Trading on Sentiment E-Book

Richard L. Peterson

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Serie: Wiley Finance Editions
• Sprache: Englisch

Seitenzahl: 543

Veröffentlichungsjahr: 2016

Beschreibung

In his debut book on trading psychology, Inside the Investor’s Brain, Rich­ard Peterson demonstrated how managing emotions helps top investors outperform. Now, in Trading on Sentiment, he takes you inside the science of crowd psychol­ogy and demonstrates that not only do price patterns exist, but the most predictable ones are rooted in our shared human nature.

Peterson’s team developed text analysis engines to mine data - topics, beliefs, and emotions - from social media.  Based on that data, they put together a market-neutral social media-based hedge fund that beat the S&P 500 by more than twenty-four percent—through the 2008 financial crisis. In this groundbreaking guide, he shows you how they did it and why it worked. Applying algorithms to so­cial media data opened up an unprecedented world of insight into the elusive patterns of investor sentiment driving repeating market moves. Inside, you gain a privi­leged look at the media content that moves investors, along with time-tested techniques to make the smart moves—even when it doesn’t feel right. This book digs underneath technicals and fundamentals to explain the primary mover of market prices - the global information flow and how investors react to it.  It provides the expert guidance you need to develop a competitive edge, manage risk, and overcome our sometimes-flawed human nature.  Learn how traders are using sentiment analysis and statistical tools to extract value from media data in order to:

• Foresee important price moves using an understanding of how investors process news.
• Make more profitable investment decisions by identifying when prices are trending, when trends are turning, and when sharp market moves are likely to reverse.
• Use media sentiment to improve value and momentum investing returns.
• Avoid the pitfalls of unique price patterns found in commodities, currencies, and during speculative bubbles

Trading on Sentiment deepens your understanding of markets and supplies you with the tools and techniques to beat global markets— whether they’re going up, down, or sideways.

Leseprobe

Table of Contents

Title Page

Copyright

Dedication

About the Author

Preface

Mathematical Mayhem

Framing the Issue

Trading on Sentiment

The Book

Notes

Acknowledgments

Part One: Foundations

Chapter 1: Perception and the Brain

A Long Estrangement

The Beauty Contest

What Moves Traders?

Chained to the Mast

The Brain: Structure and Function

Emotion versus Reason

In Summary

Notes

Chapter 2: Mind and Emotion

Crowds Moving Markets

Emotional Priming

Feelings and Finance

How Emotions Move Traders

Arousal, Stress, and Urgency

Anger, Fear, and Gloom

Information Impact

In Summary

Notes

Chapter 3: Information Processing

Media and the Flash Crash

Diversity Breakdowns

Price Patterns

Information Characteristics

A Scarce Resource

What's in a Name?

“All That Glitters”

Profiting From Social Inattention

In Summary

Notes

Chapter 4: Sentimental Markets

Reflexivity

Sentiment

Understanding Media

The Thomson Reuters MarketPsych Indices

In Summary

Notes

Chapter 5: Finding Signal in the Noise

Can Investment Research Be Believed?

Data Biases

Nonlinear Characteristics

Exploring Sentiment Data

Statistical Models

Cross-Sectional Models

Decision Trees

Moving Average Crossovers

Can Sentiment Be Trusted?

In Summary

Notes

Part Two: Short-term Patterns

Chapter 6: Information Impact

The Need for Speed

Bankruptcy Deja Vu

Faster than a Speeding Specialist

A Rehash of the #HashCrash

Breaking News

Leaking News

News Momentum

Gross National Happiness

Social Sentiment

The Human Advantage

In Summary

Notes

Chapter 7: Daily Reversals

Social Media and Insider Trading

Daily Reversals Research

Global Price Forecasts

In Summary

Notes

Chapter 8: Weekly Deceptions

Weekly Reversals

Emotions in Markets

Tricky Sentiments

The Lessons of Magic

In Summary

Notes

Chapter 9: The Only Thing to Fear

Estimates of Fear

To Catch a Falling Knife

Good News for People Who Love Bad News

Panics and Bounces

The Fear Factor in Markets

In Summary

Notes

Chapter 10: Buy on the Rumor

Price Forecasts

Sweet Anticipation

Earnings and IPOs

The Neuroscience of Disappointed Expectations

Tricks of the Stock Promoters' Trade

In Summary

Notes

Part Three: Long-term Patterns

Chapter 11: Trends and Price Momentum

What Causes Trends?

