Predatory Trading and Crowded Exits

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James Clunie. Predatory Trading and Crowded Exits

Publishing Details

About the Author

Acknowledgements

Preface. What this book is about

Who this book is for

How the book is structured

Introduction

Chapter 1. The Ecology of Markets. Fair value

Many financial models assume a fair value exists

The problem of simplifying assumptions

Informed traders versus noise traders

Why smart arbitrageurs don’t always win…

The role of clients

Delayed arbitrage

Tidal waves and market bubbles

Don’t be a hero!

Reverse broking

More complicated worlds

The ecology of markets

Ever-changing cycles

Adaptive markets

Cross-market trading

Free money

Figure 1.1 - Barclays PLC share price, relative share price and trading volume around 31 October 2008 MCN issue

Figure 1.2 - Stock lending activity around 31 October 2008 MCN issue

Short-sale constraints

To what extent do short-sale constraints play a role in limiting arbitrage?

What next?

Endnotes

Chapter 2. Predatory trading

Margin call

Loan covenants

Regulatory limits on financial companies

Predictable behaviour

Metallgesellschaft AG

Futures roll-overs

Open-ended funds

Fire sales

The perfect predator

Brunnermeier and Pedersen model

Figure 2.1 - Price over time for a risky asset subject to liquidation and predatory trading

Short sellers

SOES

LTCM

Credit downgrades

Figure 2.2 - RBS downgraded to BB by S&P on 19 January 2009

Figure 2.3 - ABN AMRO downgraded to BB+ by S&P on 20 January 2009

Multiple predators, multiple prey

Predatory trading and stock price manipulation

Strategic trader vs the arbs

A closer look at index-fund predation

US evidence

S&P 500 index reviews

The UK is different

Analysis of FTSE 350 Index

Table 2.1 - Summary statistics for additions/deletions to FTSE 350 Index

Figure 2.4 - Cumulative abnormal returns around the revision and implementation dates of additions to the FTSE 350 Index

Figure 2.5 - Cumulative abnormal returns around the revision and implementation dates of deletions to the FTSE 350 Index

Table 2.2 - Price effects around the revision date and implementation dates for additions to (and deletions from) the FTSE 350 Index

Index funds evolve…

Risks in anticipating index revisions

Revision risk

Fundamental risk

Conditions that suit index-fund predation

Dimensional Fund Advisors (DFA) 9-10 Fund

The ethics of predatory trading

The regulator’s view

What about the ethics of predatory trading?

Different ethical perspectives

Virtue ethics

Consequentialist perspective

Contractualism

But should we expect market participants to behave ethically in the sense meant by ethicists?

A framework for testing the ethics of predatory trading practices

Example

Table 2.3: Ethical test matrix for example case

Changing the player

Changing the nature of the information

Chinese walls

Endnotes

Chapter 3. Crowded Exits

Punch Taverns

Figure 3.1 - Percentage of shares outstanding on loan for Punch Taverns (April 2007 – April 2009)

Figure 3.2 - Ratio of the number of shares on loan to the normal number of shares traded each day (days to cover ratio) for Punch Taverns (April 2007 – 2009)

Rational imitation strategy

Causes of crowded exits

Data

Using stock-lending data to examine the risks in short-selling

Crowded positions

Days-to-cover ratio

Table 3.1 - Portfolios based on simple sorts

Returns on the high DCR portfolios

Table 3.2 - Presents the cumulative abnormal returns associated with portfolios of stocks where short positions are ‘crowded’

Crowded exits

Methodology for identifying exceptional short covering

RNS announcements

Filtering possible arbitrage stocks

Estimating cumulative abnormal returns

Table 3.3 - Abnormal Returns around Crowded Exits

Results

Summary

A warning about using empirical evidence to develop quantitative strategies

Counter-performativity

Does it matter if stock prices occasionally diverge from equilibrium levels?

Endnotes

Chapter 4. Stop Losses

Why use stop losses?

1. Client confidence

2. Risk-control mechanisms

3. Momentum strategies

Loss-realisation aversion

A problem with stop losses

How do stop losses influence portfolio returns?

Figure 4.1 - Simulated stock returns without stop-loss rule

Figure 4.2 - Simulated stock returns with stop-loss rule

Figure 4.3 - Changes in the simulated return distribution caused by a stop-loss rule

Figure 4.4 - Distribution of monthly stock returns

Figure 4.5 - Stock returns without stop-loss rule

Figure 4.6 - Stock returns with stop-loss rule

Figure 4.7 - Conditional distribution of stock returns post stop-loss limit breach

Figure 4.8 - Cost to portfolio performance from using a stop loss rule

Profit taking

US data

Figure 4.9 - Cumulative performance for randomly selected portfolios using a -15% stop-loss rule

Figure 4.10 - Distribution of monthly returns for randomly-selected portfolios using a -15% stop-loss rule

Table 4.1 - Results for applying various stop-loss rules to randomly-selected portfolios of us stocks

An aversion to realising losses

What about the professionals?

