Machine Intelligence Dynamic Global Equity | Voya Investment Management

Machine Intelligence Dynamic Global Equity

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 7/31/251 Month3 MonthYTD1yr3yr5yr10yrSince Inception (11/01/21)
Gross-0.187.4511.0816.2314.15--8.61
Net-0.237.2710.6315.4313.36--7.86
Index*1.2911.9110.8815.7215.83--8.55

* 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

Investment Team

Russell Shtern

Russell Shtern, CFA

Portfolio Manager, Voya Machine Intelligence

Years of Experience: 25

Years with Voya: 3

Russell Shtern is a portfolio manager with the Voya Machine Intelligence (VMI) team at Voya Investment Management. Prior to joining Voya, he was a senior portfolio manager at Franklin Templeton, managing smart beta and active multi-factor equity strategies. Prior to that, Russell worked at QS Investors (a Legg Mason affiliate) as head of equity portfolio management and trading. Previously, he was a lead portfolio manager with the diversification based investing equity and tax managed equity strategies at Deutsche AM Quantitative Strategies group. Russell earned a BBA with honors in finance and a minor in economics from Pace University. He is a CFA® Charterholder.
Sanne de Boer

Sanne V de Boer, PhD, CFA

Head of Systematic Equities

Years of Experience: 22

Years with Voya: 10 *

Sanne de Boer is a managing director and head of systematic equities at Voya Investment Management, responsible for overseeing the firm’s quantitative equity research agenda as well as portfolio management for its suite of active quant, machine intelligence, and index funds. Previously at Voya, he was director of quantitative equity research. Prior to joining Voya, he was a senior research analyst for quantitative strategies for Invesco. Previously, he was a research analyst for global quantitative equities at QS and before that he was with ING Investment Management, Voya’s predecessor firm. Sanne’s research has been published in the Journal of Asset Management and the Journal of Investing, among other prominent publications. He has lectured on decision science and quantitative investing at New York University, Columbia University, and the National University of Singapore, and regularly presents at industry conferences. Sanne earned a PhD in operations research from the Massachusetts Institute of Technology and an MS in mathematics and an MA in econometrics cum laude from the Vrije Universiteit Amsterdam and is a CFA® Charterholder.

* Years with Voya is not consecutive; Individual has re-joined the firm.

Kai Yee Wong

Kai Yee Wong

Portfolio Manager

Years of Experience: 33

Years with Voya: 13

Kai Yee Wong is a portfolio manager on the quantitative equity team at Voya Investment Management responsible for the index, research enhanced index and smart beta strategies. Prior to joining the firm, she worked as a senior equity portfolio manager at Northern Trust responsible for managing various global indices including developed, emerging, real estate. Prior to that, Kai Yee was a portfolio manager with Deutsche Bank. Previously, she held roles with Bankers Trust and Bank of Tokyo. Kai Yee earned a BS from New York University Stern School of Business.

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.

The Voya Machine Intelligence (VMI) team employs a proprietary machine learning approach to identify and exploit persistent patterns in company data. The approach leverages (non-linear) machine learning ("ML") models for fundamental analysis. The ML models employed do not utilize generative AI algorithms.

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