Notable slowdown in the RON exchange rate on May 20
2024-05-20
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
OVERALL TREND OF EXCHANGE RATES
Looking at the provided dataset, it shows the variation in the exchange rate over a 4-hour period. The trend of the RON exchange rate presents a slight increase in the initial timestamps, reaching the highest value of 0.29828. This trend is followed by a gradual decline throughout the observed time period, with the lowest value of 0.29705 recorded. Hence, in the selected timeframe, the exchange rates trended slightly downwards. It's noteworthy to specify this does not look into longer-term trend changes and is limited by the dataset provided.
SEASONALITY OR RECURRING PATTERNS
Given the limited timeframe, a clear pattern of seasonality cannot be directly observed. The foreign exchange market is highly influenced by market opening and closing hours, release of financial news, as well as other external factors. Furthermore, the nature of forex markets often do exhibit intra-day reversion to the mean which could appear seasonal but is a characteristic of the asset class. To detect any seasonality or recurring patterns, a more extensive data set that encompasses a longer timeframe (e.g., 24-hour cycle, weekly, monthly, annually) is recommended.
OUTLIERS IN THE DATASET
In a financial market context, outliers can often represent periods of high volatility due to sharp movements in prices. In our dataset, there are no observable outliers or instances where the exchange rate deviates greatly from the general trend. While a few minor fluctuations can be observed, these are rather minor and typical in a forex exchange data. The rates mostly oscillate around the average value and there is no significant spike in the data, hence, it can be concluded that this dataset does not contain any major outliers. However, outliers detection would benefit from using statistical models which can compare actual deviations to those expected under typical market conditions.