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.
Join us for the 2024 State Street Foundations of Investing Seminar 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 fourth annual State Street Foundations of Investing Seminar Series, where our team of academic and industry experts will go back to basics, covering the core principles of modern investing.
Connecting theory to practice, our Global Markets research experts and academic partners will cover topics such as inflation, liquidity, private markets, and much more. We have plenty of new sessions this year, in addition to the most popular sessions from last year. Register for specific seminars or join them all. So close your email, silence your phone, and prepare some tough questions. We’ll see you there!
Thursday, July 18, 2024
9am HK
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, August 1, 2024
9am HK
Understanding Chinese Policies and Cross Asset Implications
Ben Luk and Yuting Shao, State Street Global Markets Research | REGISTER NOW
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.
Thursday, August 8, 2024
9am HK
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 15, 2024
9am HK
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.
Thursday, August 22, 2024
9am HK
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.
Thursday, August 29, 2024
9am HK
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.
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 | Replay Coming Soon
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 Partner| Replay Coming Soon
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| Replay Coming Soon
In an increasingly interconnected world, it is impossible to succeed as an investor without a firm grasp on economic fundamentals and policy levers. In this session, Robin Greenwood ꟷ the George Gund Professor of Finance ꟷ will review the fundamental tenets of central banking with a focus on the main questions global investors should be thinking about in 2024.
Tuesday, July 16, 2024
10am EST
The Limits of Diversification
Will Kinlaw, Senior Managing Director, Head of Global Markets Research, State Street Global Markets | Replay Coming Soon
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
Inflation Explained: Measurement, Causes, and Latest Trends
Alberto Cavallo, Harvard Business School, State Street Associates Academic Partner| REGISTER NOW
The recent trends indicate that the inflation crisis is ending. However, central banks and investors remain vigilant and cautious about the potential future trajectory, reflecting the persistent uncertainties in the economic landscape. In this session, Alberto Cavallo ꟷ 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) ꟷ will discuss the fundamentals of how inflation is measured, what drives it, and how to think about the risk to investors in 2025
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.
Tuesday, July 30, 2024
9am EST
Understanding Chinese Policies and Cross Asset Implications
Ben Luk and Yuting Shao, State Street Global Markets Research | REGISTER NOW
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.
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.
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, 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, Michael Metcalfe, and David Turkington
Published in the Journal of Investment Management, Third Quarter 2023 and recipient of the 2023 Harry Markowitz Award.
We use novel statistical techniques to measure the time-varying influence of cost push, demand pull, inflation expectations, monetary policy, and fiscal policy on inflation regimes.
It can be hard to pin down what causes inflation, and often a range of views are put forth. The shifting nature of inflation regimes makes this challenge even more daunting. In our latest research we take a data-driven view of the key drivers of inflation, comparing the economic circumstances at any point in time to those that prevail during various historical regimes. We identify four prototypical inflation regimes: stable, rising steady, rising volatile, and disinflation. And we apply a method originally introduced for predicting the business cycle to disentangle and attribute the determinants of U.S. inflation to eight macroeconomic variables. The results are intuitive and carry interesting policy implications. As of early 2022, fiscal spending stands out as the main determinant of the current inflation regime.