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Journal of Economics and Finance

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01.02.2017

Are standard asset pricing factors long-range dependent?

verfasst von: Benjamin Rainer Auer

Erschienen in: Journal of Economics and Finance | Ausgabe 1/2018

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Abstract

Factor portfolios derived from phenomena identified in the cross-section of stock returns have become vital parts of modern investment products and financial models. Even though much has been learned about the properties of these portfolios in recent years, one issue still remains unaddressed. Are factor returns long-range dependent (LRD)? We seek to answer this important research question because if factor returns were LRD, optimal portfolio decisions and traditional asset pricing methods/tests based on these factors would be severely biased and the validity of a large strand of prior research would be compromised. Specifically, using Hurst exponent approaches within rescaled range and detrended fluctuation frameworks, we analyse the presence of LRD in the returns of factor portfolios formed based on size, book-to-market, momentum and beta characteristics. For the periods from 1931 to 2014 (US market) and 1990 to 2014 (20 international markets) and supported by several robustness checks, we find no systematic evidence of persistence or anti-persistence in the factor returns. This implies that the factor use can be considered unproblematic in both asset management and asset pricing.

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¹ We use the term ‘factor returns’ because we do not wish to take sides in the debate about whether they are anomalous returns or represent premia compensating for certain types of risk.

² Also, note that the conclusions of some tests of the efficient market hypothesis or stock market rationality also hang precariously on the presence or absence of long-term memory (see Merton 1987).

³ A typical example of LRD is given by Granger-type fractionally differenced (FD) time-series models (see Campbell et al. 1997). Consider an AR(1) series with slope ϕ=0.5 and a FD series with differencing parameter κ=1/3. Although both series have first-order autocorrelation of 0.500, at lag 5 (10, 25) the AR(1) correlation is 0.031 (0.001, 0.000) whereas the FD series has correlation 0.295 (0.235, 0.173), declining to only 0.109 at lag 100.

⁴ Recent applications show that they are useful as standards that can distinguish emerging capital markets from mature capital markets (see Eom et al. 2008; Auer, 2016b).

⁵ There are some online sources offering international factor returns (e.g., the data library of Kenneth French). However, they often do not provide the beta factor and also do not cover as many countries as we do.

⁶ Individual issues are assigned to markets based on the location of the primary exchange. For companies traded in multiple markets, the primary trading vehicle identified by Compustat/XpressFeed is used.

⁷ Book equity is obtained as shareholders’ equity minus the preferred stock value (PSTKRV, PSTKL or PSKT depending on availability). Shareholders’ equity is measured by stockholders’ equity (SEQ) or, if not available, the sum of common equity (CEQ) and preferred stocks (PSTK). If both SEQ and CEQ are unavailable, shareholders’ equity is proxied by total assets (TA) minus the sum of total liability (LT) and minority interest (MIB).

⁸ For firms with fiscal year ending in December this approach of Asness et al. (2013a) delivers the same measure as in Fama and French (1992). For firms with fiscal year not ending in December, prices at the fiscal year end date are used while Fama and French (1992) use December prices for all firms.

⁹ This definition is in line with recent evidence that short-term prior returns contribute little to momentum profits (see Novy-Marx 2012). Its slightly different from the seminal momentum studies of Jegadeesh and Titman (1993, 2001) using returns over the past 3 to 12 months.

¹⁰ For US securities, the size breakpoint is the median NYSE market equity. For international securities, it is the 80th percentile by country.

¹¹ The book-to-market breakpoints are the 70th and 30th percentiles. Also note that Fama and French (1992, 1993, 1996, 2012) use independent sorts. However, we prefer conditional sorts, as proposed by Asness et al. (2013a), because they ensure a balanced number of stocks in each portfolio.

¹² The momentum breakpoints are the 70th and 30th percentiles.

¹³ This choice of countries is motivated by a focus on developed markets listed in the MSCI market classification (see https://www.msci.com/market-classification). Some developed markets (Israel and Portugal), emerging markets and frontier markets in the MSCI classification cannot be considered because of insufficient sample sizes.

¹⁴ Note that the tail behaviour of this kind of GARCH specifications often remains too short (see Bollerslev and Wooldridge 1992). However, this is no disadvantage for our analysis because RRA is robust to heavy tails.

