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Seven Steps of Investment Research Process

In this step, the Investment Research Analysts collaborate with the Data & Analytics Team to ensure the accuracy and cleanliness of the migrated data. Data is treated as a critical asset, and maintaining its integrity is a top priority. The analysts thoroughly validate and verify the data, eliminating any errors or inconsistencies.

Factors play a crucial role in our investment analysis, and each factor consists of multiple underlying parameters such as Return on Capital Employed (ROCE), Return on Equity (ROE), Change in Free Cash Flow (FCF) for the Quality factor, among others. To ensure the reliability and legitimacy of these parameters, we adhere to a rigorous development process. Once a parameter is defined, it undergoes a hygiene check and is then sent to the IT department for implementation, becoming a valuable addition to our Parameter Library.

In this standardized process, each developed parameter undergoes robustness testing. The purpose is to distinguish between strong and weak parameters. Only the robust parameters that exhibit a high degree of reliability and effectiveness are selected for further analysis and used in backtesting various portfolio strategies.

Different methodologies can be employed to determine the weightings of assets within a portfolio. These methodologies include equal-weighted, market capitalization-weighted, reverse market capitalization-weighted, and Z-score weighted, among others. The research team has the flexibility to develop and implement various weighting methods by leveraging the options available in the Research Library. These methodologies are instrumental in dynamically backtesting different portfolio strategies.

Once the relevant parameters have been developed and thoroughly tested for robustness, and the weighting methodologies have been established, the research team can proceed to create a portfolio strategy. They have the freedom to customize the strategy by selecting dynamic inputs such as the portfolio universe, the number of stocks, the selection criteria, the weighting method, and the rebalancing frequency and period. These choices enable them to tailor the portfolio strategy to their specific investment objectives and preferences.

To evaluate the performance of the backtested portfolio strategy, the historical Net Asset Value (NAV) of the model portfolio is analyzed using the proprietary SMART BETA Dashboard. A comprehensive range of metrics, including historical cumulative returns, point-to-point returns, rolling returns, calendar year returns, Sharpe and Sortino ratios, and volatility, are compared with relevant benchmarks. This analysis provides insights into how the backtested model portfolio has performed in relation to the benchmarks.

In this final step, the Research Output document, containing the details of the portfolio strategy, is shared with the entire Investment Committee for discussion and evaluation. The committee assesses the merit and demerit of the strategy based on the research output. If the strategy is deemed promising and has successfully completed all audit checks, it may be finalized for implementation in actual portfolios, both existing and new ones.

Through this process, our aim is to minimize human biases as much as possible and create rule-based methodologies. These methodologies are designed to generate risk adjusted returns that have potential to outperform the benchmark over the long term. The ultimate objective is to simplify the investing experience for both investors and partners.

By implementing rule-based investing methodologies, we ensure that portfolios remain true to their intended investment strategies. This means that the portfolios are aligned with their designated objectives and consistently follow predefined rules.

Furthermore, this approach reduces the need for frequent human intervention in day-to-day portfolio management. With clearly defined rules in place, the reliance on subjective decision-making is minimized. This leads to portfolios with increased consistency and reliability.