Investor Underreaction

Timing Trends with Moving Averages

Augmented Momentum

Going with the Flow

In Summary

Notes

Chapter 12: Value Investing

The Manic-Depressive Mr. Market

Value Investing

Selling to Optimists

Value Traps and Catalysts

In Summary

Notes

Chapter 13: Anger and Mistrust

Anger under the Microscope

The Value of Anger

Trust

Who Trusts Bankers?

The Trust Factor

Trust and Forgiveness

In Summary

Notes

Chapter 14: The Psychology of Leadership

Blaming Management

The Emotional Value of Human Sacrifice

Superstar CEOs

Buy Mistrusted Leadership

Buy Unstable Governments

Using Text Analytics to Improve Executive Communication

Conclusion

In Summary

Notes

Chapter 15: Navigating Uncertainty

How Investors Deal with Uncertainty

The Warrior Gene

Uncertainty about Asset Prices

Uncertainty at Boeing

Uncertainty across Equities

Certainly Doubtful

In Summary

Notes

Part Four: Complex Patterns and Unique Assets

Chapter 16: Optionality

Optionality and Wealth Creation

Habits Are Hard to Break

Catalysts

Detecting Optionality

In Summary

Notes

Chapter 17: Blowing Bubbles

A Brief History of Bubbles

Irrational Exuberance

Laboratory Bubbles

Staging a Bubble

The Bubble Checklist

In Summary

Notes

Chapter 18: Timing Bubble Tops

How Bubbles Pop

Brains of Steel

The Peak-End Rule

The Bubbleometer

Arbitraging Stock Speculation

Global Mean-Reversion

Keeping the House Money

In Summary

Notes

Chapter 19: Commodity Sentiment Analysis

What Drives Oil Prices?

The Predictability of Fear, Violence, and Oil Price Volatility

When the Heat Stays On

Commodity Psychology

In Summary

Notes

Chapter 20: Currency Characteristics

The Value of Uncertainty

Information Flow

Timing Currencies with MACDs

Using Currency Sentiment in Trading

In Summary

Notes

Chapter 21: Economic Indicators

Earnings Forecasts

Predicting Economic Activity

Quantifying Economic Pressures

News Flow as a Leading Economic Indicator

MPMI Results

Live Nowcasting

New Economic Indicators

In Summary

Notes

Chapter 22: Sentiment Regimes

Regime Dependency

Strategy-Shifting

Anomalies by Regime

Emotion versus Fact

In Search of Consistency

In Summary

Notes

Part Five: Managing the Mind

Chapter 23: Mental Hygiene

Traits of Super-Forecasters

Adaptability

The Power of Not Knowing

Stress Management Is Risk Management

Facing Your Fears

Reversing Stress

In Summary

Notes

Postscript

Appendix A: Understanding the Thomson Reuters MarketPsych Indices

Source Type Customization

Lexical Analysis

Entity Identification and Correlate Filtering

Linguistic Analysis Flow

Sentence-Level Example

Creating an Index

Source Text

Index Construction

Asset Classes Covered

TRMI Definitions

Visual Validation

Notes

Appendix B: Methods for Modeling Economic Activity

Professional Economic News vs. Social Media

Analysis of Single TRMI

Testing Methodology

Selected Models and Algorithms Tuning

Results Table

Notes

Glossary

Index

End User License Agreement

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Guide

Table of Contents

Begin Reading

List of Illustrations

Chapter 1: Perception and the Brain

Figure 1.1 “Ulysses and the Sirens.” Herbert James Draper, 1909.

Figure 1.2 A depiction of the brain. The limbic system is seen situated underneath the cortex. The prefrontal cortex lies behind the forehead.

Chapter 2: Mind and Emotion

Figure 2.1 S&P 500 monthly price bars versus media sentiment moving averages (200- and 500-day) from January 1998 through January 2015.

Figure 2.2 The sentiment-derived Thomson Reuters MarketPsych Indices (TRMI) plotted on the affective circumplex.

Chapter 3: Information Processing

Figure 3.1 Sentiment about the S&P 500 expressed in news and social media, simple 60-minute average, on May 6, 2010.

Figure 3.2 Fear about the S&P 500 expressed in news and social media, simple 60-minute average, on May 6, 2010.