¹⁵ While this is the most frequently used procedure, there are also versions that differ in the sub-sample (distinct vs. overlapping) and scatter-plot construction (averages vs. all points) (see Mielniczuk and Wojdyłło 2007).

¹⁶ For more details and potential drawbacks of Eq. 9, see Sánchez Granero et al. (2008). Also note that our results do not change significantly when the correction concerning (9) is omitted.

¹⁷ Interestingly, in our application, this simple procedure yields results similar to the bootstrap test proposed by Grau-Carles (2005) and recently used by Cajueiro and Tabak (2008, 2010) and Souza et al. (2008).

¹⁸ These intervals refer to minimum sub-sample sizes of n _{m
i
n}>50. However, they are also good approximations for smaller n _{m
i
n}. Detailed values for other sizes and confidence levels are tabulated in Weron (2002).

¹⁹ This is why a new strand of the literature seeks to construct new types of size factors that may revive the size effect. One prominent example in this field is the size factor of Asness et al. (2015). Its main idea is to control for ‘junk’, i.e., stocks of companies that are small, have low average returns and are typically distressed or illiquid.

²⁰ For the sake of brevity, we do not report the filter results. However, they are available upon request.

²¹ Switching to a 95 % confidence interval causes a few breaches. However, a picture of insignificant LRD in most time-windows remains.

²² Note that Weron (2002) also constructs his simulated confidence intervals for the PRM and finds that the resulting values are close to the classic normality-based interval of the method.

²³ Similar arguments hold when we follow Kang et al. (2009) and Mohammadi and Su (2010) by using alternative GARCH types, namely the TGARCH, EGARCH, CGARCH, IGARCH and FIGARCH models.

²⁴ To back up this result, we have extended the study of Kristoufek (2012), which compares the performance of various Hurst exponent approaches in a variety of different memory and distribution settings, by our filtered procedure. Our results show that (i) non-normal GARCH residuals do not bias the H estimator and that (ii) the application of the filter leads to more precise estimates (in terms of a lower mean absolute error) of the population H than the estimator of Lo (1991).

²⁵ If one interprets cross-sectional effects as market anomalies, absence of long memory in factor returns does not imply efficient markets because the performance of the portfolios still originates from abnormal predictability.

Alvarez J, Rodriguez E (2008) Short-Term Predictability of Crude Oil Markets: A Detrended Fluctuation Analysis Approach. Energy Econ 30(5):2645–2656CrossRef

Amenc N, El Bied S, Martellini L (2003) Predictability in Hedge Fund Returns. Financ Anal J 59(5):32–46CrossRef

Andrews D, Guggenberger P (2003) A Bias-Reduced Log-Peridogram Regression Estimator for the Long-Memory Parameter. Econometrica 71(2):675–712CrossRef

Anis A, Lloyd E (1976) The Expected Value of the Adjusted Rescaled Rescaled Hurst Range of Independent Normal Summands. Biometrika 63(1):111–116CrossRef

Artmann S, Finter P, Kempf A, Koch S, Theissen E (2012) The Cross-Section of German Stock Returns: New Data and New Evidence. Schmalenbach Bus Rev 64(1):20–43CrossRef

Asness C, Frazzini A, Israel R, Moskowitz T, Pedersen L (2015) Size Matters, If You Control For Junk. Unpublished Manuscript, AQR Capital Management, Greenwich

Asness C, Frazzini A, Pedersen L (2013a) Quality Minus Junk. Unpublished Manuscript, AQR Capital Management, Greenwich

Asness C, Moskowitz T, Pedersen L (2013b) Value and Momentum Everywhere. J Financ 68(3):929–985

Auer B (2016a) On the Performance of Simple Trading Rules Derived From the Fractal Dynamics of Gold and Silver Price Fluctuations. Financ Res Lett 16:255–267

Auer B (2016b) On Time-Varying Predictability of Emerging Stock Market Returns. Emerg Mark Rev 27:1–13

Auer B (2016c) Pure Return Persistence, Hurst Exponents, and Hedge Fund Selection - A Practical Note. J Asset Manag 17(5):319–330

