We address the problem of partial index tracking, replicating a benchmark index using a small number of assets. Accurate tracking with a sparse portfolio is extensively studied as a classic finance problem. However in practice, a tracking portfolio must also be diverse in order to minimise risk -- a requirement which h…
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48 results for “diversity index”
Contrary to conventional economic growth theory, which reduces a country's output to one aggregate variable (GDP), product diversity is central to economic development, as recent 'economic complexity' research suggests. A country's product diversity reflects its diversity of knowhow or 'capabilities'. Researchers propo…
New statistical Minkowski distances for Gaussian mixtures with closed-form formulas.
problem Computing distances for Gaussian mixture models efficiently.
method Proposed novel statistical distances based on Minkowski's inequality for Gaussian mixtures.
result Closed-form formula for Gaussian mixture models with integer exponents.
In the past few years, a lot of attention has been devoted to multimedia indexing by fusing multimodal informations. Two kinds of fusion schemes are generally considered: The early fusion and the late fusion. We focus on late classifier fusion, where one combines the scores of each modality at the decision level. To ta…
Study shows diverse data sources improve cryptocurrency forecasting models.
problem Improving cryptocurrency market forecasting accuracy.
method Integrating various data types, including on-chain metrics, traditional indices, and macroeconomic indicators.
result Data source diversity significantly enhances forecasting model performance.
Gradient descent can efficiently learn a target function with diverse and near-orthogonal features.
problem Learning a target function with additive structure and diverse features.
method Gradient descent training of a two-layer neural network.
result A large subset of polynomial target functions can be efficiently learned.
MAP-Elites generates diverse trading strategies for improved execution performance.
problem Optimizing trading execution schedules in volatile market conditions.
method Quality-diversity algorithm (MAP-Elites) generating a portfolio of specialized strategies.
result Diverse strategies achieve 8-10% performance improvements, validating quality-diversity methods.
Cash Flow Entropyq-fin.GN
In this paper we aim to find a measure for the diversity of cash flows between agents in an economy. We argue that cash flows can be linked to probabilities of finding a currency unit in a given cash flow. We then use the information entropy as a natural measure of diversity. This leads to a hirarchical inequality meas…
Study benchmarks 26 clustering validity measures.
problem Determining the best clustering solution from candidates.
method Enhanced revision of previous methodology with three sub-methodologies.
result Comprehensive evaluation of 26 internal validity indexes.
AFP-CKSAAP predicts antifreeze proteins using k-spaced amino acid pairs with deep neural networks.
problem Predicting antifreeze proteins due to their diverse sequence characteristics.
method Deep neural network with skipped connections and ReLU non-linearity to learn protein sequence descriptors.
result AFP-CKSAAP achieves excellent prediction scores and high Youden's index (0.82) on independent dataset.
MallowsPO enhances LLM fine-tuning with a dispersion index of human preferences.
problem Lack of diversity in human preferences in DPO.
method Developed a dispersion index based on Mallows' theory to characterize preference diversity.
result Demonstrated improved performance in various tasks using the dispersion index.
This paper measures the intensity of implicit government guarantees using PMC index model.
problem Excessive local government debt due to implicit government guarantees.
method Text mining of policy documents related to municipal investment bonds, PMC index model.
result Recent policies have reduced the intensity of implicit government guarantees.
Entropy measure quantifies volatility correlation and risk diversity in asset portfolios.
problem Quantifying volatility correlation and risk diversity in asset portfolios.
method Kullback-Leibler cluster entropy for empirical and model probability distributions of realized volatility.
result Portfolio built on diversity indexes derived from Kullback-Leibler entropy measure of realized volatility exhibits better performance.
Using daily returns of the S&P 500 stocks from 2001 to 2011, we perform a backtesting study of the portfolio optimization strategy based on the extreme risk index (ERI). This method uses multivariate extreme value theory to minimize the probability of large portfolio losses. With more than 400 stocks to choose from, ou…
Ancestry improves genealogy search results by ranking diverse record types.
problem Ranking diverse genealogy records equitably from various sources.
method Customized Coordinate Ascent, Stochastic Search, Normalized Cumulative Entropy.
result Demonstrated effectiveness of algorithms in improving relevance and diversity.
Proposes a transfer learning framework for sparse SIMs without raw source data.
problem Lack of direct access to raw source data and known link functions in transfer learning.
method Source-data-free framework based on SIM, using summary statistics and a multilayer perceptron.
result Consistent improvements over existing approaches in synthetic and real-world data.
Enhances model OOD detection by diversifying feature representation.
problem Limited diversity in model ensembles for OOD detection.
method Integrates diverse training tasks into model ensembles to generate distinct feature representations.
result Superior performance of Multi-Comprehension Ensemble in OOD detection.
