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Machine Intelligence Dynamic Small Cap

Approach

A dynamic strategy that seeks to identify high quality small cap companies with strong potential to out-perform. Applies the latest machine intelligence tools to conduct a deep analysis of financial, fundamental and ESG qualities of over 2,000 companies and to generate a high conviction portfolio without static style or factor biases.

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 10/31/241 Month3 MonthYTD1yr3yr5yr10yrSince Inception (1/01/21)
Gross-2.25-0.647.9731.472.38--7.27
Net-2.32-0.837.3130.511.62--6.48
Index*-1.44-2.249.5634.07-0.05--4.19

* Russell 2000 Index

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

Gareth Shepherd

Gareth Shepherd, PhD, CFA

Co-head Voya Machine Intelligence, Portfolio Manager

Years of Experience: 26

Years with Voya: 4

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.
Russell Shtern

Russell Shtern, CFA

Portfolio Manager, Voya Machine Intelligence

Years of Experience: 24

Years with Voya: 2

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.
Vincent Costa

Vincent Costa, CFA

Chief Investment Officer, Equities

Years of Experience: 39

Years with Voya: 18

Vincent Costa is chief investment officer, equities at Voya Investment Management and also serves as a portfolio manager for the active quantitative and fundamental large cap value strategies. Previously at Voya, he was head of the global quantitative equity team. 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 and a BS in quantitative business analysis from Pennsylvania State University. He is a CFA® Charterholder.

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.

The principal risks are generally those attributable to stock investing. Holdings are subject to market, issuer, and other risks, and their values may fluctuate. Market risk is the risk that securities may decline in value due to factors affecting the securities markets or particular industries. Issuer risk is the risk that the value of a security may decline for reasons specific to the issuer, such as changes in its financial condition. More particularly, the strategy invests in smaller companies, which may be more susceptible to price swings than larger companies because they have fewer resources and more limited products, and many are dependent on a few key managers. 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.

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