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THE IMPACT OF THE US EMPLOYMENT REPORT ON THE GOLD SPOT RATE

Year 2023, Volume: 10 Issue: 2, 108 - 120, 30.06.2023
https://doi.org/10.17261/Pressacademia.2023.1733

Abstract

Purpose- Considering the various financial markets, it can be observed that macroeconomic events such as announcement releases might affect the volatility and the direction of price movements in the related markets. While some announcements might play a substantial role in this subject, some might be categorized as unessential announcements in the economic calendars. Reports related to the employment situation, inflation, growth of the domestic product, and commodity reservations of a country are crucial points on the schedule of investors and traders all around the globe. However, reports coming from countries with a major economic share have a much more significant effect on the market. In that regard, researchers are more interested in the evaluation of economic events of countries like the United States, United Kingdom, Germany, and China. In that regard, this study focuses on the impact of the U.S. employment situation report on the XAU/USD spot exchange rate.
Methodology- In the first part of the study, the significance of relevant factors of the announcement has been evaluated to specify the importance of the elements included in the employment report. In that interest, an OLS regression model has been developed in the first step. Furthermore, the face and statistical validity phases have been controlled to improve the efficiency of the model. The second part of the study focuses on the direction of the price movement respectively after specific periods from the report’s release. To satisfy the desired goal of the study, two various models have been applied to the data to evaluate the two models and their performances. The first model is based on logistic regression approaches while the second model benefits from XGboost regression. Accuracy metrics have been evaluated for both models to decide on the healthiness of the performances.
Findings- Findings demonstrate that the gold spot exchange rate reacts strongly to the announced nonfarm payroll employment figure, while the market takes its revision of the prior month and unemployment rate as additional data around the release of the announcement. Results suggest that employment reports labeled as “bad news” for the U.S. economy caused an increase in the exchange rate of the gold spot. Price discovery for different time intervals after the announcement release shows that the first 10 minutes are the most crucial. Time intervals before the announcement release imply that exchange rate changes are regular and there is not any recognizable pattern for price movements before the announcement release, while abnormal returns start to show up just after the release of the announcement.
Conclusion- To sum up, the impact of the announcement report on the price movement of the gold spot is undeniable. However, uncertainties increase before the announcement, and volatility increases after the announcement. Various statuses lead to specific movements in the market. While the uncertainties are lower before the announcement, the price movement of the gold spot would be diverse to the status of the announcement.

