Bulgarian Lev Forecast

Not for Invesment, Informational Purposes Only

Summary of Yesterday

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Statistical Measures

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    1. Understanding the Trend

    The exchange rates data provided fluctuates quite often due to various factors. With an overall insignificant slope of variation, I can say that the rates are relatively stable. A few isolated instances seem to indicate a slight increase or decrease in rates, but the larger picture suggests that no substantial or progressive change constitutes an "Overall Trend". Hence, from a broader perspective, the trend of the exchange rates within this specific timeframe (2024-02-29 00:00:02 to 2024-02-29 23:55:02) appears to be neutral or stable.

    2. Seasonality and Recurring Patterns

    Regarding seasonality or recurring patterns, the exchange rates do not appear to follow a distinct pattern within the interval provided. For thorough confirmation, more extensive historical data may be required. The fluctuations are random and do not seem to be seasonally adjusting at any discernible level. Therefore, it's difficult to assess any seasonality with the limited data.

    3. Outliers in the Exchange Rates

    The highest value in the dataset is 0.75379 (at 2024-02-29 04:05:03), and the lowest value is 0.74921 (at 2024-02-29 20:05:02 and 20:10:02). These data points may be considered outliers after a more in-depth statistical analysis. However, the deviation from the mean rate does not seem significant enough to label them as extreme outliers. These might be due to normal volatility in exchange rates. An in-depth identification of outliers would require a specific statistical model, which is beyond the provided information and requirements.

Summary of Yesterday

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Statistical Measures

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    1. Overall Trend Analysis

    After analysing the provided data, it can be observed that the overall trend of the BGN exchange rate doesn't follow a strictly increasing or decreasing trend but is rather oscillatory in nature over time. The value of BGN tends to go up and down within a certain range. It starts at 00:00:02 with a value of 0.7504 and ends at 23:55:02 with a slightly higher value of 0.75198. However, throughout the time series data, there is no clear extensive pattern suggesting a significant continuous uptrend or downtrend.

    2. Seasonality and Recurring Patterns

    With regard to seasonality or recurring patterns, it is difficult to determine any clear patterns within the provided time range due to the high frequency and volatility inherent to exchange rates. The rate oscillates frequently in a non-significant range, indicating highly variable time-variant behaviour. So, there doesn't appear to be a consistent to-and-fro pattern or seasonality that could help predict future rates.

    3. Outliers and Unexpected Events

    Analyzing time-series data for outliers is subject to the nature of the data. The provided data does not seem to exhibit major sudden jumps or extreme values which usually indicate the presence of outliers. The exchange rates seem to deviate within what seems to be a normal range for such data. However, this does not mean that future data will not contain outliers, as the financial market can often be influenced by unexpected macroeconomic events leading to such anomalies.

    Please note that for a deeper and more accurate understanding, statistical tests and techniques like Autoregressive Integrated Moving Average (ARIMA) or Generalized Autoregressive Conditional Heteroskedasticity (GARCH) can be employed to model and analyse this type of data which has not been considered in this analysis.

Summary of Yesterday

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Statistical Measures

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Summary of Last Month

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Statistical Measures

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    1. Understanding of the overall trend of exchange rates

    Upon analyzing the given dataset, it becomes clear that the overall trend of the exchange rates fluctuates slightly but sees a general incremental increase over time. The exchange rate begins at 0.74716, hits a low point of around 0.74714, before slowly increasing and ending at 0.74933. Therefore, there has been overall appreciation of the currency during the period under consideration.

    2. Identification of seasonality or recurring patterns

    Establishing clear seasonal patterns or recurring trends in the given dataset is challenging due to its short duration. However, there appear to be minor cyclical patterns where the rate increases gradually before experiencing a slight slump, then the cycle repeats. This typical pattern indicates that the exchange rate might be influenced by certain time-bound factors such as market open/close hours, but such an inference must be substantiated with more data and over a longer period.

    3. Identification of outliers

    Proper outlier identification requires robust statistical modeling, which might not be feasible with this univariate dataset. That said, there are a few instances where the rate changes are relatively substantial, including the jump from 0.74766 at 01:45 to 0.74859 at 01:55, and the dip from 0.75157 at 06:55 to 0.75011 at 07:00. Though potential outliers, these fluctuation might be normal in a volatile financial market, and shouldn't be deemed anomalies without further investigation.

    In conclusion, although the dataset exhibits a slight upward trend and repetitive cycles of rise and fall, making any concrete assertions about its behavior would necessitate a more in-depth analysis using a larger dataset over an extended period.

