Executive summary
This report is about forecasting the exchange rate between the British Pound (GBP) and U.S. Dollar (USD). Currency traders use several methods in forecasting to be able to gain higher profit. One method in forecasting is the use of data and the corresponding analysis, and tracing price trends that may appear in sequence or in cluster. The technical analysis (TA) method is a scientific way of creating a forecast, involving the recording and clustering of the variables and looking for past price trends. The agent can use the results in buying or selling currencies. There are several aids that currency traders use to get the precise trend, or a good forecast. In the currency market are tools, newsletters, and other print materials, selling information about price data. Others use gut feeling, but this is unscientific and may lead to a questionable situation for the agent.
This paper discussed the different variables and used technical approach (TA) in the analysis of the variables. The hypothesis was tested and the null hypothesis – that gut feeling is more effective than using data in the forecast – was rejected because it lacked evidence from empirical studies.
Hypothesis
The report tested the hypothesis that 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.
Background
Knowing the trend in exchange rates enables forecasters to determine international currency involved in business and the risks and benefits in international business. A forecast is a look at an unpredictable future and the near-certainty of international cash flows can be seen by historical price trends. Thus, our methodology involves the study of the relationship of the British Pound and the U.S. Dollar (USD) in terms of the past currency behavior.
The research question is: Can data-driven decision making create competitive edge for foreign currency traders?
Methodology
We used historical data from the website Exchange Rates.org.uk to answer our research question and prove the hypothesis. The significance of the research question and in obtaining a clear forecast of the exchange rate between the British Pound (GBP) and the U.S. Dollar (USD) is that this can help international traders assess benefits, risks and other challenges in international trade. Currency forecasts seem not normal because they include expected mistakes (Frankel & Froot, 1987 as cited in Chang & Osler, 1999).
This report used Technical Analysis (TA) to find out some price patterns, such as price repetitions in the past. The short-range moving average (SRMA), a model of Technical Analysis (TA) uses 30 days of information, while the short-range moving average (LRMA) uses 180 days of information.
Technical analysis (TA)
Technical analysis is effective because of the way orders are clustered. There is what analysts say ‘a concentration of demand … and supply’ (Osler, 2003, p. 1792), which can guide the trader. Order clustering in currency markets is about ‘stop-loss and take-profit orders’ (Osler, 2003, p. 1792), which refers to buy or sell currencies. A ‘stop-loss (take profit) buy order’ leads to purchase of currency if the market rate rises or falls to a particular level. For instance, when an agent places an order, say for example in a dealing bank, the orders cluster strongly at round numbers. Round numbers end in 00 or.50. Orders cluster at round numbers because large orders ‘benefit from precise pricing’ and have to be placed at round numbers rather than small orders. Agents tend to place orders at round numbers to reduce time and mistakes in their dealings with dealers (Grossman et al., 1997 as cited in Osler, 2003, p. 1793). Behavioral reason is another reason cause (Yule, 1927 as cited in Osler, 2003). Stop-loss orders tend to cluster strongly, and these clusters cannot be simply predicted because information about orders is unknown. Stop-loss orders also contribute to large, quick, price moves, also known as ‘price cascades’ (Osler, 2003, p. 1794). But even if information is lacking, agents can refer their research at past trends, in which sell and buy orders are in cluster. There are examples of these trends in the accompanying data.
The null hypothesis that currency traders use gut feeling to forecast exchange rate is hereby rejected because agents use historical data before they place their orders. Without these data they may use gut feeling, but in the stock market there are many who provide or sell data and information about trends in the market which can influence the buying and selling of currencies, such as the GBP and the USD.
The variables
Nominal values
The variables or data are shown in the appendix, which provide the 180-day information, retrieved from ExchangeRates.org.uk. Each of these values are called nominal values; meaning all of them are nominal because they provide the value of exchange rate of GBP and USD for the given period. ‘Nominal’ is like a name and in this instance, it refers to a name of the variable or the data itself.
The variables were collected and measured by researchers from the website, according to the current exchange rate in the stock market and based on the law of supply and demand. An example of the nominal value of exchange rate for September 30 is provided in the website ExchangeRates.org.uk, which is 1.6215 USD for every 1 GBP. The data collected on 30 September 2014 showed a nominal value of 5 GBP, equivalent to 8.1075 USD. All the others are also called nominal values.
Ordinal values
In the list of values or variables for the 180-day information listed in the website, i.e. from 23 June 2014 to 30 September 2014, the minimum is 1.6095 and the maximum is 1.7161. The ordinal data lists the variables, not in order. But the site has shown the 180-day information for simplification. When these data are reached, the signal is for the agent to sell or buy (ExchangeRates.org.uk, 2014).
Interval values
The interval data provided by the website for the 180-day information (2 April to 30 September 2014) reveals a pattern of intervals, i.e. for every 6-day interval, there is a repetition of the values, or a difference of zero. For example, from 28 June to 4 July, the exchange rate of 1 GBP = 1.716 is repeated in 5 Jul, or a difference of 0 for 5 and 6 July. Six days after, 11 Jul 2014, the value of 1.7107 is repeated, or there is a 0 difference between 11 July and 12 July. The interval of six days and a repetition of that value when the sixth day occurs is a pattern that can be drawn from the 180-day information for the GBP-USD exchange rate. The mean and standard deviation for the six-day data can be calculated here.
Take-profit orders cluster at round numbers ending in 00. No such round numbers occur in the 180-day information. Exchange rates would tend to rise in answer to predominance of buy orders or take orders (Evans & Lyons, 1999; Rime, 2000; Evans, 2001 as cited in Osler, 2003).
Conclusion
Drawing from the theory discussed above, the 180-day information (in the appendix) creates six-day clusters of the various data from the website. This signals the agent to buy or sell. Traders use this signal because it creates a trend. In the context of products and services, the signal refers to the demand and supply. Naturally, if there is big demand, the seller sells at higher price. If there is less demand, it can be interpreted as there is no signal. Currency traders may interpret this as gut feeling, but again there is no evidence for the so-called gut feeling. What they usually examine is the preponderance of data provided by the website, or any other organization providing currency data.
The first hypothesis that foreign currency traders who use data for decision-making are likely to gain competitive edge in foreign exchange is proven in the statistical analysis. The TA provided for clustering as a means of helping the agent in analyzing the stop-loss and take-profit orders. However, the null hypothesis that foreign currency traders can also use gut feeling is hereby rejected. There is no empirical evidence to substantiate the null hypothesis. The research question was also addressed in the discussion, i.e. data-driven decision making creates competitive edge for foreign currency traders.
References
Chang, P. & Osler, C. (1999). Methodical madness: Technical analysis and the irrationality of exchange-rate forecasts. The Economic Journal, 109(458), 636-661.
ExchangeRates.org.uk: British pound (GBP) to US dollar (USD) exchange rate history. (2014). Web.
Osler, C. (2003). Currency orders and exchange rate dynamics: An explanation for the predictive success of technical analysis. The Journal of Finance, 58(5), 1791-1819.
Appendix
Source: ExchangeRates.org.uk(2014)