Global Dynamic Allocation

• Rule-Based:

  The strategy follows a specific set of rules or criteria, ensuring decisions are consistent and not influenced by human emotions like fear or greed.

• Data-Driven:

  Systematic strategies often rely on quantitative data, in this case, transforming macroeconomic fundamentals into numerical values.

• Reproducible:

  Since decisions are based on predefined rules, the strategy can be tested on historical data, known as backtesting, to evaluate its past performance. This process also helps to optimize future performance.

The macroeconomic cycle represents the fluctuations in the overall economy, encompassing phases such as growth, recession, and recovery. Each phase presents distinct opportunities. Our strategies are designed to capture these opportunities by interpreting signals from macroeconomic data, ensuring that investments are quantitatively aligned with the current economic environment.

Backtesting involves using historical data to test how a strategy would have performed.

To ensure its accuracy, we:

• Employ high-quality, unbiased, point in time data.
• Account for all transaction costs and slippages.
• Regularly update and cross-verify our data sources.
• Conduct out-of-sample tests to confirm our findings.

While our strategies are founded on robust fundamental research and thorough testing, it's crucial to recognize that markets aren't always rational. Nevertheless, we believe that, over the long term, fundamentals prevail. Thus, this strategy is well-positioned to continue outperforming.

In addition, as time progresses, more data is generated, which can improve the precision of the asset allocation. This is likely to result in enhanced performance.

Overfitting occurs when a model is too closely tailored to historical data and may not perform well on new data. We address this by:

• Ensuring our systems make fundamental sense. We avoid black-box models.
• Keeping our models parsimonious, focusing on essential variables.
• Employing out-of-sample validation.
• Our propietary analyst traning process includes assessing their performance on different subsets of data.

Our strategy's investment universe includes gold, USD, and CHF. These assets have a history of appreciating during turbulent market conditions, thereby reducing drawdowns. Additionally, our systems are trained to anticipate the likelihood of such market disruptions and adjust the portfolio accordingly to mitigate potential declines.