Examines how transaction costs affect systematic portfolios.
problem Impact of proportional transaction costs on systematic portfolios.
method Empirical study with various portfolio types and configurations.
result Proposes a method to smooth transaction costs.
MetaTrader combines diverse expert strategies to optimize portfolio performance.
problem Optimizing portfolio performance in changing financial markets.
method Two-stage RL approach: imitation learning followed by a meta-policy.
result MetaTrader significantly outperforms state-of-the-art baselines in balancing profits and risks.
NETpred uses graph models to predict multiple market indices.
problem Predicting multiple market indices with high accuracy.
method NETpred constructs a heterogeneous graph of related indices and stocks, selects representative nodes, and uses semi-supervised learning to predict index labels.
result NETpred outperforms state-of-the-art methods by 3%-5% in F-score on various datasets.
This work uses diffusion models for accurate signal recovery from semi-parametric models.
problem Recovering signals from semi-parametric single index models with discontinuous link functions.
method Proposes an efficient reconstruction method using diffusion models that requires one round of sampling and inversion.
result Demonstrates more accurate reconstructions with fewer evaluations compared to competing methods.
Supporting evidence for adaptive feature program across diverse models.
problem Analyzing feature learning in neural networks.
method Over-parameterized sequence models and feature error measure (FEM).
result FEM is decreasing during training of adaptive feature models.
Ensemble model predicts AD progression from CN status with high accuracy.
problem Early prediction of clinical progression from cognitively normal to mild cognitive impairment or Alzheimer's disease.
method Ensemble survival analysis combining penalized Cox regression, advanced survival models, and aggregation techniques.
result Ensemble model achieved peak C-index of 0.907 and integrated time-dependent AUC of 0.904, outperforming baseline models.
Proposes a tail-adaptive shrinkage method for robust sparse estimation.
problem Robust Bayesian methods for high-dimensional regression under diverse sparse regimes.
method Global-local-tail (GLT) Gaussian mixture distribution with tail-adaptive shrinkage.
result GLT posterior contracts at minimax optimal rate for sparse normal mean models.
Generative Fractional Diffusion Models improve image diversity and quality.
problem Diffusion models struggle with diversity, mode-collapse, and slow convergence.
method Replaces light-tailed BM with fractional Brownian motion (fBM) and its Markov approximation (MA-fBM).
result GFDM achieves greater diversity and quality in image generation.
Study evaluates reinforcement learning for trading diverse stocks, finds Q-learning outperforms.
problem Evaluating reinforcement learning for trading diverse stocks.
method Implemented Value Iteration (VI), State-action-reward-state-action (SARSA), and Q-Learning on a diverse stock portfolio dataset.
result Q-learning performs better than VI and SARSA during testing, but performance varies based on market conditions.
Proposes DILATE and STRIPE++ for precise time series forecasting.
problem Non-stationary signals with sudden changes.
method Incorporates shape and temporal criteria in deep learning models.
result Improves precision in deterministic and probabilistic forecasting.
The paper uses TDA to select stocks for a sparse portfolio, improving performance across market scenarios.
problem Sparse portfolio selection in financial markets.
method Topological data analysis (TDA) for clustering stock price movements.
result The TDA-based clustering strategy significantly enhances sparse portfolio performance.
Divergence Phase Index: A Riesz-Transform Framework for Multidimensional Phase Difference Analysisstat.ML
DPI quantifies phase differences in 1D and multidimensional signals using Riesz transform.
problem Quantifying phase differences in signals of varying dimensions.
method Riesz transform framework for harmonic analysis.
result DPI detects hypersynchronization and subtle changes in images and artworks.
We propose a novel investment decision strategy (IDS) based on deep learning. The performance of many IDSs is affected by stock similarity. Most existing stock similarity measurements have the problems: (a) The linear nature of many measurements cannot capture nonlinear stock dynamics; (b) The estimation of many simila…
Research predicts healthcare index movements using historical OHLC data.
problem Predicting the directional movement of healthcare indices based on historical data.
method Supervised classification task with a one-step-ahead rolling window, using a diverse feature set including OHLC ratios.
result Robust predictive performance with accuracy exceeding 0.8 and Matthews correlation coefficients above 0.6, highlighting the importance of nowcasting features.
DINOSAUR improves retrieval by accounting for embedding uncertainty in recommender systems.
problem Retrieval bias towards popular items due to noisy embeddings.
method Samples multiple embeddings per item and queries with sampled embeddings to account for uncertainty.
result Improves coverage of long-tail niche content without sacrificing recall.
