock network and highly connected stocks should be warned at early stages to avoid the market panic.
    Date: 2017–05
  6. By: Mukherjee, Sacchidananda (National Institute of Public Finance and Policy)
    Abstract: Growing demand for public expenditures, limitations in expanding fiscal space and limited scope to deviate from common harmonized tax system under the proposed Goods and Services Tax (GST) regime may induce the states to look for opportunities to xpand revenue mobilization through alternative channels (e.g. non-tax revenue mobilization). An assessment of the existing tax efficiency (or tax effort) and strengthening tax administration could be one of such alternatives available for states to pursue. Tax administration is as important as tax base to augment revenues of a state. Efficiency of tax administration helps a state to achieve a stable tax regime which is conducive for introduction of tax reforms measures like GST. Buoyancy of tax revenues of a state is not only dependent on growth in tax base and structure of taxes but also on the state of tax administration. Many papers have been written to estimate tax effort of Indian states. Taking this exercise to the next level, this paper focuses on measuring tax effort and identifying factors that explain variations in the tax effort across states. In measuring tax potential, an attempt has been made to differentiate between factors that determine the tax base and factors that constrain the state from utilizing the available base. The exercise looks at comprehensive revenue collection under Value Added Tax of general category states for the period 2001-02 to 2013-14.
    Keywords: Tax capacity ; Tax efficiency ; Value Added Tax (VAT) ; Stochastic Frontier Approach ; Panel Data Analysis ; States of India
    JEL: H21 H71 H77
    Date: 2017–05

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