Strategy Construction Methodology

4 Pillars of Methodology

1. Signal Generation
   • Primary allocation signals generated by machine learning (ML) models trained using factor data, for opportunistic allocation
   • Clear delineation of the training, validation and out-sample periods to eliminate overfitting
2. Risk Optimization
   • Aligns allocation to underlying market price cycles
   • Resultant risk reward of at-least 1:3 with a maximum holding period of 30 days, provides guard rails at point of allocation
3. Allocation Caps
   • Weights allocations to assets are made to respect overall Asset, Sector and Group cap constraints
   • Rebalancing caps applied align final allocations to market liquidity, making the strategy executable
4. Portfolio Construction
   • Each strategy is based on Markowitz model and built by including liquid future contracts
   • Independent return streams are reinforced by applying diversification, and allocation across the curve

In summary, AiLA follows a differentiated strategy construction approach to alpha generation with uncorrelated returns​

Overview

Background

AILA strategies can be classified into three different types:

1. Seasonal Strategies
2. Event Driven Strategies
3. Trade Flow Strategies

These strategies were then decomposed to see if there is:

• A strong correlation to a particular factor
• Can any single or set of factors explain most of the alpha
• Is the alpha truly a residual which cannot be explained by these 14 factors

Strategies Considered for Analysis

1. AILAS002: Euro power & gas strategy
2. AILAF010: Diversified commodity strategy
3. AILAF012: Event driven diversified Commodity
4. AILAS043: Agriculture strategy - Grains, Oilseeds and Softs
5. AILAS066: Base metals strategy

Regression Methodology

Multivariable regression and correlation analysis of return streams of each strategy with factor returns streams:

A total of 14 factors were considered in the evaluation:

Factor Decomposition

• The strategy is uncorrelated to all the 14 factors considered and demonstrates it has a residual alpha which is not explained by the 14 factors considered here
• The key factors which have a statistically significance are Backwardation and EU Power Beta

• The strategy is uncorrelated to all the 14 factors considered and demonstrates it has a residual alpha which is not explained by the 14 factors considered here
• The key factors which have a statistically significance are Backwardation, Value, Precious Metals Beta, Softs Beta, and Oil Beta

• The strategy is uncorrelated to all the 14 factors considered and demonstrates it has a residual alpha which is not explained by the 14 factors considered here
• The key factors which have a statistically significance are Curve Carry, Trend, Vol Carry, Commodities Beta, EU power Beta, and Oil Beta

• The strategy is uncorrelated to all the 14 factors considered and demonstrates it has a residual alpha which is not explained by the 14 factors considered here
• The key factors which have a statistically significance are Backwardation, Vol Carry, Commodities Beta, Softs Beta, and Grains/Oilseeds Beta

• The strategy is uncorrelated to all the 14 factors considered and demonstrates it has a residual alpha which is not explained by the 14 factors considered here
• The key factors which have a statistically significance are Trend, Vol Carry, and Base Metals Beta

Conclusions

• There is very little correlation between these strategies and standard factors.
• Base metal and Agricultural strategies do show a slightly positive correlation to the respective sectoral factors.

• F012 Strategy as designed has negative correlation to most factors as it's expected to perform when there is a sudden market correction.
• The highlighted factors show which are statistically significant to the underlying strategy.