This study examines gender bias in Dutch newspapers from 1950-1990 using word embeddings.
problem Examining gender bias in historical newspapers.
method Word embeddings to measure bias changes over time.
result Clear differences in gender bias and changes within newspapers over time.
Analyzes ESG impact on stock market performance using social media and news data.
problem Understanding the impact of ESG news on stock market performance.
method Summarized live ESG data from social media and news, created sentiment index, calculated stock price changes, and compared sentiment to performance.
result ESG sentiment correlates with stock price changes, indicating its impact on market performance.
We report a data mining pipeline and subsequent analysis to understand the core periphery power structure created in three national newspapers in Bangladesh, as depicted by statements made by people appearing in news. Statements made by one actor about another actor can be considered a form of public conversation. Name…
This article is written for the online newspaper "The Photon" published by the Department of Physics, University of Maryland. The article describes econophysics research done in the group of Victor Yakovenko. It briefly surveys the subjects "Statistical Mechanics of Money, Income, and Wealth" and "Probability Distribut…
News sentiment in U.S. economic newspapers has become more persistent over 45 years.
problem Understanding the temporal dynamics of U.S. economic news sentiment over time.
method Daily economic news sentiment index from 1980-2025, analyzed using sentiment indexes.
result News sentiment states have become more persistent, with longer residence times in optimistic or pessimistic regimes.
According to the definition of the London Interbank Offered Rate (LIBOR), contributing banks should give fair estimates of their own borrowing costs in the interbank market. Between 2007 and 2009, several banks made inappropriate submissions of LIBOR, sometimes motivated by profit-seeking from their trading positions. …
We show that power-law analyses of financial commentaries from newspaper web-sites can be used to identify stock market bubbles, supplementing traditional volatility analyses. Using a four-year corpus of 17,713 online, finance-related articles (10M+ words) from the Financial Times, the New York Times, and the BBC, we s…
The efficient market hypothesis has been considered one of the most controversial arguments in finance, with the academia divided between who claims the impossibility of beating the market and who believes that it is possible to gain over the average profits. If the hypothesis holds, it means, as suggested by Burton Ma…
Sabrina integrates financial data and domain knowledge for better visualization.
problem Scattered financial data across various sources makes it hard for analysts to understand the economy.
method Sabrina uses a pipeline to fuse firm-specific and macroeconomic data, visualizing it in a unified interface.
result Sabrina aids financial analysts in their analysis process, as shown in a user study.
Unified HS and related methods with explicit modeling assumptions.
problem Lack of clear assumptions in HS methods for Value-at-Risk.
method Explicitly defined parametric model for asset returns and extraction of innovation process.
result HS and related methods require more assumptions than commonly acknowledged.
The link between different psychophysiological measures during emotion episodes is not well understood. To analyse the functional relationship between electroencephalography (EEG) and facial electromyography (EMG), we apply historical function-on-function regression models to EEG and EMG data that were simultaneously r…
ArtificialReplay improves data efficiency in bandits using historical data.
problem Data inefficiency in warm-starting bandit algorithms.
method ArtificialReplay, a meta-algorithm for incorporating historical data into any bandit algorithm.
result ArtificialReplay uses only a fraction of historical data compared to a full warm-start approach, achieving identical regret.
ADR helps LLMs find and use historical analogies for foresight analysis.
problem LLMs struggle to find relevant historical analogies due to surface-level matching.
method Proposes CANA framework with mechanism alignment and cross-analogy confirmation.
result CANA improves historical analogy generation by up to 10%.
Proposes dynamic borrowing method for historical data in clinical trials.
problem Insufficient statistical power in rare and pediatric disease clinical trials.
method Dynamic borrowing method based on frequentist approach using similarity measures.
result Demonstrates usefulness of dynamic borrowing in reanalyzing clinical trial data.
Data describing historical economic growth are analysed. Included in the analysis is the world and regional economic growth. The analysis demonstrates that historical economic growth had a natural tendency to follow hyperbolic distributions. Parameters describing hyperbolic distributions have been determined. A search …
This study reviews techniques to estimate volatility and price Variance Swaps.
problem Estimating historical volatility and pricing Variance Swaps.
method Review of existing techniques.
result Discussion of various methods to estimate volatility and price Variance Swaps.
Improved Bayesian inference using power priors with historical data.
problem Improving Bayesian inference with historical data.
method Generalized power priors that adapt to the α parameter of Amari's α-divergence. result Improved performance through appropriate choices of the α parameter. Develops algorithms to exploit historical and pre-clustered arm information in bandit problems.
problem Optimizing decision-making in multi-armed bandit and contextual bandit problems with historical observations and pre-clustered arms.
method META algorithm that combines historical observations and pre-clustering information, deriving regret bounds for various scenarios.
result META algorithm effectively balances between using historical observations and clustering, outperforming the other in different scenarios.
Combines experimental and historical data for robust policy evaluation.
problem Policy evaluation with mixed data sources, especially experimental vs historical.
method Linear integration of estimators from experimental and historical data, optimized for MSE minimization.
result Proposed estimators outperform traditional methods in ridesharing company data.