Auer B, Hoffmann A (2016) Do Carry Trades Show Signs of Long Memory?. Q Rev Econ Financ 61:201–208CrossRef

Ausloos M (2000) Statistical Physics in Foreign Exchange Currency and Stock Markets. Physica A 285(1-2):48–65CrossRef

Baillie R (1996) Long Memory Processes and Fractional Integration in Econometrics. J Econ 73(1):5–59CrossRef

Bali T, Cakici N, Whitelaw R (2011) Maxing Out: Stocks as Lotteries and the Cross-Section of Expected Returns. J Financ Econ 99(2):427–446CrossRef

Barkoulas J, Baum C (1996) Long-Term Dependence in Stock Returns. Econ Lett 53(3):253–259CrossRef

Barunik J, Kristoufek L (2010) On Hurst Exponent Estimation Under Heavy-Tailed Distributions. Physica A 389(18):3844–3855CrossRef

Batten J, Ciner C, Lucey B, Szilagyi P (2013) The Structure of Gold and Silver Spread Returns. Quant Finan 13(4):561–570CrossRef

Batten J, Ellis C, Fethertson T (2008) Sample Period Selection and Long-Term Dependence: New Evidence from the Dow Jones Index. Chaos, Solitons Fractals 36(5):1126–1140

Batten J, Hamada M (2009) The Compass Rose Pattern in Electricity Prices. Chaos 19(4):043106CrossRef

Bollerslev T, Wooldridge J (1992) Quasi-Maximum Likelihood Estimation and Inference in Dynamic Models with Time-Varying Covariances. Econ Rev 11 (2):143–172CrossRef

Cajueiro D, Tabak B (2004a) Ranking Efficiency for Emerging Markets. Chaos, Solitons Fractals 22(2):349–352

Cajueiro D, Tabak B (2004b) The Hurst Exponent Over Time: Testing the Assertion that Emerging Markets Are Becoming More Efficient. Physica A 336 (3-4):521–537

Cajueiro D, Tabak B (2005a) Ranking Efficiency for Emerging Equity Markets II. Chaos, Solitons Fractals 23(2):671–675

Cajueiro D, Tabak B (2005b) Testing for Time-Varying Long-Range Dependence in Volatility for Emerging Markets. Physica A 346(3-4):577–588

Cajueiro D, Tabak B (2008) Testing for Long-Range Dependence in World Stock Markets. Chaos, Solitons Fractals 37(3):918–927CrossRef

Cajueiro D, Tabak B (2010) Fluctuation Dynamics in US Interest Rates and the Role of Monetary Policy. Financ Res Lett 7(3):163–169CrossRef

Campbell J, Lo A, MacKinlay A (1997) The Econometrics of Financial Markets. Princeton University Press, Princeton

Carbone A, Castelli G, Stanley H (2004) Time-Dependent Hurst Exponent in Financial Time Series. Physica A 344(1-2):267–271CrossRef

Carhart M (1997) On Persistence in Mutual Fund Performance. J Financ 52 (1):57–82CrossRef

Chamoli A, Bansal A, Dimri V (2007) Wavelet and Rescaled Range Approach for the Hurst Coefficient for Short and Long Time Series. Comput Geosci 33(1):83–93CrossRef

Chordia T, Subrahmanyam A, Tong Q (2014) Have Capital Market Anomalies Attenuated in the Recent Era of High Liquidity and Trading Activitiy?. J Account Econ 58(1):41–58CrossRef

Clark A (2005) The Use of Hurst and Effective Return in Investing. Quant Finan 5(1):1–8CrossRef

Crato N, Ray B (2000) Memory in Returns and Volatilities of Futures’ Contracts. J Futur Mark 20(6):525–543CrossRef

De Santis G, Gérard B (1997) International Asset Pricing and Portfolio Diversification with Time-Varying Risk. J Financ 52(5):1881–1912CrossRef

De Souza C, Gokcan S (2004) Hedge Fund Investing: A Quantitative Aproach to Hedge Fund Manager Selection and De-Selection. J Wealth Manag 6(4):52–73CrossRef

Di Matteo T (2007) Multi-Scaling in Finance. Quant Finan 7(1):21–36CrossRef

Ellis C, Hudson C (2007) Scale-Adjusted Volatility and the Dow Jones Index. Physica A 378(2):374–386CrossRef

