Franklin K2 ActusRay European Alpha UCITS Fund

Investment Objective

The Fund's investment objective is to seek capital appreciation with lower volatility relative to the European equity markets.

The manager employs a Discretionary Probabilistic Investing process which is a synthesis of 1) a quantitative base, and 2) discretionary adjustments. The quantitative base uses fundamental, sentiment, technical and alternative data, and employs advanced statistics, natural language processing and artificial intelligence techniques. A large number of positions are held to enhance decision making and control risk The strategy is near neutral by beta, country, sector, market capitalisation and currency.

Latest Meeting Note

Meeting 26 Jun 2023

ActusRay Partners was founded in October 2019 by Andrew Alexander and Patrick Cheung. The firm's investment philosophy and the core of its investment team, can be traced back to the Quant Hedge Funds team at Macquarie Bank from 2007 to 2...

ActusRay Partners was founded in October 2019 by Andrew Alexander and Patrick Cheung. The firm's investment philosophy and the core of its investment team, can be traced back to the Quant Hedge Funds team at Macquarie Bank from 2007 to 2012. The strategy centres around a concept termed "discretionary probabilistic investing", which uniquely merges discretionary and quantitative techniques in equity analysis and portfolio construction. The process aims to utilise quant as extensively as possible to harness the power of probabilities, incorporating big data, alternative data, and advanced statistical techniques across both traditional quant and behavioural bias models. This process is supplemented by human input to address quant deficiencies. By identifying what quant is missing, a more repeatable methodology can be crafted. To derive benefits from using probabilities, the team has identified three essential steps: create bias, maintain bias, and repeat many times. The team "creates bias" through their proprietary linear and non-linear alpha models. The former encompasses several factor building blocks, with over 150 signals across their models, applied to market data, forecasts, P&L data, and balance sheet information. This is coupled with non-linear signals centred around biases, sentiment, turnover, etc. An additional step in creating bias is their human interaction, which addresses the deficiencies of quant and factors quant might overlook, such as data mining susceptibility, nuances, and slow evolution. The team utilises a suite of proprietary systems and models that they claim assist in maintaining this bias. This is then applied to their universe of 4,200 European companies. The strategy currently focuses on European markets due to reasons including, but not limited to: less exposure to liquidity distortions, fewer Europe-only quant competitors, greater country diversity, and a stable set of market rules. The outcome is a portfolio of 800 companies, roughly equally split. The construction process is unique in comparison to most other funds. They typically implement between 50 and 100 trades a day, ranging from 1 to 6 bps. Consequently, they often take a long time to scale into positions (often discontinuously). This can further help the team identify any quant deficiencies with the stock once it is firmly on their radar. The final book is approximately dollar neutral across approximately 100 risk factors, encompassing country, sector, size, currency, and thematics.

Performance

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC YTD
2023 0.9 0.8 0.8 0.0 0.4 0.0 0.6 0.3 0.1 0.4 0.9 0.6 0.5
2022 0.6 0.5 0.3 0.8 0.3 0.5 0.3 0.5 0.8 0.9 0.3 0.7 0.3

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