Data Wrangling, Imputation, and Normalization Techniques

Data Wrangling Fundamentals

Data wrangling is the process of bringing diverse, messy, or sometimes downright chaotic information into a consistent, structured format suitable for analysis. If you’ve ever tried consolidating daily stock returns from three or four different data vendors, you know how quickly things get complicated. Each vendor might format ticker symbols differently, or perhaps one data source logs monthly data and another logs weekly. That’s when you realize: you can’t just grab data off the shelf and run your fancy regression—first, you have to make it speak the same language.

In a typical Level II exam scenario, you may stumble upon a vignette providing large tables of stock prices, corporate fundamentals, and macroeconomic indicators. The question might involve merging them into one coherent dataset before modeling future returns. Let’s say you have data from two CSV files and one Excel workbook. Their column structures differ. The time frames don’t sync exactly. Some columns are labeled “PX_LAST,” others “Close,” or “Adjusted Price.” And oh—some stock tickers read “AAPL” while others read “Apple Inc.” That’s data wrangling in a nutshell: aligning these details carefully so your final dataset is correct and consistent.

Here are some key practices:

• Integrate multiple datasets using matching keys. Financial data is often linked by ticker symbols, company IDs (e.g., SEDOL, CUSIP), or exact date/time stamps. Check for variations in naming or date formatting (e.g., 2025/05/01 vs. 01-May-25 vs. 5/1/25).
• Resolve schema mismatches. Maybe you have “last_price” in one dataset and “price_close” in another. Standardize these to a single, uniform field.
• Use scripts in Python or R to reshape tables. In Python, the Pandas library’s merge, concat, and pivot methods are your best friends. In R, you might rely on dplyr’s join functions.

Below is a miniature data-wrangling pipeline diagram (in Mermaid syntax) illustrating how raw data flows into a well-defined format:

    graph LR
	A["Raw Data Sources"] --> B["Combine & Merge<br/> (Python: pd.merge / R: dplyr)"]
	B["Combine & Merge<br/> (Python: pd.merge / R: dplyr)"] --> C["Cleanup / Standardization<br/> (Renaming Fields, Date Format)"]
	C["Cleanup / Standardization<br/> (Renaming Fields, Date Format)"] --> D["Final Structured Dataset"]

In my experience, ensuring consistent column naming and date alignment feels tedious, but it’s absolutely vital. After all, if you can’t trust your data, you’ll have a seriously questionable final analysis.

Imputation of Missing Values

Missing data is almost inevitable—especially in big financial datasets. Some asset prices might be missing on certain days, certain exchange rate fields might be blank, or fundamental data might be missing for smaller firms. There are several ways to address these gaps:

Mean or Median Imputation

This is by far the simplest and often the first approach you learn. If a variable is missing, say, a daily trading volume, replace it with the mean or median of the available observations. For instance, if you have 200 days of volume data and only 2 days contain missing values, you can fill those days with the average volume. This can be acceptable when data are missing at random and you don’t expect major outliers to skew your results significantly.

However, be cautious: if the data are missing systematically—like all Fridays for a certain stock for some reason—mean imputation could introduce bias. Also, in financial contexts, volume could be heavily skewed. Using the median instead of the mean might better preserve the dataset’s structure.

Forward Fill and Backward Fill

These methods are especially popular in time-series contexts. Forward fill (or “last observation carried forward”) takes the last known data point and simply keeps using it for all subsequent missing observations until a new valid data point appears. Backward fill does the opposite—pulling in future observations to fill earlier gaps.

In finance, forward fill is common for missing price data over weekends or holidays. If a Sunday’s closing price is missing, you might just use Saturday’s price until Monday’s price appears. But watch out: if a big event happened over the weekend, forward fill might distort your dataset.

Multiple Imputation

If you’re dealing with large and complex datasets—perhaps you have hundreds of missing data points for certain illiquid stocks—you might go for a more statistically sound approach. Multiple imputation involves creating several different “plausible” versions of the dataset by using predictive models (like regression or machine learning models) to fill missing values. Then you run your final analysis on each plausible dataset and combine the results. This approach helps incorporate the uncertainty around missing data, theoretically giving more reliable inferences.

