arXiv research

Study optimal portfolio strategies with periodic evaluation under short-selling prohibition.

This paper optimizes portfolio management in incomplete markets with stochastic factors, considering periodic wealth evaluations.

The paper proposes a dynamic risk measure approach for evaluating defined-contribution pension funds.

Study optimal portfolio management with periodic evaluations in stochastic models, considering convex constraints.

Study compares three performance metrics of Bangladeshi banks.

Study evaluates cryptocurrency markets, focusing on Bitcoin.

Long term investment is one of the major investment strategies. However, calculating intrinsic value of some company and evaluating shares for long term investment is not easy, since analyst have to care about a large number of financial indicators and evaluate them in a right manner. So far, little help in predicting …

A contextual bandit method evaluates and improves inventory control policies.

Model explains periodic trading in financial markets through game theory.

We consider a basic model of multi-period trading, which can be used to evaluate the performance of a trading strategy. We describe a framework for single-period optimization, where the trades in each period are found by solving a convex optimization problem that trades off expected return, risk, transaction cost and h…

PER-ETD improves ETD by reducing variance to polynomial complexity.

New framework finds periodic policies in reset-free MDPs with sublinear regret.

Pharmaceutical targeting is one of key inputs for making sales and marketing strategy planning. Targeting list is built on predicting physician's sales potential of certain type of patient. In this paper, we present a time-sensitive targeting framework leveraging time series model to predict patient's disease and treat…

The problem of automatic and accurate forecasting of time-series data has always been an interesting challenge for the machine learning and forecasting community. A majority of the real-world time-series problems have non-stationary characteristics that make the understanding of trend and seasonality difficult. Our int…

MPPN network improves long-term time series forecasting accuracy.

An Abelian differential gives rise to a flat structure (translation surface) on the underlying Riemann surface. In some directions the directional flow on the flat surface may contain a periodic region that is made up of maximal cylinders filled by parallel geodesics of the same length. The growth rate of the number of…

In this paper we formally analyse the use of sparse filtering algorithms to perform covariate shift adaptation. We provide a theoretical analysis of sparse filtering by evaluating the conditions required to perform covariate shift adaptation. We prove that sparse filtering can perform adaptation only if the conditional…

Multi-dimensional state-integrals of products of Faddeev's quantum dilogarithms arise frequently in Quantum Topology, quantum Teichmüller theory and complex Chern--Simons theory. Using the quasi-periodicity property of the quantum dilogarithm, we evaluate 1-dimensional state-integrals at rational points and express the…

Paper proposes a novel trading strategy combining clustering and reinforcement learning for multi-period portfolio management.

LLMs struggle to outperform markets over long periods and diverse stocks.

AMSAs adaptively manage crypto-currency trading by selecting multiple strategies based on market conditions.

An ensemble method enhances cryptocurrency trading strategies using deep reinforcement learning.

We investigate the dynamics of correlations present between pairs of industry indices of US stocks traded in US markets by studying correlation based networks and spectral properties of the correlation matrix. The study is performed by using 49 industry index time series computed by K. French and E. Fama during the tim…

Study improves S&P 500 volatility forecasting through regime-switching methods.

ADPSGD optimizes SGD convergence by varying averaging periods based on model variance.

This study optimizes trading strategy parameters using walk-forward techniques and finds robust performance.

Applicability of the concept of financial log-periodicity is discussed and encouragingly verified for various phases of the world stock markets development in the period 2000-2010. In particular, a speculative forecasting scenario designed in the end of 2004, that properly predicted the world stock market increases in …

This study evaluates a dynamic pairs trading strategy in cryptocurrencies using cointegration tests.

p-index approach shows efficient-contrarian strategy outperforms others in low-sentiment periods

Evaluating AI investment strategies

New method decomposes Markov chain rewards into persistent and transient components.

New bounds assess policy evaluation under unobserved confounders, showing model-based methods are more effective.

TailedTS dataset benchmarks heavy-tailed time series forecasting and periodicity quantification.

Study finds ESG investments more resilient than traditional equity indices during market turmoil.

The use of target networks has been a popular and key component of recent deep Q-learning algorithms for reinforcement learning, yet little is known from the theory side. In this work, we introduce a new family of target-based temporal difference (TD) learning algorithms and provide theoretical analysis on their conver…

We propose a prediction model based on the minority game in which traders continuously evaluate a complete set of trading strategies with different memory lengths using the strategies' past performance. Based on the chosen trading strategy they determine their prediction of the movement for the following time period of…

Develops RL algorithm for non-Markovian, non-stationary reward streams.

Study evaluates deep learning models for cryptocurrency price prediction.

Methodology that recently lead us to predict to an amazing accuracy the date (July 11, 2008) of reverse of the oil price up trend is briefly summarized and some further aspects of the related oil price dynamics elaborated. This methodology is based on the concept of discrete scale invariance whose finance-prediction-or…

We propose an efficient protocol for decentralized training of deep neural networks from distributed data sources. The proposed protocol allows to handle different phases of model training equally well and to quickly adapt to concept drifts. This leads to a reduction of communication by an order of magnitude compared t…

Paper addresses OPE for dependent bandit samples using MDS and batch updates.

There are non-vanishing price responses across different stocks in correlated financial markets. We further study this issue by performing different averages, which identify active and passive cross-responses. The two average cross-responses show different characteristic dependences on the time lag. The passive cross-r…

Bayesian realized EGARCH models improve tail risk forecasting.

This article proposes a communication-efficient decentralized deep learning algorithm, coined layer-wise federated group ADMM (L-FGADMM). To minimize an empirical risk, every worker in L-FGADMM periodically communicates with two neighbors, in which the periods are separately adjusted for different layers of its deep ne…

Study compares forecasting models for European financial markets and cryptocurrencies, finding hybrid ETS-ANN model best.

The paper uses double machine learning to estimate dynamic treatment effects robustly.

Study small eigenvalues of Riemann surfaces degenerating with Kähler metrics.

Complex non-linear interactions between banks and assets we model by two time-dependent Erdős Renyi network models where each node, representing bank, can invest either to a single asset (model I) or multiple assets (model II). We use dynamical network approach to evaluate the collective financial failure---systemic ri…