Summary of Last Week

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Statistical Measures

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    Overall Trend of Exchange Rates

    Overall, the exchange rates have a slight fluctuating movement throughout the time period. From the given data, there doesn't appear to be a consistent increase, decrease, or stabilization in the data. The value fluctuates between approximately 0.73988 and 0.74800, depicting a volatile exchange rate environment.

    Seasonality or Recurring Patterns

    From the given dataset, it doesn't appear that there is a clear-cut seasonality or recurring pattern at a specific time point. In a traditional sense, seasonality would refer to specific times of a day, week, month, or year when rates see an increase or decrease consistently, but this pattern does not seem observable with the dataset provided.

    Outliers Identification

    Identifying outliers requires statistical analysis and deriving conclusions based on deviations from mean and other statistical measures, and it might be challenging to spot just by manually observing the data. Additionally, exchange rate movements are relatively complex and are influenced by several variables, including market conditions, economic indicators, political instability, among others. Therefore, it's potentially challenging to identify 'outliers' as such because significant deviations might be attributable to a certain event or announcement, which isn't considered in the current data analysis.

Summary of Yesterday

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Statistical Measures

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    1. Overall Trend of Exchange Rates

    Observing the data, you can see an overall fluctuating trend in the exchange rates. The rates seem to have been largely within the range of around 0.742 to 0.748 over the provided period from 19th February 2024 to 23rd February 2024. The values are minute yet they signify important changes in the exchange rate.

    The rate started relatively low at approximately 0.742 on the 19th of February 2024. Then it started increasing slightly till it reached a high of approximately 0.7480 on the 20th of February. Then it started to decrease again in a fluctuating pattern till reaching approximately 0.742 around the 22nd of February. At last, the rate showed a slight increase and decreased again to reach approximately 0.74736 by the 23rd of February.

    2. Identification of Seasonality

    By reviewing the dataset, it appears to be minimal to no clear seasonality or recurring patterns in the exchange rates for the considered period. It is noteworthy that within the scope of the given data, from 19th-23rd February 2024, the time frame of observable seasonality is quite short, which makes it difficult to identify a strong seasonality pattern.

    3. Outliers Identification

    As for outliers, or instances where the exchange rate significantly deviates from what is expected based on the trend or seasonality, it's difficult to identify these without more context, like the standard deviation or the overall distribution of exchange rates. Considering that the range of the exchange rate throughout the given period is quite narrow (0.742 to 0.748), there don't appear to be any major outliers.

    Note

    An essential aspect of financial analysis is considering external factors like market opening/closing hours, weekends/holidays, and the release of key financial news and reports. However, as per request, these factors have not been included in this analysis.

    Additionally, forecasting future exchange rates requires a more sophisticated model and a larger time series dataset. This initial analysis only covered an overview of what happened in the specific time period provided.

Summary of Yesterday

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Statistical Measures

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    Understanding the Trend of Exchange Rates

    From an initial glance of the dataset, the exchange rates display slight fluctuations, with rates that both increase and decrease throughout the period. There does not appear to be a strong trend towards either increasing or decreasing rates overall. More specific insights would require deeper analysis and computations using statistical models.

    Identifying Seasonality or Recurring Patterns

    • Given the format and the contents of the data, it is challenging to conclusively detect any seasonality or recurring patterns.
    • This is mainly because the data points are spaced at intervals of approximately 5 minutes and there is no clear cyclical pattern detectable at this frequency. Any seasonal patterns would usually be identifiable at larger time scales (e.g., daily, monthly, or yearly).

    Noting any Outliers

    A more detailed statistical analysis would be required to reliably detect outliers within this data. However, at a superficial level, there are no extreme fluctuations in the exchange rate that stand out as being markedly different from the rest of the dataset.

    The exchange rates seem to be relatively stable, fluctuating between approximately 0.745 and 0.747. There do not appear to be any instances where the exchange rate diverges significantly from this range.

    External Factors

    As per the request, external factors such as market opening/closing hours, weekends/holidays, and the release of key financial news and reports were not taken into consideration for this analysis.

    However, it is important to note that these factors could potentially have significant impacts on the exchange rates. This means that a more comprehensive analysis that does take these factors into account may yield different insights.

    Forecasting Future Rates

    As per the request, this analysis does not include any forecasting of future exchange rates.

    However, it is worth noting that forecasting future exchange rates would involve building a predictive model based on the historical data. This model would attempt to capture the underlying trend and any seasonal patterns, and could then be used to project future rates.

    Given the volatility of exchange rates, such a predictive model would also need to incorporate measures of uncertainty, providing a range of plausible values rather than a single point forecast.