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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. 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.
    3. 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.
    4. Richard Hanania, 2021. "Cui Bono? Partisanship and Attitudes Toward Refugees," Social Science Quarterly, Southwestern Social Science Association, vol. 102(1), pages 166-178, January.
    5. 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.

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. 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.
    3. 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).
    4. 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.
    5. Jean-Charles Bricongne & Baptiste Meunier & Raquel Caldeira, 2024. "Should Central Banks Care About Text Mining? A Literature Review," Working papers 950, Banque de France.
    6. 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.
    7. 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.
    8. Shen, Dehua & Urquhart, Andrew & Wang, Pengfei, 2019. "Does twitter predict Bitcoin?," Economics Letters, Elsevier, vol. 174(C), pages 118-122.
    9. Na, Haejung & Kim, Soonho, 2021. "Predicting stock prices based on informed traders’ activities using deep neural networks," Economics Letters, Elsevier, vol. 204(C).
    10. 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).
    11. 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).
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. Santi, Caterina, 2023. "Investor climate sentiment and financial markets," International Review of Financial Analysis, Elsevier, vol. 86(C).
    18. 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.
    19. 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.
    20. 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).
    21. 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.
    22. 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.
    23. 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.
    24. 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).
    25. 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).
    26. 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.

More information

Research fields, statistics, top rankings, if available.

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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