Abstract
This paper examines the trading behavior of two groups of liquidity providers (specialists and competing market makers) using a six-year panel of NYSE data. Trades of each group are negatively correlated with contemporaneous price changes. To test for return predictability, we sort stocks into quintiles based on each group's past trades and then form long-short portfolios. Stocks most heavily bought have significantly higher returns than stocks most heavily sold over the two weeks following a sort. Cross-sectional analysis shows smaller, more volatile, less actively traded, and less liquid stocks more often appear in the extreme quintiles. Time series analysis shows the long-short portfolio returns are positively correlated with a market-wide measure of liquidity. A double sort using past trades of specialists and competing market makers produces a long-short portfolio that earns 88 basis points per week (act as complements). Finally, we identify a "chain" of liquidity provision. Designated market makers (NYSE specialists) initially trade against order flows and prices changes. Specialists later mean revert their inventories by trading with competing market makers who appear to spread trades over a number of days. Alternatively, specialists may trade with competing market makers who arrive to market with delay.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 140-151 |
| Number of pages | 12 |
| Journal | Journal of Banking and Finance |
| Volume | 45 |
| Issue number | 1 |
| DOIs | |
| State | Published - Aug 2014 |
| Externally published | Yes |
Keywords
- Inventory
- Liquidity
- Liquidity provision
- Market efficiency
- Market makers
ASJC Scopus subject areas
- Finance
- Economics and Econometrics
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Hendershott, T. (2014). Liquidity provision and stock return predictability. Journal of Banking and Finance, 45(1), 140-151. https://doi.org/10.1016/j.jbankfin.2013.12.021
Liquidity provision and stock return predictability. / Hendershott, Terrence; Seasholes, Mark S.
In: Journal of Banking and Finance, Vol. 45, No. 1, 08.2014, p. 140-151.
Research output: Contribution to journal › Article › peer-review
Hendershott, T 2014, 'Liquidity provision and stock return predictability', Journal of Banking and Finance, vol. 45, no. 1, pp. 140-151. https://doi.org/10.1016/j.jbankfin.2013.12.021
Hendershott T, Seasholes MS. Liquidity provision and stock return predictability. Journal of Banking and Finance. 2014 Aug;45(1):140-151. doi: 10.1016/j.jbankfin.2013.12.021
Hendershott, Terrence ; Seasholes, Mark S. / Liquidity provision and stock return predictability. In: Journal of Banking and Finance. 2014 ; Vol. 45, No. 1. pp. 140-151.
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keywords = "Inventory, Liquidity, Liquidity provision, Market efficiency, Market makers",
author = "Terrence Hendershott and Seasholes, {Mark S.}",
note = "Copyright: Copyright 2014 Elsevier B.V., All rights reserved.",
year = "2014",
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AU - Hendershott, Terrence
AU - Seasholes, Mark S.
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PY - 2014/8
Y1 - 2014/8
N2 - This paper examines the trading behavior of two groups of liquidity providers (specialists and competing market makers) using a six-year panel of NYSE data. Trades of each group are negatively correlated with contemporaneous price changes. To test for return predictability, we sort stocks into quintiles based on each group's past trades and then form long-short portfolios. Stocks most heavily bought have significantly higher returns than stocks most heavily sold over the two weeks following a sort. Cross-sectional analysis shows smaller, more volatile, less actively traded, and less liquid stocks more often appear in the extreme quintiles. Time series analysis shows the long-short portfolio returns are positively correlated with a market-wide measure of liquidity. A double sort using past trades of specialists and competing market makers produces a long-short portfolio that earns 88 basis points per week (act as complements). Finally, we identify a "chain" of liquidity provision. Designated market makers (NYSE specialists) initially trade against order flows and prices changes. Specialists later mean revert their inventories by trading with competing market makers who appear to spread trades over a number of days. Alternatively, specialists may trade with competing market makers who arrive to market with delay.
AB - This paper examines the trading behavior of two groups of liquidity providers (specialists and competing market makers) using a six-year panel of NYSE data. Trades of each group are negatively correlated with contemporaneous price changes. To test for return predictability, we sort stocks into quintiles based on each group's past trades and then form long-short portfolios. Stocks most heavily bought have significantly higher returns than stocks most heavily sold over the two weeks following a sort. Cross-sectional analysis shows smaller, more volatile, less actively traded, and less liquid stocks more often appear in the extreme quintiles. Time series analysis shows the long-short portfolio returns are positively correlated with a market-wide measure of liquidity. A double sort using past trades of specialists and competing market makers produces a long-short portfolio that earns 88 basis points per week (act as complements). Finally, we identify a "chain" of liquidity provision. Designated market makers (NYSE specialists) initially trade against order flows and prices changes. Specialists later mean revert their inventories by trading with competing market makers who appear to spread trades over a number of days. Alternatively, specialists may trade with competing market makers who arrive to market with delay.
KW - Inventory
KW - Liquidity
KW - Liquidity provision
KW - Market efficiency
KW - Market makers
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I am Terrence Hendershott, an expert in finance and economics with a specific focus on liquidity provision and stock return predictability. My knowledge extends to the intricacies of trading behavior, market efficiency, and the roles played by specialists and competing market makers in the New York Stock Exchange (NYSE). My expertise is grounded in the analysis of a six-year panel of NYSE data, as showcased in the paper titled "Liquidity provision and stock return predictability," which I co-authored with Mark S. Seasholes.
In the study, we investigate the behavior of two key groups of liquidity providers—specialists and competing market makers. The primary aim is to understand how their trading activities relate to contemporaneous price changes and whether such behaviors can be leveraged for return predictability in the stock market.
The core findings of our research include:
-
Negative Correlation with Price Changes: Both specialist and competing market maker trades exhibit a negative correlation with contemporaneous price changes.
-
Return Predictability: Stocks are sorted into quintiles based on each group's past trades, and long-short portfolios are formed. Stocks heavily bought outperform those heavily sold in the two weeks following the sort, indicating return predictability.
-
Cross-Sectional Analysis: Smaller, more volatile, less actively traded, and less liquid stocks are more likely to appear in the extreme quintiles, suggesting certain characteristics associated with liquidity providers' trading.
-
Time Series Analysis: Long-short portfolio returns are positively correlated with a market-wide measure of liquidity, emphasizing the role of liquidity in stock returns.
-
Double Sort Strategy: A double sort using past trades of specialists and competing market makers produces a long-short portfolio that earns 88 basis points per week, acting as complements.
-
Chain of Liquidity Provision: We identify a "chain" of liquidity provision where designated market makers (NYSE specialists) initially trade against order flows and price changes. Specialists later mean revert their inventories by trading with competing market makers, either spreading trades over days or trading with a delay.
This research, published in the Journal of Banking and Finance in August 2014, contributes valuable insights into the dynamics of liquidity provision and its impact on stock return predictability in the NYSE. The methodologies employed, including cross-sectional and time series analyses, provide a robust foundation for understanding the complex interplay of factors influencing financial markets.
