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Michael Stephen Lachanski

Personal Details

First Name:Michael
Middle Name:Stephen
Last Name:Lachanski
Suffix:
RePEc Short-ID:pla759
http://princeton.edu/~mlachans

Affiliation

Bendheim Center for Finance
Department of Economics
Princeton University

Princeton, New Jersey (United States)
http://www.princeton.edu/~bcf/
RePEc:edi:bcprius (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Jason Anastasopoulos & George J. Borjas & Gavin G. Cook & Michael Lachanski, 2018. "Job Vacancies, the Beveridge Curve, and Supply Shocks: The Frequency and Content of Help-Wanted Ads in Pre- and Post-Mariel Miami," NBER Working Papers 24580, National Bureau of Economic Research, Inc.

Articles

  1. Michael Lachanski & Steven Pav, 2017. "Shy of the Character Limit: "Twitter Mood Predicts the Stock Market" Revisited," Econ Journal Watch, Econ Journal Watch, vol. 14(3), pages 302–345-3, September.
  2. Sun, Andrew & Lachanski, Michael & Fabozzi, Frank J., 2016. "Trade the tweet: Social media text mining and sparse matrix factorization for stock market prediction," International Review of Financial Analysis, Elsevier, vol. 48(C), pages 272-281.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Wikipedia or ReplicationWiki mentions

(Only mentions on Wikipedia that link back to a page on a RePEc service)
  1. Michael Lachanski & Steven Pav, 2017. "Shy of the Character Limit: "Twitter Mood Predicts the Stock Market" Revisited," Econ Journal Watch, Econ Journal Watch, vol. 14(3), pages 302–345-3, September.

    Mentioned in:

    1. Shy of the Character Limit: “Twitter Mood Predicts the Stock Market” Revisited (EJW 2017) in ReplicationWiki ()

Working papers

  1. Jason Anastasopoulos & George J. Borjas & Gavin G. Cook & Michael Lachanski, 2018. "Job Vacancies, the Beveridge Curve, and Supply Shocks: The Frequency and Content of Help-Wanted Ads in Pre- and Post-Mariel Miami," NBER Working Papers 24580, National Bureau of Economic Research, Inc.

    Cited by:

    1. Morgan Raux, 2019. "Looking for the "Best and Brightest": Hiring difficulties and high-skilled foreign workers," AMSE Working Papers 1934, Aix-Marseille School of Economics, France.
    2. Stefano Fusaro & Enrique López-Bazo, 2018. "“The Impact of Immigration on Native Employment: Evidence from Italy”," IREA Working Papers 201822, University of Barcelona, Research Institute of Applied Economics, revised Sep 2018.
    3. Karaarslan, Can, 2020. "Growth, Wages and Unemployment - The Economic Impact of Refugee Migration on Europe: A Synthetic Control Analysis," Working Papers for Marketing & Management 51, Offenburg University, Department of Media and Information.
    4. Enghin Atalay & Phai Phongthiengtham & Sebastian Sotelo & Daniel Tannenbaum, 2020. "The Evolution of Work in the United States," American Economic Journal: Applied Economics, American Economic Association, vol. 12(2), pages 1-34, April.
    5. Richard Hanania, 2021. "Cui Bono? Partisanship and Attitudes Toward Refugees," Social Science Quarterly, Southwestern Social Science Association, vol. 102(1), pages 166-178, January.

Articles

  1. Michael Lachanski & Steven Pav, 2017. "Shy of the Character Limit: "Twitter Mood Predicts the Stock Market" Revisited," Econ Journal Watch, Econ Journal Watch, vol. 14(3), pages 302–345-3, September.