References

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  • Barndorff-Nielsen, O. E., & Shephard, N. (2004). Power and bipower variation with stochastic volatility and jumps. Journal of Financial Econometrics, 2(1), 1-37.
  • Cai, J., Cheung, Y.-L., Lee, R. S., & Melvin, M. (2001). ‘Once-in-a-generation’yen volatility in 1998: fundamentals, intervention, and order flow. Journal of International Money and Finance, 20(3), 327-347.
  • Caporale, G. M., & Plastun, A. (2021). Gold and oil prices: abnormal returns, momentum and contrarian effects. Financial Markets and Portfolio Management, 35(3), 353-368.
  • Chaboud, A., Chernenko, S., Howorka, E., Iyer, R. S., Liu, D., & Wright, J. H. (2004). The high-frequency effects of US macroeconomic data releases on prices and trading activity in the global interdealer foreign exchange market. Available at SSRN 625181.
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  • Chen, Y.-L., & Gau, Y.-F. (2010). News announcements and price discovery in foreign exchange spot and futures markets. Journal of Banking & Finance, 34(7), 1628-1636.
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  • DeGennaro, R. P., & Shrieves, R. E. (1997). Public information releases, private information arrival and volatility in the foreign exchange market. Journal of Empirical Finance, 4(4), 295-315.
  • Dezhkam, A., & Manzuri, M. T. (2023). Forecasting stock market for an efficient portfolio by combining XGBoost and Hilbert–Huang transform. Engineering Applications of Artificial Intelligence, 118, 105626.
  • Ederington, L. H., Fernando, C. S., Hoelscher, S. A., Lee, T. K., & Linn, S. C. (2019). A review of the evidence on the relation between crude oil prices and petroleum product prices. Journal of Commodity Markets, 13, 1-15.
  • Evans, K., & Speight, A. (2010). International macroeconomic announcements and intraday euro exchange rate volatility. Journal of the Japanese and international economies, 24(4), 552-568.
  • Evans, K. P., & Speight, A. E. (2010). Intraday periodicity, calendar and announcement effects in Euro exchange rate volatility. Research in International Business and Finance, 24(1), 82-101.
  • Evans, M. D., & Lyons, R. K. (2002). Order flow and exchange rate dynamics. Journal of Political Economy, 110(1), 170-180.
  • Fama, E. F., Fisher, L., Jensen, M. C., & Roll, R. (1969). The adjustment of stock prices to new information. International Economic Review, 10(1), 1-21.
  • Fleming, M. J., & Remolona, E. M. (1999). Price formation and liquidity in the US Treasury market: The response to public information. The Journal of Finance, 54(5), 1901-1915.
  • Gau, Y.-F., & Wu, Z.-X. (2017). Macroeconomic announcements and price discovery in the foreign exchange market. Journal of International Money and Finance, 79, 232-254.
  • Hajek, P., & Novotny, J. (2022). Fuzzy rule-based prediction of gold prices using news affect. Expert Systems with Applications, 193, 116487.
  • Han, Y., Kim, J., & Enke, D. (2023). A machine learning trading system for the stock market based on N-period Min-Max labeling using XGBoost. Expert Systems with Applications, 211, 118581.
  • Melvin, M., & Yin, X. (2000). Public information arrival, exchange rate volatility, and quote frequency. The Economic Journal, 110(465), 644-661.
  • Nikkinen, J., Omran, M., Sahlström, P., & Äijö, J. (2006). Global stock market reactions to scheduled US macroeconomic news announcements. Global Finance Journal, 17(1), 92-104.
  • Smales, L. A., & Yang, Y. (2015). The importance of belief dispersion in the response of gold futures to macroeconomic announcements. International Review of Financial Analysis, 41, 292-302.
  • Sun, E. W., Rezania, O., Rachev, S. T., & Fabozzi, F. J. (2011). Analysis of the intraday effects of economic releases on the currency market. Journal of International Money and Finance, 30(4), 692-707.
  • Ye, Z. J., & Schuller, B. W. (2021). Capturing dynamics of post-earnings-announcement drift using a genetic algorithm-optimized XGBoost. Expert Systems with Applications, 177, 114892.
  • Yun, K. K., Yoon, S. W., & Won, D. (2021). Prediction of stock price direction using a hybrid GA-XGBoost algorithm with a three-stage feature engineering process. Expert Systems with Applications, 186, 115716.
Year 2023, Volume: 10 Issue: 2, 108 - 120, 30.06.2023
https://doi.org/10.17261/Pressacademia.2023.1733

