About

George M. Constantinides is the Leo Melamed Professor of Finance at The University of Chicago Booth School of Business. His research focuses on the causes of the historically observed premium of equity returns over bond returns, known as the equity premium, as well as the value premium and the size premium. He studies the pricing and hedging of fixed-income securities, options, futures, and other derivatives, along with the effects of transaction costs and taxes on the pricing of derivatives, and portfolio management. He has published numerous papers in distinguished academic periodicals covering topics such as asset pricing with countercyclical household consumption risk, mispricing of S&P 500 index options, rational asset prices, and new perspectives on the equity premium puzzle. Constantinides is a former president of the American Finance Association and of the Society for Financial Studies. He is also a research associate at the National Bureau of Economic Research and serves as a director and trustee of the Dimensional Fund Advisors' family of funds and trusts.

Research topics

• Microeconomics
• Economics
• Market economy
• Monetary economics
• Business
• Public economics
• Econometrics

Selected publications

• Carbon Uncertainty and Asset Prices 

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• Sentiment and Environmental Performance

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Welfare Costs of Idiosyncratic and Aggregate Consumption Shocks

  The Review of Asset Pricing Studies · 2025-02-06 · 1 citations

  article1st authorCorresponding

  Abstract I estimate the welfare benefits of eliminating idiosyncratic consumption shocks in the United States related (unrelated) to the business cycle as 36%–39% (lower than 1%) of household utility. Estimates of the former exceed earlier ones because I distinguish between idiosyncratic shocks related/unrelated to the business cycle, estimate the negative skewness of shocks, target moments of idiosyncratic shocks from household-level CEX data, and target market moments. Benefits of eliminating aggregate shocks are lower than 1% of utility. Policy should facilitate the insurance of idiosyncratic shocks related to the business cycle, such as job layoffs, with proof that individuals diligently seek suitable employment during periods of unemployment. (JEL D31, D52, E32, E44, G01, G12)

  PublisherDOI

• Convergence for Discrete Parameter Update Schemes

  arXiv (Cornell University) · 2025-12-03

  preprintOpen accessSenior author

  Modern deep learning models require immense computational resources, motivating research into low-precision training. Quantised training addresses this by representing training components in low-bit integers, but typically relies on discretising real-valued updates. We introduce an alternative approach where the update rule itself is discrete, avoiding the quantisation of continuous updates by design. We establish convergence guarantees for a general class of such discrete schemes, and present a multinomial update rule as a concrete example, supported by empirical evaluation. This perspective opens new avenues for efficient training, particularly for models with inherently discrete structure.

  PublisherOA PDFDOI

• Diagnostic Expectations and the Macroeconomy

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

  ArXiv.org · 2025-12-01

  preprintOpen access

  We present hls4ml, a free and open-source platform that translates machine learning (ML) models from modern deep learning frameworks into high-level synthesis (HLS) code that can be integrated into full designs for field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs). With its flexible and modular design, hls4ml supports a large number of deep learning frameworks and can target HLS compilers from several vendors, including Vitis HLS, Intel oneAPI and Catapult HLS. Together with a wider eco-system for software-hardware co-design, hls4ml has enabled the acceleration of ML inference in a wide range of commercial and scientific applications where low latency, resource usage, and power consumption are critical. In this paper, we describe the structure and functionality of the hls4ml platform. The overarching design considerations for the generated HLS code are discussed, together with selected performance results.

  PublisherOA PDFDOI

• Diagnostic Expectations and the Macroeconomy

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Sentiment, Productivity, and Economic Growth

  Journal of Financial and Quantitative Analysis · 2025-08-11 · 1 citations

  articleOpen access1st authorCorresponding

  Abstract Earlier research finds correlation between sentiment and future economic growth, but disagrees on the channel that explains this result. We shed new light on this issue by exploiting cross-sectional variation in country size and market efficiency. We find that sentiment shocks in the largest advanced economies increase economic activity, but only temporarily and without affecting productivity. Conversely, sentiment shocks in smaller or less advanced economies predict prolonged economic growth and a corresponding increase in productivity. The results support the view that sentiment can create economic booms, although only in economies where sentiment and fundamentals are harder to disentangle.

  PublisherDOI

• Sentiment, Productivity, and Economic Growth

  SSRN Electronic Journal · 2023-01-01 · 2 citations

  articleOpen access1st authorCorresponding
  PublisherDOI

• Welfare Costs of Idiosyncratic and Aggregate Consumption Shocks

  SSRN Electronic Journal · 2023-01-01 · 1 citations

  preprintOpen access1st authorCorresponding
  PublisherDOI

Frequent coauthors

• Mike Gallmeyer

  Financial Research (Hungary)

  225 shared

• Andrea Tamoni

  Financial Research (Hungary)

  225 shared

• Yukun Liu

  East China Normal University

  225 shared

• Jianjun Miao

  Boston University

  225 shared

• Sydney C. Ludvigson

  225 shared

• Indrajit Mitra

  Federal Reserve Bank of Atlanta

  225 shared

• Yang Liu

  Guangdong University of Petrochemical Technology

  225 shared

• Francisco Palomino

  225 shared

Education

• B.A.

  Oxford University

• Other

  Indiana University

• Other

  Indiana University

Awards & honors

• Distinguished Alumni Award Honorees