Eom C, Choi S, Oh G, Jung W (2008) Hurst Exponent and Prediction Based Weak-Form Efficient Market Hypothesis of Stock Markets. Physica A 387 (18):4630–4636CrossRef

Fama E, French K (1992) The Cross-Section of Expected Stock Returns. J Financ 47(2):427–465CrossRef

Fama E, French K (1993) Common Risk Factors in the Returns on Stocks and Bonds. J Financ Econ 33(1):3–56CrossRef

Fama E, French K (1996) Multifactor Explanations of Asset Pricing Anomalies. J Financ 51(1):55–84CrossRef

Fama E, French K (1998) Value versus Growth: The International Evidence. J Financ 53(6):1975–1999CrossRef

Fama E, French K (2012) Size, Value, and Momentum in International Stock Returns. J Financ Econ 105(3):457–472CrossRef

Fama E, French K (2015) A Five-Factor Asset Pricing Model. J Financ Econ 116(1):1–22CrossRef

Fernandez V (2011) Alternative Estimators of Long-Range Dependence. Stud Nonlinear Dyn Econ 15(2):1–37

Frazzini A, Pedersen L (2014) Betting Against Beta. J Financ Econ 111 (1):1–25CrossRef

Geweke J, Porter-Hudak S (1983) The Estimaton and Application of Long Memory Time Series Models. J Time Ser Anal 4(4):221–238CrossRef

Giraitis L, Kokoszka P, Leipus R, Teyssière G (2003) Rescaled Variance and Related Tests for Long Memory in Volatility and Levels. J Econ 112(2):265–294CrossRef

Grau-Carles P (2000) Empirical Evidence of Long-Range Correlations in Stock Returns. Physica A 287(3-4):396–404CrossRef

Grau-Carles P (2005) Tests of Long Memory: A Bootstrap Approach. Comput Econ 25(2):103–113CrossRef

Grech D, Mazur Z (2004) Can One Make Any Crash Prediction in Finance Using the Local Hurst Exponent Idea? Physica A 336(1-2):133–145CrossRef

Grech D, Pamula G (2008) The Local Hurst Exponent of the Financial Time Series in the Vicinity of Crashes on the Polish Stock Exchange Market. Physica A 387(16-17):4299–4308CrossRef

Griffin J (2002) Are the Fama and French Factors Global or Country Specific?. Rev Financ Stud 15(3):783–803CrossRef

Hamori S (1992) Test of C-CAPM for Japan: 1980-1988. Econ Lett 38(1):67–72CrossRef

Harvey C, Liu Y, Zhu H (2016) ... and the Cross-Section of Expected Returns. Rev Financ Stud 29(1):5–68CrossRef

Hull M, McGroarty F (2014) Do Emerging Markets Become More Efficient As They Develop? Long Memory Persistence in Equity Indices. Emerg Mark Rev 18:45–61CrossRef

Hurst H (1951) Long Term Storage Capacity of Reservoirs. Trans Am Soc Civ Eng 116:770–799

Ivanova K, Ausloos M (2002) Are EUR and GBP Different Words For the Same Currency?. Eur Phys J B 27:239–247

Jacobs H (2015) What Explains the Dynamics of 100 Anomalies?. J Bank Financ 57:65–85CrossRef

Jacobsen B (1996) Long Term Dependence in Stock Returns. J Empir Financ 3(4):393–417CrossRef

Jegadeesh N, Titman S (1993) Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. J Financ 48(1):65–91CrossRef

Jegadeesh N, Titman S (2001) Profitability of Momentum Strategies: An Evaluation of Alternative Explanations. J Financ 56(2):699–720CrossRef

Kang S, Kang S, Yoon S (2009) Forecasting Volatility of Crude Oil Markets. Energy Econ 31(1):119–125CrossRef

Kantelhardt J (2009) Fractal and Multifractal Time Series. In: Meyers R (ed) Encyclopedia of Complexity and Systems Science. Springer, New York, pp 3754-3779

Kristoufek L (2012) How Are Rescaled Range Analyses Affected by Different Memory and Distributional Properties? A Monte Carlo Study. Physica A 391 (17):4252–4260CrossRef

