arXiv subjects

Hangyi Zhao

Publications and source records attributed to Hangyi Zhao.

2 recordsLinked to original sources

arXiv2026

Insider Purchases Far Below the 52-Week High: Decomposing the Disclosure Reaction in Microcap Equities

Purchases reported under transaction code P on SEC Form 4 by insiders of U.S. equities with an estimated filing-date capitalization of USD 30 million to USD 500 million (13,534 lines, 1,192 issuers, 2018-2024) are followed by a first-day abnormal return that rises steeply with the stock's distance below its 52-week high: 4.13% in the quintile farthest below the high against 0.86% nearest it (two-way clustered t = 9.77); random non-event days of the same issuers show 0.14%. Five tests with decision rules fixed in advance characterize the gradient. Most of it is scale: the beaten-down stocks are 3.10 times as volatile, and with a full set of controls the raw gap fails its pre-specified bar (0.94 points, t = 2.11). Per unit of the stock's own volatility the reaction is 2.51 times as large far below the high (t = 7.49), 1.28 to 3.50 on other estimators, though a variance-weighted slope shows none. The gradient is larger than for insider sales by the same issuers and for positive earnings surprises as a class; against the strongest surprises the difference is imprecise. Dropping purchases with a concurrent 8-K leaves the raw gradient intact (t = 7.13), but the per-risk gradient no longer clears the controls (t = 2.52). The reaction runs for two to three sessions; the 29-day drift is imprecise (two-way t = 1.70) and a calendar-time portfolio that skips the first day earns no significant alpha. The analysis quantifies sensitivity to price adjustment and benchmark specification: mixing price bases misassigns run-up buckets, and carrying the estimation-window intercept supplies 57% of the 30-day gradient under that benchmark. A gradient-boosting classifier (test AUC 0.676) is indistinguishable from logistic regression. A separate large-cap extension schedules USD 29,075,559 a year of buyer-cluster flow but fails every matched-comparison gate.

q-fin.ST↗

arXiv2026

Bilateral Trade Under Heavy-Tailed Valuations: Minimax Regret without a Variance Bound

In contextual bilateral trade under full feedback, the posted price does not affect which valuations are observed. We show that in this model such action-independent feedback removes the polynomial adaptation penalty familiar from heavy-tailed bandits: fully parameter-free algorithms attain the oracle minimax $T$-exponents up to logarithmic factors, with no knowledge of the moment order $p \in (1,2)$ or its scale $σ_p$, and -- in the nonparametric case -- none of the effective Hölder smoothness $β\in (0,1]$. The statistic that makes model selection possible is a paired squared-loss difference, whose noise-square term cancels exactly, leaving noise damped by the candidate gap. The resulting bilateral-trade regret rates are new. Trader valuations have bounded conditional densities and heavy tails -- finite $p$-th moments for some $p \in (1,2)$, with possibly infinite variance. An epoch-based algorithm with truncated means achieves regret $\widetilde{O}(T^{(2-p)/p})$ in the parametric model and $\widetilde{O}(T^{1-2β(p-1)/(βp + d(p-1))})$ when the market value function is $β$-Hölder, with matching $Ω(\cdot)$ lower bounds -- under a mild nondegeneracy condition -- via Assouad's method and a fixed-support mixture construction -- characterizing the minimax rate in $T$ up to logarithmic factors over the effective smoothness range $β\in (0,1]$, interpolating between the classical nonparametric rate at $p{=}2$ and the trivial linear rate as $p \to 1^+$. The enabling structural step extends the self-bounding property of Bachoc et al. (ICML 2025) from bounded to real-valued valuations: within our conditionally independent, conditionally centered noise model, bounded conditional densities and finite first moments suffice for the expected regret of any price $π$ to satisfy $\mathbb{E}[g(m,V,W) - g(π,V,W)] \le L|m-π|^2$ -- no second moment is needed.

stat.ML↗