Top

Review of Quantitative Finance and Accounting

Published in:

01-10-2016 | Original Research

When noise trading fades, volatility rises

Author: Jinliang Li

Published in: Review of Quantitative Finance and Accounting | Issue 3/2016

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

We hypothesize and test an inverse relation between liquidity and price volatility derived from microstructure theory. Two important facets of liquidity trading are examined: volume and noisiness. As represented by the expected turnover rate (volume) and realized average commission cost per share (noisiness) of NYSE equity trading, both facets are found negatively associated with the ex post and ex ante return volatilities of the NYSE stock portfolios and the NYSE composite index futures. Furthermore, the inverse association between noisiness and volatility is amplified in times of market crisis. The negative noisiness–volatility relation is also supported by our analysis on the effects of trade size on price volatility. The overall results demonstrate that volatility increases as noise trading declines.

previous article Economic growth potential creating a real put and the resulting valuation of the firm

next article CAR associated with SEO share lockups: Real or illusionary?

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Available only for authorised users

Noise in the sense of a large number of small events is often a causal factor much more powerful than a small number of large events can be (Black 1986). The terms “noise trading” and “liquidity trading” are used interchangeably in the market microstructure literature. We use “noise trading” to emphasize the nosiness and “liquidity trading” to emphasize the volume of liquidity.

This view is consistent with the work of Huang and Wang (2009), which postulates that endogenous illiquidity can lead to market crashes in the absence of any aggregate shocks.

Shleifer and Summers (1990) discuss arbitrage limits and conclude that “news alone does not move stock prices; uninformed changes in demand move them too.” Irrational noise traders with erroneous stochastic beliefs may affect prices and earn higher expected returns (De Long et al. 1990). In the presence of heterogeneous beliefs, Daigler and Wiley (1999) and Shalen (1993) have found that uninformed trading may cause price volatility.

Brennan and Subrahmanyam (1996) combine diverse empirical techniques from asset pricing and market microstructure research to study this relation. They find the microstructure parameters of market depth and illiquidity to be related to stock return premia. They relate illiquidity to adverse selection costs, which are costs associated with the discovery of private information that are paid by liquidity traders.

Johnson (2008) provides a model in which the equilibrium price-response measure of liquidity reflects the average risk-bearing capacity of the economy and volume reflects the changing contribution of individuals to that average. In that model, volume and liquidity are unrelated but jointly determined.

By AIC and BIC, an AR(1) process with a time drift is adequate to fit the logarithm quarterly turnover rate.

French et al. (1987) use one lagged cross-variance in Eq. (2) and make no adjustment for the mean return. We also estimate the quarterly volatility following their method. The estimates following French et al. (1987) are all positive and very close to the estimates in Eq. (2). The empirical analysis using the French et al. estimates of quarterly volatility is consistent with the analysis using the estimates in Eq. (2). For brevity, we do not report the analysis using the French et al. estimates.

According to Eq. (3), the association between \(\widetilde{{\Delta {\text{ACR}}_{\text{t}} }}\) and quarterly volatility (variance) can be written as \(\sigma_{\text{t}}^{2} = \exp \left( {2\varphi_{1} \cdot \widetilde{{\Delta {\text{ACR}}_{\text{t}} }} } \right) \cdot \exp \left[ {2\left( {\mu + \sum\nolimits_{{{\text{m}} = 2}}^{\text{M}} {\varphi_{\text{m}} \cdot {\text{f}}_{{{\text{m}},{\text{t}}}} + \frac{{1 - \theta {\text{L}}}}{{1 - \rho {\text{L}}}}\varepsilon_{\text{t}} } } \right)} \right]\), where \({\text{f}}_{{{\text{m}},{\text{t}}}}\) refers to the explanatory variables in addition to \(\widetilde{{\Delta {\text{ACR}}_{\text{t}} }}\). The marginal proportion of the volatility that is explained by \(\widetilde{{\Delta {\text{ACR}}_{\text{t}} }}\) is \(\exp \left( {2\varphi_{1} \cdot \widetilde{{\Delta {\text{ACR}}_{\text{t}} }} } \right) - 1\).

We also run the regressions with restrictions of the GARCH parameters and yield essentially identical results.

In the following reported GARCH regressions, we include the MA term. We also run the GARCH regressions without this MA term, and yield qualitatively similar results.

It is well known that the residual series in the GARCH process is strictly stationary with finite second moment provided α + β < 1 (see Tsay 2005: page 14). Engle and Rangel (2008: page 1191) explicitly point out that when α+β<1 , the conditional variance reverts to its mean value at a geometric rate of α + β.

