Short-term foreign exchange forecasting: decision making based on expert polls
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Author(s)Jaehyung AnLink to ORCID Index: https://orcid.org/0000-0001-5410-7506
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Mikhail DorofeevLink to ORCID Index: https://orcid.org/0000-0002-2829-9900
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DOIhttp://dx.doi.org/10.21511/imfi.16(4).2019.19
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Article InfoVolume 16 2019, Issue #4, pp. 215-228
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The paper aims to analyze the decision making based on expert polls for short-term foreign exchange (FX) forecasting from the viewpoint of the economic behavior theory. The paper offers the assessment of the problem of decision making for forecasting and investment into foreign currency. This study analyzes the relative accuracy of expert polls and forecasts, based on historical data, in the prediction of the most liquid currency pairs (EUR/USD, USD/JPY, GBP/USD) as well as USD/RUB currency pair on time horizons 1, 2, 6, and 12 months. Observation period lasted from January 2018 to January 2019. For EUR/USD (56-62 experts), the polls were more accurate than historical simulations. For GBP/USD (28-70 experts), historical simulations were more accurate than polls. For USD/JPY and USD/RUB, historical simulations are better earlier, while polls are slightly better later. The main conclusion is that EUR/USD historical modeling is usually less accurate on the horizon more than half a year as compared with expert polls for making the decisions about the future exchange rate.
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JEL Classification (Paper profile tab)G11, D81, D84
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References55
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Tables0
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Figures4
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- Figure 1. Mean absolute error of expert polls and forecasts EFA against the real rate EUR/USD (1, 3, 6 months and 1 year)
- Figure 2. Mean absolute error of expert polls and forecasts EFA against the real rate USD/JPY (1, 3, 6 months and 1 year)
- Figure 3. Mean absolute error of expert polls and forecasts EFA against the real rate GBP/USD (1, 3, 6 months and 1 year)
- Figure 4. Mean absolute error of expert polls and forecasts EFA against the real rate RUB/USD (1, 3, 6 months and 1 year)
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- An, J., Mikhaylov, A., & Moiseev, N. (2019b). Oil Price Predictors: Machine Learning Approach. International Journal of Energy Economics and Policy, 9(5), 2019, 1-6.
- An, J., Mikhaylov, A., Lopatin, E., Moiseev, N., Richter, U. H., Varyash, I., Dooyum, Y. D., Oganov, A., & Bertelsen, R. G. (2019a). Bioenergy potential of Russia: method of evaluating costs. International Journal of Energy Economics and Policy, 9(5), 244-251.
- Armstrong, J. S. (1980). The Seer-Sucker theory: The value of experts in forecasting. Technology Review, 83, 16-24.
- Armstrong, J. S. (2001). Combining forecasts. In Principles of Forecasting: A Handbook for Researchers and Practitioners (pp. 417-439).
- Armstrong, J. S., Green, K. C., & Graefe, A. (2015). Golden Rule of Forecasting: Be conservative. Journal of Business Research, 68(8), 1717-1731.
- Backus, D. K., & Crucini, M. J. (2000). Oil Prices and the Terms of Trade. Journal of International Economics, 50(1), 185-213.
- Baumeister, C., & Peersman, G. (2008). Time-varying effects of oil supply shocks on the US Economy.
- Biemer, P. P. (2010). Total survey error: Design, implementation, and evaluation. Public Opinion Quarterly, 74(5), 817-848.
- Buchanan, W. (1986). Election predictions: An empirical assessment. Public Opinion Quarterly, 50(2), 222-227.
- Buetzer, S., Habib, M., & Stracca, L. (2012). Global Exchange Rate Configurations: Do Oil Shocks Matter? (Working Paper, European Central Bank).
- Cooper, I., & Priestly, R. (2009). Time-varying risk premiums and the output gap. Review of Financial Studies, 22(7), 2801-2833.
- Denisova, V. (2019). Energy efficiency as a way to ecological safety: evidence from Russia. International journal of energy economics and policy, 9(5), 32-37.
