Feature Engineering: The Unsung Hero of Deep Learning in Equity Prediction

By Sid Ghatak

“In quant finance, the secret isn’t the algorithm—it’s what you feed it.”

In today’s era of accessible computing power and open-source machine learning, many focus on finding the perfect algorithm. But anyone who’s implemented a live equity prediction system knows the real differentiator is feature engineering. This practice—transforming raw, messy data into the optimal inputs for a predictive model—is what separates backtested illusions from real, durable alpha.

The Model Isn’t Enough: Why Features Matter

There’s a misconception that deep learning will automatically “discover” alpha with enough data. In practice, even the most sophisticated neural networks succeed only with high-quality, context-rich features. Traditional quant models have relied on hand-crafted features—ratios, spreads, indicators, and classifications—built on economic logic and human intuition. These models offer transparency, but are usually limited in the complexity of relationships they capture.

Deep learning promises to reveal more nuanced, nonlinear dynamics. However, greater flexibility is a double-edged sword: neural nets are prone to overfitting and can be misled by irrelevant patterns if inputs aren’t carefully curated. When it comes to equity prediction, the quality and construction of your features matter even more than the complexity of your model architecture.

What is Feature Engineering in Equities?

Feature engineering is both an art and a science. It means converting vast stores of financial data—prices, volumes, company filings, and alternative sources—into signals a model can effectively use. Well-constructed features capture key financial patterns, like momentum or mean reversion, in a way that’s accessible to a machine learning model. Poorly engineered features introduce noise or lead to overfitting.

Technical features such as moving averages, RSI, and momentum scores create signals from price history. Fundamental features like valuation multiples, profit margins, and leverage ratios ground the model in company realities. Alternative data features draw on news sentiment, web analytics, or satellite counts, offering new sources of edge. Composite features such as principal components or regime indicators combine multiple signals into a more powerful summary.

In deep learning, features aren’t just inputs—they’re the scaffolding that allows the model to “understand” the market.

The Reality of “Automatic” Feature Learning

It’s tempting to believe that deep learning models can uncover every meaningful signal in data on their own, just as neural networks in vision find edges and shapes. In real-world finance, it’s more complicated. Market data is noisy, ever-changing, and typically less abundant than in other fields. The most effective approaches combine engineered features with model-driven insights. The highest-performing strategies blend classic, thoughtfully engineered signals with model-driven transformations, letting deep learning amplify—rather than replace—human expertise.

Why Feature Engineering is So Difficult in Finance

Signal-to-noise and data-snooping are major pitfalls. Financial markets generate many possible predictors, but only a small fraction have true predictive power. It’s easy to mistake random patterns for real signals—especially without rigorous out-of-sample testing. Many “winning” factors fail in live trading.

Non-stationarity and regime change are constant threats. A feature that once offered predictive value can lose its edge—or even invert—as market conditions change. The challenge is not just to find the right signals, but to maintain and adapt them as markets evolve.

Complexity and dimensionality can overwhelm a model. More features do not always mean better performance. Too many can lead to overfitting, too few can mean missing critical information. Balancing feature quantity and quality requires systematic reduction techniques and deep experience.

Domain knowledge matters.

Understanding which features make economic sense helps prevent models from chasing irrelevant relationships.

Best Practices: Building Winning Features

Start with economic logic. Domain knowledge should inform which features to try—such as earnings surprises, momentum, or liquidity.

Reduce redundancy. Use feature selection techniques or dimensionality reduction to avoid overfitting and keep models manageable.

Blend manual and automated engineering. Allow the model to learn from a strong foundation of curated features.

Handle alternative data with care. New data sources are valuable but demand their own engineering—think NLP for text or CNNs for imagery.

Validate out-of-sample. Robust backtesting, walk-forward analysis, and stress tests confirm that features add real value.

Prioritize explainability. Tools that reveal which features the model is using help with debugging, compliance, and trust.

Real-World Examples

LSTM networks excel at handling the sequential nature of stock prices. When provided with well-structured input data—including histories of returns and engineered technical signals—these models deliver superior performance versus traditional approaches. The real value comes not from raw prices, but from features that reflect economic logic and technical insight.

Reshaping time series and technical indicators into image-like matrices enables convolutional neural networks to spot complex patterns that classic models miss. This transformation is a sophisticated form of feature engineering, making it easier for models to “see” what matters.

Simply throwing every possible signal into a model dilutes predictive power. Consistently stronger and more stable results come from systematically ranking and filtering features, using only the best-performing signals.

Bridging the Gap: Deep Learning and Classic Quant

Leading practitioners now combine curated feature sets—grounded in economic rationale—with the flexibility of deep learning models. Sometimes this even means embedding classic factors into neural architectures or adding financial constraints for sensible results. Advances in explainable AI make it possible to understand not just that a model works, but why.

What’s Next? The Evolving Frontier

As machine learning techniques become more accessible, the real competitive edge shifts to those who can engineer and maintain better features. Automation, alternative data, and explainable AI are pushing the boundaries—but the need for careful, ongoing adaptation remains critical. Feature engineering is the lens that keeps your sights set on alpha.

Conclusion: Edge is Engineered

When evaluating any AI-powered equity strategy, the most important question isn’t what model is used, but how its features are built and maintained. In today’s markets, real edge is engineered—thoughtfully and systematically, with a blend of human insight and machine learning power. If deep learning is the engine, feature engineering is the fuel. In a crowded, efficient market, what you feed your model still determines your results. That’s the uncorrelated truth.

Sid Ghatak
CEO
Increase Alpha, LLC

Increase Alpha is a predictive intelligence platform engineered to extract signal from noise in modern markets. Backed by four years of forward-tested results, we deliver AI-powered insights tailored for institutional desks. Our proprietary deep learning architecture is free of LLMs, free of look-ahead bias, and fully explainable—built on public data and refined over a decade.