Asia Pacific Journal of Business Review

Kyung Hee University Management Research Institute (경희대학교 경영연구원)
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Applying Keyword Analysis to Predicting Agriculture Product Price Index: The Case of the Chinese Farming Market

  • Wang, Zhi-yuan (School of Management, Kyung Hee University) ;
  • Kwon, Ohbyung (School of Management, Kyung Hee University) ;
  • Liu, Fan (College of Economics and Management, Shandong University of Science and Technology)
  • Received : 2016.06.17
  • Accepted : 2016.06.30
  • Published : 2016.08.30
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Abstract

The prediction of prices of agricultural products in the agriculture IT sector plays a significant role in the economic life of consumers and anyone engaged in agricultural business, and as these prices fluctuate more often than do other prices, the prediction of these prices holds a great deal of research promise. For this reason, academic literature has provided studies on the factors influencing the prices of agricultural products and the price index. However, as these factors vary, they are difficult to predict, resulting in the challenge of acquiring quantitative data. China is one example of a country without a reliable prediction system for prices of agricultural products. Fortunately, disclosed heterogeneous data can be found on the Internet, which allows for the effective collection of factors related to the prediction of these product prices through the use of text mining. The data provided online is valuable in that they reflect the opinions of the general public in real-time. Accordingly, this study aims to use heterogeneous data from the Internet and suggest a model predicting the prices of agricultural products before functional analyses. Toward this end, data analyses were conducted on the Chinese agricultural products market, one of the largest markets in the world.