Kristoufek L, Vosvrda M (2013) Measuring Capital Market Efficiency: Global and Local Correlations Structure. Physica A 392(1):184–193CrossRef

Ledoit O, Wolf M (2008) Robust Performance Hypothesis Testing with the Sharpe Ratio. J Empir Financ 15(5):850–859CrossRef

Liew J, Vassalou M (2000) Can Book-to-Market, Size and Momentum be Risk Factors that Predict Economic Growth?. J Financ Econ 57(2):221–245CrossRef

Lo A (1991) Long-Term Memory in Stock Market Prices. Econometrica 59 (5):1279–1313CrossRef

Lo A, MacKinlay C (1988) Stock Market Prices Do Not Follow Random Walks: Evidence from a Simple Specification Test. Rev Financ Stud 1(1):41–66CrossRef

Lucey B (2004) Robust Estimates of Daily Seasonality in the Irish Equity Market. Appl Financ Econ 14(7):517–523CrossRef

Maheswaran S, Sims C (1993) Empirical Implications of Arbitrage-Free Asset Markets. In: Phillips P (ed) Models, Methods and Applications of Econometrics. Blackwell, Oxford, pp 301-316

Mandelbrot B (1971) When Can Price be Arbitraged Efficiently? A Limit to the Validity of the Random Walk and Martingale Models. Rev Econ Stat 53(3):225–236CrossRef

Mandelbrot B (1972) Statistical Methodology for Nonperiodic Cycles: From the Covariance to R/S Analysis. Ann Econ Soc Meas 1(3):259–290

Mandelbrot B (1975) Limit Theorems on the Self-Normalized Range for Weakly and Strongly Dependent Processes. Zeitschrift für Wahrscheinlichkeitstheorie und verwandte Gebiete 31(4):271–285CrossRef

Mandelbrot B (1997) Fractals and Scaling in Finance. Springer, BerlinCrossRef

Mandelbrot B, Wallis J (1969) Some Long-Run Properties of Geophysical Records. Water Resour Res 5(2):321–340CrossRef

McLean R, Pontiff J (2016) Does Academic Research Destroy Stock Return Predictability?. J Financ 71(1):5–32CrossRef

Merton R (1987) On the Current State of the Stock Market Rationality Hypothesis. In: Dornbusch R, Fischer F (eds) Macroeconomics and Finance: Essays in Honor of Franco Modigliani. MIT Press, Cambridge, pp 93-124

Mielniczuk J, Wojdyłło P (2007) Estimation of Hurst Exponent Revisited. Comput Stat Data Anal 51(9):4510–4525CrossRef

Mohammadi H, Su L (2010) International Evidence on Crude Oil Price Dynamics: Applications of ARIMA-GARCH Models. Energy Econ 32(5):1001–1008CrossRef

Moreira J, Silva J, Kamphorst S (1994) On the Fractal Dimension of Self-Affine Profiles. J Phys A Math Gen 27(24):8079–8089CrossRef

Mulligan R (2004) Fractal Analysis of Highly Volatile Markets: An Application to Technology Equities. Q Rev Econ Financ 44(1):155–179CrossRef

Rahmani B (2006) A Multifractal Detrended Fluctuation Description of Iranian Rial-US Dollar Exchange Rate. Physica A 367:328–336CrossRef

Novy-Marx R (2012) Is Momentum Really Momentum?. J Financ Econ 103 (3):429–453CrossRef

Novy-Marx R (2013) The Other Side of Value: The Gross Profitability Premium. J Financ Econ 108(1):1–28CrossRef

Ohanissian A, Russell J, Tsay R (2004) True or Spurious Long Memory in Volatility: Does it Matter for Pricing Options?. Unpublished Manuscript, University of Chicago

Pastor L, Stambaugh R (2003) Liquidity Risk and Expected Stock Returns. J Polit Econ 111(3):642–685CrossRef

Peng C, Buldyrev S, Havlin S, Simons M, Stanley H, Goldberger A (1994) Mosaic Organization of DNA Nucleotides. Phys Rev E 49(2):1685–1689CrossRef

Peters E (1992) R/S Analysis Using Logarithmic Returns. Financ Anal J 48 (6):81–82CrossRef

