DELUSION AND MADNESS OF THE CROWDS: COLLECTIVE PERCEPTION IN PAKISTANI EXCHANGE MARKETS
Keywords:
Exchange rates, Collective perceptions, Google trends, Vector-Autoregression, web search, Wisdom of Crowds.Abstract
The current study attempts to examine the associationbetween collective perception and exchange rates in Pakistan. We argue that people search online for information on currency exchange rates,and this online searching activity is transformed into data that could reflect people’s interest in a given currency.The current study used Google Trends data of seven pairs of currencies to account for the level of interest in these currencies in Pakistan. Pairs of currencies include United Arab Emirates Dirham, Saudi Arabian Riyal, US dollar, Kuwaiti dinar, Qatari riyal, Omani riyal, and Canadian dollar against Pakistani rupees. Currencies are selected based on the highest level of remittances received in these currencies. The study has utilized data from 2010 to 2019 and used vector- autoregressive models forestimations.The results showed a significant impact of the collective perception measured through google trends data on exchange rates in Pakistani exchange markets.The authors analyzed the Google trends search queries for only seven pairs of currencies against the exchange rate in Pakistan. To be safe in the current and future, Investors in foreign and local currency exchange markets can benefit from the findings of this study at large. So, we argue that investors seeking information on exchange rate trends in Pakistan could utilize Google Trends information to forecast the future and make decisions accordingly. Google is widely used globally and hence in Pakistan due to the emerging trends of heavily relying on google searches for so many reasons. More specifically, exchange rates are searched on google, and thus google trends show the trends per click. This study is the first to investigate the google trends search data associated with the exchange rates in Pakistani markets with a broader view of collection perception in Pakistan.
References
Askitas, N. and Zimmermann, K. F. (2011) ‘Google Econometrics and Unemployment Forecasting’, SSRN Electronic Journal. DOI: 10.2139/ssrn.1465341.
Bonabeau, E. (2009) ‘Decisions 2.0: The Power of Collective Intelligence Eric Bonabeau’, MIT Sloan Management Review, 50(2), pp. 45–52. Available at: http://www.icosystem.com/site/wp- content/uploads/2013/04/Bonabeau_Sloan-Mgmt-Review_Collective-Decisions.pdf (Accessed: 27 February 2020). Brabham, D. C. (2012) ‘The myth of amateur crowds: A critical discourse analysis of crowdsourcing coverage’, Information Communication and Society, 15(3), pp. 394–410. DOI: 10.1080/1369118X.2011.641991.
Choi, H. and Varian, H. (2012a) ‘Predicting the Present with Google Trends’, Economic Record, 88(SUPPL.1), pp. 2–9. DOI: 10.1111/j.1475-4932.2012.00809.x.
Choi, H. and Varian, H. (2012b) ‘Predicting the Present with Google Trends’, Economic Record, 88(SUPPL.1), pp. 2–9. DOI: 10.1111/j.1475-4932.2012.00809.x.
Cooper, C. P. et al. (2005) ‘Cancer Internet search activity on a major search engine, United States 2001-2003’,
Journal of Medical Internet Research, 7(3), p. e36. DOI: 10.2196/jmir.7.3.e36.
D’Amuri, F. and Marcucci, J. (2012) ‘“Google It!” Forecasting the US Unemployment Rate with A Google Job Search Index’, SSRN Electronic Journal. DOI: 10.2139/ssrn.1594132.
D’Avanzo, E., Pilato, G. and Lytras, M. (2017) ‘Using Twitter sentiment and emotions analysis of Google Trends for decisions making’, Program, 51(3). DOI: 10.1108/PROG-02-2016-0015.
Dilmaghani, M. (2019) ‘Workopolis or The Pirate Bay: what does Google Trends say about the unemployment
rate?’, Journal of Economic Studies, 46(2), pp. 422–445. DOI: 10.1108/JES-11-2017-0346.
Ettredge, M., Gerdes, J. and Karuga, G. (2005) ‘Using web-based search data to predict macroeconomic statistics’,
Communications of the ACM, pp. 87–92. DOI: 10.1145/1096000.1096010.
Fondeur, Y. and Karamé, F. (2013) ‘Can Google data help predict French youth unemployment?’, Economic Modelling, 30(1), pp. 117–125. DOI: 10.1016/j.econmod.2012.07.017.
