LOB prediction asks whether the visible book predicts the next move — and the honest answer is ‘yes, weakly, at horizons where it’s hard to monetize.’ The literature runs from handcrafted imbalance features through the DeepLOB lineage to transformers and beyond, with evaluation pitfalls (label construction, horizon overlap, fee-blind backtests) recurring in every generation. Our own T-KAN replication review is a live case study in branch four.

  flowchart TD
  ROOT["Limit-Order-Book Prediction Research<br>644 papers"]
  ROOT --> S0["Imbalance & handcrafted features<br>127 papers"]
  ROOT --> S1["Deep LOB models<br>49 papers"]
  ROOT --> S2["Transformers & new architectures<br>17 papers"]
  ROOT --> S3["Evaluation & pitfalls<br>59 papers"]
  ROOT --> S4["Price impact & flow dynamics<br>114 papers"]

644 papers mapped · ranked by rigor-weighted score · regenerated automatically as the daily pipeline scores new papers.

Imbalance & handcrafted features

What the book’s shape measures before any learning.

Deep LOB models

CNN/LSTM architectures on raw book states — the DeepLOB lineage.

Transformers & new architectures

Attention, KANs, and the current frontier.

Evaluation & pitfalls

Labels, horizons, costs, and why accuracy ≠ P&L.

Price impact & flow dynamics

The mechanism underneath: how flow moves price.


Evaluation lens for this field: see the guides index · Full hub: /topics/market-microstructure/