Not everyone is averse to realising losses

Does this impact asset prices?

Short-sellers and stop losses

Do short-sellers cover in response to book losses?

Methodology

Equation 4.1

Table 4.2 - Results using log of market cap on loan as dependent variable

Table 4.3 - Dependent variable: market capitalisation on loan

Does the use of stop losses hurt short-sellers’ returns?

Table 4.4 - Cumulative abnormal returns after short covering

Short-sellers cover in response to book losses. Why?

Portfolio diversification

Tax

Capital constraints

Myopic loss aversion

Endnotes

Chapter 5. Manipulation

Citigroup

1. Trade-based manipulation

2. Information-based manipulation

3. Action-based manipulation

Stock pools

Identifying manipulation

Manipulation around share issues

Case study: a placing of stock

Figure 5.1 - Share price, market relative share price and turnover by shares for Scottish and Southern Energy around its share placing in January 2009

Case study: an underwritten rights issue

Figure 5.2 - Share price, market relative share price and turnover by shares around the 2008 Centrica rights Issue

Figure 5.3 - Stock lending activity around the 2008 Centrica rights Issue

Manipulating the shorts

Volkswagen AG

Short squeezes

Figure 5.4 - Illustrates the relationship between ‘crowded positions’, ‘crowded exits’, ‘short squeezes’ and ‘manipulative short squeezes’

Characteristics of a manipulative short squeeze

Definition of an ‘apparent manipulative short squeeze’

Separating apparent manipulative short squeezes from noise trading

Estimating abnormal returns around apparent manipulative short squeezes

Figure 5.5 - Timeline representing the three phases of an apparent manipulative short squeeze

Results

Figure 5.6 - Abnormal returns around apparent manipulative short squeezes

Figure 5.7 - Cumulative abnormal returns by day (starting from day -3)

Robustness checks

An alternative approach

Characteristics of stock subject to apparent manipulative short squeezes

Could knowledge of these characteristics assist in predicting manipulative short squeezes?

The Volkswagen case

Figure 5.8 - Market data for Volkswagen AG shares (autumn 2008)

Figure 5.9 - Percentage of shares outstanding on loan, stock-loan utilisation rate and average stock-loan fee for Volkswagen AG ordinary shares around the event date (day 0)

Endnotes

Chapter 6. Final Thoughts

Flexibility

Predatory trading

Ethics

Forced traders

Only the paranoid survive

Ever-changing cycles

Short selling

Manipulation

Stop losses

Appendix 1. The Market Model

Endnote

Appendix 2. Abnormal returns

Bibliography

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James Clunie works at Scottish Widows Investment Partnership (SWIP), where he is responsible for managing a UK equity long-short fund and a long-only fund. Previously, he was at the University of Edinburgh for four years, conducting research into stock lending and short-selling. He also set up and ran their Masters programme in Finance and Investment. Prior to this, Clunie worked at Murray Johnstone International, where he was head of asset allocation, and at Aberdeen Asset Management, where he was head of global equities. He graduated with a BSc (Hons) in Mathematics and Statistics and recently completed his PhD on indirect short-selling constraints, both at the University of Edinburgh. He is a chartered financial analyst.

Special thanks go to Nelly Terekhova for her research assistance on this book.

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The trader asked his assistant to construct a spreadsheet of recent prices of the two bonds, which supported the view that it was indeed an anomaly and thus a trading opportunity. Having first made the necessary purchases and short sales to take advantage of it, the trader then phoned a contact in an investment bank to direct his attention to the anomaly – ‘There is at least half a point in that trade, and there is zero market risk’ – and sent him the spreadsheet.

The purpose of this activity is to encourage dissemination of the idea and to alert other arbitrageurs to the opportunity. This has two effects: first, it lowers the risk of greater divergence of the position from fair value, so limiting margin calls and the risk of performance-based arbitrage. Secondly, it might bring the trades of other actors forward in time, thus reducing synchronisation risk. This suggests a social dimension to arbitrage, well beyond simply identifying mis-priced securities. Where such reverse broking is based on the interpretation of factual information (as opposed to false rumours) it is an entirely legitimate activity.

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