The Inference-Compute Frontier and a Latency-Efficient Architecture for Limit Order Book Prediction
arXiv:2606.25986v1 Announce Type: new Abstract: We study whether a scaling-law-style inference-compute frontier appears in limit order book prediction. Using FI-2010 and a suite of models ranging from small decision trees to neural LOB architectures, we find that the realized empirical frontier of predictive loss versus structural forward work is well summarized by a power law. In particular, with MLPLOB held out as an architecture family, a power-law fit to the low- and mid-compute non-MLPLOB frontier extrapolates across multiple orders of magnitude and attains $R^2=0.941$ on the excluded hig
The continuous maturation of AI research in financial applications, particularly in areas requiring high-speed decision-making and optimal resource allocation, drives this exploration of compute frontiers in LOB prediction.
This research provides insights into the fundamental trade-offs between computational resources and predictive accuracy in high-frequency trading, identifying scaling laws that will shape future infrastructure and algorithmic development.
Understanding the inference-compute frontier allows for more efficient design and deployment of AI models for limit order book prediction, potentially optimizing resource allocation in financial markets.
- · High-frequency trading firms
- · Financial AI developers
- · Cloud computing providers
- · Semiconductor companies
- · Firms with compute-inefficient trading strategies
- · Legacy financial institutions slow to adopt AI
Increased efficiency in financial market prediction and trading strategies through optimized AI architectures.
Heightened competition in high-frequency trading as advanced AI models become more accessible and efficient.
Potential for algorithmic instability or flash crashes if widespread adoption of highly optimized, complex AI financial models interacts unpredictably.
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Read at arXiv cs.LG