Garcia Martinez, M. (2015) ‘Solver engagement in knowledge sharing in crowdsourcing communities: Exploring the link to creativity’, Research Policy, 44(8), pp. 1419–1430. DOI: 10.1016/j.respol.2015.05.010.
Goel, S. et al. (2010) ‘Predicting consumer behaviour with web search’, Proceedings of the National Academy of Sciences of the United States of America, 107(41), pp. 17486–17490. DOI: 10.1073/pnas.1005962107.
Hand, C. and Judge, G. (2012) ‘Searching for the picture: Forecasting UK cinema admissions using Google trends data’, Applied Economics Letters, 19(11), pp. 1051–1055. DOI: 10.1080/13504851.2011.613744.
Kosonen, M. et al. (2013) ‘My idea is our idea! Supporting user-driven innovation activities in crowdsourcing communities’, in International Journal of Innovation Management. Imperial College Press. DOI: 10.1142/S1363919613400100.
Kosonen, M. et al. (2014) ‘User motivation and knowledge sharing in idea crowdsourcing’, International Journal of Innovation Management, 18(5). DOI: 10.1142/S1363919614500315.
Kristoufek, L. (2013) ‘Can google trends search queries contribute to risk diversification?’, Scientific Reports, 3, p. 2713. DOI: 10.1038/srep02713.
Mackay, C. (2015) Extraordinary popular delusions and the madness of crowds., Extraordinary popular delusions and the madness of crowds.DOI: 10.1037/14716-000.
Majchrzak, A. and Malhotra, A. (2016) ‘Effect of knowledge-sharing trajectories on innovative outcomes in temporary online crowds’, Information Systems Research, 27(4), pp. 685–703. DOI: 10.1287/isre.2016.0669.
McLaren, N. and Shanbhogue, R. (2012) ‘Using Internet Search Data as Economic Indicators’, SSRN Electronic Journal. DOI: 10.2139/ssrn.1865276.
Miner, T. (2005)’Book Review: The Wisdom of Crowds: Why the Many are Smarter than the Few, and how Collective Wisdom Shapes Business, Economies, Societies, and Nations’, Journal of Experiential Education, 27(3), pp. 351–352. DOI: 10.1177/105382590502700325.
Preis, T., Moat, H. S. and Eugene Stanley, H. (2013) ‘Quantifying trading behaviour in financial markets using google trends’, Scientific Reports, 3(1), pp. 1–6. DOI: 10.1038/srep01684.
Preis, T., Reith, D. and Stanley, H. E. (2010) ‘Complex dynamics of our economic life on different scales: insights from search engine query data, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 368(1933), pp. 5707–5719. DOI: 10.1098/rsta.2010.0284.
Reed, M. (2015) ‘Social network influence on consistent choice’, Journal of Choice Modelling, 17, pp. 28–38. DOI: 10.1016/j.jocm.2015.12.004.
Reed, M. and Ankouri, K. (2019) ‘Collective Perception and Exchange Rates’, Journal of Behavioral Finance, 20(1), pp. 53–65. DOI: 10.1080/15427560.2018.1461100.
Shang, W., Chen, H. and Livoti, C. (2017) ‘Adverse drug reaction early warning using user search data’, Online Information Review, 41(4), pp. 524–536. DOI: 10.1108/OIR-10-2015-0341.
Suhoy, T. (2009) ‘Query Indices and a 2008 Downturn: Israeli Data’, Bank of Israel Working Papers, pp. 1–33. DOI: Suhoy.
Vaughan, L. (2014) ‘Discovering business information from search engine query data, Online Information Review, 38(4), pp. 562–574. DOI: 10.1108/OIR-08-2013-0190.
Wu, L. (2013) ‘The Future of Prediction : How Google Searches Foreshadow Housing Prices and Sales’, pp. 1–43. Available at: https://www.nber.org/chapters/c12994 (Accessed: 6 March 2020).
Wu, L. and Brynjolfsson, E. (2009) ‘The future of prediction: How google searches foreshadow housing prices and quantities’, in ICIS 2009 Proceedings - Thirtieth International Conference on Information Systems, pp. 1–43. DOI: 10.2139/ssrn.2022293.
Surowiecki, J. The Wisdom of Crowds. New York: Doubleday, 2004
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
