From positions, attained by modern theoretical physics in understanding of the universe bases, the methodological and philosophical analysis of fundamental physical concepts and their formal and informal connections with the real economic measurings is carried out. Procedures for heterogeneous economic time determinati…
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48 results for “economic series”
Study uses IMFs and neural networks to predict economic time series, enhancing interpretability.
problem Improving prediction accuracy and interpretability of economic time series.
method Intrinsic Mode Functions (IMFs) derived from economic time series, combined with DeepSHAP for interpretability.
result The last IMFs are most influential, and high-frequency IMFs introduce noise.
Examines predictability and complexity of economic time series using symbolic dynamics and entropy.
problem Understanding the predictability and complexity of economic time series.
method Symbolic dynamics and Information theory (entropy and uncertainty).
result Economic time series are complex and can be expressed in terms of information production.
Surveying machine learning methods for economic forecasting.
problem Improving accuracy of economic forecasts using machine learning.
method Nowcasting, textual data, panel and tensor data, high-dimensional Granger causality tests, time series cross-validation, classification with economic losses.
result Recent advances in machine learning methods enhance economic forecasting accuracy.
'Ergodicity economics' is criticized as pseudoscience.
problem Flawed conceptual basis of mainstream economic theory.
method Claims 'ergodicity economics' is more parsimonious and clearer.
result Peters' approach has not produced falsifiable implications.
Fuzzy clustering of ordinal time series based on two novel distances with economic applicationsstat.ML
Paper introduces novel distances for clustering ordinal time series.
problem Clustering ordinal time series with discrete response.
method Introduces two novel distances and fuzzy clustering algorithms.
result Fuzzy clustering algorithms accurately group series from similar stochastic processes.
A new SOHP filter improves trend estimation in economic time series.
problem Improving trend estimation in nonlinear economic time series.
method Recursive application of one-sided HP filter on updated cyclical components, combined with an incremental HP filtering algorithm.
result Better performance of SOHP filter compared to other HP-type filters on real economic data.
Paper classifies economic states and optimizes portfolios for stagflationary environments.
problem Economic uncertainty and stagflationary conditions.
method Mathematical techniques for analyzing multivariate time series, economic driver analysis, self-similarity identification, and portfolio optimization.
result Constructs economic state classifications and computes economic state integrals.
A dangerously brief history of the developments of the main ideas in economics, as observed by a physicist, is given. This was published in 'Econophysics of Stock and Other Markets', Eds. A. Chatterjee, B. K. Chakrabarti, New Economic Windows Series, Springer, Milan, 2006, pp~219-224.
Analyzes how economic policies affect wealth distribution in Bitcoin token economy.
problem Impact of economic policies on wealth distribution in token economies.
method Eliminated noise in wealth distribution data using macroeconomic and microeconomic time series. Causality analysis between BIPs and wealth distribution data.
result Proposed a structure for economic policy taxonomy in token economies.
In this editorial guide for the special issue on econophysics, we give a unique review of this young but quickly growing discipline. A suggestive taxonomy of the development is proposed by making a distinction between classical econophysics and modern econophysics. For each of these two stages of development, we identi…
Analyzes Indian commercial dynamism using time series data.
problem Understanding commercial dynamism in India.
method Time series analysis of various economic indicators.
result Detailed insights into growth rate, trade balance, etc.
Threshold levels in Economicsq-fin.ST
In this paper, we present theorems specifying the critical values for series associated with debts arranged in the order of their duration.
New framework detects time-varying economic persistence.
problem Time-varying persistence in economic shocks.
method Localized regression techniques to identify evolving heterogeneity.
result Substantial persistence variations align with macroeconomic events.
