Advanced Predictive Analytics
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SMM069 Advanced Predictive Analytics – Group Coursework
1 Introduction
In this coursework, you will analyse commodity futures price data using regression models and statistical
techniques that we have covered in this module. The dataset spans monthly observations from 29 July 2011
to 31 May 2024. This introduction is designed to familiarise you with the data structure, the concept of
factor models, the specifics of the dataset you will be using, and the objectives of this coursework.
1.1 Commodity Futures Price Data and S&P GSCI Index
Commodity futures prices are volatile and are influenced by a multitude of factors. In this coursework, you
will work with the commodities that influence the S&P Goldman Sachs Commodity Index (S&P GSCI), a
widely recognised benchmark for investment in commodity markets. The S&P GSCI Index represents the
performance of a diversified group of commodities, providing a broad measure of the commodity market’s
overall performance. It includes various commodities such as energy, metals, agriculture, and livestock,
making it a comprehensive indicator of commodity futures price movements.
1.2 Factor Models
A factor model is a financial model that explains the returns of an asset through various underlying factors.
These factors are variables that are considered to influence commodity futures prices. In this coursework,
you will use factor models to analyse commodity futures price data. Factor models are based on i) observable
factors that are computed from observational data and are based on expert opinion or ii) engineered factors
that could be extracted via Machine Learning models like Principal component analysis (PCA) or Factor
Analysis (FA). This coursework relies on a factor model with eight observational factors, and are described
as follows:
• Momentum: Measures the tendency of a commodity futures price to continue moving in its current
direction. It is calculated based on the cumulative excess returns of the commodity over the previous
12 months.
• Basis: The basis for each commodity is the difference between the future prices of the nearby and
next-to-nearby futures contracts. This difference provides insights into the market’s expectations of
future price movements.
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• Basis-Momentum: This factor combines the basis and momentum effects. It measures the difference
in momentum between the nearby and next-to-nearby futures contracts.
• Skewness: Measures the asymmetry of the commodity futures price distributions. It helps in un-
derstanding the likelihood of extreme future price movements by analysing the daily returns of the
commodity over the past 12 months.
• Inflation Beta: Represents the sensitivity of the commodity futures price to changes in inflation. It
shows how the commodity’s future price responds to unexpected changes in monthly inflation rates
over the past 60 months.
• Volatility: Defined as the variance-per-absolute mean of the first-nearby futures returns over the prior
36 months. It indicates the stability or instability of a commodity’s future price.
• Open Interest: Indicates the level of market activity and liquidity. It is defined as the monthly
change in the total number of outstanding contracts for a commodity.
• Value: Represents the intrinsic worth of a commodity based on fundamental factors such as supply
and demand. It is measured as the average future price of the nearby futures contract from 4.5 to 5.5
years ago compared to the current future price.
The data for these risk factors will be provided to you, so you do not need to calculate them by your-
self. Your task is to use these factors to analyse the commodity futures price data, explain movements in
commodity futures prices, and determine which factors have the greatest impact.
1.3 Data Structure and Assignment
Each group will be provided with THREE CSV files:
1. S&P GSCI Index and Commodities Future Price Daily Data: A CSV file provided to the
daily index values of the S&P GSCI Index and commodity futures prices from 03 Oct. 2005 to 09
May 2024.