\(\widetilde{{\Delta {\text{ACR}}_{\text{t}} }}\) is related to the variance of daily stock price returns in the following way: \(\sigma_{i,t}^{2} = \exp \left( {\varphi_{1} \cdot \widetilde{{\Delta {\text{ACR}}_{\text{t}} }} } \right) \cdot \exp \left( {m + \sum\nolimits_{n = 2}^{N} {\varphi_{n} \cdot f_{n,t} } } \right) \cdot h_{i,t}\), where \(\varphi_{n}\) (n = 1 to N) denotes the coefficient of \(\widetilde{{\Delta {\text{ACR}}_{\text{t}} }}\) or other corresponding liquidity variables in the regressions. The marginal (proportional) effect of \(\widetilde{{\Delta {\text{ACR}}_{\text{t}} }}\) on return variance is thus \({ \exp }(\varphi_{1} \cdot \widetilde{{\Delta {\text{ACR}}_{\text{t}} }}) - 1\).

This paper focuses on “micro” liquidity (trading volume and transaction costs), while the government often refers to “macro” liquidity (money supply in general or fund flows between asset sectors).

Jones (2002) reports a similar estimate.

Report that brokers charge a per-share commission to institutional traders as a convenient way of charging a predetermined fixed fee for broker services.

We use the 30 DJIA component companies as of the beginning of 2004. They are 3 M, Alcoa, Altria Group, American Express, AT&T, Boeing, Caterpillar, Citigroup, Du Pont, Eastman Kodak, EXXON Mobil, General Electric, General Motors, Hewlett–Packard, Home Depot, Honeywell International, Intel, IBM, International Paper, Johnson & Johnson, J. P. Morgan Chase, Coca-Cola, McDonald’s, Merck, Microsoft Corporation, Proctor & Gamble, SBC Communications, United Technologies, Wal-Mart Stores, and Walt Disney. AT&T, International Paper, and Eastman Kodak were subsequently replaced by AIG, Pfizer, and Verizon on April 8, 2004. However, AIG was later removed from the index on September 22, 2008.

NYSE TAQ databank traces back to the beginning of 1993.

Admati AR, Pfleiderer P (1988) A theory of intraday patterns: volume and price variability. Rev Financ Stud 1:3–40CrossRef

Amihud Y (2002) Illiquidity and stock returns: cross-section and time-series effects. J Financ Mark 5:31–56CrossRef

Amihud Y, Mendelson H (1986) Asset pricing and the bid-ask spread. J Financ Econ 17:223–249CrossRef

Amihud Y, Mendelson H, Lauterbach B (1997) Market microstructure and securities values: evidence from the Tel Aviv Stock Exchange. J Financ Econ 45:365–390CrossRef

Andersen TG, Bollerslev T (1997) Heterogeneous information arrivals and return volatility dynamics: uncovering the long run in high-frequency returns. J Finance 52:975–1005CrossRef

Bae K, Karolyi GA, Stulz RM (2003) A new approach to measuring financial contagion. Rev Financ Stud 16:717–763CrossRef

Bailey W, Jagtiani J (1994) Foreign ownership restrictions and stock prices in the Thai capital market. J Financ Econ 36:57–87CrossRef

Barber BM, Odean T (2000) Trading is hazardous to your wealth: the common stock investment performance of individual investors. J Finance 55:773–806CrossRef

Barber BM, Odean T (2008) All that glitters: the effect of attention and news on the buying behavior of individual and institutional investors. Rev Financ Stud 21:785–818CrossRef

Barclay M, Warner J (1993) Stealth trading and volatility: which trades move prices. J Financ Econ 34:281–305CrossRef

Bekaert G, Harvey G, Lundblad C (2007) Liquidity of emerging markets: lessons from emerging markets. Rev Financ Stud 20:1783–1831CrossRef

Berkman H, Eleswarapu V (1998) Short-term traders and liquidity: a test using Bombay Stock Exchange data. J Financ Econ 47:339–355CrossRef

Berkowitz SA, Logue DE, Noser EA (1988) The total cost of transactions on the NYSE. J Finance 43:97–112CrossRef

Bessembinder H, Seguin PJ (1992) Futures trading activity and stock price volatility. J Finance 47:2015–2034CrossRef

Bessembinder H, Seguin PJ (1993) Price volatility, trading volume, and market depth: evidence from futures markets. J Financ Quant Anal 28:21–39CrossRef

Bhar R, Malliaris A (1998) Volume and volatility in foreign currency futures markets. Rev Quant Financ Account 10:285–302CrossRef

Black F (1986) Noise. J Finance 41:529–543CrossRef

Bloomfield R, O’Hara M, Saar G (2009) How noise trading affects markets: an experimental analysis. Rev Financ Stud 22:2275–2302CrossRef