Sector identification in a set of stock return time series traded at the London Stock Exchangecond-mat.dis-nn
We compare some methods recently used in the literature to detect the existence of a certain degree of common behavior of stock returns belonging to the same economic sector. Specifically, we discuss methods based on random matrix theory and hierarchical clustering techniques. We apply these methods to a portfolio of s…
We have analyzed the Indices of Industrial Production (Seasonal Adjustment Index) for a long period of 240 months (January 1988 to December 2007) to develop a deeper understanding of the economic shocks. The angular frequencies estimated using the Hilbert transformation, are almost identical for the 16 industrial secto…
We test a historical price time series in a financial market (the NASDAQ 100 index) for a statistical property known as detailed balance. The presence of detailed balance would imply that the market can be modeled by a stochastic process based on a Markov chain, thus leading to equilibrium. In economic terms, a positiv…
The paper proposes a method of financial time series forecasting taking into account the semantics of news. For the semantic analysis of financial news the sampling of negative and positive words in economic sense was formed based on Loughran McDonald Master Dictionary. The sampling included the words with high frequen…
Alpha-R1 uses LLMs to reason about economic factors and news for better alpha screening.
problem Challenges in data-driven investment strategies due to signal decay and regime shifts.
method Reinforcement learning trained on 8B parameters to evaluate alpha relevance under changing market conditions.
result Empirically outperforms benchmark strategies and shows improved robustness to alpha decay.
This paper develops copula-based models for forecasting multivariate realized volatility.
problem Forecasting multivariate realized volatility matrices with hidden dependence structure.
method Copula-based time series models to capture hidden dependence structure and ensure positive definiteness.
result Copula-based models achieve significant performance in volatility matrix forecasting.
Economic sector identification in a set of stocks traded at the New York Stock Exchange: a comparative analysisphysics.soc-ph
We review some methods recently used in the literature to detect the existence of a certain degree of common behavior of stock returns belonging to the same economic sector. Specifically, we discuss methods based on random matrix theory and hierarchical clustering techniques. We apply these methods to a set of stocks t…
Economic systems are similar with physic systems for their large number of individuals and the exist of equilibrium. In this paper, we present a model applying the equilibrium statistical model in economic systems. Consistent with statistical physics, we define a series of concepts, such as economic temperature, econom…
This paper extends forecast reconciliation to non-linearly constrained time series.
problem Forecasting time series with non-linear constraints.
method Non-linearly Constrained Reconciliation (NLCR) algorithm that adjusts forecasts to meet non-linear constraints.
result NLCR significantly improves forecast accuracy compared to benchmarks.
I propose a frequency domain adaptation of the Expectation Maximization (EM) algorithm to group a family of time series in classes of similar dynamic structure. It does this by viewing the magnitude of the discrete Fourier transform (DFT) of each signal (or power spectrum) as a probability density/mass function (pdf/pm…
In this paper, we introduce quantile coherency to measure general dependence structures emerging in the joint distribution in the frequency domain and argue that this type of dependence is natural for economic time series but remains invisible when only the traditional analysis is employed. We define estimators which c…
Paper forecasts commodity price spikes using AI and economic news.
problem Accurate forecasting of commodity price spikes for economic stability.
method Hybrid framework combining historical data and semantic signals from economic news.
result Model achieves high AUC and accuracy in detecting price shocks.
In this research the technology of complex Markov chains is applied to predict financial time series. The main distinction of complex or high-order Markov Chains and simple first-order ones is the existing of aftereffect or memory. The technology proposes prediction with the hierarchy of time discretization intervals a…
Proposes a stochastic model for South African actuarial use.
problem Long-term forecasting for South African institutions.
method Modeling economic series, estimating parameters, testing stability.
result Validated model for long-term forecasts.
The econophysics approach to socio-economic systems is based on the assumption of their complexity. Such assumption inevitably lead to another assumption, namely that underlying interconnections within socio-economic systems, particularly financial markets, are nonlinear, which is shown to be true even in mainstream ec…
Forecasting time series data is an important subject in economics, business, and finance. Traditionally, there are several techniques to effectively forecast the next lag of time series data such as univariate Autoregressive (AR), univariate Moving Average (MA), Simple Exponential Smoothing (SES), and more notably Auto…
A two-component model for the evolution of real GDP per capita in the USA is presented and tested. The first component of the GDP growth rate represents an economic trend and is inversely proportional to the attained level of real GDP per capita itself, with the nominator being constant through time. The second compone…
In this paper, we perform statistical segmentation and clustering analysis of the Dow Jones Industrial Average time series between January 1997 and August 2008. Modeling the index movements and log-index movements as stationary Gaussian processes, we find a total of 116 and 119 statistically stationary segments respect…
We have recently introduced the ``thermal optimal path'' (TOP) method to investigate the real-time lead-lag structure between two time series. The TOP method consists in searching for a robust noise-averaged optimal path of the distance matrix along which the two time series have the greatest similarity. Here, we gener…
This study examines how economic policy uncertainty impacts commodity prices across different crises.
problem Impact of economic policy uncertainty on commodity prices during various crises.
method Wavelet coherence analysis of time series data.
result Commodity prices are more correlated during global financial and Covid-19 crises.