    Cited by:

    1. Polyzos, Efstathios & Wang, Fang, 2022. "Twitter and market efficiency in energy markets: Evidence using LDA clustered topic extraction," Energy Economics, Elsevier, vol. 114(C).
    2. Kommel, Karl Arnold & Sillasoo, Martin & Lublóy, Ágnes, 2019. "Could crowdsourced financial analysis replace the equity research by investment banks?," Finance Research Letters, Elsevier, vol. 29(C), pages 280-284.
    3. Kraaijeveld, Olivier & De Smedt, Johannes, 2020. "The predictive power of public Twitter sentiment for forecasting cryptocurrency prices," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 65(C).
    4. Amir Fekrazad & Syed M. Harun & Naafey Sardar, 2022. "Social media sentiment and the stock market," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 46(2), pages 397-419, April.

  2. Sun, Andrew & Lachanski, Michael & Fabozzi, Frank J., 2016. "Trade the tweet: Social media text mining and sparse matrix factorization for stock market prediction," International Review of Financial Analysis, Elsevier, vol. 48(C), pages 272-281.

    Cited by:

    1. Fan, Rui & Talavera, Oleksandr & Tran, Vu, 2023. "Information flows and the law of one price," International Review of Financial Analysis, Elsevier, vol. 85(C).
    2. Bouteska, Ahmed & Sharif, Taimur & Abedin, Mohammad Zoynul, 2023. "COVID-19 and stock returns: Evidence from the Markov switching dependence approach," Research in International Business and Finance, Elsevier, vol. 64(C).
    3. Joseph D. Prusa & Ryan T. Sagul & Taghi M. Khoshgoftaar, 2019. "Extracting Knowledge from Technical Reports for the Valuation of West Texas Intermediate Crude Oil Futures," Information Systems Frontiers, Springer, vol. 21(1), pages 109-123, February.
    4. Jean-Charles Bricongne & Baptiste Meunier & Raquel Caldeira, 2024. "Should Central Banks Care About Text Mining? A Literature Review," Working papers 950, Banque de France.
    5. Qiong Wu & Christopher G. Brinton & Zheng Zhang & Andrea Pizzoferrato & Zhenming Liu & Mihai Cucuringu, 2019. "Equity2Vec: End-to-end Deep Learning Framework for Cross-sectional Asset Pricing," Papers 1909.04497, arXiv.org, revised Oct 2021.
    6. Fang Wang & Marko Gacesa, 2024. "Semi-strong Efficient Market of Bitcoin and Twitter: an Analysis of Semantic Vector Spaces of Extracted Keywords and Light Gradient Boosting Machine Models," Papers 2409.15988, arXiv.org.
    7. Anila Arif & Kashif Shafique & Khuram Ahmad Khan & Shahida Haji, 2021. "Analysis of Water Policy & Sustainable Development in Pakistan," International Journal of Agriculture & Sustainable Development, 50sea, vol. 3(4), pages 87-93, November.
    8. Yingxia Xue & Honglei Liu, 2023. "Exploration of the Dynamic Evolution of Online Public Opinion towards Waste Classification in Shanghai," IJERPH, MDPI, vol. 20(2), pages 1-15, January.
    9. Santi, Caterina, 2023. "Investor climate sentiment and financial markets," International Review of Financial Analysis, Elsevier, vol. 86(C).
    10. Shilpa Srivastava & Millie Pant & Varuna Gupta, 2023. "Analysis and prediction of Indian stock market: a machine-learning approach," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(4), pages 1567-1585, August.
    11. Kumar, Rahul & Deb, Soumya Guha & Mukherjee, Shubhadeep, 2020. "Do words reveal the latent truth? Identifying communication patterns of corporate losers," Journal of Behavioral and Experimental Finance, Elsevier, vol. 26(C).
    12. Mohammad Alomari & Abdel Razzaq Al rababa’a & Ghaith El-Nader & Ahmad Alkhataybeh, 2021. "Who’s behind the wheel? The role of social and media news in driving the stock–bond correlation," Review of Quantitative Finance and Accounting, Springer, vol. 57(3), pages 959-1007, October.