Bollerslev T (1987) A conditionally heteroskedastic time-series model for speculation prices and rates of return. Rev Econ Stat 69:542–547CrossRef

Box GEP, Pierce DA (1970) Distribution of residual autocorrelations in autoregressive-integrated moving average time-series models. J Am Stat Assoc 65:1509–1526CrossRef

Brennan MJ, Subrahmanyam A (1996) Market microstructure and asset pricing: on the compensation for illiquidity in stock returns. J Financ Econ 41:441–464CrossRef

Brennan MJ, Subrahmanyam A (1998) The determinants of average trade size. J Bus 71:1–25CrossRef

Campbell JY, Grossman SJ, Wang J (1993) Trading volume and serial correlation in stock returns. Q J Econ 108:905–939CrossRef

Chakravarty S (2001) Stealth-trading: Which traders’ trades move stock prices? J Financ Econ 61:289–307CrossRef

Chan K, Fong W (2000) Trade size, order imbalance, and the volatility–volume relation. J Financ Econ 57:247–273CrossRef

Chiang TC, Doong S (2001) Empirical analysis of stock returns and volatility: evidence from seven Asian stock markets based on TAR-GARCH model. Rev Quant Financ Account 17:301–318CrossRef

Chordia T, Roll R, Subrahmanyam A (2000) Commonality in liquidity. J Financ Econ 56:3–28CrossRef

Chordia T, Subrahmanyam A, Anshuman VR (2001) Trading activity and expected stock returns. J Financ Econ 59:3–32CrossRef

Chordia T, Sarkar A, Subrahmanyam A (2005) An empirical analysis of stock and bond market liquidity. Rev Financ Stud 18:85–129CrossRef

Daigler RT, Wiley MK (1999) The impact of trader type on the futures volatility–volume relation. J Finance 54:2297–2316CrossRef

Datar VT, Naik NY, Radcliffe R (1998) Liquidity and stock returns: an alternative test. J Financ Mark 1:203–219CrossRef

De Long JB, Shleifer A, Summers LH, Waldmann RJ (1990) Noise trader risk in financial markets. J Polit Econ 98:703–738CrossRef

Duffie D, Pedersen LH, Singleton KJ (2003) Modeling sovereign yield spreads: a case study of Russian debt. J Finance 58:119–159CrossRef

Dungey M, Renée F, González-Hermosillo B, Martin V (2006) Contagion in international bond markets during the Russian and the LTCM crises. J Financ Stab 2:1–27CrossRef

Easley D, Kiefer NM, O’Hara M, Paperman JB (1996) Liquidity, information, and infrequently traded stocks. J Finance 51:1405–1436CrossRef

Easley D, Kiefer NM, O’Hara M (1997) One day in the life of a very common stock. Rev Financ Stud 10:805–835CrossRef

Engle RF, Rangel JG (2008) The spline-GARCH model for low-frequency volatility and its global macroeconomic causes. Rev Financ Stud 21:1187–1222CrossRef

Epps TW, Epps ML (1976) The stochastic dependence of security price changes and transaction volumes: implications for the mixture-of-distributions hypothesis. Econometrica 44:305–321CrossRef

Evans MDD, Lyons RK (2002) Time varying liquidity in foreign exchange. J Monet Econ 49:1025–1051CrossRef

Forbes KJ, Rigobon R (2002) No contagion, only interdependence: measuring stock market comovements. J Finance 57:2223–2261CrossRef

French KR, Schwert GW, Stambaugh RF (1987) Expected stock returns and volatility. J Financ Econ 19:3–29CrossRef

Gallant AG, Rossi PE, Tauchen G (1992) Stock prices and volume. Rev Financ Stud 5:199–242CrossRef

Glosten LR, Milgrom PR (1985) Bid, ask, and transaction prices in a specialist market with heterogeneously informed traders. J Financ Econ 14:71–100CrossRef

Hamao Y, Masulis R, Ng V (1990) Correlations in price changes and volatility across international stock markets. Rev Financ Stud 3:281–307CrossRef

Harris L (1986) A transaction data study of weekly and intradaily patterns in stock returns. J Financ Econ 16:99–117CrossRef

Harris M, Raviv A (1993) Differences of opinion make a horse race. Rev Financ Stud 6:473–506CrossRef

Huang J, Wang J (2009) Liquidity and market crashes. Rev Financ Stud 22:2607–2643CrossRef

Huang C, Lin T, Yu C, Hoe S (2006) Volatility–volume relationships among types of traders considering the investment limitation to foreign investors. Rev Pac Basin Financ Mark Polic 9:575–596CrossRef