Figure 3.3 Price impact of pre-market Reuters news conditioned upon social media buzz.

Figure 3.4 Price impact of intraday Reuters MRG news alerts conditioned upon social media (SM) buzz.

Chapter 4: Sentimental Markets

Figure 4.1 Infographic explaining large-scale text analytics.

Chapter 5: Finding Signal in the Noise

Figure 5.1 An image of the Turkish lira (TRY) price (dark line) versus the Buzz TRMI from October 1, 2013, through May 4, 2014.

Figure 5.2 Histogram of daily Fear TRMI values for individual stocks from 1998 to 2015.

Figure 5.3 Natural Gas (Henry Hub) prices plotted against MACD 10–30 supplyVsDemand TRMI averages, October 1, 2013, to May 4, 2014.

Figure 5.4 Weekly S&P 500 candlestick prices versus two 500-day moving averages of the US500 Sentiment TRMI for news and social media.

Figure 5.5 Monthly expressions of optimism in social media for the S&P 500. Horizontal bars represent the monthly 17-year average optimism. Wavy lines represent the month-by-month optimism from 1998 to 2014.

Figure 5.6 Correlation matrix for selected daily S&P 500 (US500) TRMI.

Figure 5.7 An equity curve derived by arbitraging the weekly average media Sentiment TRMI for individual U.S. stocks.

Chapter 6: Information Impact

Figure 6.1 The impact of nonfarm payrolls on the U.S. dollar futures contract over minutes.

Figure 6.2 The impact of nonfarm payrolls on the U.S. dollar futures contract over milliseconds.

Figure 6.3 News of United Airline's 2002 bankruptcy filing moved the stock price dramatically when republished in 2008 on Bloomberg.

Figure 6.4 The S&P 500 on April 23, 2013, the day of the #HashCrash.

Figure 6.5 Merger news impact and stock price returns.

Figure 6.6 An equity curve derived by arbitraging the daily average news optimism TRMI for individual U.S. stocks.

Figure 6.7 An equity curve derived by arbitraging the daily average news earningsForecast TRMI for individual U.S. stocks.

Figure 6.8 An equity curve derived by arbitraging the daily average news sentiment TRMI for individual Canadian stocks.

Chapter 7: Daily Reversals

Figure 7.1 Breakdown of stock price predictions in social media by positive or negative direction.

Figure 7.2 Next-day accuracy of social media stock price predictions for a Fortune 100 stock.

Chapter 8: Weekly Deceptions

Figure 8.1 An equity curve representing weekly mean-reversion returns derived in a cross-sectional rotation model of the social media–based Sentiment TRMI.

Figure 8.2 An equity curve representing weekly mean-reversion returns from a cross-sectional model of the news media-based Trust TRMI.

Chapter 9: The Only Thing to Fear

Figure 9.1 Fear TRMI versus the candlestick price chart of the Leisure and Entertainment ETF (PEJ) during the swine flu scare in 2009.

Figure 9.2 The decline of the Turkish lira versus the Buzz TRMI, 2013–2014.

Figure 9.3 Value of the Russian ruble versus the U.S. dollar and the ruble's Buzz TRMI.

Chapter 10: Buy on the Rumor

Figure 10.1 Speculative excitement around the iPhone 5 release in 2013, evident in this MACD 30–90 of the marketRisk TRMI for Apple Inc.

Chapter 11: Trends and Price Momentum

Figure 11.1 Momentum returns from a long-only momentum strategy based on the top 5 percent by past price performance of the S&P 500 constituents.

Figure 11.2 S&P 500 candlestick chart with Sentiment TRMI MACD 200–500 from January 1, 2000 through July 1, 2015.

Figure 11.3 Crude oil price versus the priceDirection TRMI MACD (30–90), May 2014 to July 2015.

Figure 11.4 The returns of momentum strategies are boosted by innovation perceptions. The light gray line is the equity curve of Figure 11.1, included for comparison.

Chapter 12: Value Investing

Figure 12.1 Equity curve of a long-only value strategy based on the S&P 500.

Figure 12.2 Equity curve of a long-only value strategy based on the intersection of value (high E/P) and high news fear. The light gray line is the equity curve from Figure 12.1, included for comparison.

Chapter 13: Anger and Mistrust

Figure 13.1 A depiction of the Groupon (GRPN) share price versus a MACD (90–200) of media anger. Surges in anger are correlated with a falling share price, while declines are associated with price rises.

