Economic rational

A factor as defined by Alpha Centauri and our investing framework needs to meet the following criteria: a) economic rational b) established c) academically / economically recognized d) sustainable and e) accessible

High data quality

“Point-in-time” data (PIT) are used along the complete process chain – from development, implementation up to risk managment. This approach is an important pre-requisite to draw realistic conclusions from historical perspectives. It allows to synthesize and extract past effects and subsequently correctly fine tune parameters to harvest factors under live market conditions.

Integrated risk model

A leading, commercial risk model is an integral element of our framework. It is used along the supply chain from R&D to actual implementation. Adaption of a stringent risk model early on in the process allows to model and anticipate possible risks prior to trading. Furthermore, a full set of controls can be directly employed to mitigate and address these risks.

Another key advantage of a vertically highly integrated risk model is a real-time risk analysis and risk management system.

High technical quality

In order to meet high data quality requirements and stringent risk model integration an in-house database is used for production. Automated processes and application programming interfaces were developed to fully utilize the advantages of a risk based production line .

Additionally all relevant data input is independantly verified in-house and checked for quality before being employed in the multi factor model.

Resulting factors

The current investment framework can be best described by the following factors:

• Growth: growth potential based on profit and dividends
• (Low) Risk: below-average volatility levels
• Momentum: historical price development
• Quality: stable finances based on e. g. debt ratio and liquidity
• Size: low market capitalization
• Value: favorable valuation with respect to cash flow and profits

Targeted premia extraction

Alpha Centauri’s multi factor framework is based on stable return expectations combined with systematic risk diversification. A timing-free mathematical model combines and optimizes individual factor signals and weights yielding a selection of favored stocks.

Optimized portfolio implementation

Modern portfolio construction tools are being employed to control key portfolio metrics such as: a) benchmark tracking error and beta b) risk composition c) sector and country allocation and d) liquidity and size of portfolio

The resulting portfolio is characterized by a controlled near-benchmark risk structure while producing historically significant excess returns.