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 Mark Kritzman, Huili Song, and David Turkington.
We show how warping time renders stock price bubbles comparable, revealing common patterns that investors can use to detect new bubbles and time exposure to their rise and fall.
Can history offer a guide to understanding future stock-price bubbles? The answer is yes, but we have to learn how to bend time. Thankfully, a method called dynamic time warping offers the solution. Previous bubbles occur at different paces: some rise fast and others slowly, some crash after weeks while others persist for years. By stretching and shrinking the timeline of thousands of bubble events, we systematically place them side by side and find more commonalities in their attributes' patterns than a calendar view suggests. We then use various attributes collectively to assess the likelihood of a developing bubble and identify its lifecycle stage, from inception to peak to conclusion. A simple trading rule seeking to invest in bubble run-ups and post-crash over reactions, while avoiding the peak, generates compelling performance in out-of-sample backtests.
By Mark Kritzman, Huili Song, and David Turkington.
We show how warping time renders stock price bubbles comparable, revealing common patterns that investors can use to detect new bubbles and time exposure to their rise and fall.
Can history offer a guide to understanding future stock-price bubbles? The answer is yes, but we have to learn how to bend time. Thankfully, a method called dynamic time warping offers the solution. Previous bubbles occur at different paces: some rise fast and others slowly, some crash after weeks while others persist for years. By stretching and shrinking the timeline of thousands of bubble events, we systematically place them side by side and find more commonalities in their attributes' patterns than a calendar view suggests. We then use various attributes collectively to assess the likelihood of a developing bubble and identify its lifecycle stage, from inception to peak to conclusion. A simple trading rule seeking to invest in bubble run-ups and post-crash over reactions, while avoiding the peak, generates compelling performance in out-of-sample backtests.
By Alexander Cheema-Fox, Megan Czasonis, Piyush Kontu and George Serafeim
We explore the world’s first set of financial accounting data on firms’ sustainable activities.
Though sustainable investing has grown in popularity over the past decade, measuring sustainability remains a key challenge. Investors often rely on environmental criteria—such as analyst ratings and carbon emissions—that are insufficient or rely on qualitative analysis. However, for the first time, with the advent of the EU’s Taxonomy for Sustainable Activities, investors have access to financial accounting data that follows standardized and transparent criteria for quantifying the percentage of a firm’s revenues and expenditures that align with sustainable activities. In a recent paper, we explore this novel dataset for a cross-section of large European firms, documenting patterns and analysing how firms’ aligned activities relate to fundamentals and environment ratings. We find that the EU Taxonomy data provide information that is distinct from existing sources and offers insights that can help investors and regulators, alike.
By Alexander Cheema-Fox, Megan Czasonis, Piyush Kontu and George Serafeim
We explore the world’s first set of financial accounting data on firms’ sustainable activities.
Though sustainable investing has grown in popularity over the past decade, measuring sustainability remains a key challenge. Investors often rely on environmental criteria—such as analyst ratings and carbon emissions—that are insufficient or rely on qualitative analysis. However, for the first time, with the advent of the EU’s Taxonomy for Sustainable Activities, investors have access to financial accounting data that follows standardized and transparent criteria for quantifying the percentage of a firm’s revenues and expenditures that align with sustainable activities. In a recent paper, we explore this novel dataset for a cross-section of large European firms, documenting patterns and analysing how firms’ aligned activities relate to fundamentals and environment ratings. We find that the EU Taxonomy data provide information that is distinct from existing sources and offers insights that can help investors and regulators, alike.
By Megan Czasonis, Mark Kritzman, and David Turkington.
We propose a new currency hedging technique called full-scale hedging, which accounts for the complexities of diversification.
Diversification is nuanced and summary statistics, such as correlation, fail to capture complexities that lie below the surface. For investors, these complexities matter—accounting for them can make the difference between an effective, or ineffective, hedging strategy. In the case of currencies, investors often determine risk-minimizing hedge ratios based on the portfolio’s betas to those currencies or with mean-variance optimization. In both cases, the optimal solution depends crucially on the correlation between the currencies and assets in the portfolio. But correlation is an unreliable estimate of the diversification investors actually care about: the co-occurrences of the cumulative returns of the portfolio and currencies over the investment horizon. We propose a new currency hedging technique called full-scale hedging, which addresses these challenges by considering the full distribution of co-occurrences between currencies and the portfolio.
By Megan Czasonis, Mark Kritzman, and David Turkington.
We propose a new currency hedging technique called full-scale hedging, which accounts for the complexities of diversification.
