Sentiment trading with large language models
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Daron Acemoglu & David Autor & Jonathon Hazell & Pascual Restrepo, 2022.
"Artificial Intelligence and Jobs: Evidence from Online Vacancies,"
Journal of Labor Economics, University of Chicago Press, vol. 40(S1), pages 293-340.
- Acemoglu, Daron & Autor, David & Hazell, Jonathon & Restrepo, Pascual, 2022. "Artificial intelligence and jobs: evidence from online vacancies," LSE Research Online Documents on Economics 113325, London School of Economics and Political Science, LSE Library.
- Stephen Hansen & Michael McMahon & Andrea Prat, 2018.
"Transparency and Deliberation Within the FOMC: A Computational Linguistics Approach,"
The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(2), pages 801-870.
- Stephen Eliot Hansen & Michael McMahon & Andrea Prat, 2014. "Transparency and deliberation within the FOMC: A computational linguistics approach," Economics Working Papers 1425, Department of Economics and Business, Universitat Pompeu Fabra.
- Stephen Hansen & Michael McMahon & Andrea Prat, 2014. "Transparency and Deliberation within the FOMC: A Computational Linguistics Approach," CEP Discussion Papers dp1276, Centre for Economic Performance, LSE.
- Hansen, Stephen & McMahon, Michael & Prat, Andrea, 2014. "Transparency and deliberation within the FOMC: a computational linguistics approach," LSE Research Online Documents on Economics 58072, London School of Economics and Political Science, LSE Library.
- Stephen Hansen & Michael McMahon & Andrea Prat, 2014. "Transparency and Deliberation within the FOMC: a Computational Linguistics Approach," Working Papers 762, Barcelona School of Economics.
- Prat, Andrea & McMahon, Michael & Hansen, Stephen, 2014. "Transparency and Deliberation within the FOMC: a Computational Linguistics Approach," CEPR Discussion Papers 9994, C.E.P.R. Discussion Papers.
- Hansen, Stephen & McMahon, Michael & Prat, Andrea, 2014. "Transparency and deliberation within the FOMC: a computational linguistics approach," LSE Research Online Documents on Economics 60287, London School of Economics and Political Science, LSE Library.
- Stephen Hansen & Michael McMahon & Andrea Prat, 2014. "Transparency and Deliberation within the FOMC: a Computational Linguistics Approach," Discussion Papers 1411, Centre for Macroeconomics (CFM).
- Malcolm Baker & Jeffrey Wurgler, 2006.
"Investor Sentiment and the Cross‐Section of Stock Returns,"
Journal of Finance, American Finance Association, vol. 61(4), pages 1645-1680, August.
- Malcolm Baker & Jeffrey Wurgler, 2004. "Investor Sentiment and the Cross-Section of Stock Returns," NBER Working Papers 10449, National Bureau of Economic Research, Inc.
- Calomiris, Charles W. & Mamaysky, Harry, 2019. "How news and its context drive risk and returns around the world," Journal of Financial Economics, Elsevier, vol. 133(2), pages 299-336.
- Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016.
"Measuring Economic Policy Uncertainty,"
The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
- Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2015. "Measuring Economic Policy Uncertainty," Economics Working Papers 15111, Hoover Institution, Stanford University.
- Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2015. "Measuring Economic Policy Uncertainty," NBER Working Papers 21633, National Bureau of Economic Research, Inc.
- Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2015. "Measuring Economic Policy Uncertainty," CEP Discussion Papers dp1379, Centre for Economic Performance, LSE.
- Baker, Scott R. & Bloom, Nicholas & Davis, Steven J., 2015. "Measuring economic policy uncertainty," LSE Research Online Documents on Economics 64986, London School of Economics and Political Science, LSE Library.
- Davis, Steven & Bloom, Nicholas & Baker, Scott, 2015. "Measuring Economic Policy Uncertainty," CEPR Discussion Papers 10900, C.E.P.R. Discussion Papers.
- Jegadeesh, Narasimhan & Wu, Di, 2013. "Word power: A new approach for content analysis," Journal of Financial Economics, Elsevier, vol. 110(3), pages 712-729.
- Gerard Hoberg & Gordon Phillips, 2016.
"Text-Based Network Industries and Endogenous Product Differentiation,"
Journal of Political Economy, University of Chicago Press, vol. 124(5), pages 1423-1465.
