A Literature Review on the Impact of Macroeconomic Expectations on Equity Risk Premium
DOI:
https://doi.org/10.54097/p68szh73Keywords:
Macroeconomic Expectations, Equity Risk Premium, Asset Pricing, Textual Analysis, Large Language Models.Abstract
This paper systematically reviews domestic and foreign literature on the correlation between macroeconomic expectations and equity risk premium, sorting out their definitions, measurement methods, transmission channels, heterogeneous characteristics and cutting-edge research. Macroeconomic expectations affect equity risk premium through three major channels: corporate earnings, discount rate, as well as uncertainty and risk appetite, while research conclusions diverge due to different measurement approaches and market environments. The measurement tools of core variables have evolved from traditional surveys and macro proxies to text mining and large language models. Current studies are limited by inconsistent measurement criteria, insufficient identification of transmission mechanisms and shallow application of artificial intelligence. Future research shall promote integrated and standardized indicators, refine channel identification, conduct localized analysis based on China’s capital market, and deepen the application of large language models in asset pricing, so as to provide theoretical support for market investment, policy expectation management and financial stability.
Downloads
References
[1] Adam K, Nagel S. Expectations data in asset pricing[R]. NBER Working Paper No.29977. Cambridge, MA: National Bureau of Economic Research, 2022. DOI: https://doi.org/10.3386/w29977
[2] Manski C F. Survey measurement of probabilistic macroeconomic expectations: progress and promise[J]. NBER Macroeconomics Annual, 2018, 32(1): 411-471. DOI: https://doi.org/10.1086/696061
[3] Dominitz J, Manski C F. How should we measure consumer confidence? [J]. Journal of Economic Perspectives, 2004, 18(2): 51-66. DOI: https://doi.org/10.1257/0895330041371303
[4] Croushore D, Stark T. Fifty years of the survey of professional forecasters [EB/OL]. Philadelphia: Federal Reserve Bank of Philadelphia, 2019.
[5] Zarifhonarvar A. Generating inflation expectations with large language models[J]. Journal of Monetary Economics, 2026, 157(C): 103859. DOI: https://doi.org/10.1016/j.jmoneco.2025.103859
[6] Campbell, S. D., & Diebold, F. X. (2009). Stock Returns and Expected Business Conditions: Half a Century of Direct Evidence. Journal of Business & Economic Statistics, 27(2), 266–278. DOI: https://doi.org/10.1198/jbes.2009.0025
[7] Welch I, Goyal A. A comprehensive look at the empirical performance of equity premium prediction[J]. The Review of Financial Studies, 2008, 21(4): 1455-1508. DOI: https://doi.org/10.1093/rfs/hhm014
[8] Li X‑M. New evidence on economic policy uncertainty and equity premium[J]. Pacific‑Basin Finance Journal, 2017, 46A: 41‑56. DOI: https://doi.org/10.1016/j.pacfin.2017.08.005
[9] Ma F, Cao J. The Chinese equity premium predictability: Evidence from a long historical data[J]. Finance Research Letters, 2023, 53(C): 103668. DOI: https://doi.org/10.1016/j.frl.2023.103668
[10] Shapiro A H, Sudhof M, Wilson D J. Measuring News Sentiment[R]. Federal Reserve Bank of San Francisco Working Paper 2017‑01, 2020. DOI: https://doi.org/10.24148/erwp2017-01
[11] Kalamara E, Turrell A, Redl C, et al. Making text count: Economic forecasting using newspaper text[J]. Journal of Applied Econometrics, 2022, 37(5): 896‑919. DOI: https://doi.org/10.1002/jae.2907
[12] Lee J, Stevens N, Han S C, et al. A survey of large language models in finance (FinLLMs)[EB/OL]. arXiv:2402.02315, 2024.
[13] Bybee J L. The Ghost in the Machine: Generating Beliefs with Large Language Models[R]. Chicago Booth School of Business Working Paper, 2025.
[14] Kwon B, Park T, Rungcharoenkitkul P, Smets F. Parsing the pulse: decomposing macroeconomic sentiment with LLMs[R]. BIS Working Paper No. 1294, Bank for International Settlements, 2025.
