What I do when I'm free.

Why So Serious? V2 — Prediction-Market Execution Lab

Forward-monitored dashboard for Kalshi BTC/ETH markets, cross-venue execution context, and q=10 paper strategy tracking

A live research dashboard for BTC and ETH prediction-market execution experiments. It tracks core paper strategies, challenger monitors, and observational watchlists while keeping forward performance separate from historical research evidence, refreshed through a read-only workflow.

Research-only system; no real orders are placed from this dashboard.

Develops a stylized cross-market execution model in which a required trade in an underlying asset changes the settlement distribution of a related digital claim before the digital market fully reprices. The note derives when a same-direction digital overlay can offset part of the incremental cost of concentrated execution, with the result governed by settlement-relevant impact, market segmentation, and digital-market depth.

Part of a broader research agenda on how linked markets restore equilibrium when execution, liquidity, and information are incorporated at different speeds. Planned extensions include endogenous repricing, latency and sequencing, transient versus permanent impact, and reference-price design.

This note extends structural credit models by linking the shape of the default barrier to information from firms' 10-K maturity disclosures. Rather than treating the barrier as fixed or purely exogenous, the model allows it to reflect the underlying debt profile and rollover structure of the firm. The result is a more flexible framework for interpreting default risk and distance to default in a way that is closer to the firm's actual financing structure.

Currently being extended into a more detailed version and tested against CDS term-structure data.

Written and submitted in four weeks for the UCLA Fink Center Human × AI Conference, this paper studies whether the volatility smile observed in prediction markets reflects genuine information or is largely a mechanical consequence of bounded prices. Using Kalshi FOMC contracts, it develops a logit-space framework to separate boundary effects from belief dispersion and examines how those effects evolve as markets approach resolution.

Developed through a human-AI research workflow spanning literature review, coding, model development, empirical testing, and drafting. The framework is now being extended to a broader universe of event markets.