Internship - HeadgPoint Presentation

Overview


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  1. Introduction
    • Context about the goals and what was done
  2. Statistical Analysis: Identifying Relationships in Commodity Prices
    • Applied Principal Component Analysis
  3. Spread and Relationships analysis in the Financial Marketplace
    • Analysis of the correlations found within the financial marketplace
  4. Closing and References



GitHub

1. Introduction

  • Data about closing prices across a selected group of commodities
  • The primary objective is to comprehend the key market relationships among these commodities, offering insights and tools for managing spreads.
  • To make this possible, the structure was split in two sessions.
    • The first will deal with an Exploratory Data Analysis (EDA) trough Multivariate Analysis, mainly using the Principal Component (PC).
    • The final session entailed the relationships found in the Principal Component Analysis, interpreting and providing insights about five main categories found relevant.


Table 1: Commodities Categories and Studied Relationships

Table 1: Commodities Categories and Studied Relationships

2. Exploratory Data Analysis

Exploratory Data Analysis Principal Component Analysis

  • Application of Principal Component Analysis (PCA): Identify primary relationships.
  • Addressing Time Series Challenges: Discussing high correlations, seasonal fluctuations, and inherent trends.
  • Transformation: From daily closing future prices into logarithmic returns for data processing.

2.1 Unconditional, time-invariant correlations among commodity returns

  • Initial Step in Analysis: Utilizing a correlation matrix.
  • Revealing Long-Term Associations: Unveiling underlying patterns, dependencies, and interdependence.


Figure 1: Correlation Matrix


2.2 Principal Component Analysis (PCA)

  • PCA: Offers a comprehensive approach beyond surface-level correlations, reducing data dimensions while capturing significant patterns.
  • PC1 and PC2 respectively capture 27.9% and 12.8% of the total data variance.
  • Arrows’ length and direction indicate relationship strength and direction.

Figure 2: PCA Biplot - Commodities and Relationship


  • Evaluation of Representation Quality
    • Kaiser’s Rule: Retaining components with eigenvalues greater than 1.
    • Scree Plot: Identifying where eigenvalues start to level off.
    • Cosine Squared: Revealing variable contribution to PCs via correlation between original variables and respective components.

Figure 3: Scree Plot


Figure 4: PCA Biplot - Correlation Streght

Figure 4: PCA Biplot - Correlation Streght


  • Hard commodities: Oil products, such as Brent Crude and Heating Oil present close relationship, similar to Wheat Kansas and Chicago.
  • Soft commodities: Such coffee, cotton, sugar tend to stay together, but impact in a lower level in the provided data.
  • Finally, the soybean related products show similar impact and by logic are also correlated, similar to the livestock.

3. Spread Analysis

Overview


  • 3.1 Crack Spread Oil Market
  • 3.2 Soybean Crush
  • 3.3 Cattle Crush
  • 3.4 Dynamic Between Chicago and Kansas Wheat
  • 3.5 Soft Commodities


3.1 Crack Spread Oil Market

  • Crack Spread: A metric reflecting refining margins, representing the disparity between refined product (e.g., gasoline) prices and crude oil prices.
  • Spread Analysis: A positive spread denotes profitability, showcasing higher prices for refined products than crude oil.
  • Seasonal shifts: Seasonal changes in demand influence the spread; summers witness increased demand for unleaded gasoline, whereas winters favor heating oil. Narrow spreads prompt price adjustments, favoring contracts for heating oil and gasoline over crude oil for profitability.

Figure 5: Subplot for Brent Crude, Heating Oil, and Oil Crush Spread


3.2 Soybean Crush

  • Crush Spread: quantifies the difference between the value of soybeans and their processed byproducts, serving as a gauge for potential profit margins in soybean processing.

  • Spread Trading: Involves simultaneous purchase and sale of different contracts, while hedging mitigates potential losses by adopting a long position in Soybean futures and short positions in Soybean Meal and Soybean Oil futures.

  • Strategy: Speculators utilize the soybean crush spread to capitalize on market mispricing, going long on soybean futures while shorting soybean oil and meal futures, assuming undervalued processing costs.

  • Short Position in Futures: An investor takes a short position in futures when they anticipate that the prices of commodities will decrease in the future. By doing so, they aim to sell the commodity at a high price now and then buy it back at a lower price later to cover the contract. This way, they lock in a higher selling price before the anticipated decrease.

  • Long Position in Futures: Conversely, an investor takes a long position in futures when they anticipate that the prices of commodities will rise in the future. Here, they aim to buy the commodity at a lower price now and sell it later at a higher price to cover the contract. This allows them to benefit from the anticipated increase in prices.

