We offer an intuitive and interactive way to engage with State Street Global Markets'® research. With Insights, you can read market commentary from our team of macro strategists and explore the most interesting trends in our daily indicators of investor behavior, risk, inflation and sentiment based on award-winning research.1

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Chart of the Week21 Jun 2024
Looking More and More Disappointing for China

Throughout the past year, the PBoC’s top priority is avoiding excessive Yuan volatility. The combination of stronger daily fixing and tighter monetary policy through draining liquidity has pushed the currency significantly higher against its trade-weighted basket despite broad weakening of EM against the USD. However, the consequence to tighter policies means economic growth remains uneven with consumption and investment hardly recovering on the back of weaker credit impulse. Our China macro momentum index has started to deteriorate despite a brief rebound at the start of the year. While we acknowledge better investor sentiment towards Chinese assets, especially on the back of their commitment to tackle the property crisis, it's time to use this opportunity and start prioritizing growth over the currency.

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Institutional investors cash out despite cyclical doubts


  • The State Street Holdings Indicators showed that long-term investor allocations to equities rose 33bps to 53.7%.
  • Cash holdings well a further 0.4 percentage points to 18.4%, this is the first time in tenth months that cash holdings are below their long-term average.
  • Fixed income holdings were left largely unchanged after their sharp rise last month.


  • The State Street Risk Appetite Index bounced back to 0.09 in May revealing a modest risk on bias across the month.
  • Within this moderate risk on environment long-term investors began to reassess their USD overweight once again.
  • Long-term investors rediscovered their appetite for higher yielding FX and fixed income instruments in May.
Research on the go

The State Street Global Markets® Insights app provides you with instant access to our cutting-edge research, data-driven indicators and timely expert commentary on global macro trends, multi-asset strategy, investor behavior, daily inflation, media sentiment, risk regimes, liquidity, currencies, portfolio construction and more.

Powered by our longstanding partnerships with renowned academics and unique proprietary data sources, Global Markets Research provides an essential perspective on markets that is not available anywhere else.

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We offer intuitive and interactive ways to engage with State Street Global Markets' research.

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Our home section is at the core of Insights and is the starting point for exploring our research. We curate a feed of the most relevant and exciting content that is of greatest interest to you, based on your research preferences, saved views, and our Insights AI engine.

Engage with our research

In Research, explore our catalogue of research by filtering on publication type, author, or theme. Once you’re satisfied with your search, sort the results by the latest or most popular research meeting your criteria and subscribe to future publications or authors.

Interact with our indicators

Choose how you want to engage with our indicators. Browse our Indicator section to analyze trends across market segments and through time.

Research a Trade Idea

In Research a Trade Idea, look across flagship indicator pairings in investor behavior, risk, inflation and sentiment to get a feel for the investment environment in the market segment of your choosing.

Signal Studio

Or, if you’re looking for a more hands-on approach, visit our Signal Studio and craft your own indicator combinations.

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Our Research

Our expansive suite of investment indicators provides investors with an information edge. We provide measures of:

  • Investor Behavior
  • Risk Regimes
  • Media Sentiment
  • Consumer Prices
Aggregated and anonymized measures of institutional behavior can identify persistent trends in buying, selling, positioning, and agreement across investment styles, currencies, sectors, countries, and asset classes.
Market commentary

Our team of macro strategists produce regular commentary on equity, fixed income, and currency markets around the world. We offer a differentiated view on global market trends, risk, and opportunities.

Thought leadership

Access 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.

Latest papers


Join us for the 2024 State Street Summer Sessions Webinar Series


Time to review the fundamentals of finance and investing! Even the most sophisticated investors can benefit from an occasional tune up. Join us for our 4th annual State Street Summer Sessions, where our team of academic and industry experts will go back to basics and cover the core principles of modern investing.

Connecting theory to practice, our presenters will cover topics including inflation, liquidity, and private markets into context. We have plenty of new sessions this year, in addition to the most popular sessions from last year. You can register for specific seminars or join us for them all. So close your email, silence your phone, and prepare some good questions. We’ll see you there!




Thursday June 27, 2024

9am EST

Geopolitics and Markets

Daniel Drezner, Tufts University’s Fletcher School of Law and Diplomacy, State Street Associates Academic Partner | REGISTER NOW

The past few years have highlighted a sea change in how governments approach their own economies and the global economy, adding an additional layer of uncertainty to markets.  Geopolitical hotspots have the potential to generate significant economic fallouts.  The year of elections is only half over, and the biggest votes are coming soon.  Political analyst Daniel Drezner dissects the role that politics will be playing in the months to come.



Tuesday, July 9, 2024

11am EST

Generative AI, Climate Solutions and Investment Implications

George Serafeim, Harvard Business School, State Street Associates Academic PartnerREGISTER NOW

Presenting an application of Generative AI to identify climate technologies and innovation and the implications for growth, risk and valuation across different sectors of the economy.



