Skip to main content
SpringerLink
Log in

Differences in the effects of seller-initiated versus buyer-initiated crowded trades in stock markets

  • Regular Article
  • Published:
Journal of Economic Interaction and Coordination Aims and scope Submit manuscript

Abstract

This paper illustrates the differences in the effects of seller-initiated versus buyer-initiated crowded trades in stock markets. First, a one-period multi-investor model is proposed to describe how crowded trades affect stock prices. Further, we theoretically decompose the crowded trades into buyer-initiated crowded trades and seller-initiated crowded trades and, respectively, analyse their effects on stock prices. An empirical study is conducted to examine the theoretical model, obtaining the following results. First, stock prices increase with crowded trades; second, stock prices are positively related to buyer-initiated crowded trades, but negatively related to seller-initiated crowded trades; and third, the effects of crowded trades, buyer-initiated crowded trades and seller-initiated crowded trades on stock prices are stronger for the younger stocks, lower price earnings ratio stocks, lower earnings per share stocks, and lower fixed asset ratio stocks. Collectively, our results can provide new insights into the roles of crowded trades on stock prices.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Notes

  1. The limit-up of the price limit is 10%, and the limit-down of the price limit is − 10% in Chinese stock markets.

  2. The Fama–French three factors of Chinese stock markets are calculated by the method of Fama and French (1992, 1993) and were obtained from RESSET.

  3. RESSET Financial Research Database (RESSET) is mainly for colleges and universities, financial research institutions and research departments of financial enterprises in China, providing support for empirical research and model test. RESSET is designed by numerous experts from Tsinghua University, Peking University, and the London School of Economics.

  4. Wind is the provider of financial data, information and services in mainland China. Wind has built a top-tier financial database focusing on securities data, with a wide coverage of equities, funds, bonds, foreign exchanges, insurance, futures, derivatives, commodities, macro-economy and financial news. The timely updated information is always there to satisfy institutional investors' diversified needs. Knowing the demand diversification among investment institutions, research institutes, academic institutes and government bodies, Wind has developed series of professional analytics and applications for indexing, data extraction and analysis, portfolio management and many other areas. With all these tools, users could get real-time, accurate and complete financial data and information and the analytical results.

  5. In comparison, Chordia and Subrahmanyam (2004) use a sample for an average of 1322 NYSE stocks over 132 months from January 1988 to December 1998, about 4 billion transaction data. Kumar and Lee (2006) use a sample with 1,854,776 trades of individual stocks from one discount broker over 6 years (1991–1996). Bailey et al. (2009) use the transaction data of 198 stocks, including the current components of the Shanghai 180 index plus 18 stocks that were replaced after December 2003 for the period from October 2003 to March 2004.

  6. Here, firm age (A) is the list age of stock; price earnings ratio (PE) is the ratio of stock price to per-share earnings; earnings per share (EPS) is the portion of a company's profit allocated to each outstanding share of common stock; and fixed assets (PPE) is the ratio of fixed assets to total assets.

  7. \( C_{i,t}^{\mathrm{Rmrf}} \) is the day-t residual for individual stock \( i \) in the following equation: \( C_{i,t} = c_{0} + c_{1} {\mathrm{Rmrf}}_{t} + \varepsilon_{i,t} \). Using the same way, we can calculate \( C_{i,t}^{{{\mathrm{b}},{\mathrm{Rmrf}}}} \), \( C_{i,t}^{{{\mathrm{s}},{\mathrm{Rmrf}}}} \), \( C_{p,t}^{\mathrm{Rmrf}} \), \( C_{p,t}^{{{\mathrm{b}},{\mathrm{Rmrf}}}} \), and \( C_{p,t}^{{{\mathrm{s}},{\mathrm{Rmrf}}}} \).

  8. The Shanghai Stock Exchange and Shenzhen Stock Exchange paused initial public offerings (IPO) from November 2012 to January 2014; therefore, we may remove the impacts of trading rules and IPO underpricing on stock returns by selecting the subsample from January 4, 2013, to June 26, 2013.