- Gerard Hoberg & Gordon M. Phillips, 2010. "Text-Based Network Industries and Endogenous Product Differentiation," NBER Working Papers 15991, National Bureau of Economic Research, Inc.
- Richard Frankel & Jared Jennings & Joshua Lee, 2022. "Disclosure Sentiment: Machine Learning vs. Dictionary Methods," Management Science, INFORMS, vol. 68(7), pages 5514-5532, July.
- Diego García, 2013. "Sentiment during Recessions," Journal of Finance, American Finance Association, vol. 68(3), pages 1267-1300, June.
- Pekka Malo & Ankur Sinha & Pekka Korhonen & Jyrki Wallenius & Pyry Takala, 2014.
"Good debt or bad debt: Detecting semantic orientations in economic texts,"
Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(4), pages 782-796, April.
- Pekka Malo & Ankur Sinha & Pyry Takala & Pekka Korhonen & Jyrki Wallenius, 2013. "Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts," Papers 1307.5336, arXiv.org, revised Jul 2013.
- Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
- Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, June.
- A. Craig MacKinlay, 1997. "Event Studies in Economics and Finance," Journal of Economic Literature, American Economic Association, vol. 35(1), pages 13-39, March.
- Tim Loughran & Bill Mcdonald, 2011. "When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks," Journal of Finance, American Finance Association, vol. 66(1), pages 35-65, February.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Alejandro Lopez-Lira & Yuehua Tang, 2023. "Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models," Papers 2304.07619, arXiv.org, revised Sep 2023.
- Obaid, Khaled & Pukthuanthong, Kuntara, 2022. "A picture is worth a thousand words: Measuring investor sentiment by combining machine learning and photos from news," Journal of Financial Economics, Elsevier, vol. 144(1), pages 273-297.
- García, Diego & Hu, Xiaowen & Rohrer, Maximilian, 2023. "The colour of finance words," Journal of Financial Economics, Elsevier, vol. 147(3), pages 525-549.
- Anand, Abhinav & Basu, Sankarshan & Pathak, Jalaj & Thampy, Ashok, 2021. "The impact of sentiment on emerging stock markets," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 161-177.
- Jin, Xuejun & Chen, Cheng & Yang, Xiaolan, 2024. "The effect of international media news on the global stock market," International Review of Economics & Finance, Elsevier, vol. 89(PA), pages 50-69.
- Ardia, David & Bluteau, Keven & Boudt, Kris, 2022.
"Media abnormal tone, earnings announcements, and the stock market,"
Journal of Financial Markets, Elsevier, vol. 61(C).
- David Ardia & Keven Bluteau & Kris Boudt, 2021. "Media abnormal tone, earnings announcements, and the stock market," Papers 2110.10800, arXiv.org.
- Ahmed, Yousry & Elshandidy, Tamer, 2016. "The effect of bidder conservatism on M&A decisions: Text-based evidence from US 10-K filings," International Review of Financial Analysis, Elsevier, vol. 46(C), pages 176-190.
- Ahmad, Khurshid & Han, JingGuang & Hutson, Elaine & Kearney, Colm & Liu, Sha, 2016.
"Media-expressed negative tone and firm-level stock returns,"
Journal of Corporate Finance, Elsevier, vol. 37(C), pages 152-172.
- Khurshid Ahmad & JingGuang Han & Elaine Hutson & Colm Kearney & Sha Liu, 2016. "Media-expressed negative tone and firm-level stock returns," Open Access publications 10197/8208, Research Repository, University College Dublin.
- Travis Adams & Andrea Ajello & Diego Silva & Francisco Vazquez-Grande, 2023. "More than Words: Twitter Chatter and Financial Market Sentiment," Papers 2305.16164, arXiv.org.
- John Garcia, 2021. "Analyst herding and firm-level investor sentiment," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 35(4), pages 461-494, December.
- Fraiberger, Samuel P. & Lee, Do & Puy, Damien & Ranciere, Romain, 2021.
"Media sentiment and international asset prices,"
Journal of International Economics, Elsevier, vol. 133(C).
- Fraiberger,Samuel Paul & Lee,Do & Puy,Damien & Rancier,Romain, 2018. "Media Sentiment and International Asset Prices," Policy Research Working Paper Series 8649, The World Bank.
