Summary of Yesterday
- Difference of Opening & Closing:
- Daily High:
- Daily Low:
- Difference of Daily High & Low:
- Standard Deviation:
First, it is important to understand that given the granular time intervals per data entry (5 minute intervals), we are dealing with intraday data. Intraday data analysis allows us to understand the behavior of the exchange rate throughout each day, instead of looking at closing day prices.
1. Overall Trend of the Exchange Rates
By observing the given dataset and considering that the smallest value is 1.34356 BSD, the highest being 1.3513 BSD, and there seem to be many fluctuations between these two values. Various peaks and troughs are noted, which suggests volatility in the exchange rate and this makes it difficult to indicate a clear overarching upward or downward trend. Instead, the exchange rate appears to have a lot of intraday fluctuations, hinting towards a somewhat oscillating behavior rather than a pronounced general increasing or decreasing trend. To derive a significant conclusion, a statistical measure such as linear regression may be needed to analyze, envision, and grasp such a trend efficiently.
2. Seasonality or Recurring Patterns
From the given data, it's a challenge to conclusively state any definitive seasonality or recurring patterns. Generally, exchange rates are influenced by a multitude of different factors, including but not limited to macroeconomic indicators and international trade balances. Furthermore, given that the data covers a very short period of just a single day, it's almost impossible to identify any seasonality. As such, longer duration data would be necessary to adequately identify any potential patterns or seasonality in the exchange rates.
3. Outliers in the Exchange Rates
When comparing each exchange rate to its immediate neighbors, no extreme price jumps that might be classified as outliers can be immediately detected. Nonetheless, the notable leap from 1.34434 BSD to 1.34766 BSD within a 5-minute window might be something worth investigating further. A detailed statistical analysis using methods like the Z-Score could help in identifying potential outliers more accurately. However, it is crucial to remember that in financial markets, the perception of what constitutes an 'outlier' can often be subjective and dependent on market context.
In conclusion, the given dataset exhibits noticeable intraday volatility, a lack of observable overarching trend, and a potential absence of recognizable seasonality. Finally, while potential outliers are present, they are scarce and somewhat consistent with the intraday variability that is common in financial markets.