Dramatic Fluctuations Shape Trading Day for AZN Forex Market
2024-03-11
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
1. Overall Trend of Exchange Rates
Upon examining the time-series dataset on the AZN exchange rate, it does not clearly follow a simple linear trend - either increasing, decreasing, or remaining stable. The exchange rates exhibit considerable fluctuations over the time period covered. However, it appears that the rates generally oscillate around a certain level, suggesting a potential mean-reverting behavior. Given the inherent volatility of exchange rates, short term fluctuations are normal. Further statistical tests would be needed to confirm if there's a significant upward or downward trend over time.
2. Seasonality or Recurring Patterns
Due to the granularity of the dataset (exchange rate fluctuations across different timestamps within the same day), it's challenging to identify clear patterns of seasonality. Seasonality, when commonly detected, usually occurs over larger timescales (e.g., monthly, quarterly, or yearly cycles). However, certain times of the day (e.g., opening & closing of markets) might show higher volatility but more data would be required to confirm such intraday patterns.
3. Outliers in Exchange Rates
There are several instances where the exchange rate differs significantly from the surrounding data points. These could potentially be considered as outliers. However, in the context of financial markets, these sharp changes could be attributed to a variety of factors such as sudden changes in market sentiment, release of key economic data, central bank interventions, etc. For a thorough analysis, a statistical outlier detection method could pinpoint these substantial deviations, which can then be investigated further for potential causes.
It should be noted that further analysis, including a more concrete statistical breakdown and potentially a predictive model, would help provide more accurate insights into the trend, seasonality and outliers within the data.