arXiv research

An optimal algorithm for multi-armed bandits with constraints.

The paper evaluates index-based allocation policies using data from randomized control trials.

New algorithm uses Whittle index to improve Q-learning for restless bandits.

A new tail-shape index based on Value at Risk and Expected Shortfall.

We consider the restless Markov bandit problem, in which the state of each arm evolves according to a Markov process independently of the learner's actions. We suggest an algorithm that after $T$ steps achieves $\tilde{O}(\sqrt{T})$ regret with respect to the best policy that knows the distributions of all arms. No ass…

Policy iteration is a family of algorithms that are used to find an optimal policy for a given Markov Decision Problem (MDP). Simple Policy iteration (SPI) is a type of policy iteration where the strategy is to change the policy at exactly one improvable state at every step. Melekopoglou and Condon [1990] showed an exp…

Investment tool predicts higher returns for Madrid real estate units.

Paper develops a two-population model to assess longevity basis risk.

Indices of acceptability are well suited to frame the axiomatic features of many performance measures, associated to terminal random cash flows.We extend this notion to classes of càdlàg processes modelling cash flows over a fixed investment horizon.We provide a representation result for bounded paths. We suggest an ac…

Traditional voxel-level multiple testing procedures in neuroimaging, mostly $p$-value based, often ignore the spatial correlations among neighboring voxels and thus suffer from substantial loss of power. We extend the local-significance-index based procedure originally developed for the hidden Markov chain models, whic…

Using a two-point correlation technique, we study emergence of market efficiency in the emergent Russian futures market by focusing on lagged correlations. The correlation strength of leader-follower effects in the lagged inter-market correlations on the hourly time frame is seen to be significant initially (2009-2011)…

Paper proposes a federated learning framework for relative fairness.

New algorithms improve privacy in bandit problems with partial information.

Optimal stock price prediction model using recurrent neural networks with RMSprop optimizer.

Deep learning predicts S&P 500 index direction.

Notwithstanding almost forty years of efforts, the market for paintings still lacks a widely accepted price index. In this paper, we introduce a simple and intuitive metric to construct such index. Our metric is based on the price of a painting divided by its area. This formulation rests on a solid mathematical foundat…

Quantum-assisted VAE improves similarity search in high-dimensional datasets.

Paper introduces a new measure combining entropy and Gini index.

We propose the use of statistical emulators for the purpose of valuing mortality-linked contracts in stochastic mortality models. Such models typically require (nested) evaluation of expected values of nonlinear functionals of multi-dimensional stochastic processes. Except in the simplest cases, no closed-form expressi…

A new clustering evaluation index based on density estimation.

Financial markets are well known for their dramatic dynamics and consequences that affect much of the world's population. Consequently, much research has aimed at understanding, identifying and forecasting crashes and rebounds in financial markets. The Johansen-Ledoit-Sornette (JLS) model provides an operational framew…

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 …

This paper assesses the hedge effectiveness of an index-based longevity swap and a longevity cap. Although swaps are a natural instrument for hedging longevity risk, derivatives with non-linear pay-offs, such as longevity caps, also provide downside protection. A tractable stochastic mortality model with age dependent …

Paper proposes a natural hedging framework with graphical assessment for longevity risk management.

We propose using five data-driven community detection approaches from social networks to partition the label space for the task of multi-label classification as an alternative to random partitioning into equal subsets as performed by RAkELd: modularity-maximizing fastgreedy and leading eigenvector, infomap, walktrap an…

The importance of considering the volumes to analyze stock prices movements can be considered as a well-accepted practice in the financial area. However, when we look at the scientific production in this field, we still cannot find a unified model that includes volume and price variations for stock assessment purposes.…

GP-LSTM model predicts stock returns and volatility more accurately.

Study shows COVID-19 increases stock market crash risk in China.

Large vessel occlusion (LVO) plays an important role in the diagnosis of acute ischemic stroke. Identifying LVO of patients in the early stage on admission would significantly lower the probabilities of suffering from severe effects due to stroke or even save their lives. In this paper, we utilized both structural and …

New turbulence index using TDA detects financial market transitions.

Paper uses Time Series Transformer for bank stability prediction.

Bank transactions help predict macroeconomic indexes faster and more accurately.

Paper models demand and solvency for index insurance, combining traditional and measurable index-based coverage.

Develops a new cluster validity index to find multiple optimal cluster numbers.

Regarding the intraday sequence of high frequency returns of the S&P index as daily realizations of a given stochastic process, we first demonstrate that the scaling properties of the aggregated return distribution can be employed to define a martingale stochastic model which consistently replicates conditioned expecta…

Hybrid LSTM-PPO optimizes dynamic portfolios with better performance.

ContextWIN uses neural networks and reinforcement learning to optimize decisions in dynamic environments.

Optimizes hybrid insurance contracts for heavy-tailed losses.

Improves energy efficiency of neuromorphic hardware by optimizing memory organization and encoding schemes.

New Hilbert bundles with ends defined from indexed bases.

A new stock index model simplifies high-dimensional stock data.

Neural Index Policy for multi-action bandits with heterogeneous budgets.

The paper introduces a US crime index to assess financial losses from property and cyber crimes.

Systematizes blockchain decentralization taxonomy and metrics.

Digital transformation boosts corporate financial asset allocation, especially short-term.

A large consensus now seems to take for granted that the distributions of empirical returns of financial time series are regularly varying, with a tail exponent close to 3. We revisit this results and use standard tests as well as develop a battery of new non-parametric and parametric tests (in particular with stretche…

The theory of multilayer networks is in its early stages, and its development provides vital methods for understanding complex systems. Multilayer networks, in their multiplex form, have been introduced within the last three years to analysing the structure of financial systems, and existing studies have modelled and e…

HARNet improves volatility forecasting using deep neural networks.