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.
Deep Belief Network reduces false positives in risky host detection.
A pair of points in a riemannian manifold is secure if the geodesics between the points can be blocked by a finite number of point obstacles; otherwise the pair of points is insecure. A manifold is secure if all pairs of points in are secure. A manifold is insecure if there exists an insecure point pair, and to…
This paper deals with an optimal position management problem for a market maker who has to face uncertain customer order flows in an illiquid market, where the market maker's continuous trading incurs a stochastic linear price impact. Although the execution timing is uncertain, the market maker can also ask its OTC cou…
A new backtesting framework for Expected Shortfall simplifies risk measurement.
Secure submodel learning protects privacy in federated learning.
SLIP secures LLMs on edge devices by splitting computation and protecting sensitive parts.
Consider an American option that pays G(X^*_t) when exercised at time t, where G is a positive increasing function, X^*_t := \sup_{s\le t}X_s, and X_s is the price of the underlying security at time s. Assuming zero interest rates, we show that the seller of this option can hedge his position by trading in the underlyi…
A strategy to beat benchmarks by investing in heavily shorted but fundamentally sound securities.
Italian banks use swaps to hedge against rising interest rates, offsetting losses on debt securities.
This paper studies the problem of maximizing expected utility from terminal wealth combining a static position in derivative securities, which we assume can be traded only at time zero, with a traditional dynamic trading strategy in stocks. We work in the framework of a general semi-martingale model and consider a util…
QFNN-FFD uses quantum computing and FL for secure financial fraud detection.
We present a general framework for measuring the liquidity risk. The theoretical framework defines a class of risk measures that incorporate the liquidity risk into the standard risk measures. We consider a one-period risk measurement model. The liquidity risk is defined as the risk that a given security or a portfolio…
Security, privacy, and fairness have become critical in the era of data science and machine learning. More and more we see that achieving universally secure, private, and fair systems is practically impossible. We have seen for example how generative adversarial networks can be used to learn about the expected private …
Develops risk measures for markets with constraints and costs.
Discriminatory trade liberalization policies are becoming more popular among world economies. Countries are motivated to enter for regional trade agreements to capture faster economic growth for alleviating poverty. In developing economies like most of the member countries of the Association of South East Asian Nations…
A Riemannian manifold is said to be uniformly secure if there is a finite number such that all geodesics connecting an arbitrary pair of points in the manifold can be blocked by point obstacles. We prove that the number of geodesics with length between every pair of points in a uniformly secure manifol…
In this work, we study a dynamic portfolio optimization problem related to pairs trading, which is an investment strategy that matches a long position in one security with a short position in another security with similar characteristics. The relationship between pairs, called a spread, is modeled by a Gaussian mean-re…
We utilize a fundamentally different model of trading costs to look at the effect of the opening of the Hong Kong Shanghai Connect that links the stock exchanges in the two cities, arguably the biggest event in international business and finance since Christopher Columbus set sail for India. We design a novel methodolo…
Paper evaluates ML's resilience in detecting ransomware.
The paper finds optimal levels for traders in mean-reverting markets.
The paper certifies decision trees against evasion attacks using program analysis.
Study shows long-term debt impacts financial growth of non-financial firms listed at Nairobi Securities Exchange.
Extends random dot product graph model to handle multiple graphs.
A new mathematical framework simplifies securitization structuring.
New approach detects adversarial samples with certifiable guarantees.
Optimizes a portfolio for an investor preferring accepted securities over a reference security.
This paper makes a small step towards a non-stochastic version of superhedging duality relations in the case of one traded security with a continuous price path. Namely, we prove the coincidence of game-theoretic and measure-theoretic expectation for lower semicontinuous positive functionals. We consider a new broad de…
Study shows Bitcoin security tied to mining rewards and prices.
Study assesses short-term debt's impact on non-financial firms' financial growth.
A pair of points in a riemannian manifold makes a secure configuration if the totality of geodesics connecting them can be blocked by a finite set. The manifold is secure if every configuration is secure. We investigate the security of compact, locally symmetric spaces.
Deep neural networks detect cyberthreats from Twitter.
This paper explores security threats in ML systems and proposes mitigation techniques.
We say that a pair of points x and y is secure if there exist a finite set of blocking points such that any geodesic between x and y passes through one of the blocking points. The main point of this paper is to exhibit new examples of blocking phenomena both in the manifold and the billiard table setting. As an approac…
Paper proposes a recursive PLS model for optimal response to security threats.
Paper proposes Pcomp classification for binary classification with pairwise confidence comparisons.
Industry lacks tools to secure ML systems, study finds.
Sparse oblique decision tree improves security rules for renewable power systems.
In this article we perform a computational study of Polyrakis algorithms presented in [12,13]. These algorithms are used for the determination of the vector sublattice and the minimal lattice-subspace generated by a finite set of positive vectors of R^k. The study demonstrates that our findings can be very useful in th…
RL models improve target control in SSGs for security applications.
The paper evaluates methods for explaining deep learning in security.
A riemannian manifold is secure if the geodesics between any pair of points in the manifold can be blocked by a finite number of point obstacles. Compact, flat manifolds are secure. A standing conjecture says that these are the only secure, compact riemannian manifolds. The conjecture claims, in particular, that a riem…
Security issues are crucial in a number of machine learning applications, especially in scenarios dealing with human activity rather than natural phenomena (e.g., information ranking, spam detection, malware detection, etc.). It is to be expected in such cases that learning algorithms will have to deal with manipulated…
New model values equity-linked securities with guaranteed return.
Securely trains regression models with secret sharing for data collaboration.
Abstract: Cyber-security challenges tackled with machine learning.
Secure linear regression at speed of plaintext methods.
Paper proposes a secure protocol for federated learning.
There are some statistical anomalies in the Chinese stock market, i.e., positive return skewness, anti-leverage effect (positive returns induce higher volatility than negative returns); and reverse volatility asymmetry (contemporaneous return-volatility correlation is positive). In this paper, we first confirm the exis…