The investment selection process involves a systematic and data-driven  approach, applying mathematical and statistical models to identify  underlyings that align with specific investment criteria based on key  indicator and ratios defined by the Investment Management. 

Initially, historical returns, volatility and correlations are analyzed to gauge a  stock's past performance and risk characteristics.

Then we use macroeconomic factors to understand the prevailing business  cycle, guiding investment decisions based on sector and industry  performance trends. The goal is to achieve a better allocation in sectors,  ensuring that our investment strategy is finely tuned to the broader economic  landscape and positioned for optimal performance in varying market  conditions.

The process also incorporates a factor investing model, identifying key  metrics such as earnings growth and financial health, which are historically  associated with strong stock performance. These factors are then weighted  and integrated into a scoring system, allowing for a quantitative assessment  of each underlying's investment potential.

The final selection involves combining scores from various models to identify  underlyings that consistently demonstrate strength across multiple criteria.  Importantly, this approach is dynamic, requiring continuous monitoring and  adjustments to adapt to changing market conditions and ensure the  ongoing alignment of the portfolio with the investor's goals and risk  tolerance. 

The initial Data Base is reduced to a filtered population that will be the input  list for the next step (optimization).

Once the underlyings have been filtered, you can evaluate the performance  and statistics of each one individually. For example, we use scripts to  calculate the Sharpe ratio, or other metrics that indicate the past  performance of a stock. Additionally, Monte Carlo simulations and GARCH  models are employed to simulate potential future scenarios and assess risk. 

These advanced modeling techniques contribute to a more robust  understanding of potential returns. Importantly, this approach remains  dynamic, requiring continuous monitoring and adjustments to adapt to  market changes and ensure alignment with investor goals and risk tolerance.

Our enhaced optimization model consider factors such as diversification,  volatility and sector’s correlation to determine the optimal portfolio of  underlyings that balances risk and return based on the performance target.  For example, you could use a model that maximizes the expected return of  the portfolio subject to constraints on the volatility of the portfolio.

With the optimal portfolio a list of performance sensitivities, statics and  simulations are tested to identify key metrics of expected performance. 

As an example, for risk sensitivity Value-at-Risk, Short-Fall, Risk Contribution,  etc. is calculated through an specific time period. Regarding to the forward  simulations Monte Carlo and GARCH model are used to generate expected  scenario of portfolio performance.

Once the portfolio has been defined and tested, it is important to perform a  fundamental analysis of the selected underlyings. 

This step can help to ensure that the portfolio remains aligned with the  investor's long-term objectives. Additionally, the model should be regularly  monitored to make sure that the portfolio remains optimal and adjust it  when necessary.