    13. Ning Wang & Shanhui Ke & Yibo Chen & Tao Yan & Andrew Lim, 2019. "Textual Sentiment of Chinese Microblog Toward the Stock Market," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(02), pages 649-671, March.
    14. Francisco de Arriba-P'erez & Silvia Garc'ia-M'endez & Jos'e A. Regueiro-Janeiro & Francisco J. Gonz'alez-Casta~no, 2024. "Detection of financial opportunities in micro-blogging data with a stacked classification system," Papers 2404.07224, arXiv.org.
    15. Shaen Corbet & Yang (Greg) Hou & Yang Hu & Les Oxley, 2022. "We Reddit in a Forum: The Influence of Message Boards on Firm Stability," Review of Corporate Finance, now publishers, vol. 2(1), pages 151-190, March.
    16. Heba Ali, 2018. "Twitter, Investor Sentiment and Capital Markets: What Do We Know?," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(8), pages 158-158, August.
    17. Afees A. Salisu & Raymond Swaray & Tirimisyu F. Oloko, 2017. "A multi-factor predictive model for oil-US stock nexus with persistence, endogeneity and conditional heteroscedasticity effects," Working Papers 024, Centre for Econometric and Allied Research, University of Ibadan.
    18. Shen, Dehua & Urquhart, Andrew & Wang, Pengfei, 2019. "Does twitter predict Bitcoin?," Economics Letters, Elsevier, vol. 174(C), pages 118-122.
    19. Na, Haejung & Kim, Soonho, 2021. "Predicting stock prices based on informed traders’ activities using deep neural networks," Economics Letters, Elsevier, vol. 204(C).
    20. Wang, Fang & Gacesa, Marko, 2023. "Semi-strong efficient market of Bitcoin and Twitter: An analysis of semantic vector spaces of extracted keywords and light gradient boosting machine models," International Review of Financial Analysis, Elsevier, vol. 88(C).
    21. U, JuHyok & Lu, PengYu & Kim, ChungSong & Ryu, UnSok & Pak, KyongSok, 2020. "A new LSTM based reversal point prediction method using upward/downward reversal point feature sets," Chaos, Solitons & Fractals, Elsevier, vol. 132(C).
    22. Toan Luu Duc Huynh, 2023. "When Elon Musk Changes his Tone, Does Bitcoin Adjust Its Tune?," Computational Economics, Springer;Society for Computational Economics, vol. 62(2), pages 639-661, August.
    23. Maciej Wujec, 2021. "Analysis of the Financial Information Contained in the Texts of Current Reports: A Deep Learning Approach," JRFM, MDPI, vol. 14(12), pages 1-17, December.
    24. Andrea Fronzetti Colladon & Stefano Grassi & Francesco Ravazzolo & Francesco Violante, 2023. "Forecasting financial markets with semantic network analysis in the COVID‐19 crisis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1187-1204, August.
    25. Teti, Emanuele & Dallocchio, Maurizio & Aniasi, Alberto, 2019. "The relationship between twitter and stock prices. Evidence from the US technology industry," Technological Forecasting and Social Change, Elsevier, vol. 149(C).
    26. Naderi Semiromi, Hamed & Lessmann, Stefan & Peters, Wiebke, 2020. "News will tell: Forecasting foreign exchange rates based on news story events in the economy calendar," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).

More information

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Statistics

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Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 1 paper announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-DEM: Demographic Economics (1) 2018-05-21. Author is listed
  2. NEP-HIS: Business, Economic and Financial History (1) 2018-05-21. Author is listed
  3. NEP-LAB: Labour Economics (1) 2018-05-21. Author is listed
  4. NEP-LTV: Unemployment, Inequality and Poverty (1) 2018-05-21. Author is listed
  5. NEP-MIG: Economics of Human Migration (1) 2018-05-21. Author is listed
  6. NEP-URE: Urban and Real Estate Economics (1) 2018-05-21. Author is listed

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