Figure 13.2 A depiction of the declining media trust in Petrobras following revelations of corruption at the company; a simple 90-day and 200-day average of trust are superimposed on the stock price.

Figure 13.3 A chart of Barclay's stock price with a MACD (30–200) of the Trust TRMI superimposed. The LIBOR-rigging scandal caused a sharp fall in stock price and trust, both of which quickly rebounded.

Figure 13.4 An equity curve derived from arbitraging monthly news trust across U.S. stocks.

Figure 13.5 An equity curve derived from arbitraging trust across global stock indexes in a monthly rotation model.

Chapter 14: The Psychology of Leadership

Figure 14.1 Netflix (NFLX) share price following a change in subscription strategy. The managementTrust TRMI MACD (30–90) is plotted against the share price.

Figure 14.2 Equity curve derived from arbitraging managementTrust across the yearly news and social media about individual U.S. equities.

Figure 14.3 The value of the governmentInstability TRMI on a global heat map in 2014.

Figure 14.4 An equity curve derived via a 12-month rotating arbitrage of the primary stock indexes in the countries with the most unstable governments, as ranked by the governmentInstability TRMI, versus the most stable governments.

Chapter 15: Navigating Uncertainty

Figure 15.1 Investors who perform well on the MarketPsych Gambing Task (

y

-axis) on average report higher past investment performance (

x

-axis).

Figure 15.2 Decision tree depicting the future monthly direction of Boeing (BA) stock under differing conditions of uncertainty and volatility from 2007 to 2013.

Figure 15.3 Equity curve derived by arbitraging annual uncertainty across the most buzzed-about U.S. stocks in the media.

Chapter 17: Blowing Bubbles

Figure 17.1 The anatomy of a bubble based on the Nasdaq bubble of 1996–2002.

Chapter 18: Timing Bubble Tops

Figure 18.1 Sir Isaac Newton's investments during the South Seas bubble.

Figure 18.2 Nasdaq Composite candlestick chart versus a MACD (30–200) of the marketRisk TRMI (a.k.a. the Bubbleometer), 1998–2002.

Figure 18.3 Shanghai Composite candlestick chart versus a MACD (30–90) of the marketRisk TRMI (a.k.a. the Bubbleometer), 2014–2015.

Figure 18.4 Equity curve derived from annual arbitrage of news-derived marketRisk TMRI across individual U.S. stocks.

Figure 18.5 An equity curve produced from an annual marketRisk arbitrage across global stock indexes, based on shorting the quintile of countries with the greatest marketRisk and buying the quintile with the least, based on news media.

Chapter 19: Commodity Sentiment Analysis

Figure 19.1 The beginning of the gold bear market. Gold prices versus a Gold Sentiment MACD (90–200), November 2012 through July 2013.

Figure 19.2 A depiction of a crude oil–derived weapon called Greek Fire.

Figure 19.3 Threats to forcibly dismantle the Iranian nuclear program, and counter-threats from Iran to close the Strait of Hormuz, lead to surges in the crude oil fear index, violence index, and the price itself.

Chapter 20: Currency Characteristics

Figure 20.1 Equity curve derived from arbitraging currency uncertainty across the top eight currencies (top two long, bottom two short) in the media with 1-week look-back and prediction horizons.

Figure 20.2 Equity curve derived from arbitraging country uncertainty across the top eight currencies (top two long, bottom two short) in the media with 12-month look-back and prediction horizons.

Figure 20.3 Equity curve derived from arbitraging currency priceForecast across the top 10 currencies (top one long, bottom one short) in the media with one-week look-back and prediction horizons.

Figure 20.4 Equity curve derived from arbitraging country-level Trust across the top eight currencies in the media (top two long, bottom two short) with 12-month look-back and prediction horizons.

Figure 20.5 Japanese Yen priceForecast MACD (90–200) versus the JPY/USD, July 2012 to July 2015.

Chapter 21: Economic Indicators

Figure 21.1 Interactive map displaying the economicGrowth TRMI for the third quarter of 2015, where dark shading indicates positive economic growth, and light represents economic contraction mentioned in the media.

Figure 21.2 Plots of the U.S. PMI (top) and the 200-day simple average of the U.S. economicGrowth TRMI (from both news and social media).

