The ML based AiLA allocations originate from identified opportunities, given the prevailing market conditions.
The percentage of allocated capital can therefore vary significantly, as well as rapidly, over time.
Can we force the index to maintain its allocation percentage within a given range?
What is the corresponding impact on performance?
Illustrate impact for an AiLA index based on 102 selected assets using long-only (LO) allocations, for which allocation tends to vary more over time.
Implement allocation range functionality based on adjusting holding periods associated with active signals and awareness of contract maturities.
Assess magnitude of performance impact from such allocation range criteria.
The allocation percentage is affected by different constraints and targets in the index construction process.
However, often the number of times of allocation is the main aspect for how much capital that is allocated, which in turn depends on the ML model’s ability to identify opportunities.
This results in periods with few trade opportunities and hence low allocation, which change as conditions vary.
For the comparison, the allocation percentage is forced to be sustained within a given (min/max) range by,
Forcing allocations to be applied outside the tenor set that is considered optimal by the system.
Adjusting holding periods when necessary.
Prioritize w.r.t maturities among contracts with signal.
The methodology is not expected in any way to be optimal, but intended to serve as a first order assessment of the impact from this type of functionality.
The index performance was investigated for different allocation percentage ranges, i.e. within a min/max value.
Forcing the index to maintain a minimum allocation percentage reduced the performance noticeably, e.g. by requiring an allocation percentage in a range close to 100% reduced the performance by about half.
AiLA index performance appears on a risk adjusted basis,
Higher than from a standard index like GSCI/BCOM.
However, the correlation to GSCI/BCOM increases with allocation range converging towards 100%.
Typically, the correlation increase from about 60% (AiLA LO) to almost 80% (100/100).
Sustaining a larger allocation by forcing the system away from its specific trading recommendations, significantly reduce the performance due to the opportunistic nature of the ML based AiLA signals.
An approach to maintain the percentage of allocated capital within a min/max range was implemented within the AiLA index framework.
The functionality was used to investigate the expected impact on performance for an AiLA index based on 102 selected assets and long-only signals, for which the allocation percentage tends to vary the most.
The methodology is by no means optimal, but appears suitable for the purpose of assessing the impact at a general level.
The AiLA index performance results were significantly reduced when the system was forced to sustain an allocation percentage within a range narrowing towards 100%.
On a risk adjusted basis the AiLA index performance was generally found higher than the performance of the GSCI/BCOM indices, however, the positive correlation increased from about 60% to 80% with a narrowing allocation range converging towards 100%.
The results suggest that the performance associated with the ML based AiLA signals is significantly reduced when the system is forced away from its specific trading recommendations in order to maintain a higher allocation percentage, which is expected due to the opportunistic nature of the signals with underlying models trying to identify trading opportunities given the prevailing market conditions.