A quantitative trading firm is building a model to predict the end-of-day price volatility of a specific Exchange-Traded Fund (ETF). The team is using two primary data sources:
The ETF's historical daily Open, High, Low, and Close (OHLC) prices, recorded once at the end of each trading day.
A real-time social media sentiment score related to the ETF's underlying assets, captured and timestamped every minute during trading hours.
The team's initial approach involves a direct join of the two datasets on the calendar date, which results in the daily OHLC data being duplicated for every one-minute sentiment reading. Which data issue is most fundamentally compromising the model's integrity, and what is the correct first step to remediate it?
The model will suffer from multicollinearity. A Variance Inflation Factor (VIF) analysis should be run to identify and remove the high correlation between sentiment and price movements.
The datasets have a granularity misalignment. The one-minute sentiment data must be aggregated into a daily summary statistic (e.g., mean, total, or final value) before being joined with the daily OHLC data.
The model has insufficient features. The team should engineer new features by creating lagged observations of the sentiment data to capture its delayed impact on daily prices.
The data exhibits non-stationarity. Both time series should be made stationary using differencing before any modeling is attempted to avoid spurious correlations.
The correct answer identifies granularity misalignment as the primary issue. The ETF data has a daily granularity, while the sentiment data has a minute-level granularity. A direct join on the date creates an invalid representation, as the daily metrics are not comparable to the minute-level metrics. The appropriate first step is to aggregate the high-granularity sentiment data to match the low-granularity target variable (daily volatility). This can be done by calculating daily statistics like the mean, max, min, or a volume-weighted average of the minute-by-minute sentiment scores.
Non-stationarity: While financial time series are often non-stationary (meaning their statistical properties change over time), and this will likely need to be addressed, it is a characteristic of the individual series, not the structural problem of combining them. The granularity must be aligned before non-stationarity can be properly assessed and treated on the combined dataset.
Multicollinearity: This issue occurs when two or more predictor variables are highly correlated. It cannot be properly evaluated until all features are at the same level of granularity. Furthermore, the scenario describes a feature (sentiment) and a target (volatility), not two features.
Insufficient Features: While creating lagged variables is a valid feature engineering technique for time-series models, it is a subsequent step. It is impossible to create meaningful daily lags from the sentiment data before it has been aggregated to a consistent daily level. Addressing the granularity misalignment is a prerequisite for effective feature engineering.
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Why is granularity alignment important in data integration for modeling?
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What are common strategies to aggregate high-granularity data like minute-level sentiment statistics into a lower granularity format?
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How does granularity misalignment differ from other challenges like non-stationarity in time-series data?