arXiv research

Malware allegedly developed by nation-states, also known as advanced persistent threats (APT), are becoming more common. The task of attributing an APT to a specific nation-state or classifying it to the correct APT family is challenging for several reasons. First, each nation-state has more than a single cyber unit th…

In recent years numerous advanced malware, aka advanced persistent threats (APT) are allegedly developed by nation-states. The task of attributing an APT to a specific nation-state is extremely challenging for several reasons. Each nation-state has usually more than a single cyber unit that develops such advanced malwa…

We generalize the Arbitrage Pricing Theory (APT) to include the contribution of virtual arbitrage opportunities. We model the arbitrage return by a stochastic process. The latter is incorporated in the APT framework to calculate the correction to the APT due to the virtual arbitrage opportunities. The resulting relatio…

Study on time-varying APT validity in Japanese stock market.

Deep learning detects APT attacks with high accuracy and low false positives.

How can one perform Bayesian inference on stochastic simulators with intractable likelihoods? A recent approach is to learn the posterior from adaptively proposed simulations using neural network-based conditional density estimators. However, existing methods are limited to a narrow range of proposal distributions or r…

APT-Gen generates tasks to help RL learn in hard problems.

We contrast Arbitrage Pricing Theory (APT), the theoretical basis for the development of financial instruments, with a dynamical picture of an interacting market, in a simple setting. The proliferation of financial instruments apparently provides more means for risk diversification, making the market more efficient and…

Paper proposes nested MLMC for SNPE with intractable likelihoods.

Study uses APT and QR to identify risk factors affecting crude oil returns.

We consider infinite dimensional optimization problems motivated by the financial model called Arbitrage Pricing Theory. Using probabilistic and functional analytic tools, we provide a dual characterization of the super-replication cost. Then, we show the existence of optimal strategies for investors maximizing their e…

Two proofs of Kalman Theorem using flows of vector fields.

Study proposes local effective dimension to measure model capacity and generalization error.

Investigates methods to regularize quantile regression for accurate predictions.

MTRGL learns temporal correlations from multi-modal data for improved pair trading.

Determining the number of clusters present in a dataset is an important problem in cluster analysis. Conventional clustering techniques generally assume this parameter to be provided up front. %user supplied. %Recently, robustness of any given clustering algorithm is analyzed to measure cluster stability/instability wh…

New framework shows much of equity market risk may come from asset returns themselves.

In this paper we study a new reinforcement learning setting where the environment is non-rewarding, contains several possibly related objects of various controllability, and where an apt agent Bob acts independently, with non-observable intentions. We argue that this setting defines a realistic scenario and we present …

A new tree-based model for multivariate responses interprets piecewise linear regimes.

Meta learning improves with contextualizers that adapt to examples.

CPCMs integrate causal drivers for robust portfolio optimization.

Generative Adversarial Network purifies images from steganography without degrading quality.

"Feint Attack", as a new type of APT attack, has become the focus of attention. It adopts a multi-stage attacks mode which can be concluded as a combination of virtual attacks and real attacks. Under the cover of virtual attacks, real attacks can achieve the real purpose of the attacker, as a result, it often caused hu…

Deep active inference agents learn complex environments using Monte-Carlo methods.

Behavioral finance has become an increasingly important subfield of finance. However the main parts of behavioral finance, prospect theory included, understand financial markets through individual investment behavior. Behavioral finance thereby ignores any interaction between participants. We introduce a socio-financia…

Study on theoretical limits of $\ell_0$ sparse-regression algorithms using Fl RDT.

Study finds price-based clustering outperforms AI and human methods in stock market analysis.