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.
A pricing principle is introduced for non-attainable claims in incomplete markets.
Coupled entropy corrects flaws in Tsallis entropy for complex systems.
We developed a strategic of optimal portfolio based on information theory and Tsallis statistics. The growth rate of a stock market is defined by using -deformed functions and we find that the wealth after n days with the optimal portfolio is given by a -exponential function. In this context, the asymptotic optim…
We proposed the agent-based model of financial markets where agents (or traders) are represented by three-state spins located on the plane lattice or social network. The spin variable represents only the individual opinion (advice) that each trader gives to his nearest neighbors. In the model the agents can be consider…
Modeling financial markets with a novel order flow model.
The distribution of intertrade durations, defined as the waiting times between two consecutive transactions, is investigated based upon the limit order book data of 23 liquid Chinese stocks listed on the Shenzhen Stock Exchange in the whole year 2003. A scaling pattern is observed in the distributions of intertrade dur…
New method calibrates reference distributions for bounded support.
This work introduces a new probabilistic process for regularization in machine learning.
The paper studies convergence rates of Tsallis entropic regularization in optimal transport.
In this paper, we present a new class of Markov decision processes (MDPs), called Tsallis MDPs, with Tsallis entropy maximization, which generalizes existing maximum entropy reinforcement learning (RL). A Tsallis MDP provides a unified framework for the original RL problem and RL with various types of entropy, includin…
Simplified proof for Tsallis-INF algorithm without conjugate functions.
Modified Bakry-Émery criterion inequality for Tsallis entropy monotonicity.
Paper finds a new principle for optimizing consumption and wealth using Tsallis entropy.
The construction of efficient and effective decision trees remains a key topic in machine learning because of their simplicity and flexibility. A lot of heuristic algorithms have been proposed to construct near-optimal decision trees. ID3, C4.5 and CART are classical decision tree algorithms and the split criteria they…
This is full length article (draft version) where problem number of topics in Topic Modeling is discussed. We proposed idea that Renyi and Tsallis entropy can be used for identification of optimal number in large textual collections. We also report results of numerical experiments of Semantic stability for 4 topic mode…
In this study, we analyze the aerospace stocks prices in order to characterize the sector behavior. The data analyzed cover the period from January 1987 to April 1999. We present a new index for the aerospace sector and we investigate the statistical characteristics of this index. Our results show that this index is we…
Develops a Best-of-Both-Worlds algorithm for linear contextual bandits with Tsallis entropy.
A hybrid impurity measure balances theoretical soundness and computational efficiency.
Estimate relaxation times in nonextensive systems using gradient flow for Tsallis entropy maximization.
Optimal control in latent factor models uses Tsallis entropy for exploration.
This work broadens calibeating to various proper losses using Bregman divergence.
This work generalizes calibeating for a broader range of proper losses using Bregman divergence.
Improved regret bounds for Tsallis-INF in adversarial bandits and corruptions.
This paper introduces a new potential function using Tsallis entropy for neural network optimization.
This study uses Tsallis entropy to analyze diversification and integration in Italian stock market companies.
Study analyzes stock market dynamics using Tsallis statistics and GHE, revealing pre-bubble and post-bubble market characteristics.
Paper presents characteristic function of Tsallis q-Gaussian and its applications.
In this paper, we propose a novel maximum causal Tsallis entropy (MCTE) framework for imitation learning which can efficiently learn a sparse multi-modal policy distribution from demonstrations. We provide the full mathematical analysis of the proposed framework. First, the optimal solution of an MCTE problem is shown …
We find the wealth distribution for an economic agent in the financial market, in analogy with standard derivation of generaliz Boltzman (Tsallis) factor in statistical mechanics. In this respect, Tsallis entropic index separates two different regimes, the large and small size market. The Pareto like wealth distributio…
Study on utility maximization with Tsallis entropy in reinforcement learning.
The aim of this paper is to compare statistical properties of a bubble period with those of the anti-bubble period in stock markets. We investigate the statistical properties of daily data for the Nikkei 225 index in the 28-year period from January 1975 to April 2003, corresponded to the periods of bubbles and anti-bub…
Earlier studies have shown that stock market distributions can be well described by distributions derived from Tsallis entropy, which is a generalization of Shannon entropy to non-extensive systems. In this paper, Tsallis relative entropy (TRE), which is the generalization of Kullback-Leibler relative entropy (KLRE) to…
The paper calculates bounds for risk metrics and entropies under partial information constraints.
New risk measure considers horizon risk and interest rate uncertainty.
This work models financial market returns with asymmetric Tsallis distributions, improving fit over symmetric q-Gaussians.
Being able to forcast extreme volatility is a central issue in financial risk management. We present a large volatility predicting method based on the distribution of recurrence intervals between volatilities exceeding a certain threshold for a fixed expected recurrence time . We find that the recurrence inter…
Deep Q-EP improves image modeling by stacking Q-EP layers.
New method uses asymmetric Tsallis relative entropy for better risk assessment in financial portfolios.
We recently showed that the S&P500 stock market index is well described by Tsallis non-extensive statistics and nonlinear Fokker-Planck time evolution. We argued that these results should be applicable to a broad range of markets and exchanges where anomalous diffusion and `heavy' tails of the distribution are present.…
The theoretical basis for a candidate variational principle for the information bottleneck (IB) method is formulated within the ambit of the generalized nonadditive statistics of Tsallis. Given a nonadditivity parameter , the role of the \textit{additive duality} of nonadditive statistics () in relating…
Recently deep reinforcement learning (DRL) has achieved outstanding success on solving many difficult and large-scale RL problems. However the high sample cost required for effective learning often makes DRL unaffordable in resource-limited applications. With the aim of improving sample efficiency and learning performa…
In this paper we study the possible microscopic origin of heavy-tailed probability density distributions for the price variation of financial instruments. We extend the standard log-normal process to include another random component in the so-called stochastic volatility models. We study these models under an assumptio…
Sparse RSP routing improves graph exploration and classification.
One of the major issues studied in finance that has always intrigued, both scholars and practitioners, and to which no unified theory has yet been discovered, is the reason why prices move over time. Since there are several well-known traditional techniques in the literature to measure stock market volatility, a centra…
Distributions derived from non-extensive Tsallis statistics are closely connected with dynamics described by a nonlinear Fokker-Planck equation. The combination shows promise in describing stochastic processes with power-law distributions and superdiffusive dynamics. We investigate intra-day price changes in the S&P500…
New spatiotemporal Besov process improves CT image reconstruction and other inverse problems.
We derive an algorithm that achieves the optimal (within constants) pseudo-regret in both adversarial and stochastic multi-armed bandits without prior knowledge of the regime and time horizon. The algorithm is based on online mirror descent (OMD) with Tsallis entropy regularization with power and reduced-varian…
q-VAE extracts disentangled latent spaces for robot control and dynamic systems.