Peters E (1994) Fractal Market Analysis: Applying Chaos Theory to Investment and Economics. Wiley, New York

Petkova R (2006) Do the Fama-French Factors Proxy for Innovations in Predictive Variables?. J Financ 61(2):581–612CrossRef

Qian B, Rasheed K (2004) Hurst Exponent and Financial Market Predictability. In: Proceedings of the 2nd IASTED Inernational Conference on Financial Engineering and Applications, pp 203–209

Qian B, Rasheed K (2006) Stock Market Prediction With Multiple Classifiers. Appl Intell 26(1):25–33CrossRef

Sánchez Granero M, Trinidad Segovia J, García Pérez J (2008) Some Comments on Hurst Exponent and the Long Memory Processes on Capital Markets. Physica A 387(22):5543–5551CrossRef

Schwert W, Seguin P (1990) Heteroskedasticity in Stock Returns. J Financ 45(4):1129–1155CrossRef

Sensoy A, Tabak B (2015) Time-Varying Long Term Memory in the European Union Stock Markets. Physica A 436:147–158CrossRef

Sharpe W (1966) Mutual Fund Performance. J Bus 39(1):119–138CrossRef

Souza S, Tabak B, Cajueiro D (2008) Long-Range Dependence in Exchange Rates: The Case of the European Monetary System. Int J Theor Appl Financ 11 (2):199–223CrossRef

Szilagyi P, Batten J (2007) Covered Interest Parity Arbitrage and Temporal Long-Term Dependence Between the US Dollar and the Yen. Physica A 376:409–421CrossRef

Tabak B, Cajueiro D (2007) Are the Crude Oil Markets Becoming Weakly Efficient Over Time? A Test for Time-Varying Long-Range Dependence in Prices and Volatility. Energy Econ 29(1):28–36CrossRef

Taqqu M, Teverovsky V, Willinger W (1995) Estimators for Long-Range Dependence: An Empirical Study. Fractals 3(4):785–798CrossRef

Teverovsky V, Taqqu M, Willinger W (1999) A Critical Look at Lo’s Modified R/S Statistic. J Stat Plan Inf 80(1-2):211–227CrossRef

Tofallis C (2008) Investment Volatility: A Critique of Standard Beta Estimation and a Simple Way Forward. Eur J Oper Res 187(3):1358–1367CrossRef

Tsay R (2005) Analysis of Financial Time Series, 2nd Edn. Wiley, HobokenCrossRef

van Dijk M (2011) Is Size Dead? A Review of the Size Effect in Equity Returns. J Bank Financ 35(12):3263–3274

Vandewalle N, Ausloos M, Boveroux P (1997) Detrended Fluctuation Analysis of the Foreign Exchange Market. In: Kertesz J, Kondor I (eds) Econophysics - An Emergent Science: Proccedings of the 1st Workshop on Econophysics. Budapest University of Technology and Economics, Budapest, pp 36–49

Wallis J, Matalas N (1970) Small Sample Properties of H and K-Estimators of the Hurst Coefficient h. Water Resour Res 6(6):1583–1594CrossRef

Wang Y, Liu L (2010) Is WTI Crude Oil Market Becoming Weakly Efficient Over Time? New Evidence From Multiscale Analysis Based on Detrended Fluctuation Analysis. Energy Econ 32(5):987–992CrossRef

Wang Y, Wei Y, Wu C (2011) Detrendet Fluctuation Analysis on Spot and Futures Markets of West Texas Intermediate Crude Oil. Physica A 390(5):864–875CrossRef

Weron R (2002) Estimating Long-Range Dependence: Finite Sample Properties and Confidence Intervals. Physica A 312(1-2):285–299CrossRef

Willinger W, Taqqu M, Teverovsky V (1999) Stock Market Prices and Long-Range Dependence. Finance Stochast 3:1–13CrossRef

Titel: Are standard asset pricing factors long-range dependent?
verfasst von: Benjamin Rainer Auer
Publikationsdatum: 01.02.2017
Verlag: Springer US
Erschienen in: Journal of Economics and Finance / Ausgabe 1/2018
Print ISSN: 1055-0925
Elektronische ISSN: 1938-9744
DOI: https://doi.org/10.1007/s12197-017-9385-y

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