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.
Study shows randomized strategies can't be Nash equilibria in markets with transient price impact.
Derives explicit investment strategy with random endowment.
Study explores strategies for randomized allocation in delayed rewards bandits.
Study optimal hedging for claims with random weights in discrete time.
We study signal recovery on graphs based on two sampling strategies: random sampling and experimentally designed sampling. We propose a new class of smooth graph signals, called approximately bandlimited, which generalizes the bandlimited class and is similar to the globally smooth class. We then propose two recovery s…
In this paper we focus on the beneficial role of random strategies in social sciences by means of simple mathematical and computational models. We briefly review recent results obtained by two of us in previous contributions for the case of the Peter principle and the efficiency of a Parliament. Then, we develop a new …
A new sampling strategy for random Fourier features reduces computation time and improves prediction performance.
Study optimal consumption and investment strategies with constraints in a market with random coefficients.
The paper solves MMV and MV problems with random coefficients and finds shared optimal strategies.
Investors with asymmetric information play a game to optimize their portfolios.
The paper analyzes investment and consumption strategies under uncertain market conditions.
The paper examines how insurers can select claims for fraud investigation, proposing a randomized approach.
ESM-CNN uses error feedback to build a random CNN for time series forecasting.
Study optimal investment-reinsurance strategy for insurers under random coefficients and jumps.
In this paper we explore the specific role of randomness in financial markets, inspired by the beneficial role of noise in many physical systems and in previous applications to complex socio- economic systems. After a short introduction, we study the performance of some of the most used trading strategies in predicting…
In this paper, making use of recent statistical physics techniques and models, we address the specific role of randomness in financial markets, both at the micro and the macro level. In particular, we review some recent results obtained about the effectiveness of random strategies of investment, compared with some of t…
Trading strategy uses Hoeffding's Inequality to predict financial regime change.
Random investment strategies outperform sensible ones, even with forecasts.
Optimal exit strategies of CPT gamblers in unfair gambles
Model financial network dynamics to avoid systemic risk.
Study ratio-limit boundaries for random walks on hyperbolic groups.
This paper solves the consumption-investment problem under Epstein-Zin preferences on a random horizon. In an incomplete market, we take the random horizon to be a stopping time adapted to the market filtration, generated by all observable, but not necessarily tradable, state processes. Contrary to prior studies, we do…
Optimal wealth strategy derived for jump-diffusion models with liabilities.
We consider the problem of how to assign treatment in a randomized experiment, in which the correlation among the outcomes is informed by a network available pre-intervention. Working within the potential outcome causal framework, we develop a class of models that posit such a correlation structure among the outcomes. …
New method designs fairer transport plans with uncertainty.
Exact simulation method for market impact estimation under various execution strategies.
Novel strategy benchmarks observational studies against randomized trials.
Adaptive market-making strategy improves profit by adjusting to order flow.
Proposes a new consumption strategy based on martingale principles.
This paper examines from an experimental perspective random forests, the increasingly used statistical method for classification and regression problems introduced by Leo Breiman in 2001. It first aims at confirming, known but sparse, advice for using random forests and at proposing some complementary remarks for both …
We present an explicit hedging strategy, which enables to prove arbitrageness of market incorporating at least two assets depending on the same random factor. The implied Black-Scholes volatility, computed taking into account the form of the graph of the option price, related to our strategy, demonstrates the "skewness…
We check the claims that data from Google Trends contain enough data to predict future financial index returns. We first discuss the many subtle (and less subtle) biases that may affect the backtest of a trading strategy, particularly when based on such data. Expectedly, the choice of keywords is crucial: by using an i…
Crowdsourcing platforms are now extensively used for conducting subjective pairwise comparison studies. In this setting, a pairwise comparison dataset is typically gathered via random sampling, either \emph{with} or \emph{without} replacement. In this paper, we use tools from random graph theory to analyze these two ra…
This paper studies an optimal trading problem that incorporates the trader's market view on the terminal asset price distribution and uninformative noise embedded in the asset price dynamics. We model the underlying asset price evolution by an exponential randomized Brownian bridge (rBb) and consider various prior dist…
Single tree outperforms random forest in testing accuracy.
We study the problem of sampling k-bandlimited signals on graphs. We propose two sampling strategies that consist in selecting a small subset of nodes at random. The first strategy is non-adaptive, i.e., independent of the graph structure, and its performance depends on a parameter called the graph coherence. On the co…
In this article we will propose a completely new point of view for solving one of the most important paradoxes concerning game theory. The solution develop shifts the focus from the result to the strategy s ability to operate in a cognitive way by exploiting useful information about the system. In order to determine fr…
Improved sampling efficiency with Random Reshuffling for Langevin dynamics.
New method speeds up solving orthogonality constrained problems.
We consider properties of the measurement intensity of a random variable for which the probability density function represented by the corresponding Wigner function attains negative values on a part of the domain. We consider a simple economic interpretation of this problem. This model is used to present the applic…
We propose a new set of stylized facts quantifying the structure of financial markets. The key idea is to study the combined structure of both investment strategies and prices in order to open a qualitatively new level of understanding of financial and economic markets. We study the detailed order flow on the Shenzhen …
New walk extraction strategies improve node embeddings in KGs.
Neural Architecture Search (NAS) aims to facilitate the design of deep networks for new tasks. Existing techniques rely on two stages: searching over the architecture space and validating the best architecture. NAS algorithms are currently compared solely based on their results on the downstream task. While intuitive, …
Calibrated strategies can be obtained by performing strategies that have no internal regret in some auxiliary game. Such strategies can be constructed explicitly with the use of Blackwell's approachability theorem, in an other auxiliary game. We establish the converse: a strategy that approaches a convex -set can be…
We present extremal constructions connected with the property of simplicial collapsibility. (1) For each , there are collapsible (and shellable) simplicial -complexes with only one free face. Also, there are non-evasive -complexes with only two free faces. (Both results are optimal in all dimensions.) (2…
Model financial network dynamics to avoid systemic risk.
Adversarial perturbations dramatically decrease the accuracy of state-of-the-art image classifiers. In this paper, we propose and analyze a simple and computationally efficient defense strategy: inject random Gaussian noise, discretize each pixel, and then feed the result into any pre-trained classifier. Theoretically,…
WildWood improves Random Forest predictions using bootstrap out-of-bag samples.