Financial Epiplexity: A Theory of Learnable Market Structure under Bounded Computation

Financial markets are hard to predict, not because price moves are purely random, but because structure is strategic, capacity-constrained, and computationally difficult. Classical information theory measures uncertainty, dependence, and directed flow through entropy, KL divergence, NMI, and transfer entropy. This paper extends that foundation to ask: how much detected structure can a bounded investor learn and reuse? We introduce financial epiplexity: a time-bounded MDL measure of learnable market structure relative to a filtration, representation, model class, and budget. The alpha-relevant object is not raw complexity, but target-specific net compression gain beyond a benchmark, penalizing the model for its description length. This framing aligns with the P-Q wedge: risk-neutral martingality closes arbitrage under a pricing measure, while epiplexity concerns real-world learnability. We prove equal entropy need not imply equal epiplexity, derive finite-sample thresholds for useful regimes, and formalize representation dependence and compute nonmonotonicity. Using the Kelly-Cover-Barron-Cover correspondence, we bound cumulative excess log-growth, Sharpe ratios, sustainable ICs, and breadth by structural bits per period, treating leverage and survival as capacity constraints. The dynamic theory models alpha decay as the migration of private bits into the market budget, crowding as mutual compressibility, and capacity as bit leakage. Finally, the strategic layer explores budget choice, signal congestion, endogenous computational depth, and Red Queen compute competition.