I am currently working on a project that involves analyzing stock market data. The data consists of daily closing prices for several stocks over a period of time. However, I’ve encountered a challenge with missing values on non-trading days, which is causing gaps in the time series. This could potentially lead to inaccurate results when performing calculations like moving averages or other time-series analyses.

I understand there are several strategies to handle missing data, such as forward-filling, backward-filling, or interpolation, but I’m unsure which method is best suited for financial data. Each method has its implications, and I want to ensure that the approach I take does not introduce bias or distort the analysis.

For context, here’s a snippet of the data with missing entries, and I’ve set the dates as the DataFrame index:

import pandas as pd

# Example data

data = {'Date': ['2021-01-01', '2021-01-02', '2021-01-04', '2021-01-05'],
'AAPL': [132.0, None, 134.0, 136.0],
'MSFT': [222.0, 223.0, None, 225.0]}

df = pd.DataFrame(data)
df['Date'] = pd.to_datetime(df['Date'])
df.set_index('Date', inplace=True)

print(df)

What I am looking for is guidance on best practices when dealing with missing financial time series data. Should I forward-fill to use the last known value, backward-fill, interpolate, or perhaps use a more sophisticated method like time-series imputation? Also, what are the potential impacts of each method on subsequent financial analysis?

I am seeking advice from the community on the most appropriate ways to approach this problem and any insights on the implications of these methods in the context of financial time series data.

I have attempted the following methods to handle the missing data:

1. Forward Filling: I used pandas.DataFrame.ffill() to propagate the last valid observation forward. However, I was concerned this method might not reflect true market behavior, as it assumes the next valid price was the same as the last available, which might not be realistic in a volatile market.

df.ffill(inplace=True)

2. Backward Filling: I also tried pandas.DataFrame.bfill(), which fills the gaps with the next available data point. However, this method assumes prior knowledge of future prices, which doesn’t make sense in a real-world scenario.

df.bfill(inplace=True)

3. Interpolation: Lastly, I attempted linear interpolation using pandas.DataFrame.interpolate(), which seemed more sophisticated but may not account for the nature of stock price movements, which are not always linear.

df.interpolate(inplace=True)

What I was expecting with these methods was to create a continuous time series without gaps that could be used for further time series analysis like calculating moving averages or volatility. My primary concern is preserving the integrity of the financial data while dealing with missing points without introducing bias. I am unsure if my approaches have been correct and am looking for guidance on the best practice for handling such situations in financial data analysis.