2024-05-14 Bahraini Dinar News
2024-05-13
Summary of Last Month
- Opening:
- Closing:
- Difference of Opening & Closing:
- Daily High:
- Daily Low:
- Difference of Daily High & Low:
Statistical Measures
- Mean:
- Standard Deviation:
Trend
Analysis of BHD Exchange Rates Over Time
For the given time series data, a comprehensive financial analysis needs to take into account factors like the overall trend, any cyclical patterns or seasonality, and outlier points. Below is the detailed assessment based on the given points:
1. Overall Trend Analysis
From the timestamp provided, it can be observed that BHD exchange rate continuously fluctuates over the timeline. However, to provide the conclusive statement about overall increase, decrease or stability, further deep statistical analysis or visual trend analysis is needed, ideally using a line graph or curve fitting techniques.
2. Seasonality or Recurring Patterns
Identifying any seasonality or recurring patterns in the exchange rates requires further computation including autocorrelation analysis. This would entail reviewing the data for regular or repeating fluctuations, which may be linked to factors like time of day, week or month. Unfortunately, the data provided does not show a clear pattern of such. The absence of a clear pattern could be due to the influence of other external variables not captured in the dataset.
3. Outliers Detection
An outlier in a distribution is a number that is more than 1.5 times the length of the half the data away from either the lower or upper quartiles. Specifically, if you have a number that is less than Q1-1.5IQR or greater than Q3 + 1.5IQR, then it is an outlier. Looking at our data, further statistical analysis such as a boxplot or IQR computation would be useful to detect the outliers. It's important to note that the extreme changes or outliers to the trend might occur due to the various macroeconomic factors influencing the currency value.
Given this is time-series data, the given analysis can further be strengthened by employing time-series modelling techniques to capture the trend, seasonality, and outlier impacts in our model. Techniques like ARIMA, SARIMA, LSTM etc. could be useful to draw a more conclusive analysis.
To conclude, the above analysis only provides a brief understanding of the exchange rates data. A more thorough analysis would be required to fully understand the factors influencing this rate - including potentially important external factors such as market conditions, policy changes, or other economic indicators.