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Forecasting Exchange Rates: Data-Driven Decision-Making Proposal Essay

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Updated: Jun 15th, 2020

Hypothesis

Foreign currency traders who use data for decision-making are likely to gain competitive edge in foreign exchange than those who rely on gut feelings.

The project area

Data for this study will be obtained from Exchange-Rates.org. Historical exchange rates between the British Pound (GBP) and the US Dollar (USD) will be used for forecasting future exchange rates. Data for analysis will be based on the preferred periods.

The technical approach (TA) will be used in forecasting the exchanges for a given period. TA relies on a subset of historical data, particularly price data (exchange rates). This approach is technical because it does not account for fundamental economic determinants that could influence exchange rates. Instead, it relies on data extrapolation obtained from historical observed trends.

Technical analysis will yield certain patterns that will be used for forecasting exchange rates between the GBP and the USD. In time-series analysis, it is imperative to determine an appropriate length of intervals because they determine the effectiveness of forecasting (Tayal, Sonawani, Ansari and Gupta 132-135). Research shows that the length of the intervals influences the accuracy of the forecast. Thus, effective selection of the length of intervals could significantly enhance the accuracy of forecasting outcomes (Tayal et al. 132-135).

SPSS or any other effective analytical tool will be used for data analysis to identify significant trends that can support decision-making. Foreign currency traders will use these significant trends to buy or sell their currencies.

A research question

Can data-driven decision-making create competitive edge for foreign currency traders?

Significance

In most cases, international transactions may take time be settled. In this regard, traders should assess exchange rate forecasts based on foreign currencies. In addition, foreign exchange traders must also understand future trends in their markets. Therefore, exchange rate forecasting is critical and can help traders to assess both benefits, risks and other challenges in trade.

Project Proposal

Forecasting has long been recognized as a fundamental tool across various industries. In the past, however, traders used various approaches to forecast and inform their decisions. One primary goal of understanding trends of exchange rate is to be able to forecast them. Given the fluctuation in exchange rate trends, forecasting exchange rates could be difficult without reliable data. The research question, therefore, would provide intellectual foundation to encourage foreign exchange traders to adopt data-driven decision-making rather than use gut feelings to make decisions.

Thomas H. Davenport noted that forecasting could be more difficult in retail industries because of several variables and constantly changing trends and influences on demands, alternative channels among others (Davenport 11). In most cases, there could be large amount of data that analysts can leverage to create accurate prediction of the exchange rate. Foreign exchange forecasting is critical for key decision-making processes, particularly in finance management, operations and budgeting among others. The use of gut feelings or manual forecasts cannot support effective decision-making. Such approaches consume management time and they could be difficult to understand due to lack of any meaningful data. In this regard, data-driven decision-making can assist managers to make effective decisions for competitive advantage. Davenport observed that there is little doubt, however, that the “aggressive adoption and exploitation of analytics has led to competitive advantage among some of the world’s most successful retailers” (Davenport 11). Therefore, data-driven decision-making can solve many challenges international traders face.

Works Cited

Davenport, Thomas H. Realizing the Potential of Retail Analytics: Plenty of Food for Those with the Appetite. Babson Park, MA: Babson Executive Education, 2009. Print.

Tayal, Devendra, Shilpa Sonawani, Gunjan Ansari, and Charu Gupta. “Fuzzy Time Series Forecasting of Low Dimensional Numerical Data.” International Journal of Engineering Research and Applications (IJERA) 2.1 (n.d): 132-135. Print.

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"Forecasting Exchange Rates: Data-Driven Decision-Making." IvyPanda, 15 June 2020, ivypanda.com/essays/forecasting-exchange-rates-data-driven-decision-making/.

1. IvyPanda. "Forecasting Exchange Rates: Data-Driven Decision-Making." June 15, 2020. https://ivypanda.com/essays/forecasting-exchange-rates-data-driven-decision-making/.


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IvyPanda. "Forecasting Exchange Rates: Data-Driven Decision-Making." June 15, 2020. https://ivypanda.com/essays/forecasting-exchange-rates-data-driven-decision-making/.

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IvyPanda. 2020. "Forecasting Exchange Rates: Data-Driven Decision-Making." June 15, 2020. https://ivypanda.com/essays/forecasting-exchange-rates-data-driven-decision-making/.

References

IvyPanda. (2020) 'Forecasting Exchange Rates: Data-Driven Decision-Making'. 15 June.

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