Sách Chuyên Sâu Về Phân Tích Hàng Hoá 2020 Advanced Positioning Flow and Sentiment Analysis Commodity Markets

 Advanced Positioning, Flow, and Sentiment Analysis in Commodity Markets - Bridging Fundamental and Technical Analysis

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  • Advanced Positioning, Flow, and Sentiment Analysis in Commodity Markets
  • The Structure of the Positioning Data
  • Performance Attribution – An Insight into Sentiment and Behavioural Analysis?
  • Concentration, Clustering, and Position Size Price Risks and Behavioural Patterns
  • ‘Dry Powder (DP)’ Analysis – An Alternative Way to Visualise Positioning
  • Advanced DP Analysis – Deeper Insights and More Variables
  • Decomposing Trading Flow and Quantifying Position Dynamics
  • Overbought/Oversold (OBOS) Analysis – The Intersection of Extremes
  • Advanced OBOS Analysis – Extremes in Sentiment and Risk
  • Sentiment Analysis – Sentiment Indices and Positioning Mismatches
  • Newsflow in Positioning Analysis
  • Flow Analysis – The ‘Flow Cube’ and the ‘Build Ratio’ in Commodity Markets
  • Chinese Commodity Markets – Analysing Flow
  • Machine Learning – A Machine’s Perspective on Positioning

For over 20 years, I have tried to better understand commodity price behaviour, and it is indeed extremely challenging. I believe that irrespective of the extent to which non fundamentals may be driving prices, or the degree to which shifts in sentiment may have caused them to decouple from prices, fundamentals ultimately win. The realignment occurs not only because commodities are tangible goods, but because they exhibit specific behavioural patterns and relationships that are driven by factors linked to physical attributes and structural dynamics within the asset class. Their positive serial correlation, or the strong persistence in changes in these factors, not only drive sustained moves in price, but also provide time for them to be identified and for these relationships to be traded and monetised. This rarely occurs in other asset classes because of the high degree of contemporaneity, meaning that price drivers typically need to be forecasted.

The serial correlation is driven by how commodities are transported and stored – in turn, a product of the extreme differences in the production and consumption locations that characterise commodities. It is also a function of how they are affected by seasonality and weather and their vulnerability to supply shocks. The nature of their demand drivers – our need to eat, our desire to be clothed, our fight to stay warm in winter and cool in summer, our obsession with fuel and power, and our dependency on commodities like metals with their unique and often non-substitutable properties.