- Rancière, Romain & Fraiberger, Samuel & , & Puy, Damien, 2018. "Media Sentiment and International Asset Prices," CEPR Discussion Papers 13366, C.E.P.R. Discussion Papers.
- Samuel P. Fraiberger & Dongyeol Lee & Mr. Damien Puy & Mr. Romain Ranciere, 2018. "Media Sentiment and International Asset Prices," IMF Working Papers 2018/274, International Monetary Fund.
- Samuel P. Fraiberger & Do Lee & Damien Puy & Romain Rancière, 2018. "Media Sentiment and International Asset Prices," NBER Working Papers 25353, National Bureau of Economic Research, Inc.
- Vegard Høghaug Larsen & Leif Anders Thorsrud, 2022.
"Asset returns, news topics, and media effects,"
Scandinavian Journal of Economics, Wiley Blackwell, vol. 124(3), pages 838-868, July.
- Vegard H. Larsen & Leif Anders Thorsrud, 2017. "Asset returns, news topics, and media effects," Working Paper 2017/17, Norges Bank.
- Vegard H�ghaug Larsen & Leif Anders Thorsrud, 2017. "Asset returns, news topics, and media effects," Working Papers No 5/2017, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
- Alejandro Bernales & Marcela Valenzuela & Ilknur Zer, 2023. "Effects of Information Overload on Financial Markets: How Much Is Too Much?," International Finance Discussion Papers 1372, Board of Governors of the Federal Reserve System (U.S.).
- Shapiro, Adam Hale & Sudhof, Moritz & Wilson, Daniel J., 2022.
"Measuring news sentiment,"
Journal of Econometrics, Elsevier, vol. 228(2), pages 221-243.
- Adam Hale Shapiro & Moritz Sudhof & Daniel J. Wilson, 2020. "Measuring News Sentiment," Working Paper Series 2017-1, Federal Reserve Bank of San Francisco.
- Ali Kabiri & Harold James & John Landon‐Lane & David Tuckett & Rickard Nyman, 2023. "The role of sentiment in the US economy: 1920 to 1934," Economic History Review, Economic History Society, vol. 76(1), pages 3-30, February.
- Hubert, Paul & Labondance, Fabien, 2021. "The signaling effects of central bank tone," European Economic Review, Elsevier, vol. 133(C).
- Cookson, J. Anthony & Moon, S. Katie & Noh, Joonki, 2020. "Imprecise and Informative: Lessons from Market Reactions to Imprecise Disclosure," SocArXiv akt2c, Center for Open Science.
- Renault, Thomas, 2017.
"Intraday online investor sentiment and return patterns in the U.S. stock market,"
Journal of Banking & Finance, Elsevier, vol. 84(C), pages 25-40.
- Thomas Renault, 2017. "Intraday online investor sentiment and return patterns in the U.S. stock market," Post-Print hal-03205113, HAL.
- Eleni Kalamara & Arthur Turrell & Chris Redl & George Kapetanios & Sujit Kapadia, 2022.
"Making text count: Economic forecasting using newspaper text,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 896-919, August.
- Kalamara, Eleni & Turrell, Arthur & Redl, Chris & Kapetanios, George & Kapadia, Sujit, 2020. "Making text count: economic forecasting using newspaper text," Bank of England working papers 865, Bank of England.
- Andrea Ajello & Diego Silva & Travis Adams & Francisco Vazquez-Grande, 2023. "More than Words: Twitter Chatter and Financial Market Sentiment," Finance and Economics Discussion Series 2023-034, Board of Governors of the Federal Reserve System (U.S.).
More about this item
Keywords
artificial intelligence investment strategies; generative pre-trained transformer (GPT); large language models; machine learning in stock return prediction; natural language processing (NLP);All these keywords.
JEL classification:
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
- G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
- G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
- G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
NEP fields
This paper has been announced in the following NEP Reports:- NEP-AIN-2024-05-13 (Artificial Intelligence)
- NEP-CMP-2024-05-13 (Computational Economics)
- NEP-FMK-2024-05-13 (Financial Markets)
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ehl:lserod:122592. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: LSERO Manager (email available below). General contact details of provider: https://edirc.repec.org/data/lsepsuk.html .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.