2024-05-03 Bahamian Dollar News
2024-05-02
Summary of Yesterday
- Opening:
- Closing:
- Difference of Opening & Closing:
- Daily High:
- Daily Low:
- Difference of Daily High & Low:
Statistical Measures
- Mean:
- Standard Deviation:
Trend
Overall Trend Analysis
From an initial visual inspection and basic calculations, one could observe that the exchange rate generally seems to decrease during the covered timeframe. Starting from a rate of approximately 1.3777 at the start of May 2, 2024, the rate goes down to 1.36994 towards the end of the day. However, this does not imply a continuous, uniform decline; there are periods with increasing and stable rates as well, which could be confirmed with a more in-depth analysis.
Seasonality and Recurring Patterns Analysis
The exchange rate data provided does not cover a span long enough to establish definitive seasonal trends or recurring patterns accurately. Data from few hours on a single day is not enough to determine daily, weekly, or monthly patterns. However, minute to minute differences, periodic fluctuations, or intra-day trends could potentially be identified with a more thorough time-series analysis.
Outliers Analysis
Identifying outliers within this dataset would depend on defining a threshold for what constitutes a significant change in the exchange rate. One approach could be to calculate the mean and standard deviation of the exchange rate changes and classify any change larger than two or three times the standard deviation as an outlier. In this specific dataset, there are some noticeable abrupt falls and rises which could be considered as potential outliers and could impact any analysis significantly.
Further Considerations
The results of this basic analysis are tentative and need further confirmation with more advanced statistical and econometric analyses. They may also be improved by considering longer time periods and additional factors such as market opening/closing hours, weekends/holidays, and the release of key financial news and reports. However, we must be careful to avoid overfitting, where our model becomes too complex and begins to fit the noise in the data rather than the underlying trend. In this case, the client has explicitly asked to avoid any form of forecast generation and to not consider any external factors.
