This study investigates the impact of information arrival on prices for 21 major global market indices for the period 1998–2018, employing quantile regression methodology. The results show that there is a contemporaneous and causal effect of volume on returns. This return-volume relation is a manifestation of systematic market-wide information that is released in an autocorrelated manner to market participants. This information is absorbed by the market participants over short horizons, within a day. This leads to uniform expectations and, in turn, lower volatility levels. The effect of volume on return is heterogeneous across the conditional quantiles, reflecting the contrasting patterns in the transmission of positive and negative news. This evidence is more pronounced when the intensity of information arrival is high (the tails of return distribution), which is consistent with the mixture of distribution hypothesis and information asymmetry hypothesis.
AndersenT. G. (1996). Return volatility and trading volume: An information flow interpretation of stochastic volatility. The Journal of Finance, 51(1), 169–204.
2.
BadshahI. U. (2013). Quantile regression analysis of the asymmetric return-volatility relation. Journal of Futures Markets, 33(3), 235–265.
3.
BakerM., & SteinJ. C. (2004). Market liquidity as a sentiment indicator. Journal of Financial Markets, 7(3), 271–299.
4.
BakerM., & WurglerJ. (2006). Investor sentiment and the cross-section of stock returns. The Journal of Finance, 61(4), 1645–1680.
5.
BarberisN., ShleiferA., & VishnyR. (1998). A model of investor sentiment. Journal of Financial Economics, 49(3), 307–343. https://doi.org/10.1016/S0304-405X(98)00027-0
6.
BaruníkJ., KočendaE., & VáchaL. (2016). Asymmetric connectedness on the U.S. stock market: Bad and good volatility spillovers. Journal of Financial Markets, 27, 55–78. https://doi.org/10.1016/j.finmar.2015.09.003
7.
BaurD. G., DimpflT., & JungR. C. (2012). Stock return autocorrelations revisited: A quantile regression approach. Journal of Empirical Finance, 19(2), 254–265. https://doi.org/10.1016/j.jempfin.2011.12.002
8.
BeberA., & PaganoM. (2013). Short-selling bans around the world: Evidence from the 2007–09 crisis. The Journal of Finance, 68(1), 343–381. https://doi.org/10.1111/j.1540-6261.2012.01802.x
9.
BellemareC., KrauseM., KrögerS., & ZhangC. (2005). Myopic loss aversion: Information feedback vs. investment flexibility. Economics Letters, 87(3), 319–324.
10.
BenSaïdaA. (2019). Good and bad volatility spillovers: An asymmetric connectedness. Journal of Financial Markets, 43, 78–95. https://doi.org/10.1016/j.finmar.2018.12.005
11.
BloomfieldR., O’haraM., & SaarG. (2015). Hidden liquidity: Some new light on dark trading. The Journal of Finance, 70(5), 2227–2274.
12.
BohlM. T., EssidB., & SiklosP. L. (2012). Do short selling restrictions destabilize stock markets? Lessons from Taiwan. The Quarterly Review of Economics and Finance, 52(2), 198–206.
13.
BohlM. T., KleinA. C., & SiklosP. L. (2014). Short-selling bans and institutional investors’ herding behaviour: Evidence from the global financial crisis. International Review of Financial Analysis, 33, 262–269. https://doi.org/10.1016/j.irfa.2014.03.004
14.
BondtW. F., & ThalerR. (1985). Does the stock market overreact?The Journal of Finance, 40(3), 793–805.
15.
CampbellJ. Y., GrossmanS. J., & WangJ. (1993). Trading volume and serial correlation in stock returns. The Quarterly Journal of Economics, 108(4), 905–939.
16.
ChanK., KotH. W., & YangZ. (2010). Effects of short-sale constraints on stock prices and trading activity: Evidence from Hong Kong and Mainland China. Working Paper. Hong Kong University of Science & Technology, Hong Kong Baptist University and Tsinghua University. http://dx.doi.org/10.2139/ssrn.1572067
17.
ChangE. C., ChengJ. W., & YuY. (2007). Short-sales constraints and price discovery: Evidence from the Hong Kong market. The Journal of Finance, 62(5), 2097–2121.
18.
ChenG., FirthM., & RuiO. M. (2001). The dynamic relation between stock returns, trading volume, and volatility. Financial Review, 36(3), 153–174.
19.
ChenS.-S. (2012). Revisiting the empirical linkages between stock returns and trading volume. Journal of Banking & Finance, 36(6), 1781–1788.
20.
ChordiaT., RollR., & SubrahmanyamA. (2000). Commonality in liquidity. Journal of Financial Economics, 56(1), 3–28.
21.
ChordiaT., RollR., & SubrahmanyamA. (2005). Evidence on the speed of convergence to market efficiency. Journal of Financial Economics, 76(2), 271–292.
22.
ChordiaT., RollR., & SubrahmanyamA. (2008). Liquidity and market efficiency. Journal of Financial Economics, 87(2), 249–268.
23.
ChordiaT., SubrahmanyamA., & AnshumanV. R. (2001). Trading activity and expected stock returns. Journal of Financial Economics, 59(1), 3–32.
24.
ChuangC.-C., KuanC.-M., & LinH.-Y. (2009). Causality in quantiles and dynamic stock return–volume relations. Journal of Banking & Finance, 33(7), 1351–1360. https://doi.org/10.1016/j.jbankfin.2009.02.013
25.
ClarkP. K. (1973). A subordinated stochastic process model with finite variance for speculative prices. Econometrica: Journal of the Econometric Society, 41(1), 135–155.
