Machine Intelligence Dynamic International Equity | Voya Investment Management

Machine Intelligence Dynamic International 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 12/31/221 Month3 MonthYTD1yr3yr5yr10yrSince Inception (11/01/21)
Composite Gross1.1817.26-14.28-14.28----12.13
Composite Net1.1317.07-14.84-14.84----12.70
Index*0.0817.34-14.45-14.45----12.35
Gross Excess Return1.10-0.090.170.17---0.22

* MSCI EAFE - NET in USD

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

No related literature found

Investment Team

Gareth Shepherd

Gareth Shepherd, PhD, CFA

Co-head of Machine Intelligence, Portfolio Manager

Years of Experience: 25

Years with Voya: 3

Gareth Shepherd is co-head of the Voya Machine Intelligence (VMI) team and a portfolio manager at Voya Investment Management. Prior to joining Voya, Gareth was a managing partner and co-founder of G Squared Capital LLP. Prior to that, he held various positions within risk and asset management in Australia, Switzerland, the U.S. and the UK. Gareth earned a PhD in applied expert systems and a Masters of Applied Science from the University of New South Wales, and a BE from the University of Queensland. He also completed a World Economic Forum sponsored Executive Masters (INSEAD, London Business School, and Columbia University) and is a CFA® Charterholder.
Vincent Costa

Vincent Costa, CFA

Chief Investment Officer, NY Equities

Years of Experience: 38

Years with Voya: 17

Vincent Costa is co-chief investment officer, NY equities at Voya Investment Management. Vincent is also the head of the global quantitative equity team and serves as a portfolio manager for the active quantitative and fundamental large cap value strategies. Previously at Voya, he was head of portfolio management for quantitative equity. Prior to joining Voya, he managed quantitative equity investments at both Merrill Lynch Investment Management and Bankers Trust Company. Vinnie earned an MBA in finance from New York University's Stern School of Business, a BS in quantitative business analysis from Pennsylvania State University, and is a CFA® Charterholder.

Russell Shtern, CFA

Portfolio Manager

Years of Experience: 23

Years with Voya: 1

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.

Disclosures

Principal Risk

Equity securities are subject to price fluctuation and possible loss of principal. Small- and mid-cap stocks involve greater risks and volatility than large-cap stocks. The manager's investment style may become out of favor and/or the manager's selection process may prove incorrect, which may have a negative impact on the Strategy's performance. The Strategy may focus its investments in certain regions or industries, increasing its vulnerability to market volatility. International investments are subject to special risks, including currency fluctuations and social, economic and political uncertainties, which could increase volatility. These risks are magnified 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.

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