- Denisova, V., Mikhaylov, А., & Lopatin, E. (2019). Blockchain Infrastructure and Growth of Global Power Consumption. International Journal of Energy Economics and Policy, 9(4), 22-29.
- Dorofeyev, M., Kоsov, M., Ponkratov, V., Masterov, A., Karaev, A., & Vasyunina, M. (2018). Trends and prospects for the development of blockchain and cryptocurrencies in the digital economy. European Research Studies Journal, 21(3), 429-445.
- Farzanegan, M. R., & Markwardt, G. (2009). The effects of oil price shocks on the Iranian economy. Energy Economics, 31(1), 134-151.
- Graefe, A. (2015). Improving forecasts using equally weighted predictors. Journal of Business Research, 68(8), 1792-1799.
- Graefe, A. (2018). Predicting elections: Experts, polls, and fundamentals. Judgment and Decision Making, 13(4), 334-344.
- Graefe, A., Armstrong, J. S., Jones, R. J. J., & Cuzán, A. G. (2014). Combining forecasts: An application to elections. International Journal of Forecasting, 30(1), 43-54.
- Graefe, A., Kјchenhoff, H., Stierle, V., & Riedl, B. (2015). Limitations of Ensemble Bayesian Model Averaging for forecasting social science problems. International Journal of Forecasting, 31(3), 943-951.
- Green, K. C., Graefe, A., & Armstrong, J. S. (2011). Forecasting principles. In M. Lovric (Ed.), International Encyclopedia of Statistical Science (pp. 527-534). Berlin Heidelberg: Springer.
- Groves, R. M., & Lyberg, L. (2010). Total survey error: Past, present, and future. Public Opinion Quarterly, 74(5), 849-879.
- Larrick, R. P., & Soll, J. B. (2006). Intuitions about combining opinions: Misappreciation of the averaging principle. Management science, 52(1), 111-127.
- Lopatin, E. (2019). Assessment of Russian banking system performance and sustainability. Banks and Bank Systems, 14(3), 202-211.
- Lopatin, E. (2019). Methodological Approaches to Research Resource Saving Industrial Enterprises. International Journal of Energy Economics and Policy, 9(4), 181-187.
- Meynkhard, A. (2019). Energy Efficient Development Model for Regions of the Russian Federation: Evidence of Crypto Mining. International Journal of Energy Economics and Policy, 9(4), 16-21.
- Meynkhard, A. (2020). Priorities of Russian Energy Policy in Russian-Chinese Relations. International Journal of Energy Economics and Policy, 10(1), 65-71.
- Mikhailov, A. (2018). Pricing in Oil Market and Using Probit Model for Analysis of Stock Market Effects. International Journal of Energy Economics and Policy, 4, 43-53.
- Mikhaylov, A. (2018). Volatility Spillover Effect between Stock and Exchange Rate in Oil Exporting Countries. International Journal of Energy Economics and Policy, 8(3), 321-326.
- Mikhaylov, A. (2019). Oil and Gas Budget Revenues in Russia after Crisis in 2015. International Journal of Energy Economics and Policy, 9(2), 375-380.
- Moiseev, N. (2017a). Forecasting time series of economic processes by model averaging across data frames of various lengths. Journal of Statistical Computation and Simulation, 87(17), 3111-3131.
- Moiseev, N. (2017b). p-Value adjustment to control type I errors in linear regression models. Journal of Statistical Computation and Simulation, 87(9), 1701-1711.
- Moiseev, N. (2017c). Linear model averaging by minimizing mean-squared forecast error unbiased estimator. Model Assisted Statistics and Applications, 11(4), 325-338.
- Moiseev, N., & Akhmadeev, B. (2017). Agent-based simulation of wealth, capital and asset distribution on stock markets. Journal of Interdisciplinary Economics, 29(2), 176-196.
- Moiseev, N., & Sorokin, A. (2018). Interval forecast for model averaging methods. Model Assisted Statistics and Applications, 18(2), 125-138.
- Morozko, Na., Morozko, Ni., & Didenko, V. (2018a). Modeling the process of financing small organizations. Journal of Reviews on Global Economics, 7, 774-783.
