![Average returns in different bins of leverage (DLV). This figure presents average gross and net DRRs (in basis points) for different DLV bins. For each group of investors (all, unskilled, and skilled), we first separate all investor‐day‐level observations into 10 bins by DLV deciles. Reported are the equally weighted averages within each bin. The left (right) panels show results for gross (net) returns. Unskilled and skilled investors are identified in Section II.C.1. [Color figure can be viewed at wileyonlinelibrary.com]](https://www.researchgate.net/publication/379033259/figure/fig1/AS:11431281472254842@1748515912363/Average-returns-in-different-bins-of-leverage-DLV-This-figurepresents-average-gross_Q320.jpg)
![Average return (in basis points) around forced buy (left plots) trades and forced sell (right plots) trades. This figure depicts results of an event study around investors' forced offsets. We plot average returns on VWAP (volume‐weighted average price). Returns are calculated as the simple return with respect to the transaction price of the forced trade, rVWAP=VWAP−P0P0$r_{VWAP}=\frac{\text{VWAP}-P_0}{P_0}$, where P0$P_0$ is the transaction price of the forced trade. The average return after 60 minutes of forced liquidations is −19.34 (50.63) bps for forced buys (sells). The average return after 90 minutes of forced liquidations is −20.07 (47.24) bps for forced buys (sells). The average return after 120 minutes of forced liquidations is −20.39 (47.77) bps for forced buys (sells). [Color figure can be viewed at wileyonlinelibrary.com]](https://www.researchgate.net/publication/379033259/figure/fig2/AS:11431281472217538@1748515912537/Average-return-in-basis-points-around-forced-buy-left-plots-trades-and-forced-sell_Q320.jpg)
![VWAP around investors' open trades. This figure depicts results from an event study around investors' open buys and sells, separately. We plot the average returns of VWAP (volume‐weighted average price) together with their 99% confidence intervals. Returns are calculated as the simple return with respect to the transaction price of the current trade rVWAP=VWAP−P0P0$r_{VWAP}=\frac{\text{VWAP}-P_0}{P_0}$, where P0$P_0$ is the transaction price of the current trade. The upper panel depicts results for unskilled investors, and the lower panel shows results for skilled investors. Unskilled and skilled investors are identified as in Section II.C.1. The upper (lower) horizontal dotted line in each plot represents investors' average transaction cost of buy‐then‐sell (sell‐then‐buy). [Color figure can be viewed at wileyonlinelibrary.com]](https://www.researchgate.net/publication/379033259/figure/fig3/AS:11431281472217539@1748515912696/VWAP-around-investors-open-trades-This-figuredepicts-results-from-an-event-study-around_Q320.jpg)
![Average returns in different bins of number of transactions (NTR) and leverage (DLV) for skilled investors. We perform dependent double sorts across all investor‐day observations. Only skilled investors' observations are included in this analysis. Skilled investors are identified as in Section II.C.1. Specifically, we first sort all observations into NTR deciles. Then, in each NTR decile, we sort observations into DLV quintiles. For each double‐sorted group, we report equally weighted average DRRs in basis points. The upper (bottom) panel shows results for gross (net) returns. [Color figure can be viewed at wileyonlinelibrary.com]](https://www.researchgate.net/publication/379033259/figure/fig4/AS:11431281472274192@1748515912879/Average-returns-in-different-bins-of-number-of-transactions-NTR-and-leverage-DLV-for_Q320.jpg)
A preview of this full-text is provided by Wiley.
Content available from The Journal of Finance
This content is subject to copyright. Terms and conditions apply.
THE JOURNAL OF FINANCE •VOL. LXXIX, NO. 2 •APRIL 2024
Leverage Is a Double-Edged Sword
AVANIDHAR SUBRAHMANYAM, KE TANG, JINGYUAN WANG,
and XUEWEI YANG
ABSTRACT
We use proprietary data on intraday transactions at a futures brokerage to analyze
how implied leverage influences trading performance. Across all investors, leverage is
negatively related to performance, due partly to increased trading costs and partly to
forced liquidations resulting from margin calls. Defining skill out-of-sample, we find
that relative performance differentials across unskilled and skilled investors persist.
Unskilled investors’ leverage amplifies losses from lottery preferences and the dis-
position effect. Leverage stimulates liquidity provision by skilled investors, and en-
hances returns. Although regulatory increases in required margins decrease skilled
investors’ returns, they enhance overall returns, and attenuate return volatility.
Avanidhar Subrahmanyam is with Anderson School of Management at UCLA. Ke Tang is with
Institute of Economics (School of Social Sciences) and PBC School of Finance, Tsinghua University.
Jingyuan Wang is with School of Economics and Management and MIIT Key Laboratory of Data
Intelligence and Management, Beihang University. Xuewei Yang is with School of Management
and Engineering and Institute of New Finance, Nanjing University. We are grateful for construc-
tive comments from two anonymous referees, Stefan Nagel (the Editor), and an Associate Editor.
We thank the anonymous brokerage firm for providing the data used in this study. We appreci-
ate comments from Bala Balachandran; Hank Bessembinder; Michael Brennan; Hui Bu; Libing
Fang; Xu Feng (Discussant); Mark Grinblatt; Haoyu Gao; Valentin Haddad; Bing Han; Xuezhong
He; Bernard Herskovic; Jieying Hong; Chris James; George Jiang; Petko Kalev; Chenxu Li; Qiang
Li; Ping Li; Xiaoquan Liu; Francis Longstaff; Muhammad Al Mamun; M. Nimalendran; Lasse
Pedersen; Cameron Peng; Baolian Wang; Bin Wang; Jia Zhai; Qunzi Zhang; Xiaoyan Zhang; Hao
Zhou; and seminar participants at Beihang University, UCLA, CUEB (Beijing, China), Chongqing
University, UESTC, University of Florida, Guangdong University of Finance, Jinan University, La
Trobe University, NJUST, University of Nottingham (Ningbo, China), Renmin University of China,
Shandong University, Tsinghua University, Xi’an Jiaotong University, XJTLU, Xidian University,
Zhejiang University, the 2021 Academy of Behavioral Finance and Economics meeting, the 2021
Annual Meeting of Quantitative Finance and Insurance Society of China, and the 2021 Annual
Meeting of the Society of Management Science and Engineering of China. We also thank Zimeng
Li for his help with the data, and Peng Zhu for his outstanding research assistance. Ke Tang ac-
knowledges support from the National Natural Science Foundation of China (Number 71973075).
Jingyuan Wang acknowledges support from the National Natural Science Foundation of China
(Numbers 72222022, 72171013, 72242101). Xuewei Yang acknowledges support from the National
Natural Science Foundation of China (Numbers 72122008, U1811462, 71720107001, 71771115,
11961141009). We have read The Journal of Finance disclosure policy and have no conflicts of
interest to disclose.
Correspondence: Xuewei Yang, School of Management and Engineering, Nanjing University, #5
Pingcang Lane, Gulou District, Nanjing 210093, Jiangsu, China;
e-mail: xwyang@nju.edu.cn
DOI: 10.1111/jofi.13316
© 2024 the American Finance Association.
1579