Thursday, July 11, 2024

9am EST

How Central Banking Relates to Markets and Economies

Robin Greenwood, Harvard Business School, State Street Associates Academic Partner| REGISTER NOW

Details forthcoming.



Tuesday, July 16, 2024

10am EST

The Limits of Diversification

Will Kinlaw, Senior Managing Director, Head of Global Markets Research, State Street Global Markets REGISTER NOW

To diversify is one of the fundamental tenets of investing. Yet what seems straightforward in theory is complex in practice. Correlations can be asymmetric and unstable through time. Moreover, correlations measured over shorter intervals do not necessarily extrapolate to longer intervals. This presentation will synthesize more than 10 years of published research into these questions, analyze the challenge from a new perspective, and propose actionable solutions to help investors construct more resilient portfolios.



Thursday, July 18, 2024

10am EST

Defining and Measuring Inflation

Alberto Cavallo, Harvard Business School, State Street Associates Academic Partner| REGISTER NOW

As the global economy reemerges from the global COVID-19 pandemic and central banks raise interest rates to contain prices, inflation risk looms large in the minds of investors. In this session, Alberto Cavallo the Thomas S. Murphy Professor of Business Administration at Harvard Business School, co-founder of PriceStats, and member of the Technical Advisory Committee of the U.S. Bureau of Labor Statistics (BLS) will discuss the fundamentals of how inflation is measured, what drives it, and how to think about the risk to investors in 2024.



Tuesday, July 23, 2024

9am EST

Investing in Private Markets

Josh Lerner, Harvard Business School, State Street Associates Academic Partner| REGISTER NOW

Throughout 2023 and the first half of 2024, private equity faced enormous challenges, navigating lower capital inflow, slower exit activity, decreased valuations and higher capital costs. In this lecture, Harvard Business School professor Josh Lerner will discuss the major factors to consider when investing in today's market conditions.

Professor Lerner will provide insight into the drivers of the historic private equity (PE) boom, current trends that are impacting the direction of the market, and secular shifts that will influence the long-term outlook of PE. The content will draw from a combination of academic research, industry data, and expert insights to provide a 360-degree view of the market landscape. From this lecture, investors will gain a foundation for positioning themselves for success amidst present and future market dynamics.



Thursday July 25, 2024

10am EST

Theory and Practice of Sentiment Analysis Using AI

Gideon Ozik, CFA, PhD, MKT MediaStats, State Street Associates Academic Partner REGISTER NOW

Analysis of textual information pertaining to stocks, bonds, and currencies can provide investors with valuable insights into market trends and investor behaviors, as well as improve their ability to predict future fluctuations of asset prices. 

In this session, we will cover various textual analysis methodologies, review advancements in AI and Large Language Models (LLM), and demonstrate practical applications such as prediction of stock returns using LLMs applied to media coverage, short squeezes using social media, treasury yields using media coverage of monetary policy, and introduce analysis of local media to forecast election outcomes.



Thursday, August 1, 2024

9am EST

Relevance-Based Prediction: A Transparent and Adaptive Alternative to Machine learning

Mark Kritzman, Founding Partner, State Street Associates, State Street Global Markets Founding Partner, CEO, Windham Capital Management, LLC, Chairman, Windham’s Investment Committee REGISTER NOW

Relevance-based prediction is a model-free approach to prediction that forms predictions as relevance-weighted averages of observed outcomes.  The relevance weights are composed of similarity and informativeness, which are both measured as Mahalanobis distances.  This prediction method deals with complexities that are beyond the reach of conventional prediction techniques such as linear regression analysis, and it does so in a way that is more transparent, more adaptive, and more theoretically justified than widely used machine learning algorithms.



Tuesday August 6, 2024

9am EST

Quant Strategies and Backtests: Building Blocks and Best Practices

Andrew Li and Alex Cheema-Fox, State Street Associates| REGISTER NOW

We explore the philosophy, mechanisms, and logistics of quantitative strategies and backtesting.  This includes how and why to formulate a backtest, modes of testing (e.g. cross-sectional relative value vs market timing), signal construction (simple linear vs machine learning), data wrangling considerations (e.g. ensuring data are point-in-time), and performance evaluation (e.g. risk-adjusted returns, turnover).  Illustrative examples from various asset classes are presented.



Thursday, August 8, 2024

9am EST

Addressing Portfolio Risk and Regimes

Meg Czasonis, State Street Associates | REGISTER NOW

Investing always entails risk, and it must be managed. But risk is a multidimensional concept which makes it challenging to measure, and even more challenging to control. In this presentation, Megan Czasonis, head of Portfolio Management Research at State Street Associates, will discuss the benefits and limitations to a range of statistical risk measures—from conventional notions of volatility and value-at-risk to more intricate measurement of losses—as well as conducing regime-specific stress tests and managing portfolio risk.



