nep-fmk New Economics Papers
on Financial Markets
Issue of 2018‒04‒16
ten papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Stock Market Efficiency Analysis using Long Spans of Data: A Multifractal Detrended Fluctuation Approach By Aviral Kumar Tiwari; Goodness C. Aye; Rangan Gupta
  2. Intermediary Asset Pricing and the Financial Crisis By Zhiguo He; Arvind Krishnamurthy
  3. Modeling stock markets through the reconstruction of market processes By Jo\~ao Pedro Rodrigues do Carmo
  4. The skewness of commodity futures returns By Adrian Fernandez-Perez; Bart Frijns; Ana-Maria Fuertes; Joelle Miffre
  5. Fear Universality and Doubt in Asset price movements By Igor Rivin
  6. Stock market activity and hormonal cycles By Bershadskii, Alexander
  7. Exploring the predictability of range-based volatility estimators using RNNs By G\'abor Petneh\'azi; J\'ozsef G\'all
  8. Banks’ holdings of and trading in government bonds By Michele Manna; Stefano Nobili
  9. Efficient market hypothesis: Evidence from the JSE equity and bond markets. By Sinazo Guduza; Andrew Phiri
  10. Cluster analysis of stocks using price movements of high frequency data from National Stock Exchange By Charu Sharma; Amber Habib; Sunil Bowry

  1. By: Aviral Kumar Tiwari (Montpellier Business School, Montpellier, France); Goodness C. Aye (Department of Economics, University of Pretoria, Pretoria, South Africa.); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, South Africa)
    Abstract: This paper investigates the multifractality and efficiency of stock markets in eight developed (Canada, France, Germany, Italy, Japan, Switzerland, UK and USA) and two emerging (India and South Africa) countries for which long span of data, covering over or nearly a century in each case, is available to avoid sample bias. We employ the Multifractal Detrended Fluctuation Analysis (MF-DFA). Our findings show that the stock markets are multifractal and mostly long-term persistent. Most markets are more efficient in the long-term than in the short-term. The findings are robust to small and large fluctuations. We draw the economic implications of these results.
    Keywords: Economic Stock market, efficiency, short-term, long-term, multifractal detrended fluctuation analysis, Hurst exponent
    JEL: C22 G10 G14 G15
    Date: 2018–04
    URL: http://d.repec.org/n?u=RePEc:pre:wpaper:201824&r=fmk
  2. By: Zhiguo He; Arvind Krishnamurthy
    Abstract: "Intermediary asset pricing'' understands asset prices and risk premia through the lens of frictions in financial intermediation. Perhaps motivated by phenomena in the financial crisis, intermediary asset pricing has been one of the fastest growing areas of research in finance. This article explains the theory behind intermediary asset pricing and in particular how it is different from other approaches to asset pricing. The article also covers selective empirical evidence in favor of intermediary asset pricing.
    JEL: E44
    Date: 2018–03
    URL: http://d.repec.org/n?u=RePEc:nbr:nberwo:24415&r=fmk
  3. By: Jo\~ao Pedro Rodrigues do Carmo
    Abstract: There are two possible ways of interpreting the seemingly stochastic nature of financial markets: the Efficient Market Hypothesis (EMH) and a set of stylized facts that drive the behavior of the markets. We show evidence for some of the stylized facts such as memory-like phenomena in price volatility in the short term, a power-law behavior and non-linear dependencies on the returns. Given this, we construct a model of the market using Markov chains. Then, we develop an algorithm that can be generalized for any N-symbol alphabet and K-length Markov chain. Using this tool, we are able to show that it's, at least, always better than a completely random model such as a Random Walk. The code is written in MATLAB and maintained in GitHub.
    Date: 2018–03
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1803.06653&r=fmk
  4. By: Adrian Fernandez-Perez (AUT - Auckland University of Technology); Bart Frijns (AUT - Auckland University of Technology); Ana-Maria Fuertes (CASS Business School); Joelle Miffre (Audencia Business School)
    Abstract: This article studies the relation between the skewness of commodity futures returns and expected returns. A trading strategy that takes long positions in commodity futures with the most negative skew and shorts those with the most positive skew generates significant excess returns that remain after controlling for exposure to well-known risk factors. A tradeable skewness factor explains the cross-section of commodity futures returns beyond exposures to standard risk premia. The impact that skewness has on future returns is explained by investors' preferences for skewness under cumulative prospect theory and selective hedging practices.
    Keywords: Commodities,Selective hedging,Futures pricing,Skewness
    Date: 2018
    URL: http://d.repec.org/n?u=RePEc:hal:journl:hal-01678744&r=fmk
  5. By: Igor Rivin
    Abstract: We take a look the changes of different asset prices over variable periods, using both traditional and spectral methods, and discover universality phenomena which hold (in some cases) across asset classes.
