The phenomenon of lost information is something that happens quite regularly especially in financial databases. It is usually due to factors such as incomplete market feeds, holidays, or discrepancies with the data providers. Missing data for traders and analysts is a critical aspect that needs to be resolved so that they can formulate sound algorithm based trading systems. Otherwise, it may result poorly on trading methodologies that have been developed. More in particular the stochastic model in regard to the trading system will be compromised as a result of poorly handled gaps.
This article aims at discussing the reasons behind missing data in trading datasets, the impacts that it creates and the best possible methods to successfully handle missing data.
Reasons Why Data is Missing in Financial Markets Datasets
Market Holidays
As exchanges do not operate everyday in many regions of the world, A few dates are usually missing data especially with regard to global markets which have non-synchronized markets.
Data Provider Errors
Data provider errors are also a common reason for missing data, in some cases the provider may be in the middle of a technical glitch or there is a technical quiet period where data feed is interrupted.
Low Liquidity
Situations of missing data can also happen in the case of years of low liquidity for small cap stocks or equity instruments in emerging markets as plenty of the data is irregular.
Time Zone Mismatches
Time zone mismatches can affect stock data especially when you are trying to join datasets of different regions.
Granularity Differences
Joining datasets which were recorded over different periods of time could also lead to the loss of data altogether (example: daily and intraday datasets).
Consequences of Absence of Information in Trading
Missing data is another issue that can greatly lower the efficiency of the model and algorithm being used for trading.
Invalid Signals: indicators such as moving averages or the RSI can provide misleading signals when there’s intraday missing data.
Incorrect Backtests: Backtests contain certain assumptions which depend on historical data. If there are gaps in the historical data, this assumption might be violated and in turn give false results on the accuracy of that strategy.
System Breakdowns: Live trading systems can also provide false orders when the houses missing data.
Potential Overfitting: Bias due to filling in missing data only makes it harder for the model to generalize the next time.
Popular Techniques for Dealing with Missing Entries
1. Leave Gaps as Is
There are scenarios where it makes sense not to fill in the blanks/gaps, for instance, if you are creating a basic visual or accessing focus and this approach helps the algorithm by skipping the areas it is not able to compute.
2. Carry forward
This method basically states that the last available value in a sequence of numbers will be used till the next known value comes in the sequence and this has been stated to be one of the fastest means to fill gaps particularly in data relating towards finance.
Example: If say a stock was last selling for ₹150, then that will be the figure en-use until a new value comes in.
3. Backward Fill
In instances when any values throughout a spread sheet or graph are missing, the next value that follows the missing one is regarded as the next available figure for use. Although not as popular as forward fill, this type of backward fill can prove to be effective amongst datasets that are predictive in nature.
4. Linear Interpolation
In computing, interpolation refers to the outlining of missing data by defining values that sit between two given ones, this method provides a fairly elegant way of computing missing data values.
Use Case: To foster the Filling up of intraday price data that had very small time intervals.
5. Mean or Median Imputation
Instead of denoting data as a blank or a question mark, missing data can be substituted by the mean or the median of the entire dataset. This is most successful method in filling up those attributes that have most small variance or standard deviations, for example, traded volumes.
Drawback: Due to this One may aggravate some of the time data series patterns that are prevalent.
6. Drop Missing Data
In some cases the missing data can be such that deleting certain rows or columns may be the most straightforward method especially in those cases where the number of missing values is relatively small or those that are insignificant to performing the data analysis.
Caution: Avoid removing the data if the number of those who are missing or more crucially important.
7. Advanced Techniques:
a. K-Nearest Neighbors (KNN) Imputation
This method indicates how the missing values are estimated based on the values of the nearest neighboring data. This method is suited for multivariate data sets.
b. Time-Series Models
Other examples of models of this kind include the ARIMA model, whereby it predicts trends and the seasonality of data to account for the missing asterisk values.
c. Machine Learning Models
Kedua, Supervised learning models can indeed deduce the missing values by using other variables in the dataset.
d. Terdapat
For a lot of missingness, Statistical or machine learning models can be used to create synthetic data to fill in the gaps
Practical Steps for Handling Missing Data
1. Identify Missing Data
First, begin that process with the visualization of the data set or simple summary so as to detect any mis – values. Typical tools used include:
Using some functions in Python, for instance, .isnull() or something similar in R to find the number of unobserved values.
Creating visualizations (e.g., heatmaps) to show degree of the unsatisfactory factors.
2. Conduct an Analysis for the Pattern of Missingness
One often attempts to seek the reasons of missing data in order to come up with the best strategy possible for filling in the missing data. The patterns can occur in the following forms:
‘Missing Completely at Random’ (MCAR). There are no relationships between data that is missing and the data that is available.
‘Missing at Random’ (MAR). The missing data may be predicted from other data that has been observed this far.
‘Not Missing at Random’ (NMAR). It means that the fact of being missing depends on other unseen data.
3. Determine Which Method Should be Used
Pick a handling method which corresponds with the set characteristics of the data and requirements of the particular strategy.
4. Try and Confirm
Subsequent to the percent of correlation that was missing being filled in, the data set should be validated by verifying the following:
If the filled values are in line with the anticipated patterns.
Strategy performance in light of the imputation.
Best Practices for Missing Data in Trading
Understand the Dataset
Examine the relationships within the data set, along with gauge the significance of the missing values before considering imputation.
Preserve Integrity
Do not conceal critical values such as prices by employing unrealistic trends such as forward fill to satisfy the imputation solution.
Log Changes
Have proper documentation concerning missing values and what was done about them to enhance reproducibility.
Use Domain Knowledge
For example, low trading activity for penny stocks does not imply missing data; rather, it is an imputation that can apply significant market knowledge.
Avoid Over-Imputation
Be careful with heavy imputations because this might lead to poor data integrity, rather, seek a reasonable balance of pattern retaining and inclusion of imputed values.
Conclusion
As it stands out, development of trading strategies and building analytical tools that find such strategies begins with missing value imputation methods for the trading datasets. Depending on the data type, the nature of missingness and the trading strategy the method fits should be selected. If all the gaps are filled then a lot of strategies will have good performance, make fewer mistakes, and make reasonable entry and exit decisions during backtesting and live execution.
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