Approach
An AI-driven opportunistic, active strategy that seeks to deliver idiosyncratic alpha without static factor or style biases. The investment process delivers a high conviction portfolio using the latest machine learning and AI techniques under a broader umbrella inclusive of human insights, traditional quant methods, and engineered financial ratios in a process we have coined "Machine Intelligence".
Key Benefits
- Differentiated strategy that combines the depth & rigor of fundamental analysis with the breadth & scalability machine learning brings
- Disciplined yet dynamic process that takes advantage of mispriced and overlooked stock opportunities and sidesteps human emotion
- Robust risk management including active screens for negative events, controversies, crowding and ESG risks
- Stable, experienced team dedicated to AI investing for over a decade
- Aims to provide returns uncorrelated to most investment strategies
Performance
Performance
| As of 11/30/24 | 1 Month | 3 Month | YTD | 1yr | 3yr | 5yr | 10yr | Since Inception (11/01/21) |
|---|---|---|---|---|---|---|---|---|
| Gross | 3.67 | 3.52 | 18.57 | 26.56 | 10.38 | - | - | 8.38 |
| Net | 3.61 | 3.34 | 17.83 | 25.69 | 9.62 | - | - | 7.63 |
| Index* | 4.59 | 4.39 | 21.85 | 27.83 | 8.79 | - | - | 7.77 |
* MSCI WORLD - NET
Past performance does not guarantee future results.
Periods greater than one year are annualized. Performance data is considered final unless indicated as preliminary. Monthly performance is based on full GIPS Composite returns. Access the GIPS page for full composite details.
The Composite performance information represents the investment results of a group of fully discretionary accounts managed with the investment objective of outperforming the benchmark. Information is subject to change at any time. Gross returns are presented after all transaction costs, but before management fees. Returns include the reinvestment of income. Net performance is shown after the deduction of a model management fee equal to the highest fee charged.
Literature
Voya Machine Intelligence Dynamic Global Equity Strategy Brief
Date: September 30, 2024
Approved For: Financial Professional or Qualified Institutional Investor Use Only
Investment Team
Gareth Shepherd, PhD, CFA
Co-head Voya Machine Intelligence, Portfolio Manager
Years of Experience: 26
Years with Voya: 4
Russell Shtern, CFA
Portfolio Manager, Voya Machine Intelligence
Years of Experience: 24
Years with Voya: 2
Vincent Costa, CFA
Chief Investment Officer, Equities
Years of Experience: 39
Years with Voya: 18
Disclosures
Principal Risk
The strategy employs a quantitative model to execute the strategy. Data imprecision, software or other technology malfunctions, programming inaccuracies and similar circumstances may impair the performance of these systems, which may negatively affect performance. Furthermore, there can be no assurance that the quantitative models used in managing the strategy will perform as anticipated or enable the strategy to achieve its objective.
All investing involves risks of fluctuating prices and the uncertainties of rates of return and yield inherent in investing. Price volatility, liquidity, and other risks that accompany an investment in equity securities of domestic and foreign companies, and small and mid sized capitalized companies. International investing does pose special risks including currency fluctuation, economic and political risks not found in investments that are solely domestic. Risks of foreign investing are generally intensified for investments in emerging markets. Investment Model: A manager's proprietary model may not adequately allow for existing or unforeseen market factors or the interplay between such factors, and even a model that performs in accordance with the manager’s intentions may underperform other investment strategies or result in greater losses than other strategies. The proprietary models used by a manager to evaluate securities or securities markets are based on the manager's understanding of the interplay of market factors and do not assure successful investment. The markets, or the prices of individual securities, may be affected by factors not foreseen in developing the models. Strategies that are actively managed, in whole or in part, according to a quantitative investment model, including models using artificial intelligence to select securities, can perform differently from the market as a whole based on the investment model and the factors used in the analysis, the weight placed on each factor, and changes from the factors' historical trends. Mistakes in the construction and implementation of the investment models (including, for example, data problems and/or software issues) may create errors or limitations that might go undetected or are discovered only after the errors or limitations have negatively impacted performance. There is no guarantee that the use of these investment models will result in effective investment decisions for the Strategy.
Artificial intelligence (AI) including natural language processing, machine learning, and other forms of AI may pose inherent risks, including but not limited to: issues with data privacy, intellectual property, consumer protection, and anti-discrimination laws; ethics and transparency concerns; information security issues; the potential for unfair bias and discrimination; quality and accuracy of inputs and outputs; technical failures and potential misuse. Reliance on information produced using AI-based technology and tools should factor in these risks.