26.
ClementsA. E., HurnA. S., & VolkovV. V. (2015). Volatility transmission in global financial markets. Journal of Empirical Finance, 32, 3–18.
27.
CopelandT. E. (1976). A model of asset trading under the assumption of sequential information arrival. The Journal of Finance, 31(4), 1149–1168.
28.
CopelandT. E., & GalaiD. (1983). Information effects on the bid-ask spread. The Journal of Finance, 38(5), 1457–1469.
29.
DarratA. F., RahmanS., & ZhongM. (2003). Intraday trading volume and return volatility of the DJIA stocks: A note. Journal of Banking & Finance, 27(10), 2035–2043.
30.
DimmockS. G., & KouwenbergR. (2010). Loss-aversion and household portfolio choice. Journal of Empirical Finance, 17(3), 441–459.
31.
EppsT. W., & EppsM. L. (1976). The stochastic dependence of security price changes and transaction volumes: Implications for the mixture-of-distributions hypothesis. Econometrica: Journal of the Econometric Society, 44(2), 305–321.
32.
FabozziF. J., FungC.-Y., LamK., & WongW.-K. (2013). Market overreaction and underreaction: Tests of the directional and magnitude effects. Applied Financial Economics, 23(18), 1469–1482. https://doi.org/10.1080/09603107.2013.829200
33.
FamaE. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383–417.
34.
FamaE. F. (1991). Efficient capital markets: II. The Journal of Finance, 46(5), 1575–1617.
35.
FamaE. F. (1998). Market efficiency, long-term returns, and behavioral finance. Journal of Financial Economics, 49(3), 283–306.
36.
FosterF. D., & ViswanathanS. (1993). Variations in trading volume, return volatility, and trading costs: Evidence on recent price formation models. The Journal of Finance, 48(1), 187–211.
37.
FrenchK. R., SchwertG. W., & StambaughR. F. (1987). Expected stock returns and volatility. Journal of Financial Economics, 19(1), 3–29.
38.
GallantA. R., RossiP. E., & TauchenG. (1992). Stock prices and volume. The Review of Financial Studies, 5(2), 199–242.
39.
JenningsR. H., StarksL. T., & FellinghamJ. C. (1981). An equilibrium model of asset trading with sequential information arrival. The Journal of Finance, 36(1), 143–161.
40.
KarpoffJ. M. (1987). The relation between price changes and trading volume: A survey. Journal of Financial and Quantitative Analysis, 22(1), 109–126.
41.
KimS. T., & StollH. R. (2014). Are trading imbalances indicative of private information?Journal of Financial Markets, 20, 151–174.
42.
KocagilA. E., & ShachmuroveY. (1998). Return-volume dynamics in futures markets. The Journal of Futures Markets (1986-1998), 18(4), 399–426.
43.
KoenkerR. (2004). Quantile regression for longitudinal data. Journal of Multivariate Analysis, 91(1), 74–89.
44.
KoenkerR. (2013). quantreg: Quantile regression. R package version 5.05. Vienna: R Foundation for Statistical Computing. http://CRAN.R-Project.Org/Package=Quantreg
45.
KoenkerR., & Bassett JrG. (1978). Regression quantiles. Econometrica: Journal of the Econometric Society, 46(1), 33–50.
46.
KoenkerR., & HallockK. F. (2001). Quantile regression. Journal of Economic Perspectives, 15(4), 143–156.
47.
KoenkerR., & MachadoJ. A. (1999). Goodness of fit and related inference processes for quantile regression. Journal of the American Statistical Association, 94(448), 1296–1310.
48.
KoenkerR., & XiaoZ. (2006). Quantile autoregression. Journal of the American Statistical Association, 101(475), 980–990. https://doi.org/10.1198/016214506000000672
49.
LamoureuxC. G., & LastrapesW. D. (1994). Endogenous trading volume and momentum in stock-return volatility. Journal of Business & Economic Statistics, 12(2), 253–260.
50.
LeeS. (2017). Does short sale restriction lower price efficiency when substitutes exist? Evidence from the Korean market. Economics Letters, 158, 77–79. https://doi.org/10.1016/j.econlet.2017.06.037
51.
LiJ., & WuC. (2006). Daily return volatility, bid-ask spreads, and information flow: Analyzing the information content of volume. The Journal of Business, 79(5), 2697–2739.
52.
LlorenteG., MichaelyR., SaarG., & WangJ. (2002). Dynamic volume-return relation of individual stocks. The Review of Financial Studies, 15(4), 1005–1047.
53.
MalinovaK., & ParkA. (2010). Trading volume in dealer markets. Journal of Financial and Quantitative Analysis, 45(6), 1447–1484.
54.
O’HaraM. (2015). High frequency market microstructure. Journal of Financial Economics, 116(2), 257–270.
55.
SchwertG. W. (1989). Why does stock market volatility change over time?The Journal of Finance, 44(5), 1115–1153.
56.
StollH. R. (2000). Presidential address: Friction. The Journal of Finance, 55(4), 1479–1514.
57.
SuominenM. (2001). Trading volume and information revelation in stock market. Journal of Financial and Quantitative Analysis, 36(4), 545–565.
58.
TauchenG. E., & PittsM. (1983). The price variability-volume relationship on speculative markets. Econometrica: Journal of the Econometric Society, 51(2), 485–505.
59.
ThalerR. H., TverskyA., KahnemanD., & SchwartzA. (1997). The effect of myopia and loss aversion on risk taking: An experimental test. The Quarterly Journal of Economics, 112(2), 647–661.
