Historical behavior

• Overall low PnL correlation for AiLA vs 3x CTA, e.g., 𝜌(daily) ≲±15%.
• AiLA captured bull market (“long trends”) from mid-2020 to Mar-2022, where Momentum strategies re-gained popularity (blue shaded period).
• AiLA also captured crash (“short trends”) around Mar-2020, not captured well by slower CTA strategies.
• AiLA typically also performed in other (“weak common trends”) periods.

Historical behavior

• AiLA alpha indices are opportunistic strategies using a systematic process.
• The strategy is driven by both micro as well as macro factors.
• Every step in the process is fully automated with no human intervention.

AiLA vs CTA factors

• 20 liquid commodity futures markets, front contracts.
• 3 CTA strategies implemented based on [Baz15].
• AiLA models aggregated for each commodity.
• All strategies applied to same commodity returns.

[Baz15] J.Baz et al., Dissecting Investment Strategies in the Cross Section and Time Series (December 4, 2015). Available at SSRN: https://ssrn.com/abstract=2695101

AiLA Different to CTA Factors

• Low return correlation between AiLA and the Momentum, Carry and Value strategies.
• AiLA appears less related to any of the CTA strategies, than the CTA strategies among each other.
• AiLA strategy returns are complementary to other systematic/quant strategies.

• AiLA products are defined by index weights, here referred to as the actual weights (AW).
• The AiLA product weights are submitted via FTP at close of NY on T 1 for execution on market close of T.
• The weight (AW) information consists of:
  • Asset (futures contract) name,
  • Weight in % of total allocated capital,
  • Position being long or short,
  • Rebalancing daily, to be executed at next close.
• The weights are determined in downstream process, focusing on “how much” to allocate to each asset, given daily allocation decisions as input.

• Inputs provided by the upstream process in the form of daily allocation decisions for individual assets.
• On a given day, the inputs provide the decision to allocate long/short or not to allocate, for each asset in the AiLA index portfolio.
• The downstream index construction process decide “how much” to allocate to each asset, by calculating index weights in three sequential steps based on the allocation inputs.

Five asset index:

• July Bean, June Copper, June Gold, Cal TTF and June WTI.
• Portfolio target: equal 20% weight per asset, however, allow asset weights up to 100%.

Dynamic weights:

• 2022 03 23: allocation opportunities for Gold, Copper and Soybean, weights increased above 20% resulting in system allocating 94.1% of capital.
• 2022 05 16: TTF only asset with allocation opportunity, 20% weight prevented by liquidity constraints resulting in system allocating 6% of capital.
• 2022 05 17: TTF allocation ends, resulting in system decision to exit 6% long TTF weight.

Varying number weights:

• Periods with large (small) nr of active assets tend to result in smaller (larger) individual weights.

• The main purpose of the upstream process is to decide “when” to allocate to the individual assets.
• The allocations are intended to capture prevailing investment opportunities.
• The opportunistic decisions are based on data driven models dedicated to individual assets.
• The allocation decisions are produced by the upstream process in four sequential steps.

Feature Data (XF)

• All feature data is processed by the DQ system and then transformed into a format which allow the model to access relevant information more easily, e.g., including but not limited to data normalization, relative/spread information, one-hot-encodings etc.
• The data is chronologically split up for the model training cycles, with three samples corresponding to consecutive periods (see chart),
  • Training: used to train model.
  • Validation: used to validate model performance as well as tune hyper-parameters.
  • Holdout: out-of-sample data to assess final performance after all model aspects have been frozen.
• Due to the extraordinary risk of over-fitting, the holdout dataset can only be used ones for the final assessment of the model performance.
• The most important features contributing to an allocation decision, associated with an asset on a given day, of any AiLA index are accessible in the AiLA product UI.

Model choice: y = f(XF)

• Model choice involves analysis of various models using the MLRD platform (Machine Learning Research and Development)
• During the training-validation cycles of the models the hyper-parameters are also tuned.
• The metrics collected, from the model run with specific emphasis on Accuracy and Signal Density form the basis of the qualitative judgement to determine the final choice of model.

Allocation Decision (A)

• Entry decision
  • The model output (y) each day provide a prediction w.r.t. achieving a risk-reward over a certain holding period.
  • For example, probability for a long position to achieve a 10% return, without loosing more than 3%, in next 15 days.
  • If probability sufficiently high for a favorable long or short risk-reward scenario, an opportunistic allocation will be entered with corresponding target parameters.
• Exit decision:
  • An allocation will be kept until one of four exit criteria is met.
  • Expects little opportunity to be invested further.
  • Hitting the risk target since start of investment.
  • Achieves the reward target since start of investment.
  • Receiving an opposing opportunity from the model.
  • Above, only exit criteria (4) depends on the feature data.

Example: Gold_CME_June decision to allocate long.

2022-04-13: Entry decision

• Favorable risk/reward predicted by micro/macro features

(* CFTC CoT NCNC = CFTC Commitments of Traders, Non-Commercial Net Contracts)

2022-04-21: Exit decision

• Hitting risk target since start of investment