References

  • Aitken M, Cumming D, Zhan F (2015) Exchange trading rules, surveillance and suspected insider trading. J Corp Finance 34:311–330

    Google Scholar 

  • Amihud Y (2002) Illiquidity and stock returns: cross-section and time-series effects. J Financ Mark 5(1):31–56

    Google Scholar 

  • Avramov D, Chordia T (2006) Asset pricing models and financial market anomalies. Rev Financ Stud 19(3):1001–1040

    Google Scholar 

  • Bailey W, Cai J, Yan LC, Wang F (2009) Stock returns, order imbalances, and commonality: evidence on individual, institutional, and proprietary investors in China. J Bank Finance 33(1):9–19

    Google Scholar 

  • Baker M, Wurgler J (2006) Investor sentiment and the cross-section of stock returns. J Finance 61(4):1645–1680

    Google Scholar 

  • Baltzer M, Jank S, Smajlbegovic E (2019) Who trades on momentum? J Financ Mark 42:56–74

    Google Scholar 

  • Barberis N, Thaler R (2003) A survey of behavioral finance. In: Constantinides GM, Harris M, Stulz RM (eds) Handbook of the economics of finance, vol 1. Elsevier, Amsterdam, pp 1053–1128

    Google Scholar 

  • Blocher J (2016) Network externalities in mutual funds. J Financ Mark 30:1–26

    Google Scholar 

  • Bollerslev T, Li SZ, Todorov V (2016) Roughing up beta: continuous versus discontinuous betas and the cross section of expected stock returns. J Financ Econ 120(3):464–490

    Google Scholar 

  • Bookstaber R, Foley MD, Tivnan BF (2016) Toward an understanding of market resilience: market liquidity and heterogeneity in the investor decision cycle. J Econ Interact Coord 11(2):205–227

    Google Scholar 

  • Brogaard J, Li D, Xia Y (2017) Stock liquidity and default risk. J Financ Econ 124(3):486–502

    Google Scholar 

  • Cen L, Lu H, Yang L (2013) Investor sentiment, disagreement, and the breadth-return relationship. Manag Sci 59(5):1076–1091

    Google Scholar 

  • Chan K, Hameed A, Kang W (2013) Stock price synchronicity and liquidity. J Financ Mark 16(3):414–438

    Google Scholar 

  • Chen J, Hong H, Stein JC (2002) Breadth of ownership and stock returns. J Financ Econ 66(2):171–205

    Google Scholar 

  • Cheung YL, Ouyang Z, Weiqiang TAN (2009) How regulatory changes affect IPO underpricing in China. China Econ Rev 20(4):692–702

    Google Scholar 

  • Chordia T, Subrahmanyam A (2004) Order imbalance and individual stock returns: theory and evidence. J Financ Econ 72(3):485–518

    Google Scholar 

  • Chordia T, Roll R, Subrahmanyam A (2001) Market liquidity and trading activity. J Finance 56(2):501–530

    Google Scholar 

  • Chordia T, Roll R, Subrahmanyam A (2002) Order imbalance, liquidity, and market returns. J Financ Econ 65(1):111–130

    Google Scholar 

  • Chordia T, Subrahmanyam A, Tong Q (2014) Have capital market anomalies attenuated in the recent era of high liquidity and trading activity? J Account Econ 58(1):41–58

    Google Scholar 

  • Chordia T, Hu J, Subrahmanyam A, Tong Q (2017) Order flow volatility and equity costs of capital. Manag Sci 65:1520–1551

    Google Scholar 

  • Cremers M, Halling M, Weinbaum D (2015) Aggregate jump and volatility risk in the cross-section of stock returns. J Finance 70(2):577–614

    Google Scholar 

  • Cumming D, Johan S, Li D (2011) Exchange trading rules and stock market liquidity. J Financ Econ 99(3):651–671

    Google Scholar 

  • Datar VT, Naik NY, Radcliffe R (1998) Liquidity and stock returns: an alternative test. J Financ Mark 1(2):203–219

    Google Scholar 

  • Ding R, Hou W (2015) Retail investor attention and stock liquidity. J Int Financ Mark Inst Money 37:12–26

    Google Scholar 

  • Eckbo BE, Norli Ø (2005) Liquidity risk, leverage and long-run IPO returns. J Corp Finance 11(1–2):1–35

    Google Scholar 

  • Eiling E (2013) Industry-specific human capital, idiosyncratic risk, and the cross-section of expected stock returns. J Finance 68(1):43–84

    Google Scholar 

  • Fama EF, French KR (1992) The cross-section of expected stock returns. J Finance 47(2):427–465

    Google Scholar 

  • Fama EF, French KR (1993) Common risk factors in the returns on stocks and bonds. J Financ Econ 33(1):3–56

    Google Scholar 

  • Fu F (2009) Idiosyncratic risk and the cross-section of expected stock returns. J Financ Econ 91(1):24–37

    Google Scholar 

  • Gerhold S, Guasoni P, Muhle-Karbe J, Schachermayer W (2014) Transaction costs, trading volume, and the liquidity premium. Finance Stoch 18(1):1–37

