arXiv · arXiv · 2026
This study develops an agent-based financial market model to explain stock-price momentum and reversal through the joint effects of local herding and delayed information diffusion. Investors form heterogeneous Gaussian beliefs about the next-period price, choose among buying, selling, and remaining inactive, and revise their action probabilities in response to neighboring investors. The local interaction structure is…
Jiahao Weng
arXiv · arXiv · 2009
Based on our "finance-prediction-oriented" methodology which involves such elements as log-periodic self-similarity, the universal preferred scaling factor lambda=2, and allows a phenomenon of the "super-bubble" we analyze the 2009 world stock market (here represented by the SP500, Hang Seng and WIG) development. We identify elements that indicate the third decade of September 2009 as a time limit for the present bul…
Stanislaw Drozdz, Pawel Oswiecimka
arXiv · arXiv · 2025
We establish a general matched filter principle for order flow normalization: optimal normalization must match the scaling behaviour of the signal-generating process. For capacity-constrained institutional investors, market capitalization normalization ($S^{MC}$) is the matched filter; for volume-targeting traders (e.g., VWAP/TWAP algorithms), trading value normalization ($S^{TV}$) is optimal. Monte Carlo simulations…
Sungwoo Kang
arXiv · arXiv · 2012
We introduce a trade strategy representation theorem for performance measurement and portable alpha in high frequency trading, by embedding a robust trading algorithm that describe portfolio manager market timing behavior, in a canonical multifactor asset pricing model. First, we present a spectral test for market timing based on behavioral transformation of the hedge factors design matrix. Second, we find that the t…
Godfrey Charles-Cadogan
arXiv · arXiv · 2026
Understanding the joint dynamics of prices and trades is central to market microstructure, where returns and order flow interact through nonlinear and state-dependent mechanisms. Linear models are interpretable but may miss these effects, while deep neural networks improve forecasting at the cost of transparency. We use neural networks as tools for structural discovery rather than only for prediction. A deep feed-for…
Manuel Naviglio, Fabrizio Lillo
arXiv · arXiv · 2026
The stability of markets hosting leveraged exchange-traded products is governed not by any single product's loop gain but by the spectral radius of a loop-gain matrix, and scalar per-product monitoring underestimates system feedback by construction. Recent work measures the self-reinforcement of a leveraged fund's daily close rebalancing through a scalar loop gain and treats cross-asset spillovers as bias. We model c…
Jihwan Woo
arXiv · arXiv · 2026
At 15-minute horizons, directional mean reversion is far stronger and more pervasive in cryptocurrency markets than in US equities: scored under one matched, strictly out-of-sample protocol, 90% of 183 Binance pairs carry significant directional reversal against 2.7% of 187 US stocks and ETFs, in every focal coin-year since 2021. The signal lives in signs, not magnitudes: lag-one return autocorrelation is near zero o…
Nadav A. Kitron, Jonathan M. Wengrowicz
arXiv · arXiv · 2026
Prediction markets increasingly list contracts settling on an asset price that holders can move by trading the underlying. We build a model showing that such contracts transfer wealth from prediction-market liquidity traders to manipulators and harm price discovery in the underlying, even as it becomes more liquid. After the launch of Polymarket's five-minute Bitcoin contract, settlement-time spot order flow spikes, …
David Dai, Ruizhe Jia, Shihao Yu
arXiv · arXiv · 2022
We analyse two models of liquidity provision to determine the retail traders' preference for marketable order routing. Order internalization is captured by a model of market makers competing for the retail order flow in a Bertrand fashion. On the other hand, the price-taking competitive liquidity providers characterize the open exchange model. We show that, when liquidity providers are risk averse, routing of the mar…
Umut Çetin, Alaina Danilova
arXiv · arXiv · 2020
We establish connections between optimal transport theory and the dynamic version of the Kyle model, including new characterizations of informed trading profits via conjugate duality and Monge-Kantorovich duality. We use these connections to extend the model to multiple assets, general distributions, and risk-averse market makers. With risk-averse market makers, liquidity is lower, assets exhibit short-term reversals…
Kerry Back, Francois Cocquemas, Ibrahim Ekren, Abraham Lioui
arXiv · arXiv · 2026
Coupled feedback networks are often monitored channel by channel even though cross-channel paths alter both stability margins and transmitted disturbances. We study identification of a structured feedback matrix L_t = Phi diag(gamma_t) in an output-only setting: no commanded, probing, or reference input exists -- only temporally separated outputs and the scheduling gains gamma_t are observed, while the coupling respo…
Jihwan Woo