How Contrastive Predictive Coding Rethinks Financial Time Series Forecasting
Learning What Markets Refuse to Tell You
The Quiet Failure of Traditional Forecasting
Financial time series forecasting has always lived with an uncomfortable truth. Markets look structured, but behave like noise. For decades, researchers relied on linear models such as ARIMA and regression, hoping stationarity and historical patterns would hold long enough to be useful. In practice, they rarely do. Exchange rates, equity returns, and macro linked assets are shaped by shocks, sentiment, and regime changes that violate nearly every classical assumption.
Deep learning promised an escape. Recurrent models such as LSTMs were designed to capture temporal dependencies and long term memory. Yet even these architectures struggle when the signal to noise ratio collapses. More data does not automatically solve the problem. More parameters often amplify overfitting. The uncomfortable result is that increasingly complex models can perform no better than naive baselines when faced with real financial data.
The study REPRESENTATION LEARNING FOR FINANCIAL TIME SERIES FORECASTING starts from that failure and asks a sharper question. Instead of forcing models to predict directly from raw prices or returns, what if we first learn representations that separate structure from randomness.
From Feature Engineering to Representation Learning
Feature engineering has long been the craft of financial modeling. Lagged returns, moving averages, volatility measures, and technical indicators are designed to expose latent structure. But this process is slow, subjective, and fragile. Domain knowledge becomes bias. What worked in one regime silently fails in another.
Representation learning offers a different philosophy. Rather than specifying what features should matter, the model learns compressed embeddings that retain information useful for future prediction. The key challenge is doing this without labels. Financial markets do not come with ground truth signals. There is no clean objective telling the model what structure looks like.
