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.
Optimal order execution strategies for brokers under reference benchmarks.
New methods improve LLM preference optimization by intelligently weighting multiple reference models.
Model shows how heterogeneity in strategies and risk tolerance affects financial market stability.
Modeling consumption and investment decisions with reference point and drawdown constraints.
This paper introduces a relative model risk measure of a product priced with a given model, with respect to another reference model for which the market is assumed to be driven. This measure allows comparing products valued with different models (pricing hypothesis) under a homogeneous framework which allows concluding…
The paper optimizes insurer's decisions on dividends, reinsurance, and capital injection under model uncertainty.
The aim of this work is to create systematic trading strategies built upon several financial crisis indicators based on the spectral properties of market dynamics. Within the limitations of our framework and data, we will demonstrate that our systematic trading strategies are able to make money, not as a result of pure…
Accelerates optimal transport computation by 10x with spectral insights.
A model-free method analyzes trading strategies using excursion paths.
GAN approach optimizes investment under market uncertainty.
The paper analyzes optimal consumption with past spending maximum as a reference.
Proposes a new framework to optimize portfolios with reduced estimation errors.
Paper explains DRL strategies for portfolio management using linear models.
In this work, we addressed the issue of combining linear classifiers using their score functions. The value of the scoring function depends on the distance from the decision boundary. Two score functions have been tested and four different combination strategies were investigated. During the experimental study, the pro…
Novel pairs trading strategy for cointegrated cryptocurrencies using copulas.
Many immunization strategies have been proposed to prevent infectious viruses from spreading through a network. In this study, we propose efficient immunization strategies to prevent a default contagion that might occur in a financial network. An essential difference from the previous studies on immunization strategy i…
This paper optimizes portfolio selection by penalizing tracking error, improving Sharpe ratio.
This paper concerns the dual risk model, dual to the risk model for insurance applications, where premiums are surplus-dependent. In such a model premiums are regarded as costs, while claims refer to profits. We calculate the mean of the cumulative discounted dividends paid until ruin, if the barrier strategy is applie…
Backtests of structured strategies lose much of their predictive power in live trading.
We present a generalization of the Simultaneous Long-Short (SLS) trading strategy described in recent control literature wherein we allow for different parameters across the short and long sides of the controller; we refer to this new strategy as Generalized SLS (GSLS). Furthermore, we investigate the conditions under …
The paper analyzes how conformal prediction works with contaminated reference data.
We consider the problem of hedging a European contingent claim in a Bachelier model with transient price impact as proposed by Almgren and Chriss. Following the approach of Rogers and Singh and Naujokat and Westray, the hedging problem can be regarded as a cost optimal tracking problem of the frictionless hedging strat…
Study replicates reference-dependent preferences impact on risk-return trade-off in Chinese stock market.
Study optimizes identifying the best arm with fixed rounds and Gaussian outcomes.
A risk-averse agent hedges her exposure to a non-tradable risk factor using a correlated traded asset and accounts for the impact of her trades on both factors. The effect of the agent's trades on is referred to as cross-impact. By solving the agent's stochastic control problem, we obtain a closed-form expr…
This paper evaluates six strategies for mitigating imbalanced data: oversampling, undersampling, ensemble methods, specialized algorithms, class weight adjustments, and a no-mitigation approach referred to as the baseline. These strategies were tested on 58 real-life binary imbalanced datasets with imbalance rates rang…
We establish a super-replication duality in a continuous-time financial model where an investor's trades adversely affect bid- and ask-prices for a risky asset and where market resilience drives the resulting spread back towards zero at an exponential rate. Similar to the literature on models with a constant spread, ou…
We study Atlas-type models of equity markets with local characteristics that depend on both name and rank, and in ways that induce a stable capital distribution. Ergodic properties and rankings of processes are examined with reference to the theory of reflected Brownian motions in polyhedral domains. In the context of …
Deep neural networks identify robust arbitrage strategies in financial markets.
Embedded ensembles improve neural network performance efficiently.
We study the pricing and hedging of derivative securities with uncertainty about the volatility of the underlying asset. Rather than taking all models from a prespecified class equally seriously, we penalise less plausible ones based on their "distance" to a reference local volatility model. In the limit for small unce…
This paper is concerned with improving the empirical convergence speed of block-coordinate descent algorithms for approximate nonnegative tensor factorization (NTF). We propose an extrapolation strategy in-between block updates, referred to as heuristic extrapolation with restarts (HER). HER significantly accelerates t…
Cluster analysis is an unsupervised learning strategy that can be employed to identify subgroups of observations in data sets of unknown structure. This strategy is particularly useful for analyzing high-dimensional data such as microarray gene expression data. Many clustering methods are available, but it is challengi…
Classification is one of the widely used analytical techniques in data science domain across different business to associate a pattern which contribute to the occurrence of certain event which is predicted with some likelihood. This Paper address a lacuna of creating some time window before the prediction actually happ…
Bayesian method for estimating quantile sets efficiently.
BS-NAS broadens and shrinks search space for optimal neural architectures.
AI methods broaden signal discovery in scientific data.
Unified definition of hallucinations in language models.
Decentralized learning achieves centralized performance via Gibbs measures.
Program synthesis is the task of automatically generating a program consistent with a specification. Recent years have seen proposal of a number of neural approaches for program synthesis, many of which adopt a sequence generation paradigm similar to neural machine translation, in which sequence-to-sequence models are …
Optimizes group testing for COVID-19 to reduce test numbers.
We introduce a dynamic credit portfolio framework where optimal investment strategies are robust against misspecifications of the reference credit model. The risk-averse investor models his fear of credit risk misspecification by considering a set of plausible alternatives whose expected log likelihood ratios are penal…
Handling previously unseen tasks after given only a few training examples continues to be a tough challenge in machine learning. We propose TapNets, neural networks augmented with task-adaptive projection for improved few-shot learning. Here, employing a meta-learning strategy with episode-based training, a network and…
We consider the problem of selecting a seed set to maximize the expected number of influenced nodes in the social network, referred to as the \textit{influence maximization} (IM) problem. We assume that the topology of the social network is prescribed while the influence probabilities among edges are unknown. In order …
Develops a strategy to minimize loss in both stochastic and adversarial environments for linear contextual bandits.
It has been shown recently that graph signals with small total variation can be accurately recovered from only few samples if the sampling set satisfies a certain condition, referred to as the network nullspace property. Based on this recovery condition, we propose a sampling strategy for smooth graph signals based on …
New strategy optimally identifies best arm in unknown variance Gaussian bandits.
Hybrid LSTM-ARIMA model outperforms other algorithms in algorithmic investment strategies.