EXAONE Forecast for Finance

The technical report outlines EXAONE Finance, a foundational model for time‑series designed specifically for financial forecasting. Unlike recent models that rely on self‑attention mechanisms and demand substantial computational resources, this approach removes self‑attention and employs two linear‑complexity operators: a one‑dimensional causal convolution that blends temporal information and an MLP‑style neural network with channel grouping that merges the various data streams. During training, a masked context‑augmentation technique is introduced, simulating the absence of contiguous data blocks, which strengthens the model’s ability to cope with the missing‑observation patterns typical of financial markets. The model was trained on a broad corpus that includes equities, currencies, commodities, crypto‑assets, bonds, and macroeconomic variables. In the FinVerse benchmark—which evaluates point‑forecast accuracy, cross‑asset classification, and portfolio profitability—EXAONE Finance outperformed competitors, securing first place across all three evaluation tiers. This improvement is significant because it enables more accurate and efficient forecasts in complex financial environments, potentially leading to better‑informed investment decisions and greater competitiveness for institutions that rely on time‑series analysis.

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