Alternative Data and NLP in Quant Strategies › Challenges and Limitations

Alternative data and NLP introduce problems that are less common in traditional quantitative datasets. Accounting for these limitations helps you form realistic expectations and build reliable systems.

When satellite parking lot data was available to only a few funds in 2015, it generated significant alpha. By 2020, with dozens of subscribers to similar services, the informational advantage had largely disappeared. Prices adjusted more quickly to the signal, leaving fewer opportunities for profitable trading.

This decay creates a perpetual search for new data sources. Academic factors may persist for decades. Alternative data advantages typically last months to years. Maintaining an edge requires continuous innovation in data sourcing and processing, not just better modeling of data you already have. The most successful alternative data firms build data moats through exclusive relationships with data providers, proprietary collection infrastructure, or analytical capabilities that competitors struggle to replicate.

Alternative datasets are typically far noisier than traditional financial data. Social media sentiment is contaminated by bots, sarcasm, and non-financial content. Satellite imagery is affected by weather and parking lot layout changes. Web scraping captures spam and irrelevant content.

This noise makes overfitting a serious risk. Machine learning models with high-dimensional text features and noisy targets can memorize spurious patterns that don't generalize. Rigorous out-of-sample validation and cross-validation are essential safeguards, though even these may not reveal biases specific to particular time periods or market conditions.

Selection biases also pervade alternative data. Social media users are not representative of the general population, credit card data captures only electronic transactions, and satellite coverage may be better for some geographies than others. These biases can create systematic prediction errors that only become apparent when market conditions change.

Alternative data requires extensive preprocessing before it can feed into models. Text must be cleaned of formatting artifacts. Entities must be identified and disambiguated. For example, does "Apple" refer to the company or the fruit? Sentiment must be extracted in context. Each preprocessing step introduces potential errors and assumptions.

Data quality issues pervade alternative datasets. News feeds contain duplicates, incomplete articles, and misattributed sources. Social media includes spam, bot content, and foreign-language posts. Web-scraped data often has parsing errors and site format changes. Geospatial data requires georeferencing, cloud removal, and temporal alignment.

Data engineering typically consumes a larger portion of alternative data projects than modeling does. The principle 'garbage in, garbage out' applies especially strongly when raw inputs are messy and unstructured.

Historical alternative data often differs from what was available in real time. News articles may be timestamped to publication time but weren't accessible until minutes or hours later. Social media APIs may return different results depending on when they're queried. Satellite images might be revised after initial release.

It's hard to build backtests that accurately reflect what data was available historically. Point-in-time data (snapshots of what was known at each date) is ideal but often impossible for alternative datasets that weren't collected until recently. This lookback bias inflates historical performance estimates.