How to Interpret Foreign Exchange Rates?

The foreign exchange rates help us determine the value of one currency in terms of another currency. The foreign exchange market is a global 24-hour market where traders from around the world buy and sell currencies for various purposes including speculation, and hedging. When you travel to another country you will need to convert your money into that country’s currency to buy stuff there.

As traders buy and sell currencies, the foreign exchange rates fluctuate constantly based on the laws of demand and supply. A currency that is in more demand compared to another currency becomes more expensive relative to other currencies and so on.

From time to time, you will need to interpret the exchange rates for your country vis-à-vis another country. For example, if you have some business in India, then you need to convert your earnings in Indian rupee (INR) back to your domestic currency (U.S. dollars for American companies).

The first thing that you need to do is determine the current exchange rate. In this case the USD/INR exchange rate. You can check the rates on websites such as XE.com, or even Google provides exchange rates in its search results. For example if you type “USD INR exchange rate” in Google search box, you will get the result as follows:

As you can see, 1 US dollar is equal to 52.95 Indian rupees today (on the date of this writing).

Now assume that your business’s Indian subsidiary made Rs. 2,500,000 profits last year. To determine how much that is in US dollar, you can use the foreign exchange rate for USD/INR. We know that as per the current exchange rate, 1 US dollar = 52.95 Indian rupees. Using this data, we can say that:

Rs 2,500,000 = 2,500,000/52.95 =  USD 47,214.35

In terms of US dollars, the Indian subsidiary has made USD 47,214.35.

You can also analyse the historical exchange rates to determine how the exchange rates have trended over time. As the exchange rates move, some currencies will appreciate while others will depreciate. For example, assume that the USD INR exchange rate 6 months back was 1 US dollar = 50 Indian rupees. So, earlier you could get 50 rupees for 1 dollar, and now you can get 52.95 rupees for the same 1 dollar. This is interpreted as the US dollar has appreciated or strengthened compared to Indian rupee. This means that the Indian goods will be cheaper for American buyers, so Americans may import more Indian goods, or will travel to India cheaper than before. Even the Indian exporters earning their revenues in US dollars will benefit because each dollar they earn will convert into higher Indian rupees.

The effect will be exactly opposite if US dollar had depreciated instead of appreciated. If dollar depreciated, then Indian exporters earning in US dollars will have to take a hit on their earnings, as they will get less rupees for every dollar, which will lower their profit margins. The firm may have to look for hedging their foreign exchange risk.

When one currency appreciates compared to another currency, it does not mean that the currency is now stronger than all other currencies. For example, if USD has appreciated against INR, it does not necessarily mean that it has appreciated against other currencies such as yen or yuan. It may have depreciated against those currencies.

Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book includes PDFs, explanations, instructions, data files, and R code for all examples.

Get the Bundle for $29 (Regular $57)
JOIN 30,000 DATA PROFESSIONALS

Free Guides - Getting Started with R and Python

Enter your name and email address below and we will email you the guides for R programming and Python.

Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.