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References

  1. Alper, C. E., S. Fendoglu and B. Saltoglu, 2008, Forecasting stock market volatilities using MIDAS regressions: An application to the emerging markets, MPRA Paper, 7460.
  2. Askitas, N. and K. F. Zimmermann, 2009, Google econometrics and unemployment forecasting. German Council for Social and Economic Data (RatSWD) Research Notes.
  3. Bank, M., M. Larch and G. Peter, 2011, Google search volume and its influence on liquidity and returns of German stocks, Financial markets and portfolio management 25, 239-264 https://doi.org/10.1007/s11408-011-0165-y
  4. Carriere-Swallow, Y. and F. Labbe, 2013, Nowcasting with Google Trends in an emerging market. Journal of Forecasting 32, 289-298. https://doi.org/10.1002/for.1252
  5. Chen, J. C. and C. C. Chen, 2010, Business cycle indication using query logs of search engines. In P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), 2010 International Conference on (pp. 270-275), IEEE.
  6. Chen, Y., X. Xue and Z. Xv, 2012, The transmission mechanism of international oil price fluctuations on the domestic prices of agricultural products: Impact based on LSTAR model.
  7. Choi, H. and H. Varian, 2012, Predicting the present with Google Trends, Economic Record 88, 2-9. https://doi.org/10.1111/j.1475-4932.2012.00809.x
  8. Da, Z., J. Engelberg and P. Gao, 2011, In search of attention, Journal of Finance 66, 1461-1499. https://doi.org/10.1111/j.1540-6261.2011.01679.x
  9. Ettredge, M., J. Gerdes and G. Karuga, 2005, Using web-based search data to predict macroeconomic statistics. Communications of the ACM 48, 87-92.
  10. Fang, S. and X. Ma, 2008, Analysis on the reason of the rising price of agricultural products, Agricultural Economics 6, 20-26.
  11. Fang, L. and J. Peress, 2009, Media coverage and the cross-section of stock returns, Journal of Finance 64, 2023-2052. https://doi.org/10.1111/j.1540-6261.2009.01493.x
  12. Fu, L., L. Deng and Y. Weng, 2014, Quantitative analysis of the effect of international crude oil price fluctuation on the price of agricultural products in China: Based on path analysis, Resource Science 36, 1418-1424.
  13. Gelman, A., 2005, Analysis of variance-why it is more important than ever, Annals of Statistics 33, 1-53. https://doi.org/10.1214/009053604000001048
  14. Groen, J. J., R. Paap and F. Ravazzolo, 2013, Real-time inflation forecasting in a changing world. Journal of Business & Economic Statistics 31, 29-44. https://doi.org/10.1080/07350015.2012.727718
  15. Goel, S., J. M. Hofman, S. Lahaie, D. M. Pennock and D. J. Watts, 2010, Predicting consumer behavior with Web search, Proceedings of the National academy of sciences 107, 17486-17490. https://doi.org/10.1073/pnas.1005962107
  16. Guzman, G., 2011, Internet search behavior as an economic forecasting tool: The case of inflation expectations. Journal of economic and social measurement 36, 119-167.
  17. Han, L. and L. Yin, 2012, Speculation or real demand: A generalized perspective of the influence factors of the international commodity prices, Economic Research 12, 77-84.
  18. Kholodilin, K. A., M. Podstawski, B., Siliverstovs and C. R. S. Burgi, 2009, Google searches as a means of improving the nowcasts of key macroeconomic variables.
  19. Lapp, J. S., 1990, Relative agricultural prices and monetary policy, Journal of Agricultural Economics 72, 622-630. https://doi.org/10.2307/1243032
  20. Li, G., 2011, Analysis on the rising prices of agricultural products in China since 2003, Chinese Rural Economy 2, 11-21.
  21. Liu, X. and Z. Xv, 2008, Cause of agricultural prices in a global perspective, International Business and Economics 6, 5-10.
  22. Liu, Y. and J. Lv, 2009, The change of RMB exchange rate to the price of agricultural products in China: An empirical analysis of the transfer effect, Modern Economic Science 3, 56-62.
  23. Lu, M., 2013, A study of China money supply impact on agricultural product price fluctuation, Master's thesis, Xinjiang University Of Finance and Economics.
  24. Luo, F. and B. Niu, 2009, Pass-through effect of international price volatility of agricultural products on the domestic prices of agricultural products : Empirical analysis on VAR model, International Trade Issues 6, 16-22.
  25. Luo, J., 2010, An empirical analysis of the relationship between the money supply and the price of agricultural products in China, Financial Times 8, 39-40.
  26. Ma, X., 2008, Analysis the causes of agricultural product prices and trend of judgment, Chinese Prices 4, 3-6.
  27. Ma, X., 2008, Agricultural prices cause analysis and judgment on the trend of current, Weishi 7, 85-88.
  28. Oh, C. and O. Sheng, 2011, Investigating predictive power of stock micro blog sentiment in forecasting future stock price directional movement, Knowledge Management and Business Intelligence.
  29. Qiu, H., 2012, Global bio-energy development and its impact on the prices of agricultural products.
  30. Qu, Y., W. Shang and S. Wang, 2013, Webpage mining for inflation emergency early warning. In Web-Age Information Management (pp. 211-222). Springer Berlin Heidelberg.
  31. Schuh, G. E., 1974, The exchange rate and US agriculture, Journal of Agricultural Economics 56, 1-13. https://doi.org/10.2307/1239342
  32. Tetlock, P. C., 2007, Giving content to investor sentiment: The role of media in the stock market. Journal of Finance 62, 1139-1168. https://doi.org/10.1111/j.1540-6261.2007.01232.x
  33. Trostle, R., 2008, Global agricultural supply and demand: factors contributing to the recent increase in food commodity prices. Washington, DC, USA: US Department of Agriculture, Economic Research Service.
  34. Vosen, S. and T. Schmidt, 2011, Forecasting private consumption: survey-based indicators vs. Google trends, Journal of Forecasting 30, 565-578. https://doi.org/10.1002/for.1213
  35. Vosen, S. and T. Schmidt, 2012, A monthly consumption indicator for Germany based on internet search query data, Applied Economics Letters 19, 683-687. https://doi.org/10.1080/13504851.2011.595673
  36. Vlastakis, N. and R. N. Markellos, 2012, Information demand and stock market volatility, Journal of Banking & Finance 36, 1808-1821. https://doi.org/10.1016/j.jbankfin.2012.02.007
  37. Wang, X. and S. Xie, 2012, How international prices of agricultural products affect the prices of agricultural products in China, Economic Research 3, 141-153.
  38. Wei, Z. and F. Xu, 2013, Analysis of production cost and the price of agricultural products, Price relationship between theory and practice 5, 77-78.
  39. Wu, L. and E. Brynjolfsson, 2013, The future of prediction: How Google searches foreshadow housing prices and sales, Available at SSRN 2022293.
  40. Yang, J., J. Huang, M. Li and Q. Shang, 2011, An analysis and policy recommendations on the price of agricultural products in China's money supply, Rural financial research 12, 58-61.
  41. Zhang, C., B. Lv, G. Peng, Y. Liu and Q. Yuan, 2012, A study on correlation between web search data and CPI, In Recent Progress in Data Engineering and Internet Technology (pp. 269-274). Springer Berlin Heidelberg.
  42. Zhang, W., P. Wan and Z. Feng, 2011, A study on the factors affecting the price of agricultural products China: Integration analysis based on VAR model, Monthly Price.

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