Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Applicability of Random Forests Forecasting to International Currency Trade:An Investigation Through Language


Affiliations
1 GITAM School of International Business, GITAM University, Visakhapatnam, Andhra Pradesh, India
2 GITAM Institute of Management, GITAM University, Visakhapatnam, Andhra Pradesh, India
     

   Subscribe/Renew Journal


The goal of this research is to study the performance of foreign exchange trade in both India and China. India and China raised rapidly in recent times and there is abundant of speculation that these countries might reach to the level of few other developed nations as far as international trade is concerned. Whereas there isn’t any doubt that these countries emerging as economic powers in the Asia-Pacific region, a lot of effort is required at international platform with respect to trade and commerce. One of such areas of competition is international currency trade. The aim of this study is to understand trends of currency trade in order to predict how likely these countries are going to emerge as best in the region. The study used certain secondary datasets from very reliable and authenticated sources. As far as statistical techniques are concerned, random walk forecasting methods were employed to test the study hypothesis. The study gathered certain evidence that though there are similarities in present and past performance, it is not likely to be the same in the future. However, the study concludes that random forests forecasting as a methodology is highly useful in studying trends in the data.

Keywords

Asia-Pacific Region, Currency Trade, International Trade, Random Walk Forecasting, Time Series Analysis.
Subscription Login to verify subscription
User
Notifications
Font Size


  • The Economist. (2017). India Foreign Exchange Reserves 1998-2017. Retrieved from https://tradingeconomics.com/india/foreign-exchange-reserves
  • Bloomberg, (April 07, 2017). Why India could be the winner of a US-China trade war. Retrieved from https:// economictimes.indiatimes.com/news/economy/foreigntrade/why-india-could-be-the-winner-of-a-us-china-trade-war/articleshow/58059316.cms
  • Dar, B. A., & Ahmad, S. (2014). Major bilateral issues between China and India. Arts Social Sci J 5(64). doi: 10.4172/2151-6200.1000064
  • Sangani, P. (Feb 08, 2008). India, China to impact global economy. Retrieved from http://economictimes.indiatimes.com/articleshow/2765861.cms
  • Tang, R., (2009). The Rise of China’s Auto Industry and Its Impact on the U.S. Motor Vehicle Industry. CRS Report for Congress. Retrieved from https://fas.org/sgp/crs/row/R40924.pdf
  • NRC. (2010). The Dragon and the Elephant: Understanding the development of innovation. National Academy of Sciences. ISBN: 978-0-30915160-3. Pg. No. 6.
  • IBEF, (2017). About Indian Economy Growth Rate & Statistics. Retrieved from https://www.ibef.org/economy/indian-economy-overview
  • Gathani, B. (2004). People of Indian origin to play larger role in development. Retrieved from http:// www.thehindubusinessline.com/2004/10/08/stories/2004100800540900.htm
  • TNAP. (2004). The Dragon and the Elephant: Understanding the Development of Innovation Capacity in China and India. Retrieved from https:// www.nap.edu/catalog/12873/the-dragon-and-the-elephant-understanding-the-development-of-innovation
  • CRISIL. (2011). Raising the growth bar. Retrieved from https://www.crisil.com/pdf/corporate/india-raising-the-bar.pdf
  • Baldacci, E., Callegari, G., Coady, D., Ding, D., Kumar, M., Tommasino, P., & Woo, J. (2010). Public Expenditures on Social Programs and Household Consumption in China. International Monetary Fund Working Paper. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.167.6981&rep=rep1&type=pdf
  • Angang, H., Linlin, H., & Zhixiao, C. (2003). China’s economic growth and poverty reduction (19782002). Retrieved from https://www.imf.org/external/ np/apd/seminars/2003/newdelhi/angang.pdf
  • The Economist. (2017). Reform of China’s ailing stateowned firms is emboldening them. Retrieved from https://www.economist.com/news/finance-and economics/21725293-outperformed-private-firms-they-are-no-longer-shrinking-share-overall
  • Merrill, S., Taylor, D., & Poole, R., (2010). The Dragon and the Elephant: Understanding the development of innovation capacity in China and India. The National Academies Press.
  • Rossi, B. (2005). Are Exchange Rates Really Random Walks? Some Evidence Robust to Parameter Instability. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.497.1697&rep=rep1&type=pdf
  • Palombizio, E., & Morris, I. (2012). Forecasting Exchange Rates using Leading Economic Indicators. Palombizio and Morris, 1:8. Retrieved from http://dx.doi.org/10.4172/scientificreports.402
  • Zhang, S., & Lowinger, T. C. (2007). The monetary exchange rate model: Long-run, short-run, and forecasting performance. Journal of Economic Integration 22(2), 397-406
  • Lam, L., Fung, L., & Yu, I.-W. (2008). Comparing Forecast Performance of Exchange Rate Models. Working Paper 08/2008. June 2008. Retrieved from http:// www.hkma.gov.hk/media/eng/publication-and-research/ research/working-papers/HKMAWP08_08_full.pdf
  • Hastie, T., Tibshirani, R., & Friedman, J. (2008). The elements of statistical learning (2nd ed.). Springer. ISBN 0-387-95284-5.
  • Hyndman, R. J. (2017). Forecast: Forecasting functions for time series and linear models. R package version 8.1. Retrieved from http://github.com/robjhyndman/forecast.
  • Hyndman, R. J., & Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for R. Journal of Statistical Software, 26(3), 1-22. Retrieved from http://www.jstatsoft.org/article/view/v027i03

Abstract Views: 2

PDF Views: 1




  • Applicability of Random Forests Forecasting to International Currency Trade:An Investigation Through Language

Abstract Views: 2  |  PDF Views: 1

Authors

Kamakshaiah Musunuru
GITAM School of International Business, GITAM University, Visakhapatnam, Andhra Pradesh, India
S. S. Prasada Rao
GITAM Institute of Management, GITAM University, Visakhapatnam, Andhra Pradesh, India

Abstract


The goal of this research is to study the performance of foreign exchange trade in both India and China. India and China raised rapidly in recent times and there is abundant of speculation that these countries might reach to the level of few other developed nations as far as international trade is concerned. Whereas there isn’t any doubt that these countries emerging as economic powers in the Asia-Pacific region, a lot of effort is required at international platform with respect to trade and commerce. One of such areas of competition is international currency trade. The aim of this study is to understand trends of currency trade in order to predict how likely these countries are going to emerge as best in the region. The study used certain secondary datasets from very reliable and authenticated sources. As far as statistical techniques are concerned, random walk forecasting methods were employed to test the study hypothesis. The study gathered certain evidence that though there are similarities in present and past performance, it is not likely to be the same in the future. However, the study concludes that random forests forecasting as a methodology is highly useful in studying trends in the data.

Keywords


Asia-Pacific Region, Currency Trade, International Trade, Random Walk Forecasting, Time Series Analysis.

References