A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
This paper tackles post-trade allocation inefficiencies and presents a uniform return allocation method.
Market makers face a trade-off between fill probability and post-fill returns, requiring contrarian strategies.
Given the return series for a set of instruments, a \emph{trading strategy} is a switching function that transfers wealth from one instrument to another at specified times. We present efficient algorithms for constructing (ex-post) trading strategies that are optimal with respect to the total return, the Sterling ratio…
Robinhood users react strongly to overnight price changes and big losers, trading quickly after extreme losses.
Study shows post-COVID commodity futures returns and volatility changed for different products.
Considering that a trader or a trading algorithm interacting with markets during continuous auctions can be modeled by an iterating procedure adjusting the price at which he posts orders at a given rhythm, this paper proposes a procedure minimizing his costs. We prove the a.s. convergence of the algorithm under assumpt…
Proposes FACT, a diagnostic for understanding group fairness trade-offs.
Algorithmic trading systems on DEXs reject most candidate tokens, but the counterfactual outcome of rejected candidates is rarely measured.
PS-DME evaluates model performance and reliability after data-dependent selection.
Develops methods to adjust prediction set coverage based on post-selection analysis.
We use the 2014 market history of two high-returning biotechnology exchange-traded funds to illustrate how ex post mean-variance analysis should not be done. Unfortunately, the way it should not be done is the way it generally is done -- to our knowledge.
Paper uses RL to optimize bid-ask spreads for diverse options.
New framework improves LLM performance by avoiding forgetting during sequential training stages.
We consider a one-period Kyle (1985) framework where the insider can be subject to a penalty if she trades. We establish existence and uniqueness of equilibrium for virtually any penalty function when noise is uniform. In equilibrium, the demand of the insider and the price functions are in general non-linear and remai…
Post-hoc explanations improve CNNs by replacing final linear layer with k-means classifier.
We consider an optimal trading problem over a finite period of time during which an investor has access to both a standard exchange and a dark pool. We take the exchange to be an order-driven market and propose a continuous-time setup for the best bid price and the market spread, both modelled by Lévy processes. Effect…
The optimal approach is to theorize after examining data, not before.
Reinforcement learning is explored as a candidate machine learning technique to enhance existing analytical solutions for optimal trade execution with elements from the market microstructure. Given a volume-to-trade, fixed time horizon and discrete trading periods, the aim is to adapt a given volume trajectory such tha…
Study examines volatility-based strategy for Chinese ETF options, improving returns in volatile markets.
Trade finance history traced from medieval origins to modern markets.
Most of the work on interpretable machine learning has focused on designing either inherently interpretable models, which typically trade-off accuracy for interpretability, or post-hoc explanation systems, whose explanation quality can be unpredictable. Our method, ExpO, is a hybridization of these approaches that regu…
We study trade-based manipulation of stock prices from the perspective of complex trading networks constructed by using detailed information of trades. A stock trading network consists of nodes and directed links, where every trader is a node and a link is formed from one trader to the other if the former sells shares …
Twitter promotes cryptocurrency pump-and-dumps, affecting trading behavior and returns.
Sentiment analysis from news and social media predicts forex market movements.
The paper analyzes regret in bilateral trade mechanisms without prior valuations.
As machine learning ascends the peak of computer science zeitgeist, the usage and experimentation with sentiment analysis using various forms of textual data seems pervasive. The effect is especially pronounced in formulating securities trading strategies, due to a plethora of reasons including the relative ease of imp…
In this paper we propose a mathematical framework to address the uncertainty emergingwhen the designer of a trading algorithm uses a threshold on a signal as a control. We rely ona theorem by Benveniste and Priouret to deduce our Inventory Asymptotic Behaviour (IAB)Theorem giving the full distribution of the inventory …
Novel OTT method for cryptocurrency trading offers high annualized profit.
Study shows SEC crypto classification led to significant market reactions.
Price impact of a trade is an important element in pre-trade and post-trade analyses. We introduce a framework to analyze the market price of liquidity risk, which allows us to derive an inhomogeneous Bernoulli ordinary differential equation. We obtain two closed form solutions, one of which reproduces the linear funct…
New methods for distributed CP improve reliability in healthcare.
This paper proposes a novel adaptive algorithm for the automated short-term trading of financial instrument. The algorithm adopts a semantic sentiment analysis technique to inspect the Twitter posts and to use them to predict the behaviour of the stock market. Indeed, the algorithm is specifically developed to take adv…
We construct a deep portfolio theory. By building on Markowitz's classic risk-return trade-off, we develop a self-contained four-step routine of encode, calibrate, validate and verify to formulate an automated and general portfolio selection process. At the heart of our algorithm are deep hierarchical compositions of p…
For classification of the high frequency trading quantities, waiting times, price increments within and between sessions are referred to as the a-, b-, and c-increments. Statistics of the a-b-c-increments are computed for the Time & Sales records posted by the Chicago Mercantile Exchange Group for the futures traded on…
Detecting real-time price impact in algo trading
CPP improves predictive model outputs for better intervention decisions.
High-frequency trading models fail due to overfitting and survivor bias.
Paper introduces Cycles Protocol to integrate trade credit into market clearing.
Study on cryptocurrency trading patterns using multifractal analysis.
Paper proposes integrating wavelet transform, channel attention, and LSTM for better stock price prediction.
Motivated by the practical challenge in monitoring the performance of a large number of algorithmic trading orders, this paper provides a methodology that leads to automatic discovery of the causes that lie behind a poor trading performance. It also gives theoretical foundations to a generic framework for real-time tra…
FairPOT balances fairness and AUC performance by selectively transforming risk scores.
In this paper we introduce kinetic equations for the evolution of the probability distribution of two goods among a huge population of agents. The leading idea is to describe the trading of these goods by means of some fundamental rules in price theory, in particular by using Cobb-Douglas utility functions for the bina…
Over-the-counter markets are at the center of the postcrisis global reform of the financial system. We show how the size and structure of such markets can undergo rapid and extensive changes when participants engage in portfolio compression, a post-trade netting technology. Tightly-knit and concentrated trading structu…
Develops a bialgebra theory for post-Lie algebras using geometric interpretations and bilinear forms.
Algorithm generates realistic metaorders from public trade data.
Framework mitigates overfitting in quantitative trading strategies.
New method identifies latent components in nonlinear mixtures without stringent assumptions.