[15] Carriero A, Pettenuzzo D, Shekhar S. Macroeconomic forecasting with large language models [EB/OL]. arXiv:2407.00890, 2024‑09‑Revised 2025. DOI: https://doi.org/10.2139/ssrn.4881094
[16] Campbell S D, Diebold F X. Stock Returns and Expected Business Conditions: Half a Century of Direct Evidence[R]. NBER Working Paper No. 11736, 2005. DOI: https://doi.org/10.3386/w11736
[17] Goyal A, Welch I. A Comprehensive Look at the Empirical Performance of Equity Premium Prediction [R]. NBER Working Paper No. 10483, 2004. DOI: https://doi.org/10.3386/w10483
[18] Amromin G, Sharpe S A. From the Horse's Mouth: Economic Conditions and Investor Expectations of Risk and Return[J]. Management Science, 2014, 60(4): 845-866. DOI: https://doi.org/10.1287/mnsc.2013.1806
[19] Bianchi F, Ludvigson S C, Ma S. Belief Distortions and Macroeconomic Fluctuations[J]. Journal of Political Economy, 2023, 131(11): 2953-3001.
[20] Min F, Wu B H, Wen F H. Drivers and transmission mechanisms of the equity premium: Evidence from the Chinese stock market[J]. Systems Engineering—Theory & Practice, 2023, 43(4): 1044-1067.
[21] De la O R, Myers S. Which Subjective Expectations Explain Asset Prices? [J]. The Review of Financial Studies, 2024, 37(6): 1929-1978. DOI: https://doi.org/10.1093/rfs/hhae009
[22] Hu G X, Pan J, Wang J, Zhu H. Premium for Heightened Uncertainty: Explaining Pre-Announcement Market Returns[J]. The Review of Financial Studies, 2022, 35(9): 4104-4153. DOI: https://doi.org/10.1016/j.jfineco.2021.09.015
[23] Ge Y F. Macroeconomic Uncertainty and Stock Returns: Evidence from Global and Chinese A‑Share Markets[D]. Shanghai: Shanghai University of Finance and Economics, 2024.
[24] Zhu L, Jiang F, Tang G, Jin F. From Macro to Micro: Sparse Macroeconomic Risks and the Cross‑Section of Stock Returns[J]. International Review of Financial Analysis, 2024, 95: 103433. DOI: https://doi.org/10.1016/j.irfa.2024.103433
[25] Van Binsbergen J H, Han X, Lopez‑Lira A. Man vs. Machine Learning: The Term Structure of Earnings Expectations and Conditional Biases[R]. NBER Working Paper No. 27843, 2021. DOI: https://doi.org/10.3386/w27843
[26] Deng Y, Wang Y, Zhou T. Macroeconomic Expectations and Expected Returns[J]. Journal of Financial and Quantitative Analysis, 2024, 59(4): 1-30. DOI: https://doi.org/10.1017/S0022109024000279
[27] Bauer M D, Swanson E T. A Reassessment of Monetary Policy Surprises and High‑Frequency Identification[R]. NBER Working Paper No. 29939, 2022. DOI: https://doi.org/10.3386/w29939
[28] Campbell J Y, Cochrane J H. By Force of Habit: A Consumption‑Based Explanation of Aggregate Stock Market Behavior[J]. Journal of Political Economy, 1999, 107(2): 205-251. DOI: https://doi.org/10.1086/250059
[29] Pastor L, Veronesi P. Uncertainty about Government Policy and Stock Prices[J]. The Journal of Finance, 2012, 67(4): 1219-1264. DOI: https://doi.org/10.1111/j.1540-6261.2012.01746.x
[30] Haase F, Neuenkirch M. Macroeconomic Expectations and State‑Dependent Factor Returns[R]. Trier: University of Trier, Research Papers in Economics No. 9/23, 2023. DOI: https://doi.org/10.2139/ssrn.4602939
[31] Qiao F, Xu L, Zhang X, Zhou H. Variance Risk Premiums in Emerging Markets[J]. Journal of Banking & Finance, 2024, 167: 107259. DOI: https://doi.org/10.1016/j.jbankfin.2024.107259
[32] Xing H W, Wang H Y. Economic Policy Uncertainty Beta Premium: An Explanation Based on the Certainty Effect[J]. Journal of Shanghai University of Finance and Economics, 2021, 23(3): 64-78.