Normalization and Scaling

Normalization, or scaling, is another big step in data preparation. Many machine learning algorithms (e.g., k-Nearest Neighbors, neural networks, or support vector machines) assume all features are on similar scales. If one variable—like a stock price that often ranges in the hundreds—dominates another—like sentiment scores around 0 to 5—the model might overweight the high-scale variable. Normalization solves this.

Min–Max Scaling

With min–max scaling, each feature is transformed to a target range, typically [0,1]. The formula is:

z-Score Standardization

z-score standardization transforms each value to represent how many standard deviations above or below the mean it lies:

Robust Scaling

If you’re dealing with heavy-tailed distributions or multiple outliers (and trust me, financial data can get super messy), robust scaling might be more appropriate. Instead of using the mean and standard deviation, robust scaling uses the median and interquartile range (IQR). This approach drastically reduces the influence of outliers because the median and quartiles are less sensitive to extreme values.

Outlier Detection and Treatment

If you’re looking at returns data, you might occasionally see an abnormally huge return—like +500% in a single day—due to some one-off event (a critical press release or a data feed glitch). The question is: do you treat that as a legitimate data point, or is it a fluke that should be removed or capped?

Statistical Thresholds

A common approach is to flag any values over a certain number of standard deviations from the mean—say 3 or 4 standard deviations. If 99% of your returns distribution is within ±5%, you can suspect that a +50% reading might be an outlier that needs thorough investigation. But keep in mind, outliers in finance can be real. Look at what happens during severe market crashes or huge rallies. Don’t just automatically remove these; analyze whether they’re legitimate.

Domain Knowledge

Sometimes you rely on knowledge of the asset class or market microstructure. For instance, if a stock rarely trades over $10 million in volume per day but you see a $50 million reading, that might still be valid if it coincided with an earnings release. If it’s valid, keep it. If not, you might choose to remove or cap the value (Winsorize).

Transformation Approaches

Winsorization caps extreme values at given percentiles—like the 1st and 99th percentile. For returns, you might decide that anything above the 99th percentile is replaced with the 99th-percentile value, and anything below the 1st percentile is replaced with the 1st-percentile value. This softens the effect of outliers without completely removing them. Just be sure to document your approach and be transparent about the reasons for your chosen thresholds.

Log Transformations

If your data has exponential growth or a strong positive skew—like stock prices over time—using the natural logarithm can make a big difference. In finance, analyzing log-transformed data often aligns better with the assumption of multiplicative processes. For example, log returns are additive over time, which can simplify certain time-series analyses.

If you ever look at how asset prices evolve, they frequently follow growth patterns that are nearly exponential over long periods. Taking logs can make these patterns more linear and more convenient to model. That said, never forget that log transformations require strictly positive data—no negative or zero values allowed.

Glossary

• Data Wrangling: Transforming and consolidating raw datasets into a format suitable for analysis or modeling.
• Imputation: Replacing missing or invalid values with estimates, often to preserve the integrity of your dataset.
• Normalization (Scaling): Rescaling data so that different variables operate on comparable scales, often crucial for machine learning algorithms.
• Winsorization: Capping extreme outliers at specified percentiles to reduce their impact.

References

• Gelman, A. & Hill, J. (2006). “Data Analysis Using Regression and Multilevel/Hierarchical Models.” Cambridge University Press.
• VanderPlas, J. (2016). “Python Data Science Handbook.” O’Reilly Media (see chapters on data wrangling with Pandas).
• Springboard Blog, “Data Wrangling Tips and Tricks” (online resource covering various data cleaning strategies).

Final Exam Tips

• Document Everything. On the exam, you might be given a scenario about missing data and asked which imputation strategy is best. In real life, always record your reasons for choosing a specific method.
• Beware of Over-Manipulation. Overly aggressive data cleaning or outlier removal can mask real phenomena—especially relevant in finance where tail events can be the most critical.
• Practice Scripts and Tools. If you’re working with big data, coding proficiency in Python or R can be a game-changer—both for exam item sets and for real-world tasks.
• Time Allocations. If a vignette describes multiple data sources with unknown columns, hold back the temptation to dive right in. Clarify the data fields, check their definitions, and ensure you understand how they’re supposed to merge.

Test Your Mastery: Data Wrangling, Imputation, and Normalization Quiz