Hvidkjaer S (2006) A trade-based analysis of momentum. Rev Financ Stud 19:457–491CrossRef

Hvidkjaer S (2008) Small trades and the cross-section of stock returns. Rev Financ Stud 21:457–491CrossRef

Jain PC, Joh G (1988) The dependence between hourly prices and trading volume. J Financ Quant Anal 23:269–283CrossRef

Johnson TC (2008) Volume, liquidity, and liquidity risk. J Financ Econ 87:388–417CrossRef

Jones CM (2002) A century of stock market liquidity and trading costs. Working paper, Columbia University

Jones CM, Lipson ML (2001) Sixteenths: direct evidence on institutional execution costs. J Financ Econ 59:253–278CrossRef

Jones CM, Kaul G, Lipson ML (1994) Transactions, volume and volatility. Rev Financ Stud 7:631–651CrossRef

Karpoff JM (1987) The relation between price changes and trading volume: a survey. J Financ Quant Anal 22:109–126CrossRef

Keim DB, Madhavan A (1997) Transactions costs and investment style: an inter-exchange analysis of institutional equity trades. J Financ Econ 46:293–319CrossRef

King MA, Wadhwani S (1990) Transmission of volatility between stock markets. Rev Financ Stud 3:5–33CrossRef

Kyle AS (1985) Continuous auctions and insider trading. Econometrica 53:1315–1335CrossRef

Lamoureux CG, Lastrapes WD (1990) Heteroskedasticity in stock return data: volume versus GARCH effects. J Finance 45:221–229CrossRef

Lesmond DA (2005) Liquidity of emerging markets. J Financ Econ 77:411–452CrossRef

Levine R, Schmukler SL (2007) Migration, spillovers, and trade diversion: the impact of internationalization on domestic stock market activity. J Bank Finance 31:1595–1612CrossRef

Li J, Wu C (2006) Daily return volatility, bid-ask spreads and information flow: analyzing the information content of volume. J Bus 79:2697–2739CrossRef

Madhavan A (1995) Consolidation, fragmentation, and the disclosure of trading information. Rev Financ Stud 8:579–603CrossRef

O’Hara M (1995) Market microstructure theory. Blackwell Publishers, Cambridge

Odean T (1998) Are investors reluctant to realize their losses? J Finance 53:1775–1798CrossRef

Odean T (1999) Do investors trade too much? Am Econ Rev 89:1279–1298CrossRef

Pagano M (1989) Endogenous market thinness and stock price volatility. Rev Econ Stud 56:269–288CrossRef

Pástor L, Stambaugh RF (2003) Liquidity risk and expected stock returns. J Polit Econ 111:642–685CrossRef

Rahman S, Lee CF, Ang KP (2002) Intraday return volatility process: evidence from NASDAQ stocks. Rev Quant Financ Account 19:155–180CrossRef

Roll R (1983) On computing mean returns and the small firm premium. J Financ Econ 12:371–386CrossRef

Rouwenhorst G (1999) Local factors and turnover in emerging markets. J Finance 54:1439–1464CrossRef

Sadka R (2006) Momentum and post-earnings-announcement drift anomalies: the role of liquidity risk. J Financ Econ 80:309–349CrossRef

Schwert GW (1989) Why does stock market volatility change over time? J Finance 44:1115–1153CrossRef

Shalen CT (1993) Volume, volatility and the dispersion of beliefs. Rev Financ Stud 6:405–434CrossRef

Shleifer A, Summers LH (1990) The noise trader approach to finance. J Econ Perspect 4:19–33CrossRef

Tauchen GE, Pitts M (1983) The price variability–volume relation on speculative markets. Econometrica 51:485–505CrossRef

Tsay RS (2005) Analysis of financial time series. Wiley, HobokenCrossRef

Wu C, Xu EX (2000) Return volatility, trading imbalance and the information content of volume. Rev Quant Financ Account 14:131–153CrossRef

Title: When noise trading fades, volatility rises
Author: Jinliang Li
Publication date: 01-10-2016
Publisher: Springer US
Published in: Review of Quantitative Finance and Accounting / Issue 3/2016
Print ISSN: 0924-865X
Electronic ISSN: 1573-7179
DOI: https://doi.org/10.1007/s11156-015-0508-2

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 3/2016

Tunneling through related-party loan guarantees: evidence from a quasi-experiment in China

Economic growth potential creating a real put and the resulting valuation of the firm

Bank performance and executive pay: tournament or teamwork

Short selling and market mispricing

CAR associated with SEO share lockups: Real or illusionary?

Recap of the 26th annual financial economics and accounting conference, November 6–7, 2015