Figure 21.3 PMI and MPMI with true out-of-sample fit shaded in darker gray—entire time series.

Figure 21.4 PMI and MPMI—detailed view of out-of-sample period in darker gray for the United States. Note that the high-frequency line represents the MPMI while the step function is Markit's PMI.

Figure 21.5 MPMI implementation as dashboard in a decision support system (current-day view of selected countries).

Figure 21.6 MPMI historical view for a single country (United States).

Chapter 22: Sentiment Regimes

Figure 22.1 Abnormal returns for equal-weighted, monthly quintile portfolios constructed using market beta. High-beta stocks outperform after negative market sentiment months, but underperform after positive sentiment months.

Figure 22.2 Post–earnings announcement drift in different sentiment conditions, where the thick lines represent the standard PEAD strategy results. Differentiating by sentiment environment leads to higher overall returns from the PEAD strategy.

Figure 22.3 A weekly arbitrage of the top versus bottom quintile of U.S. stocks ranked on the EmotionVsFact TRMI out of the top 100 mentioned in the media each year.

List of Tables

Chapter 4: Sentimental Markets

Table 4.1 Three Distinct Classes of Investment Media

Chapter 7: Daily Reversals

Table 7.1 One-Day Returns Derived from Trading against One Standard Deviation Changes in the priceForecast TRMI in Global Stock Indexes and Crude Oil

Chapter 17: Blowing Bubbles

Table 17.1 Conditions Fueling a Speculative Bubble

Table 17.2 Checklist for Bubble Staging

Chapter 19: Commodity Sentiment Analysis

Table 19.1 Future One-Month Oil Price Returns Following One-Week Surges in Oil-Related Media with References to Violence, Conflict, Fear, and Production Volume

Table 19.2 Monthly Oil Price Returns Following Periods When Monthly Average TRMI Values Are at the High End of Their Historical Range (Top X%) over the Past One Month

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Trading on Sentiment

The Power of Minds Over Markets

RICHARD L. PETERSON

Copyright © 2016 by Richard L. Peterson. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey.

Published simultaneously in Canada.

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Library of Congress Cataloging-in-Publication Data is available:

ISBN 9781119122760 (Hardcover)

ISBN 9781119163749 (ePDF)

ISBN 9781119163756 (ePub)

Cover Design: Wiley

Cover Images: brain social media © VLADGRIN/istockphoto.com; summer background

© Magnilion/istockphoto.com

To the MarketPsych team. Your inspiration and persistence created something entirely new in the world.

About the Author

From investor neuroimaging to developing sentiment-based market models, Dr. Peterson spends his time exploring the intersection of mind and markets. Dr. Peterson is CEO of MarketPsych, where he is a creative force behind the Thomson Reuters MarketPsych Indices (TRMI). The TRMI is a data feed of emotions and macroeconomic topics in social and news media covering 8,000 equities, 130 countries, 30 currencies, and 35 commodities. Dr. Peterson has published in academic journals, including Games and Economic Behavior and the Journal of Neuroscience, written textbook chapters, and is an associate editor of the Journal of Behavioral Finance. His book Inside the Investor's Brain (Hoboken, NJ: John Wiley & Sons, 2007) is in six languages, and it and MarketPsych (Hoboken, NJ: Wiley 2010) were named top financial books of the year by Kiplinger. Dr. Peterson received cum laude Electrical Engineering (B.S.), Arts (B.A.), and Doctor of Medicine degrees (M.D.) from the University of Texas. Called “Wall Street's Top Psychiatrist” by the Associated Press, he performed postdoctoral neuroeconomics research at Stanford University and is board-certified in psychiatry. He lives in California with his family.

Preface

As a 12-year-old boy I was befuddled when my father—a finance professor—gave me trading authority over a small brokerage account. At the time I didn't understand what the stock market was, and I had no idea how to proceed. He educated me on how to read stock tables in the daily newspaper (this was 1985), call a broker, and place an order. I was set free with my limited knowledge and zero experience with the goal of growing the balance.

To select investments, I first turned to the local newspaper. I reviewed the micro-text of the stock tables. The numbers didn't make sense to me—my first dead-end. For Plan B I visited the library, and the librarian referred me to dusty books from the 1960s that extolled the virtues of 'tronics stocks and Dow Theory. “Nothing for me here,” I thought. I wanted to know what to buy , not to learn ancient theory.