- Morozko, Na., Morozko, Ni., & Didenko, V. (2018b). Unbalanced liquidity management evaluation of the Russian banking sector. Journal of Reviews on Global Economics, 7, 487-496.
- Morozko, Na., Morozko, Ni., & Didenko, V. (2018c). Determinants of the savings market in Russia. Banks and Bank Systems, 13(1), 196-208.
- Morozko, Na., Morozko, Ni., & Didenko, V. (2018d). Rationale for the development strategy of small business organizations using the real options method. Academy of Strategic Management Journal, 2, 17-27.
- Nosić, A., & Weber, M. (2010). How riskily do I invest? The role of risk attitudes, risk perceptions, and overconfidence. Decision Analysis, 7(3), 282-301.
- Nyangarika, A., Mikhaylov, A., & Richter, U. (2019a). Influence Oil Price towards Economic Indicators in Russia. International Journal of Energy Economics, 9(1), 123-129.
- Nyangarika, A., Mikhaylov, A., & Richter, U. (2019b). Oil Price Factors: Forecasting on the Base of Modified Auto-regressive Integrated Moving Average Model. International Journal of Energy Economics, 9(1), pp. 149-159.
- Nyangarika, A., Mikhaylov, A., & Tang, B. J. (2018). Correlation of Oil Prices and Gross Domestic Product in Oil Producing Countries. International Journal of Energy Economics, 8(5), 42-48.
- Olomola, P. A., & Adejumo, A. V. (2006). Oil price shock and macroeconomic activities in Nigeria. International Research Journal of Finance and Economics, 3(1), 28-34.
- Osipov, V. S., Skryl, T. V., Blinova, E. A., Kosov, M. E., Zeldner, A. G., & Alekseev, A. N. (2017). Institutional Analysis of Public Administration System. International Journal of Applied Business and Economic Research, 15(15), 193-203.
- Riker, W. H. (1982). The two two-party of the system and Duverger’s law: of an essay on the history of Political science. American Political Science Review, 76(4), 753-766.
- Shirani-Mehr, H., Rothschild, D., Goel, S., & Gelman, A. (2018). Disentangling bias and variance in election polls. Journal of the American Statistical Association, 113(522), 607-614.
- Sigarev, A. V., Kosov, M. E., Buzdalina, O. B., Alandarov, R. A., & Rykova, I. N. (2018). The role of chains in the Russian retail sector. European Research Studies Journal, 21(1), 542-554.
- Silver, N. (2017, May). Conventional wisdom may be contaminating polls.
- Singer, E. (2007). Oil Price Volatility and the US Macroeconomy: 1983–2006 (Working paper). Minnesota of USA: Carleton College.
- Slepov, V. A., Burlachkov, V. K., Danko, T. P., Kosov, M. E., Volkov, I. I., Grishina, O. A., & Sekerin, V. D. (2017a). The country’s economic growth models and the potential for budgetary, monetary and private financing of gross domestic product growth. European Research Studies Journal, 20(4A), 488-500.
- Slepov, V. A., Kоsov, M. E., Burlachkov, V. K., Grishina, O. A., & Sakharov, D. M. (2019). Shadow banking: Reasons of emergence and directions of development. International Journal of Civil Engineering and Technology, 10(2), 1747-1754.
- Slepov, V., Burlachkov, V., Danko, T., Kosov, M., Volkov, I., Ivolgina, N., & Sekerin, V. (2017b). Model for integrating monetary and fiscal policies to stimulate economic growth and sustainable debt dynamics. European Research Studies Journal, 20(4).
- Soll, J. B., & Larrick, R. P. (2009). Strategies for revising judgment: How do people use others? Journal of Experimental Psychology: Learning, Memory, and Cognition, 35(3), 780-805.
- Tryndina, N., Moiseev, N., Lopatin, E., Prosekov, S., & Kejun, J. (2020). Trends in Corporate Energy Strategy of Russian Companies. International Journal of Energy Economics and Policy, 10(1), 202-207.
- Zubakin, V. A., Kosorukov, O. A., & Moiseev, N. A. (2015). Improvement of regression forecasting models. Modern applied science, 9(6), 344-353.
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