    Google Scholar 

  • Han B, Kumar A (2013) Speculative retail trading and asset prices. J Financ Quant Anal 48(2):377–404

    Google Scholar 

  • Hanson SG, Sunderam A (2014) The growth and limits of arbitrage: evidence from short interest. Rev Financ Stud 27(4):1238–1286

    Google Scholar 

  • Hausman JA (1978) Specification tests in econometrics. Econom J Econom Soc 46:1251–1271

    Google Scholar 

  • Hendershott T, Jones CM, Menkveld AJ (2011) Does algorithmic trading improve liquidity? J Finance 66(1):1–33

    Google Scholar 

  • Hong H, Li W, Ni SX, Scheinkman JA, Yan P (2015) Days to cover and stock returns. NBER working paper

  • Hou K, Moskowitz TJ (2005) Market frictions, price delay, and the cross-section of expected returns. Rev Financ Stud 18(3):981–1020

    Google Scholar 

  • Jia Y, Yang C (2017) Disagreement and the risk-return relation. Econ Model 64:97–104

    Google Scholar 

  • Kimbro MB (2005) Managing underpricing? The case of pre-IPO discretionary accruals in China. J Int Financ Manag Account 16(3):229–262

    Google Scholar 

  • Kumar A, Lee C (2006) Retail investor sentiment and return comovements. J Finance 61(5):2451–2486

    Google Scholar 

  • Lee C, Ready MJ (1991) Inferring trade direction from intraday data. J Finance 46(2):733–746

    Google Scholar 

  • Li L (2006) Can corporate governance variables explain short-term IPO returns?: A study of the Chinese market. Account Account Perform 12(1):64

    Google Scholar 

  • Li M, Zheng H (2017) Heterogeneous trading and complex price dynamics. J Econ Interact Coord 12(2):437–442

    Google Scholar 

  • Ma R, Anderson HD, Marshall BR (2018) Stock market liquidity and trading activity: Is China different? Int Rev Financ Anal 56:32–51

    Google Scholar 

  • Menkveld AJ (2014) Crowded trades: an overlooked systemic risk for central clearing counterparties. SSRN working paper

  • Pagano M (1989) Trading volume and asset liquidity. Q J Econ 104(2):255–274

    Google Scholar 

  • Pojarliev M, Richard ML (2011) Detecting crowded trades in currency funds. Financ Anal J 01:26–39

    Google Scholar 

  • Shang H, Yuan P, Huang L (2016) Macroeconomic factors and the cross-section of commodity futures returns. Int Rev Econ Finance 45:316–332

    Google Scholar 

  • Sias R, Turtle HJ, Zykaj B (2015) Hedge fund crowds and mispricing. Manag Sci 62:764–784

    Google Scholar 

  • Stein JC (2009) Presidential address: sophisticated investors and market efficiency. J Finance 64(4):1517–1548

    Google Scholar 

  • Yan P (2013) Crowded trades, short covering and momentum crashes. SSRN Working Papers. 2404272, Social Science Research Network, Inc. http://ssrn.com/abstract=2404272

  • Yang C, Zhou L (2016) Individual stock crowded trades, individual stock investor sentiment and excess returns. N Am J Econ Finance 38:39–53

    Google Scholar 

  • Zhou L, Yang C (2019) Stochastic investor sentiment, crowdedness and deviation of asset prices from fundamentals. Econ Model 79:130–140

    Google Scholar 

Download references

Acknowledgements

We are especially grateful to the editor (Prof. Thomas Lux), the Associate Editor and anonymous referees for constructive comments that have significantly improved the paper. This work was supported by the National Natural Science Foundation of China (71803051), the Natural Science Foundation of Guangdong Province (2018A030310218); the project of Guangdong Planning office of Philosophy and Social Science in 2017 (GD17XLJ04; GD17XGL14), the youth project of Department of Education of Guangdong Province (2017WQNCX014), National Science Foundation of China (71471067; 71720107002), and China Postdoctoral Science Foundation (2019M652913).

Author information

Authors and Affiliations

  1. College of Economics & Management, South China Agricultural University, Guangzhou, 510642, China

    Liyun Zhou

  2. School of Economics and Commerce, Finance and Security Center, South China University of Technology, Guangzhou, 510006, China

    Chunpeng Yang

Authors
  1. Liyun Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chunpeng Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Liyun Zhou.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, L., Yang, C. Differences in the effects of seller-initiated versus buyer-initiated crowded trades in stock markets. J Econ Interact Coord 14, 859–890 (2019). https://doi.org/10.1007/s11403-019-00264-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11403-019-00264-3

Keywords

JEL Classification

Search

Navigation