A collection of our white papers and peer-reviewed research articles related to macro / multi-asset investor behavior, hedging, risk regimes, liquidity risk, private assets, portfolio construction, and more.
By Alexander Cheema-Fox, Megan Czasonis, Piyush Kontu, and George Serafeim
We investigate reliance on carbon offsets for decarbonization, associated risks, and factors that explain variation in offset prices.
Relying on carbon offsets for decarbonization has become an increasingly contentious approach in the fight against climate change. Many companies view the purchase of credits in carbon reduction or removal projects, such as reforestation or renewable energy initiatives, as an effective way to offset their green house emissions. However, critics argue that offset reliance is a temporary solution that allows for continued emissions rather than addressing the root cause. In a recent paper, we investigate firm reliance on carbon offsets and find that companies tend to use carbon offsets as a complement to their decarbonization activities rather than a substitute. Moreover, we find little evidence that market-based or analyst-derived risk measures reflect the inherent risk associated with offset reliance. Finally, we explore key factors, such as project type and geography, that explain carbon offset quality and prices.
By Megan Czasonis, Mark Kritzman, and David Turkington
We show that relevance-based prediction offers an elegant solution to the problem of incomplete data, preserving valuable information and enhancing prediction reliability in a way that is not possible using traditional models.
When setting out to form data-driven predictions, it’s common to encounter incomplete information, such as a time series with shorter history lengths or observations with missing data. Traditional methods for addressing this challenge either discard valuable data or manufacture replacements based on limiting assumptions, leading to unreliable results. We propose a novel technique called Relevance-Based Prediction (RBP), which elegantly navigates the pitfalls of missing data by retaining more information and accounting for the relative importance of observations for which only partial data is available. We show that RBP offers an elegant solution to the problem of incomplete data, preserving valuable information and enhancing prediction reliability in a way that is not possible using traditional models.
By Mark Kritzman, and David Turkington
Evidence shows that concentrated market capitalization weights do not make an index riskier, because larger stocks are inherently more diversified and their increased weights are offset by their lower volatility compared to small stocks.
The dominance of large tech firms in market-cap-weighted indices has sparked recent concern about concentration risk, but historical data and empirical analysis suggest these fears may be unfounded. A review of nearly 90 years of market performance shows that reducing exposure based on concentration offers no timing advantage and actually worsened returns and risk. Sector-level concentration also fails to distinguish between high and low risk or strong and weak performance. Moreover, large companies tend to be safer due to their more diversified operational footprint and the increased investor and regulatory scrutiny they receive. Ultimately, the presence of concentrated capitalization weights has not proven to be a reliable indicator of bubbles or future market downturns.
By Megan Czasonis, Mark Kritzman and David Turkington
We show that relevance-based prediction captures complex relationships, like a neural network, but with the added benefit of transparency.
Many prediction tasks in economics and finance lie beyond the reach of linear regression analysis. Researchers, therefore, often turn to machine learning techniques, such as neural networks, to address these complex dynamics. A neural network has the potential to extract nearly all the useful information from a dataset, however it is difficult to implement and notoriously opaque. Alternatively, relevance-based prediction is a model free and theoretically-grounded approach that forms a prediction as a relevance-weighted average of past outcomes. In a sample application to predicting stock market volatility, we show that relevance-based prediction captures complex relationships like a neural network. However, unlike a neural network, it is remarkably transparent, revealing how each observation and variable contributes to a prediction, and disclosing the reliability of a prediction in advance.
By Alexander Cheema-Fox, Megan Czasonis, Piyush Kontu and George Serafeim
We analyse the transformative impact of life insurance platform integration on the business models, financial profiles, and market valuations of prominent alternative asset managers (AAMs).
Relying on episodic fundraising through closed-end funds has long defined the growth and operating model of alternative asset managers (AAMs). Many firms have depended on periodic capital commitments from institutional investors, which, while effective for scaling private equity and credit strategies, expose managers to cyclical fundraising pressures, limited product scope, and volatile earnings. In our recent paper, we examine the industry’s shift as leading AAMs integrate insurance platforms, finding that insurance-backed capital transforms funding from episodic to permanent, enabling accelerated AUM growth and greater revenue stability. However, this structural change introduces new complexities - such as regulatory burdens and lower valuation multiples - while fundamentally altering the risk and return profile of these firms. We further analyze how the composition of capital, revenue mix, and market perceptions have evolved, offering insights for investors navigating the rapidly changing landscape of private markets.
