MANAGE RISK MORE EFFECTIVELY AND DEVELOP A BETTER UNDERSTANDING OF COMMODITY PRICE DYNAMICS, RISK AND SENTIMENT WITH POSITIONING ANALYSIS At its core, positioning refers to the breakdown of the positions held by specific categories of trader. Positioning Analysis uses such data both in isolation and in combination with other variables to provide a powerful framework for understanding price dynamics, risk, sentiment, and behaviour. The approach in Advanced Positioning, Flow, and Sentiment Analysis in Commodity Markets - the only book of its kind on the market - bridges fundamental and technical…mehr
MANAGE RISK MORE EFFECTIVELY AND DEVELOP A BETTER UNDERSTANDING OF COMMODITY PRICE DYNAMICS, RISK AND SENTIMENT WITH POSITIONING ANALYSIS At its core, positioning refers to the breakdown of the positions held by specific categories of trader. Positioning Analysis uses such data both in isolation and in combination with other variables to provide a powerful framework for understanding price dynamics, risk, sentiment, and behaviour. The approach in Advanced Positioning, Flow, and Sentiment Analysis in Commodity Markets - the only book of its kind on the market - bridges fundamental and technical analysis to reveal how to build robust, effective models based on positioning, flow and sentiment patterns. Readers will learn to generate trading signals from detailed datasets with variables like price changes, seasonal factors, exchange rates and market-wide risk and uncertainty metrics. A continuously updated companion website allows readers to immediately employ this unique framework and to customise the models for direct use.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
MARK J S KEENAN is Managing Director, Global Commodities Strategist, and Head of Research for Asia-Pacific at Société Générale Corporate & Investment Bank (SG CIB) based in Singapore. He has over 20 years of research, trading and investment experience across all major commodity markets. Mark has worked in asset management, risk management and investment banking in both London and Singapore and developed a number of innovative trading, investment and risk management solutions for corporates, trade houses, investment institutions and hedge funds. He appears regularly on CNBC and Bloomberg television and is quoted widely in global press and media channels. He has a master's degree in Molecular and Cellular Biochemistry from Oxford University.
Inhaltsangabe
About the Author xi Acknowledgements xiii Preface xv About the Companion Website xvii Introduction 1 Chapter 1 Advanced Positioning, Flow, and Sentiment Analysis in Commodity Markets 7 1.1 Positioning Analysis - What is It? 8 1.2 The History of Positioning Data, the COT Report, and the Agencies that Provide the Data 10 1.3 Misunderstandings and Issues in Positioning Analysis 10 1.4 Futures and Options Data 17 Chapter 2 The Structure of the Positioning Data 19 2.1 The Structure of Positioning Data 20 2.2 Positioning Data for the London Metal Exchange (LME) - The MiFID Classifications 28 2.3 Other Types of Positioning and Flow Data 33 2.4 The Structure of Commodity Markets 34 Chapter 3 Performance Attribution - An Insight into Sentiment and Behavioural Analysis? 45 3.1 The Proliferation of Speculative Positioning Data 46 3.2 Why is Positioning Data Interesting 47 3.3 Measuring the Skill or Performance of MMs 48 3.4 Net Futures Positioning vs Net Trader Number Positioning or Alignment 55 Chapter 4 Concentration, Clustering, and Position Size - Price Risks and Behavioural Patterns 59 4.1 Concentration, Clustering, and Position Size 60 4.2 Concentration, Clustering, Position Size, and Their Relationship to Price Over Time 62 Chapter 5 'Dry Powder (DP)' Analysis - An Alternative Way to Visualise Positioning 71 5.1 Dry Powder (DP) Analysis 72 5.2 DP Indicator Variations 75 5.3 DP Indicators and Trading Signals 82 Chapter 6 Advanced DP Analysis - Deeper Insights and More Variables 87 6.1 Advanced DP Indicator Charts 88 6.2 DP Indicators, Price, and Curve Structure 96 6.3 DP Hedging Indicators 99 6.4 DP Factor, Fundamental, and Currency Indicators 101 Chapter 7 Decomposing Trading Flow and Quantifying Position Dynamics 111 7.1 Decomposing Net Positioning - The Methodology 112 7.2 Decomposing Long and Short Positioning - The Methodology 115 7.3 Who Has Been Driving Prices? 116 7.4 Who is Trading with Whom? 118 7.5 The Impact on Prices 122 7.6 Speed of Positioning Changes 124 Chapter 8 Overbought/Oversold (OBOS) Analysis - The Intersection of Extremes 127 8.1 The Overbought/Oversold (OBOS) Philosophy 128 8.2 Constructing the Framework 128 8.3 The OBOS Concentration Indicator 132 8.4 Key Attributes of the OBOS Approach 133 8.5 Behavioural Patterns within the Boxes 134 8.6 OBOS Variations 138 8.7 The DP/OBOS Hybrid Indicator 141 Chapter 9 Advanced OBOS Analysis - Extremes in Sentiment and Risk 147 9.1 The OBOS Framework - Clustering and Position Sizes 147 9.2 OBOS Factor Indicators 155 9.3 Commodity Risk Premia - Positioning Analysis and Risk Management 159 Chapter 10 Sentiment Analysis - Sentiment Indices and Positioning Mismatches 163 10.1 