The One Factor To Save Them All–Leverage

The One Factor To Save Them All–Leverage

January 12, 2015 Research Insights
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(Last Updated On: January 18, 2017)

Financial Intermediaries and the Cross-Section of Asset Returns

Abstract:

Financial intermediaries trade frequently in many markets using sophisticated models. Their marginal value of wealth should therefore provide a more informative stochastic discount factor (SDF) than that of a representative consumer. Guided by theory, we use shocks to the leverage of securities broker-dealers to construct an intermediary SDF. Intuitively, deteriorating funding conditions are associated with deleveraging and high marginal value of wealth. Our single-factor model prices size, book-to-market, momentum, and bond portfolios with an R2 of 77% and an average annual pricing error of 1%—performing as well as standard multifactor benchmarks designed to price these assets.

Alpha Highlight:

Financial intermediaries, such as broker-dealers, trade with high frequency, low transaction cost, and based on sophisticated models. Adjustments to broker-leader leverage is thus a signal of changes in the underlying economic conditions in the economy.

In this paper, the authors provide empirical evidence that risk exposure to broker-dealer leverage shocks can explain cross-sectional return premiums. Value, size, beta, momentum, be damned…

First, Broker-dealer (BD) leverage is calculated as follows:

2014-10-21 17_11_45-Presentation4 - Microsoft PowerPoint (Product Activation Failed)

Data can be found from Table L.128 (Q) “Security Brokers and Dealers” in Z.1 “Financial Accounts of the United States” report published by Federal Reserve Website. The data is released during the second week of March, June, September, and December. Below graph shows the download page: Step 1, Select L. 128 (Q); Step 2, Choose Format package to choose time period. Click here to download data.

2014-11-06 10_26_54-FRB Z1_ Data Download Program - Choose

The graph below from the paper indicates that during crisis, volatility and margin requirements increase, which may push broker-dealers to reduce leverage.

2014-11-06 10_19_50-Financial Intermediaries and the Cross-Section of Asset Returns.pdf - Adobe Rea
The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request.

Next the paper creates a broker-dealer leverage factor, which describes the “shock” to log leverage (seasonally adjusted). The paper predicts that this factor will explain differences in returns across assets. The authors test their factor on 41 test assets: 25 size and B/M portfolios, 10 momentum portfolios, and 6 bond portfolios.

2014-10-23 14_11_51-Financial Intermediaries and the Cross-Section of Asset Returns.pdf - Adobe Rea

Key findings:

The broker-dealer leverage risk factor outperforms CAPM, FF, FF+Mom, and other multi-factor models.

2014-10-22 16_47_05-Financial Intermediaries and the Cross-Section of Asset Returns.pdf - Adobe Rea
The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request.

How does the model price various portfolio sorts?

Besides the highest momentum portfolio (Mom 10), the test assets line up very close to 45-degree line. –> good prediction!

2014-10-22 17_27_16-Financial Intermediaries and the Cross-Section of Asset Returns.pdf - Adobe Rea
The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request.

 


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Definitions of common statistics used in our analysis are available here (towards the bottom)




About the Author

Wesley R. Gray, Ph.D.

After serving as a Captain in the United States Marine Corps, Dr. Gray received a PhD, and was a finance professor at Drexel University. Dr. Gray’s interest in entrepreneurship and behavioral finance led him to found Alpha Architect. Dr. Gray has published three books: EMBEDDED: A Marine Corps Adviser Inside the Iraqi Army, QUANTITATIVE VALUE: A Practitioner’s Guide to Automating Intelligent Investment and Eliminating Behavioral Errors, and DIY FINANCIAL ADVISOR: A Simple Solution to Build and Protect Your Wealth. His numerous published works has been highlighted on CBNC, CNN, NPR, Motley Fool, WSJ Market Watch, CFA Institute, Institutional Investor, and CBS News. Dr. Gray earned an MBA and a PhD in finance from the University of Chicago and graduated magna cum laude with a BS from The Wharton School of the University of Pennsylvania.


  • Interesting. I know some industry economists watch this metric. But is it cause, or effect, or is there an underlying cause of both factor behaviour and broker-dealer leverage? I suspect a combination of effect and underlying-cause. Market falls spook broker-dealers, and broker-dealers react to the same things as market investors.

  • Eric

    From the chart it looks as though leverage goes down at the same time that the market goes down – but the chart is small so it is hard to tell. Do changes it leverage actually precede changes in the market because if not this probably isn’t predictive?

  • John,
    You are asking the million dollar questions…I don’t think anyone has the definitive answer.

  • I think the authors main point is that sensitivity to broker dealer leverage seems to explain the cross-sectional variation in stock returns. In non geek speak: you can describe why firm X and firm Y have different returns–at a given point in time–because of their varying sensitivity to broker leverage.