DELUSION AND MADNESS OF THE CROWDS: COLLECTIVE PERCEPTION IN PAKISTANI EXCHANGE MARKETS

: 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. we 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. Above,Table VIreports the result of the vector Autoregression model. The dependent variable is Saudi Arabian Riyal to Pakistani Rupee (ER_SP), and the explanatory variables include google trend searching data of Saudi Arabian Riyal and its various lags. Results suggested that google trend data used to predict collective perception can forecastSaudi Arabian Riyal to Pakistani Rupee. Treasury bills rate and KSE-100 index are used as control variables and the lags of these variables. Empirical results showed that google trend search at 1 st lag (S_P) has a significant and positive effect on the Saudi Arabian Riyal exchange rate. nd lag Saudi Arabian Riyal The results of the Vector autoregressive model are reported in Table VIII, where the dependent variable is Kuwaiti dinar to Pakistani Rupee (ER_KP), and the explanatory variables include google trend searching data of Kuwaiti dinar and its various lags. These results suggested that google trend data used to predict collective perception can forecast Kuwaiti dinar to Pakistani Rupee. The control variables include the Treasure bills rate and KSE-100 index and their lags. The results showed that google trend search at 1 st lag (K_P) has a significant and positive effect on the Kuwaiti dinar exchange rate. Moreover, the exchange rate results at only 1 st lag are significant,which shows that the prior one-month exchange rates of Kuwaiti dinar can be used to forecast future exchange rates. The treasury bills rate also indicates that the monthly treasury bills rate at lag 2 nd and 3 rd also significantly affects the Kuwaiti dinar to Pakistani Rupee. Thus, two to three months’ treasury bills rate positively and substantially affects the Kuwaiti dinar to Pakistani rupees. In terms of the KSE-100 index, the coefficient is significant at the 1 st lag on the Kuwaiti dinar exchange rate. perception, can forecast Omani Riyal to Pakistani Rupee. The control variables include the Treasure bills rate and KSE-100 index and their lags. The results showed that google trend search at 1 st lag (O_P) has a significant and positive effect on the Omani Riyal exchange rate. Moreover, the exchange rate results at the 1st lag are substantial, which shows that prior only one-monthexchange rate of Omani Riyal can be used to forecast future exchange rates. In terms of the KSE-100 index, the coefficient is significant at 1 st and 2 nd lags on the Omani Riyal exchange rate.