The Difference between Trader Numbers and Open Interest 164 10.2 Trader Sentiment (TS) Indices 166 10.3 Mismatches in Positioning and Price Inflexion Points 172 Chapter 11 Newsflow in Positioning Analysis 177 11.1 Newsflow Data in Commodity Markets 178 11.2 Building Basic Newsflow Indices 181 11.3 Combining Newsflow with Positioning 186 11.4 Economic Policy Uncertainty 190 Chapter 12 Flow Analysis - The 'Flow Cube' and the 'Build Ratio' in Commodity Markets 191 12.1 The Flow Cube 192 12.2 The Flow Cube down the Forward Curve 198 12.3 The LBMA-i Trade Data 202 Chapter 13 Chinese Commodity Markets - Analysing Flow 203 13.1 The Chinese Commodity Exchanges 203 13.2 The Size of the Chinese Commodity Exchanges 205 13.3 Speculative vs Hedging Activity in Chinese Commodities 208 13.4 The Flow Cube and Build Ratio in Chinese Commodities 210 13.5 The OBOS Framework and the Speculation Ratio 212 Chapter 14 Machine Learning - A Machine's Perspective on Positioning 215 14.1 Introduction to Machine Learning (ML) 216 14.2 Decision Trees 217 14.3 Feature Importance 227 14.4 Random Forests 229 14.5 Using ML to Trade 232 Appendix 1 History of the COT Report 235 Appendix 2 The COT Reports 237 Index 249
About the Author xi Acknowledgements xiii Preface xv About the Companion Website xvii Introduction 1 Chapter 1 Advanced Positioning, Flow, and Sentiment Analysis in Commodity Markets 7 1.1 Positioning Analysis - What is It? 8 1.2 The History of Positioning Data, the COT Report, and the Agencies that Provide the Data 10 1.3 Misunderstandings and Issues in Positioning Analysis 10 1.4 Futures and Options Data 17 Chapter 2 The Structure of the Positioning Data 19 2.1 The Structure of Positioning Data 20 2.2 Positioning Data for the London Metal Exchange (LME) - The MiFID Classifications 28 2.3 Other Types of Positioning and Flow Data 33 2.4 The Structure of Commodity Markets 34 Chapter 3 Performance Attribution - An Insight into Sentiment and Behavioural Analysis? 45 3.1 The Proliferation of Speculative Positioning Data 46 3.2 Why is Positioning Data Interesting 47 3.3 Measuring the Skill or Performance of MMs 48 3.4 Net Futures Positioning vs Net Trader Number Positioning or Alignment 55 Chapter 4 Concentration, Clustering, and Position Size - Price Risks and Behavioural Patterns 59 4.1 Concentration, Clustering, and Position Size 60 4.2 Concentration, Clustering, Position Size, and Their Relationship to Price Over Time 62 Chapter 5 'Dry Powder (DP)' Analysis - An Alternative Way to Visualise Positioning 71 5.1 Dry Powder (DP) Analysis 72 5.2 DP Indicator Variations 75 5.3 DP Indicators and Trading Signals 82 Chapter 6 Advanced DP Analysis - Deeper Insights and More Variables 87 6.1 Advanced DP Indicator Charts 88 6.2 DP Indicators, Price, and Curve Structure 96 6.3 DP Hedging Indicators 99 6.4 DP Factor, Fundamental, and Currency Indicators 101 Chapter 7 Decomposing Trading Flow and Quantifying Position Dynamics 111 7.1 Decomposing Net Positioning - The Methodology 112 7.2 Decomposing Long and Short Positioning - The Methodology 115 7.3 Who Has Been Driving Prices? 116 7.4 Who is Trading with Whom? 118 7.5 The Impact on Prices 122 7.6 Speed of Positioning Changes 124 Chapter 8 Overbought/Oversold (OBOS) Analysis - The Intersection of Extremes 127 8.1 The Overbought/Oversold (OBOS) Philosophy 128 8.2 Constructing the Framework 128 8.3 The OBOS Concentration Indicator 132 8.4 Key Attributes of the OBOS Approach 133 8.5 Behavioural Patterns within the Boxes 134 8.6 OBOS Variations 138 8.7 The DP/OBOS Hybrid Indicator 141 Chapter 9 Advanced OBOS Analysis - Extremes in Sentiment and Risk 147 9.1 The OBOS Framework - Clustering and Position Sizes 147 9.2 OBOS Factor Indicators 155 9.3 Commodity Risk Premia - Positioning Analysis and Risk Management 159 Chapter 10 Sentiment Analysis - Sentiment Indices and Positioning Mismatches 163 10.1 The Difference between Trader Numbers and Open Interest 164 10.2 Trader Sentiment (TS) Indices 166 10.3 Mismatches in Positioning and Price Inflexion Points 172 Chapter 11 Newsflow in Positioning Analysis 177 11.1 Newsflow Data in Commodity Markets 178 11.2 Building Basic Newsflow Indices 181 11.3 Combining Newsflow with Positioning 186 11.4 Economic Policy Uncertainty 190 Chapter 12 Flow Analysis - The 'Flow Cube' and the 'Build Ratio' in Commodity Markets 191 12.1 The Flow Cube 192 12.2 The Flow Cube down the Forward Curve 198 12.3 The LBMA-i Trade Data 202 Chapter 13 Chinese Commodity Markets - Analysing Flow 203 13.1 The Chinese Commodity Exchanges 203 13.2 The Size of the Chinese Commodity Exchanges 205 13.3 Speculative vs Hedging Activity in Chinese Commodities 208 13.4 The Flow Cube and Build Ratio in Chinese Commodities 210 13.5 The OBOS Framework and the Speculation Ratio 212 Chapter 14 Machine Learning - A Machine's Perspective on Positioning 215 14.1 Introduction to Machine Learning (ML) 216 14.2 Decision Trees 217 14.3 Feature Importance 227 14.4 Random Forests 229 14.5 Using ML to Trade 232 Appendix 1 History of the COT Report 235 Appendix 2 The COT Reports 237 Index 249
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