New theory shows how multi-head attention reduces variance and decorrelates outputs.
problem Understanding and optimizing multi-head attention in neural networks.
method Developed a statistical theory linking multi-head attention to ensemble Nadaraya-Watson estimators.
result MHA variance reduction depends on head decorrelation, not just head count.
A well-interpretable measure of information has been recently proposed based on a partition obtained by intersecting a random sequence with its moving average. The partition yields disjoint sets of the sequence, which are then ranked according to their size to form a probability distribution function and finally fed in…
Dynamic Process of Money Transfer Modelsphysics.soc-ph
We have studied numerically the statistical mechanics of the dynamic phenomena, including money circulation and economic mobility, in some transfer models. The models on which our investigations were performed are the basic model proposed by A. Dragulescu and V. Yakovenko [1], the model with uniform saving rate develop…
We introduce a tensor-based clustering method to extract sparse, low-dimensional structure from high-dimensional, multi-indexed datasets. This framework is designed to enable detection of clusters of data in the presence of structural requirements which we encode as algebraic constraints in a linear program. Our cluste…
The counting grid is a grid of microtopics, sparse word/feature distributions. The generative model associated with the grid does not use these microtopics individually. Rather, it groups them in overlapping rectangular windows and uses these grouped microtopics as either mixture or admixture components. This paper bui…
We introduce a stochastic process with Wishart marginals: the generalised Wishart process (GWP). It is a collection of positive semi-definite random matrices indexed by any arbitrary dependent variable. We use it to model dynamic (e.g. time varying) covariance matrices. Unlike existing models, it can capture a diverse …
Research reveals how diversity impacts ensemble generalization in classification tasks.
problem Understanding the relationship between diversity and generalization in classification ensembles.
method Investigated diversity measurement, its relationship with generalization error, and pruning methods.
result Generalization error is reduced effectively only when diversity is increased in specific ranges, not in others.
Unified kernels for diverse applications in math and stats.
problem Unified kernels for diverse applications in math and stats.
method Unified parametric class of kernels, characterized by Sobolev spaces.
result Unified kernels encompass various known kernels and their properties.
TabSurv adapts tabular neural networks for survival analysis.
problem Survival analysis on tabular data using deep learning methods.
method Adapts modern tabular architectures to survival analysis using Weibull distribution or non-parametric prediction. Optimizes SurvHL histogram loss function.
result TabSurv consistently outperforms classical and deep learning baselines on 10 real-world survival datasets.
Paper proposes a method to minimize non-differentiable loss functions.
problem Minimizing non-differentiable and non-decomposable loss functions.
method Learn smooth relaxations of true losses through surrogate neural networks, then optimize jointly with the prediction model.
result Empirical results show the efficiency of learning surrogate losses.
Range-Based Volatility Estimators for Monitoring Market Stress: Evidence from Local Food Price Dataq-fin.ST
Study adapts OHLC volatility estimators for monitoring market stress in diverse settings.
problem Limited use of range-based volatility estimators in local commodity markets.
method Adapted OHLC volatility estimators to monitor market distress across various contexts.
result OHLC-based volatility indicators detect market disruptions missed by standard momentum indicators.
A new algorithm learns diverse policies in reinforcement learning.
problem Learning diverse behaviors in reinforcement learning.
method Proposes Maximum Entropy Diverse Exploration (MEDE) algorithm.
result The set of policies learned by MEDE capture the same modalities as the optimal maximum entropy policy.
BiPE blends intra-segment and inter-segment encodings for better length extrapolation.
problem Improving length extrapolation in language models.
method Bilevel Positional Encoding (BiPE) that separates intra-segment and inter-segment encodings.
result BiPE enhances length extrapolation across various text modalities.
This paper explores how diverse neural network ensembles improve prediction accuracy and robustness against deception.
problem Improving prediction accuracy and robustness of neural networks against adversarial attacks.
method Examines and measures ensemble diversity, develops algorithms for creating and combining diverse ensembles.
result Greater diversity in neural network ensembles leads to higher accuracy and robustness against deception.
Proposes method to discover diverse near-optimal policies in reinforcement learning.
problem Finding different solutions to the same problem in reinforcement learning.
method Formalizes problem as CMDP, uses Successor Features, proposes new diversity rewards.
result Proposed method discovers diverse near-optimal policies that are robust and distinct.
Paper proposes a fair stock trading strategy using multi-agent reinforcement learning.
problem Unfair stock trading strategies lead to long-term losses for companies.
method Multi-agent reinforcement learning system to balance revenue and fairness.
result The system optimizes individual revenue while maintaining fairness.