Abstract

References

  • Abu-Doush, I., Ahmed, B., Awadallah, M. A., Al-Betar, M. A., & Rababaah, A. R. (2023). Enhancing Multilayer Perceptron Neural Network using Archive-based Harris Hawks Optimizer to Predict Gold Prices. Journal of King Saud University-Computer and Information Sciences, 101557.
  • Andersen, T. G., & Bollerslev, T. (1998). Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International Economic Review, 885-905.
  • Andersen, T. G., Bollerslev, T., Diebold, F. X., & Ebens, H. (2001). The distribution of realized stock return volatility. Journal of Financial Economics, 61(1), 43-76.
  • Barndorff-Nielsen, O. E., & Shephard, N. (2004). Power and bipower variation with stochastic volatility and jumps. Journal of Financial Econometrics, 2(1), 1-37.
  • Cai, J., Cheung, Y.-L., Lee, R. S., & Melvin, M. (2001). ‘Once-in-a-generation’yen volatility in 1998: fundamentals, intervention, and order flow. Journal of International Money and Finance, 20(3), 327-347.
  • Caporale, G. M., & Plastun, A. (2021). Gold and oil prices: abnormal returns, momentum and contrarian effects. Financial Markets and Portfolio Management, 35(3), 353-368.
  • Chaboud, A., Chernenko, S., Howorka, E., Iyer, R. S., Liu, D., & Wright, J. H. (2004). The high-frequency effects of US macroeconomic data releases on prices and trading activity in the global interdealer foreign exchange market. Available at SSRN 625181.
  • Chatrath, A., Miao, H., Ramchander, S., & Villupuram, S. (2014). Currency jumps, cojumps and the role of macro news. Journal of International Money and Finance, 40, 42-62.
  • Chen, Y.-L., & Gau, Y.-F. (2010). News announcements and price discovery in foreign exchange spot and futures markets. Journal of Banking & Finance, 34(7), 1628-1636.
  • Christie–David, R., Chaudhry, M., & Koch, T. W. (2000). Do macroeconomics news releases affect gold and silver prices? Journal of Economics and Business, 52(5), 405-421.
  • DeGennaro, R. P., & Shrieves, R. E. (1997). Public information releases, private information arrival and volatility in the foreign exchange market. Journal of Empirical Finance, 4(4), 295-315.
  • Dezhkam, A., & Manzuri, M. T. (2023). Forecasting stock market for an efficient portfolio by combining XGBoost and Hilbert–Huang transform. Engineering Applications of Artificial Intelligence, 118, 105626.
  • Ederington, L. H., Fernando, C. S., Hoelscher, S. A., Lee, T. K., & Linn, S. C. (2019). A review of the evidence on the relation between crude oil prices and petroleum product prices. Journal of Commodity Markets, 13, 1-15.
  • Evans, K., & Speight, A. (2010). International macroeconomic announcements and intraday euro exchange rate volatility. Journal of the Japanese and international economies, 24(4), 552-568.
  • Evans, K. P., & Speight, A. E. (2010). Intraday periodicity, calendar and announcement effects in Euro exchange rate volatility. Research in International Business and Finance, 24(1), 82-101.
  • Evans, M. D., & Lyons, R. K. (2002). Order flow and exchange rate dynamics. Journal of Political Economy, 110(1), 170-180.
  • Fama, E. F., Fisher, L., Jensen, M. C., & Roll, R. (1969). The adjustment of stock prices to new information. International Economic Review, 10(1), 1-21.
  • Fleming, M. J., & Remolona, E. M. (1999). Price formation and liquidity in the US Treasury market: The response to public information. The Journal of Finance, 54(5), 1901-1915.
  • Gau, Y.-F., & Wu, Z.-X. (2017). Macroeconomic announcements and price discovery in the foreign exchange market. Journal of International Money and Finance, 79, 232-254.
  • Hajek, P., & Novotny, J. (2022). Fuzzy rule-based prediction of gold prices using news affect. Expert Systems with Applications, 193, 116487.
  • Han, Y., Kim, J., & Enke, D. (2023). A machine learning trading system for the stock market based on N-period Min-Max labeling using XGBoost. Expert Systems with Applications, 211, 118581.
  • Melvin, M., & Yin, X. (2000). Public information arrival, exchange rate volatility, and quote frequency. The Economic Journal, 110(465), 644-661.
  • Nikkinen, J., Omran, M., Sahlström, P., & Äijö, J. (2006). Global stock market reactions to scheduled US macroeconomic news announcements. Global Finance Journal, 17(1), 92-104.
  • Smales, L. A., & Yang, Y. (2015). The importance of belief dispersion in the response of gold futures to macroeconomic announcements. International Review of Financial Analysis, 41, 292-302.
  • Sun, E. W., Rezania, O., Rachev, S. T., & Fabozzi, F. J. (2011). Analysis of the intraday effects of economic releases on the currency market. Journal of International Money and Finance, 30(4), 692-707.
  • Ye, Z. J., & Schuller, B. W. (2021). Capturing dynamics of post-earnings-announcement drift using a genetic algorithm-optimized XGBoost. Expert Systems with Applications, 177, 114892.
  • Yun, K. K., Yoon, S. W., & Won, D. (2021). Prediction of stock price direction using a hybrid GA-XGBoost algorithm with a three-stage feature engineering process. Expert Systems with Applications, 186, 115716.
There are 27 citations in total.

Details

Primary Language English
Subjects Business Administration
Journal Section Articles
Authors

Nima Nıyazpour This is me 0000-0001-7369-4945

Kaya Tokmakcıgglu This is me 0000-0002-5981-299X

Publication Date June 30, 2023
Published in Issue Year 2023 Volume: 10 Issue: 2

Cite

APA Nıyazpour, N., & Tokmakcıgglu, K. (2023). THE IMPACT OF THE US EMPLOYMENT REPORT ON THE GOLD SPOT RATE. Journal of Economics Finance and Accounting, 10(2), 108-120. https://doi.org/10.17261/Pressacademia.2023.1733

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