[33] Lu R, Zhu S Y, Xu T L. Inflation Expectations and Corporate Financialization: From the Perspective of the "Inflation Illusion"[J]. Finance & Trade Economics, 2022, 43(12): 97-112.
[34] Loughran T, McDonald B. When is a liability not a liability? Textual analysis, dictionaries, and 10‑Ks[J]. Journal of Finance, 2011, 66(1): 35-65. DOI: https://doi.org/10.1111/j.1540-6261.2010.01625.x
[35] Jiang Fuwei, Meng Lingchao, Tang Guohao. Sentiment in Media Texts and Stock Return Prediction [J]. Economics (Quarterly), 2021, 21(4): 1323-1344.
[36] Yao Jiaquan, Feng Xu, Wang Zanjun, et al. Tone, Sentiment, and Market Impact: Based on a Financial Sentiment Dictionary [J]. Journal of Management Science, 2021, 24(5): 26-46.
[37] Jiang Fuwei, Liu Yumin, Meng Lingchao. Large Language Models, Text Sentiment, and Financial Markets [J]. Management World, 2024, 40(8): 42-59.
[38] Zheng Tingguo, Fan Xinyue. Big Data Methods and Their Applications in Macroeconomic Monitoring and Forecasting [J]. Finance and Economics Think Tank, 2024, 9(5): 47-96.
[39] Devlin J, Chang M‑W, Lee K, et al. BERT: Pre‑training of deep bidirectional transformers for language understanding [C] //Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Minneapolis, Minnesota: Association for Computational Linguistics, 2019: 4171–4186. DOI: https://doi.org/10.18653/v1/N19-1423
[40] Brown T B, Mann B, Ryder N, et al. Language models are few‑shot learners[C] //Advances in Neural Information Processing Systems 33 (NeurIPS 2020), 2020: 1877–1901.
[41] Araci D. FinBERT: Financial sentiment analysis with pre‑trained language models[J]. arXiv preprint arXiv:1908.10063, 2019.
[42] Yang Y, Uy M C S, Huang A. FinBERT: A pretrained language model for financial communications[J]. arXiv preprint arXiv:2006.08097, 2020.
[43] Beck E, Eckert F, Kühne L, et al. Measuring Economic Outlook in the News[J]. Swiss National Bank Working Papers, 4/2026, 2025. DOI: https://doi.org/10.2139/ssrn.6275639
[44] Chen J, Tang G, Zhou G, et al. ChatGPT, Stock Market Predictability, and Links to the Macroeconomy[J]. Available at SSRN, 2023, 4660148.
[45] Huang Y, Luk P. Measuring economic policy uncertainty in China[J]. China Economic Review, 2020, 59: 101367. DOI: https://doi.org/10.1016/j.chieco.2019.101367
[46] Greenwood R, Shleifer A. Expectations of returns and expected returns[J]. Review of Financial Studies, 2014, 27(3): 714-746. DOI: https://doi.org/10.1093/rfs/hht082
[47] Veronesi P. Stock market overreactions to bad news in good times: A rational expectations equilibrium model[J]. Review of Financial Studies, 1999, 12(5): 975-1007. DOI: https://doi.org/10.1093/rfs/12.5.975
[48] Epstein L G, Schneider M. Ambiguity, information quality, and asset pricing[J]. Journal of Finance, 2008, 63(1): 197-228. DOI: https://doi.org/10.1111/j.1540-6261.2008.01314.x
[49] Adrian T, Boyarchenko N, Giannone D. Vulnerable growth[J]. American Economic Review, 2019, 109(4): 1263-1289. DOI: https://doi.org/10.1257/aer.20161923
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Highlights in Business, Economics and Management

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.