Diversification is nuanced and summary statistics, such as correlation, fail to capture complexities that lie below the surface. For investors, these complexities matter—accounting for them can make the difference between an effective, or ineffective, hedging strategy. In the case of currencies, investors often determine risk-minimizing hedge ratios based on the portfolio’s betas to those currencies or with mean-variance optimization. In both cases, the optimal solution depends crucially on the correlation between the currencies and assets in the portfolio. But correlation is an unreliable estimate of the diversification investors actually care about: the co-occurrences of the cumulative returns of the portfolio and currencies over the investment horizon. We propose a new currency hedging technique called full-scale hedging, which addresses these challenges by considering the full distribution of co-occurrences between currencies and the portfolio.
By Mark Kritzman, Huili Song, and David Turkington.
We show how warping time renders stock price bubbles comparable, revealing common patterns that investors can use to detect new bubbles and time exposure to their rise and fall.
Can history offer a guide to understanding future stock-price bubbles? The answer is yes, but we have to learn how to bend time. Thankfully, a method called dynamic time warping offers the solution. Previous bubbles occur at different paces: some rise fast and others slowly, some crash after weeks while others persist for years. By stretching and shrinking the timeline of thousands of bubble events, we systematically place them side by side and find more commonalities in their attributes' patterns than a calendar view suggests. We then use various attributes collectively to assess the likelihood of a developing bubble and identify its lifecycle stage, from inception to peak to conclusion. A simple trading rule seeking to invest in bubble run-ups and post-crash over reactions, while avoiding the peak, generates compelling performance in out-of-sample backtests.
By Mark Kritzman, Cel Kulasekaran, and David Turkington.
We introduce a more flexible way to forecast risk and return based on the most relevant historical periods.
As economic regimes shift, investors who choose to adapt must build portfolios that match their evolving view of the future. Forecasts of asset risk and return should account for regime-specific trends. The question is how to implement this idea in practice. Typically, an analyst will find every time an economic indicator like inflation or growth was above (or below) a fixed threshold, and she will pay equal attention to every data point that qualifies. While this approach seems sensible, it also has dramatic limitations. Ideally, we should recognize that the regime labels of past events are not simple yes/no answers; they are ambiguous. We should pay more attention to some past events than others, based on their relevance. We should weigh the impact of many variables rather than just one. And we should accept that some events are relevant to more than one regime. A statistical measure of relevance, based on the Mahalanobis distance, empowers investors to analyze these nuances of regimes with rigor. We show how to estimate expected risk and return as weighted averages of the relevant past, and how these forecasts of asset performance lead to intuitive portfolios optimized for a range of possible regimes.
By Alberto Cavallo, Megan Czasonis, William Kinlaw, and David Turkington
We show how unstructured price data from online retailers can anticipate inflation shifts and enable investors to hedge inflation risk dynamically.
Investors and academics have been studying inflation, and how it affects asset prices, for more than four decades. Their findings are discouraging: there just aren’t many assets that offer a reliable hedge against inflation. Treasury Inflation Protected Securities (TIPS), introduced in 1997, represent the only U.S. asset class whose returns are linked explicitly to inflation, but they have drawbacks. For one, their yields are lower than normal treasury bonds during most periods, when inflation is low. In an ideal world, investors would capture the higher yield of treasuries when inflation is benign and shift into TIPS to capture their price appreciation when inflation expectations rise. To do this, they need a good leading indicator of the market’s collective inflation expectations. In this paper, we show how unstructured price data from online retailers, spanning millions of products captured by PriceStats®, can be used to forecast the relative performance of TIPS and treasuries.
By Musa Amadeus, Rajeev Bhargava, Michael Guidi, Marvin Loh, Gideon Ozik, and Ronnie Sadka
Read between the lines: The measurement of Fed members’ monetary tones facilitates an understanding of the dynamics of the individual monetary policy stances underlying aggregated, consensus (top-down) Fed tones.
Amadeus et al. (2022) observe that aggregated, consensus (top-down) central bank monetary tones in media contain predictive information pertaining to future weekly yield fluctuations. This article elucidates the more granular, stratified (bottom-up) dynamics underlying these relations. The predictive relationships between Fed consensus tones and yields are primarily driven by an underreaction of yields to the Fed Board of Governors’ tones between monetary policy meetings. Over short-term horizons, Treasury yields appear to price voting FOMC members’ (Board of Governors’ and Regional Bank Presidents’) tones while relatively longer-term horizon yields appear to reflect both voting and non-voting tones. Fed Regional Bank Presidents’ monetary tones are more responsive to regional inflation fluctuations than to unemployment. The analysis of the heterogeneous impacts of Fed members’ tones over distinct yield horizons provides insights pertaining to the pricing of voting and non-voting Fed members’ tones in Treasury markets.