Figure 6: Subplot for Soybean, Soybean Oil, Soybean Meal and Soybean Crush


3.3 Cattle Crush

  • Cattle crush: Is a strategic approach in agricultural commodity markets involving live cattle, feeder cattle, and corn futures.
  • Positive spread indicates profit potential, while a negative spread suggests possible losses.
  • Seasonal shifts: Significantly impact market demands. For instance, in summer, there’s heightened demand for feed, particularly corn, crucial in preparing cattle for market weight. Conversely, during winter, shifts in demand favor different types of cattle products.

Figure 7: Subplot for Feeder Cattle, Live Cattle, Corn and Cattle Crush


3.4 Dynamic Between Chicago and Kansas Wheat

  • Kansas City Hard Red Winter Wheat (KC HRW) and Chicago Wheat (Chicago SRW): KC HRW typically contains higher protein levels, historically commanding higher prices compared to SRW.
  • Spread Trading Strategy: Traders focus on the difference between the two contracts (Kansas Wheat HRW minus Chicago Wheat SRW). This spread trade involves buying Kansas and simultaneously selling Chicago to exploit the gap between these two.
  • Profit and Loss Scenario: If the gap widens, potential losses may occur. However, should the gap revert to its typical range, profit opportunities arise. If the gap remains unchanged, the trade remains neutral, yielding neither profit nor loss.
  • Spread Analysis: The spread between KC HRW and Chicago SRW saw a negative trend from 2019 until late 2021. This was primarily due to adverse weather conditions in SRW planting areas. Additionally, tensions between Ukraine and Russia contributed to a downward trend at the beginning of 2022.

Figure 8: Subplot for Wheat Kansas, Wheat Chicago and Wheat Spread


3.5 Soft Commodities

  • Soft commodities: Refer to a specific group of agricultural products that share a common trait—they are grown rather than mined or extracted.
  • Due to their dependence on natural factors like weather, crop diseases, and geopolitical events, the prices of soft commodities often exhibit volatility and are influenced by numerous global factors.

Figure 8: Subplot for Soft Commodities Closing Future Price in US$

4. Conclusion

  • Main relationships found were in the Oil, Soybean, Livestock and Wheat Market allowing to analyze the Spread and provide insights and tools to improve the decision-making while trading.

References

STOCKCO. KANSAS/CHICAGO – A SPREAD TRADE STRATEGY. Disponível em: https://stockco.co.nz/kansas-chicago-a-spread-trade-strategy/. Acesso em: 27 dez. 2023.

SUTTON-VERMEULEN. Kansas City vs. Chicago Wheat Spread: A Tale of Two Markets. 2020. Disponível em: https://www.cmegroup.com/education/articles-and-reports/kc-vs-chicago-wheat-spread-a-tale-of-two-markets.html. Acesso em: 27 dez. 2023.

CHEN, James. Crush Spread. 2022. Disponível em: https://www.investopedia.com/terms/c/crushspread.asp. Acesso em: 27 dez. 2023.

CHEN, James. Crack Spread: What it is, How to Trade It. 2021. Disponível em: https://www.investopedia.com/terms/c/crackspread.asp. Acesso em: 26 dez. 2023.

STEINER, Len. THE CATTLE CRUSH AND REVERSE CRUSH: an industry hedging tool and a financial investment opportunity. An Industry Hedging Tool And A Financial Investment Opportunity. Disponível em: https://www.cmegroup.com/education/files/the-cattle-crush-and-reverse-crush.pdf. Acesso em: 27 dez. 2023.

MEFFORD, Eli; STUDENT, M.s.. All Correlations Go to 1 in a Crisis: The Cattle Crush Spread during COVID-19 Crisis. 2021. Disponível em: All Correlations Go to 1 in a Crisis: The Cattle Crush Spread during COVID-19 Crisis. Acesso em: 28 dez. 2023.

CME GROUP. Soybean Crush Reference Guide. Disponível em: https://www.cmegroup.com/education/files/soybean-crush-reference-guide.pdf. Acesso em: 27 dez.

CME Group. Introduction to Crack Spreads. 2017. Disponível em: https://www.cmegroup.com/education/articles-and-reports/introduction-to-crack-spreads.html. Acesso em: 02 jan. 2024.2023.

ESIGNAL. Crack Spread. Disponível em: https://download.esignal.com/products/da/help/charts/chart_studies/available_chart_studies/crack.htm. Acesso em: 02 jan. 2024.

CHEN, James. Soft Commodity: Meaning and Examples vs. Hard Commodities. Disponível em: https://shorturl.at/bkptJ. Acesso em: 27 dez. 2023.

CHEN, James Ming; REHMAN, Mobeen Ur; VO, Xuan Vinh. Clustering commodity markets in space and time: clarifying returns, volatility, and trading regimes through unsupervised machine learning. Resources Policy, [S.L.], v. 73, p. 102162, out. 2021. Elsevier BV. http://dx.doi.org/10.1016/j.resourpol.2021.102162.

PLOTLY. Plotly - Time Series Interactive Graphs. Disponível em: https://plotly.com/r/time-series/. Acesso em: 02 fev. 2024.


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