By Zachary Crowell, Lee Ferridge, Michael Guidi, William Kinlaw, Gideon Ozik, and Ronnie Sadka
We present new research analyzing how media-driven narratives significantly influence currency movements, offering predictive power beyond traditional economic factors.
We introduce a novel framework for quantifying how media attention to economic and geopolitical narratives impacts currency risk and returns. By analyzing nearly a decade of digital media across 52 currency pairs, we observe that sudden shocks in media focus lead to predictable currency movements over time. These shocks are not immediately priced in, creating opportunities for return predictability. Portfolio managers can use this insight to assess exposure to specific narratives and strategically hedge or capitalize on emerging storylines. The research highlights the growing importance of media sentiment in global FX markets and its practical applications for investment strategy.
By Megan Czasonis, Ding Li, Grace (TianTian) Qiu, Huili Song, and David Turkington
We present an adaptive method for constructing an inflation robustness equity factor which outperforms attribute- and sector-based strategies during 65 inflation events from 2008 to 2025.
We introduce a novel framework to identify which equities will prove robust and which will falter during episodes of inflationary stress. By analyzing firm-level attributes and comparing them to historical performance during inflation events, we construct stock-level inflation robustness scores that capture nonlinear, conditional relationships between stock characteristics and inflation sensitivity. Tested across 65 inflation events from 2008 to 2025, these scores reliably predict stock performance, with a long-short portfolio delivering 9.1% annualized outperformance during inflationary periods. The robustness factor outperforms traditional attribute- and sector-based strategies and remains statistically significant even after controlling for sector and factor exposures. This adaptive methodology enables investors to build inflation-hedged portfolios with greater precision than conventional equity benchmarks.
By Mark Kritzman, Sébastien Page, and David Turkington
Our 2010 research paper – recently flagged by the Financial Analysts Journal as among the 23 most influential articles in its 80 year history—defended how data-drive portfolio optimization adds value when it is thoughtfully applied.
Investors rarely aspire to mediocrity, yet critics love to dismiss the quest for an “optimal” portfolio as quixotic. In an article published in the Financial Analysts Journal 15 years ago, we challenged the notion that equal-weighting (the infamous 1/N rule) beats optimization. That claim implies all information including expected returns, risks, and correlations, is worthless. By digging deeper we found a reason those studies generated such a counterintuitive conclusion: they used naïve five-year return extrapolations as forecasts. When we applied simple, sensible expectations instead, mean-variance optimization delivered clear out-of-sample value across assets, industries, factors, and stocks. Optimization is far from perfect and it deserves to be extended to address real-world complexities, but it is a far better starting point than just giving up.
By Alexander Cheema-Fox, Edward S.Cuipa III, and Robin Greenwood
Institutional investor flows can help distinguish between flights to safety and flights from carry, revealing that the CHF, JPY, and USD play distinct roles depending on interest rate differentials and whether market stress is driven by macroeconomic uncertainty or funding pressure in currency markets.
We examine the roles of safe-haven and funding currencies during market stress by analyzing how institutional investors reallocate across currencies during crises. Using proprietary FX and local-currency sovereign bond flows from State Street, we show that while the CHF, JPY, and USD all act as safe havens, investor flows and currency behavior differ substantially across crises depending on interest rate differentials and whether the episode is driven by macroeconomic uncertainty or by a carry trade unwind. A panel regression framework, leveraging FX forward and sovereign bond flows, reveals that some crises, like the 2008 Global Financial Crisis and COVID-19, reflect true flights to safety, whereas others, such as the 2024 carry crash and Liberation Day, are characterized as flights from carry involving CHF and JPY inflows and muted or negative USD flows. These findings underscore the importance of distinguishing between safe-haven and carry dynamics in currency markets and suggest that institutional investor flows offer a powerful tool for classifying crisis episodes.
By Megan Czasonis, Mark Kritzman, and David Turkington
As the race to design sophisticated data analytics continues, we show why relevance-based prediction offers an ideal way to measure the importance of an input variable to a prediction.
T-statistics act as a hallmark for rigor by pinpointing the effect of a single variable and distinguishing signal from noise. However, they have significant limitations: (1) t-stats do not capture ‘shared’ information, (2) t-stats are not prediction-specific, and (3) t-stats only consider linear relationships. In a recent paper, we introduce an alternative method, called Relevance-Based Importance (RBI), which measures the importance of every variable to the reliability of every individual prediction. RBI recognizes that it is almost never the case that a variable is always important, or that it is never important. Rather, it's more likely that variables are sometimes important, depending on the circumstance. We show that RBI brings the virtues of t-statistics but also adapts to each unique situation, making it robust to complexities where t-stats fall short.
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