Jon Chandler
Senior Research Lead
Jon Chandler has 20 years of experience in the financial services industry. Prior to joining Cutter Associates in 2014, Jon was a senior business analyst for State Street Global Advisors (SSGA), where he was involved in a number of projects for the front and middle office, including UAT design and execution for an order management system (Fidessa IMS), the implementation of a web-based due diligence platform (FundInsight), and an enterprise solution to support blended benchmarks. Prior to his project roles, Jon was a principal in fixed income trade operations at SSGA. He has extensive experience with post-trade operations and applications, such as OASYS/CTM, PORTIA, MBSExpert, and FailStation. Jon earned a bachelor of arts in history from the University of Florida and an MBA from Northeastern University.
Recent research assignments and publications include the following:
- Alternative Investment Systems
- Cutter Benchmarking: Alternative Investments
- Cutter Benchmarking: Derivatives and Collateral Management
- Cutter Benchmarking: Investment Risk
- Cutter Benchmarking: Performance Measurement and Attribution
- Derivatives and Collateral Management Solutions
- Execution Management Systems
- Managed Data Services
- Order Management Systems
- Outsourcing Solutions
- Performance Measurement and Attribution Systems
- Portfolio Analytics Solutions
- Private Debt
- Risk Management Systems
- The Evolving Front Office Support Model
Steve Go
Research Analyst
Steve Go has more than 20 years of experience spanning various roles in the financial services industry. Prior to joining Cutter Associates in 2026, Steve was a trader, having executed fundamental and technical strategies for desks with differing investment profiles across global markets. His trading experience includes roles at event-driven hedge fund Beryl Capital Management and long-only asset manager DoubleLine Capital, with exposures in equities, derivatives, fixed income, and digital assets. Prior to assuming a role in trading, Steve was a business systems analyst, having implemented business transformation projects across front, middle, and back offices of both buy and sell side firms. His business analysis experience includes roles at multi-strategy hedge fund Canyon Partners and consulting firm Deloitte while on a Lehman Brothers engagement. Steve earned both a Bachelor of Science in Computer Science and a Master of Business Administration from the University of Notre Dame.
Factor-based investing has moved from a specialized quantitative discipline to a broader operating framework for modern investment decision-making. As firms contend with expanding datasets, the ability to translate investment views into measurable exposures and repeatable decisions is becoming the standard.
Once the exclusive domain of quantitative firms, institutional investors increasingly rely on sophisticated portfolio analytics, risk models, and factor-based insights. As investment teams contend with growing volumes of data, more complex exposures, and an expanding set of risk drivers, specialized platforms have emerged to help portfolio managers combine both the art and science of portfolio construction.
While these capabilities have traditionally depended on dedicated risk, quantitative, and data science teams, AI is beginning to democratize access to investment intelligence by enabling portfolio managers to interact directly with analytics, research, and portfolio data.
Looking Through to the Drivers of Risk and Return
Factor-based investing uses observable characteristics to explain, select, weight, and manage investments. These characteristics may include macroeconomic variables such as growth, inflation, and interest rates; style factors such as value, quality, momentum, size, and volatility; and fundamental measures such as earnings, dividends, and cash flow.
Research shows that the underlying sources of risk and return within a portfolio are not only due to security selection, or the securities themselves, but also to broad-based market factors. In recent years, firms have tried to isolate and analyze those factors to make better and more informed decisions. Many experts now argue that risk factor diversification provides more holistic insights than traditional portfolio construction, which mainly relies on asset class diversification alone.
Portfolio holdings can have exposure to the same risk factors that might otherwise be unexpected, across sectors, countries, and even asset classes. For example, the industry once regarded that a 60/40 equity-to-fixed income portfolio would “safely” generate return, while mitigating risk. Through a factor-based lens, however, the implications could vary significantly depending on the macro variable, style, or fundamentals of the equity or fixed income market. High-yield corporate bonds and stocks have exposure to some of the same risk factors, leaving the portfolio undefended under certain shocks. Factor decomposition reveals concentrations that traditional labels may obscure and helps investors separate a compelling story from the portfolio mechanics used to capture it.
Separating Skill From Systematic Exposure
While firms continue to analyze drivers of risk and return using more familiar categories like sector or country, factor analysis has become more common across our industry. Many Cutter member firms now also look through to their underlying drivers of return through a factor lens.
Asset owners increasingly use factor analysis to evaluate their active managers. Outperforming the market or index is not enough to justify active management fees when similar returns are available for less through passive management products. More asset owners want to understand to what extent manager skill drives an active manager’s performance rather than beta from exposure to common factors.
Beyond manager evaluation, asset owners are applying factor analysis across the total portfolio to identify overlapping exposures, test whether allocations are delivering the intended diversification and understand how macroeconomic and style risks are distributed across public and private investments. These insights can inform strategic asset allocation, manager selection, and risk budgeting decisions, while giving investment committees a more consistent framework for comparing strategies that may appear different by asset class but respond similarly to the same underlying drivers.
Thematic Investment Is What’s Trending
This discipline is especially valuable in thematic investing, which saw a resurgence last year and continues to attract significant inflows in 2026. Thematic investing looks beyond standard sectors or regions, using measurable factors like revenue and market share to find companies linked to specific themes. It aims to invest across countries and industries expected to gain from a theme over time, though risks remain.
Themes such as AI, defense, demographic change, and energy transition cut across conventional sectors and geographies. But portfolios pursuing the same theme can differ substantially in eligibility, holdings, weighting, and risk. Factor analysis can help identify concentration risks and anticipate performance in various market conditions.
By blending thematic investing with quantitative analysis, investors can improve outcomes by reducing unexpected risks and refining portfolio design.
Factor analysis and data signals can help explain why a theme performs the way it does, neutralize unwanted factor exposure, and hedge factor risk as needed.
Factor analysis can serve as the quantitative backbone to appropriately explain how thematic portfolios perform.
Democratizing Portfolio Intelligence
Sophisticated portfolio analytics, risk models, and factor-based insights are no longer confined to quantitative teams. As investment complexity grows, firms are adopting portfolio intelligence platforms that help portfolio managers understand exposures, evaluate performance drivers, and make better decisions. AI accelerates this evolution by making advanced analytics more accessible through self-service natural-language interactions, reducing dependence on specialized teams and enabling faster investment decisions.
And this arrives just in time because client scrutiny is rising as well. Factor-based reporting makes decisions more transparent and defensible.
In our upcoming report, The Alpha in the Room: Turning Data Into Insights, we will discuss some of these themes and more, and profile portfolio intelligence platforms that help the modern portfolio manager turn data into actionable insights.