The existence of forbidden patterns, i.e., certain missing sequences in a given time series, is a recently proposed instrument of potential application in the study of time series. Forbidden patterns are related to the permutation entropy, which has the basic properties of classic chaos indicators, thus allowing to sep…
Methods for detecting structural changes, or change points, in time series data are widely used in many fields of science and engineering. This chapter sketches some basic methods for the analysis of structural changes in time series data. The exposition is confined to retrospective methods for univariate time series. …
Paper extends quantile factor analysis with probabilistic methods for better economic policy and financial condition prediction.
problem Improving accuracy in economic and financial condition prediction.
method Probabilistic quantile factor analysis with regularization and variational approximations.
result The probabilistic estimator outperforms a recent loss-based estimator in many cases.
DAMNETS generates complex network dynamics models.
problem Generating flexible and scalable models for network time series is challenging.
method Deep autoregressive model for Markovian network time series.
result DAMNETS outperforms other methods in sample quality.
Quantitative model predicts Sri Lankan stock market using NLP, clustering, and time-series forecasting.
problem Predicting economic regimes and market signals in Sri Lankan stock indices.
method Integrates NLP, clustering, and time-series forecasting; uses FinBERT for sentiment analysis, UMAP/HDBSCAN for clustering, and GRU/LSTM for forecasting.
result GRU model achieves 80.1% R-squared for daily closing price forecasts.
Dynamic functional time-series methods improve forecast accuracy for foreign exchange implied volatility surfaces.
problem Forecasting implied volatility surfaces in foreign exchange markets.
method Dynamic functional principal component analysis and multivariate functional time-series methods.
result Dynamic univariate functional time-series method shows the greatest improvement in forecast accuracy.
Enhances FAVAR models with autoencoder for better economic forecasting and interpretability.
problem Limitations of linear FAVAR models in forecasting and structural analysis.
method Introduces Grouped Sparse autoencoder with time-varying parameters.
result The Grouped Sparse autoencoder produces more interpretable factors and superior forecasting performance.
The Economic Mobility in Money Transferphysics.soc-ph
In this paper, we investigate the economic mobility in some money transfer models which have been applied into the research on wealth distribution. We demonstrate the mobility by recording the time series of agents' ranks and observing their volatility. We also compare the mobility quantitatively by employing an index,…
ReGEN-TAD detects anomalies in financial time series with interpretable models.
problem Detecting anomalies in complex financial time series with high-dimensional data.
method Integrates machine learning with econometric diagnostics in a refined convolutional--transformer architecture.
result Unified anomaly score without labeled data, robust to structured deviations.
Modelling Correlations in Portfolio Credit Riskcond-mat.stat-mech
The risk of a credit portfolio depends crucially on correlations between the probability of default (PD) in different economic sectors. Often, PD correlations have to be estimated from relatively short time series of default rates, and the resulting estimation error hinders the detection of a signal. We present statist…
The initial theoretical connections between Leontief input-output models and Markov chains were established back in 1950s. However, considering the wide variety of mathematical properties of Markov chains, there has not been a full investigation of evolving world economic networks with Markov chain formalism. Using the…
Survey of machine learning methods for time series forecasting.
problem Improving accuracy of time series predictions.
method Linear and nonlinear machine learning models, including neural networks and ensemble methods.
result Demonstrates superior predictive ability of certain machine learning models.
Paper introduces MN-DAG for modeling evolving causal relationships in multivariate time series.
problem Modeling causal relationships that evolve over time and occur at different scales.
method Probabilistic generative model based on spectral and causality theories, combined with Bayesian stochastic variational inference.
result MN-CASTLE outperforms baseline models in identifying causal relationships in multivariate time series data.
Deep learning models improve stock market portfolio returns.
problem Optimizing portfolio returns using deep learning methods.
method Deep neural networks (feedforward and LSTM) applied to stock market excess returns forecasting.
result Deep learning models deliver significant gains in portfolio certainty equivalent returns and Sharpe ratios.