    Date: 2018–03
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1803.07138&r=fmk
  6. By: Bershadskii, Alexander
    Abstract: It is shown that the 8 weeks cycle and self-organized criticality at stock markets may have a biological origin related to a 4 weeks hormonal cycle. Threshold triggering mechanism of decision making is responsible for the period doubling (8 weeks instead of 4 weeks) and for the self-organized criticality. The hormonal cycle and the self-organized criticality can serve as stabilizing factors for the stock market fluctuations dynamic.
    Keywords: stock market, S&P 500, cycles, hormonal, self-organized criticality
    JEL: C1 G1 G12
    Date: 2018–03–19
    URL: http://d.repec.org/n?u=RePEc:pra:mprapa:85298&r=fmk
  7. By: G\'abor Petneh\'azi; J\'ozsef G\'all
    Abstract: We investigate the predictability of several range-based stock volatility estimators, and compare them to the standard close-to-close estimator which is most commonly acknowledged as the volatility. The patterns of volatility changes are analyzed using LSTM recurrent neural networks, which are a state of the art method of sequence learning. We implement the analysis on all current constituents of the Dow Jones Industrial Average index, and report averaged evaluation results. We find that changes in the values of range-based estimators are more predictable than that of the estimator using daily closing values only.
    Date: 2018–03
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1803.07152&r=fmk
  8. By: Michele Manna (Bank of Italy); Stefano Nobili (Bank of Italy)
    Abstract: In this paper we examine the holdings of government securities by domestic banks along with those of five other sectors: foreign banks, foreign non-banks, the official foreign sector, the domestic central bank and domestic non-banks. We use data for 21 advanced economies from 2004 Q1 to 2016 Q2. The results offer four main insights. First, banks are reluctant to undertake major changes in their holdings of domestic bonds but do accept frequent changes of more intermediate size. Second, the foreign official sector emerges as the clearest example of a contrarian investor, buying when prices fall and selling when prices rise. Third, the greater the holdings by domestic and foreign banks, the lower the yields tend to be on 10-year benchmark sovereign bonds. Finally, in all countries included in the sample we find a positive home bias in banks’ sovereign holdings while foreign banks hold fewer bonds than predicted by a neutral portfolio measure. These results suggest that banks regard domestic government bonds as a special asset class (hence the positive bias and avoidance of major changes in inventories) which they manage in a flexible manner (hence the frequent intermediate changes and lack of systematic timing of transactions), in all likelihood to meet requests from their customers. All in all, this behaviour by domestic banks provides a positive contribution to the liquidity of the market.
    Keywords: government bond yields, investor holdings, panel cointegration
    JEL: C23 E43 G11 G12 G15 G21
    Date: 2018–03
    URL: http://d.repec.org/n?u=RePEc:bdi:wptemi:td_1166_18&r=fmk
  9. By: Sinazo Guduza (Department of Economics, Nelson Mandela University); Andrew Phiri (Department of Economics, Nelson Mandela University)
    Abstract: This study investigates weak form efficiency for 4 stock and 6 bond market return under the Johannesburg Stock Exchange (JSE) using monthly data spanning from 2002 to 2016. Our empirical strategy consists of using both individual and panel based unit root testing procedures. Moreover, we split our empirical data into two sub-samples corresponding to periods before and periods subsequent to the global financial crisis. Our empirical results point to an overwhelming evidence of weak form efficiency as the integration test fail to produce convincing evidence of unit root behaviour amongst the observed time series. The study thus confirms the efficiency of equities and debt markets in South Africa in light of the global financial crisis.
    Keywords: Equity markets, Bond market, Efficient market hypothesis, unit root tests, Johannesburg Stock Exchange (JSE), South Africa.
    JEL: C12 C13 C22 C23 G10 N27
    Date: 2017–12
    URL: http://d.repec.org/n?u=RePEc:mnd:wpaper:1718&r=fmk
  10. By: Charu Sharma (Shiv Nadar University, UP); Amber Habib (Shiv Nadar University, UP); Sunil Bowry (Shiv Nadar University, UP)
    Abstract: This paper aims to develop new techniques to describe joint behavior of stocks, beyond regression and correlation. For example, we want to identify the clusters of the stocks that move together. Our work is based on applying Kernel Principal Component Analysis(KPCA) and Functional Principal Component Analysis(FPCA) to high frequency data from NSE. Since we dealt with high frequency data with a tick size of 30 seconds, FPCA seems to be an ideal choice. FPCA is a functional variant of PCA where each sample point is considered to be a function in Hilbert space L^2. On the other hand, KPCA is an extension of PCA using kernel methods. Results obtained from FPCA and Gaussian Kernel PCA seems to be in synergy but with a lag. There were two prominent clusters that showed up in our analysis, one corresponding to the banking sector and another corresponding to the IT sector. The other smaller clusters were seen from the automobile industry and the energy sector. IT sector was seen interacting with these small clusters. The learning gained from these interactions is substantial as one can use it significantly to develop trading strategies for intraday traders.
    Date: 2018–03
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1803.09514&r=fmk

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