Tuesday August 13, 2024

10am EST

Machine Learning Interpretation and Model Fingerprint

David Turkington and Huili Song, State Street Associates| REGISTER NOW

Machine learning brings exciting opportunities to investing by utilizing advanced models capable of processing complex nonlinearity and interaction patterns that are powerful for statistical predictions. However, applying machine learning to investing also faces challenges that differ from other disciplines where machine learning has excelled. The primary challenge is the black box problem – the lack of trust and transparency in understanding the models. In this summer session, we will discuss both the opportunities and challenges of applying machine learning to investing, and presenting our solutions that help human users comprehend how a machine learning model arrives at a prediction.



Tuesday, August 20, 2024

11am EST

The Evolution of Crypto Markets

Antoinette Schoar, MIT Sloan School of Management, State Street Associates Academic Partner | REGISTER NOW

Recent developments in the crypto market saw an increasing entry of traditional financial institutions and an expanding role for centralized exchanges. We explore the implications of these trends for systemic risk, data privacy and transparency, as well as consumer financial protection.



Tuesday August 27, 2024

10am EST

Understanding Market Liquidity

Ronnie Sadka, Boston College Carroll School of Management, State Street Associates Academic Partner | REGISTER NOW

Despite having been a key determinant of asset prices for decades, liquidity is still a difficult concept to define and properly understand. In this session, we shall review the theoretical economic underpinnings of market liquidity, and discuss its multi-faceted role in determining market prices and investment strategies. Alternative measures will be introduced as well as practical applications. Further attention will be devoted to the impact of recent market trends, such as retail trading and social media on market liquidity.




Understanding Chinese Policies and Cross Asset Implications

Ben Luk and Yuting Shao, State Street Global Markets Research | REGISTRATION COMING SOON

China’s increasing importance not only in emerging markets but also globally means investors are closely following every move out of Beijing. Meanwhile, with China’s post-Covid pent-up demand start to run out of steam, continued weak prices and property sector slump underpin concerns on whether China is able to turn the macro economy around. What’s more, the 3rd Plenum and upcoming US general elections add another layer of policy and geopolitical uncertainty. In this summer session, Ben Luk and Yuting Shao take a deep dive into China’s macro economy and asset classes to try to understand the dynamics of underlying drivers and implications for emerging markets and broader global economy.

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 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.

By Megan Czasonis, Mark Kritzman, and David Turkington


Relevance-based prediction is a new approach to data-driven forecasting that serves as a favorable alternative to both linear regression analysis and machine learning. It follows from two seminal scientific innovations: Prasanta Mahalanobis’ distance measure and Claude Shannon’s information theory. Relevance-based prediction rests on three key tenets:


1) relevance, which measures the importance of an observation to a prediction;


2) fit, which measures the reliability of each individual prediction task;


3) codependence, which holds that the choice of observations and predictive variables should be determined jointly for each individual prediction task

By William Kinlaw, Mark Kritzman, and David Turkington


Conventional statistics hide important realities that investors need to know.


The correlation coefficient often fails to capture what really matters to investors. There are two reasons for this. First, investors often measure correlations using monthly data and assume that they also hold over one-year, five-year or ten-year periods. Unfortunately, in the real world, they often don’t. The second reason has to do with a fundamental misconception about diversification. The fact is, investors don’t always want it. Sure, they want it on the downside, in order to offset the poor performance of one or more assets. But on the upside they prefer all assets to rise in unison, which is the opposite of diversification. Put differently, they’d be happy to place their eggs, conveniently, in a single basket provided nobody steals it. Our research shows that correlations can vary through time based on a range of conditions including the level of interest rates, the degree of turbulence in financial markets, and the performance of major equity markets. Overall, our findings challenge the notion that returns evolve as a simple “random walk,” a critical pre-condition without which we must interpret the correlation coefficient distrustfully. To address these issues, we introduce the notion of co-occurrence and offer a new perspective on how investors should diversify portfolios.



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Our goal is to bridge the worlds of financial theory and practice with innovative research for asset managers and owners. We focus on two fundamental drivers of performance to help State Street's clients exceed their performance goals and manage risk.

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1.Peter L. Bernstein Award for Best Article in an Institutional Investor Journal in 2013; Doriot Award for Best Private Equity Research Paper in 2022; Bernstein-Fabozzi/Jacobs-Levy Award for Outstanding Article in the Journal of Portfolio Management in 2006, 2009, 2011, 2013 (2), 2014, 2015, 2016, 2021; Roger F. Murray First Prize for Research Presented at the Q Group Conference in 2012 and 2021; Graham & Dodd Scroll Award for article in the Financial Analysts Journal in 2002 and 2010.