In this paper we look at the efficacy of different risk measures on energy markets and across several different stock market indices. We use both the Value at Risk and the Tail Conditional Expectation on each of these data sets. We also consider several different durations and levels for historical risk measures. Throu…
The paper develops a method to forecast financial risk multiple steps ahead using quantile time series and historical simulation.
problem Forecasting financial risk multiple steps ahead with accurate estimation of Value-at-Risk (VaR) and Expected Shortfall (ES).
method Quantile-based, semi-parametric historical simulation estimation of VaR and ES models, using quantile loss function and resampling.
result The proposed method accurately forecasts VaR and ES one and multiple steps ahead, superior to existing methods.
Calibrates historical and implied correlations in energy markets.
problem Challenges in aligning historical correlations of futures contracts with implied volatility smiles.
method Multiplicative multi-factor Heath-Jarrow-Morton model combined with stochastic volatility from lifted Heston model, using Kemna-Vorst approximation and Fourier-based techniques.
result Remarkable joint historical and implied calibration fits on the German power market.
Econophysics embodies the recent upsurge of interest by physicists into financial economics, driven by the availability of large amount of data, job shortage in physics and the possibility of applying many-body techniques developed in statistical and theoretical physics to the understanding of the self-organizing econo…
Contextualizing financial news improves stock price predictions.
problem Predicting stock prices from financial news requires understanding historical context.
method Proposed a method using a large language model for main articles and a small model for historical context.
result Historical context significantly improves model performance across methods and time horizons.
Typically flat filling, linear or polynomial interpolation methods to generate missing historical data. We introduce a novel optimal method for recreating data generated by a diffusion process. The results are then applied to recreate historical data for stocks.
A new GNN model predicts stock trends by learning historical and future correlations.
problem Limited improvement in stock trend prediction models due to ignoring future patterns.
method DishFT-GNN framework that trains a teacher and student model to capture historical and future data correlations.
result State-of-the-art performance on real-world datasets.
Study compares VaR models and finds GARCH-FHS superior.
problem Comparing VaR models for accurate risk assessment.
method Historical Simulation, GARCH-N, GARCH-FHS models evaluated.
result GARCH-FHS provides superior performance in capturing tail risks.
New algorithm reduces online learning regret by exploiting historical invariances.
problem Stochastic non-stationary linear bandits with changing reward models.
method ISD-linUCB algorithm that learns invariances in reward model.
result Significant regret improvements in fast-changing environments with historical data.
Industry datasets used for text classification are rarely created for that purpose. In most cases, the data and target predictions are a by-product of accumulated historical data, typically fraught with noise, present in both the text-based document, as well as in the targeted labels. In this work, we address the quest…
Historical returns depend on historical closing prices and distributions. We describe how to compute adjusted closing prices from closing price/distribution data with an emphasis on spreadsheet implementation. Then the growth of a security from one date to another (1 + total return) is just the ratio of the correspondi…
RL improves market making with historical data time travel.
problem Limited ability to simulate and fully appraise the impact of actions in competitive systems.
method Introduces 'consistent data time travel' to adjust historical data time index.
result Significant improvement in agent's gain with data time travel.
Identifying the type of font (e.g., Roman, Blackletter) used in historical documents can help optical character recognition (OCR) systems produce more accurate text transcriptions. Towards this end, we present an active-learning strategy that can significantly reduce the number of labeled samples needed to train a font…
Surveying nonparametric inference with shape constraints, past and future.
problem Statistical inference under shape constraints.
method Historical overview and future directions.
result Outlook on future research directions.
Explains historical connections between vector bundle splitting and Riemann-Hilbert problems.
problem Vector bundle splitting over the Riemann sphere.
method Historical overview and connections to other mathematical problems.
result Explains the Riemann-Hilbert-Birkhoff problems and their relation to vector bundle splitting.
Two ML approaches compare in recognizing tables from historical records.
problem Recognizing rows and columns in hand-written registry books.
method Comparison of Conditional Random Field and Graph Convolutional Network.
result Both ML methods achieve an 89 F1 score for table detection.
Improves trial efficiency by adjusting for historical prognostic scores.
problem Reducing statistical uncertainty in randomized trial estimates.
method Linear covariate adjustment using a prognostic model trained on historical data.
result Prognostic covariate adjustment achieves minimum variance and reduces mean-squared error.
Explains isometric immersions and their applications.
problem Isometric immersions and their applications in math and physics.
method Historical overview and applications.
result Explains the importance and applications of isometric immersions.
EHNA learns node embeddings from historical network neighborhoods.
problem Capturing temporal information in evolving networks.
method Temporal random walk and deep learning model with attention mechanism.
result EHNA outperforms existing methods in network reconstruction and link prediction tasks.
The study uses historical revenue data to forecast music catalog cashflows and multipliers.
problem Valuation of music catalogs based on historical revenue data.
method Risk-neutral approach using discounted cashflows formula.
result Ask prices are close to multipliers justified by median song cashflows, while best bids are near multipliers justified by bottom decile cashflows.
Combines historical and market data for better portfolio selection.
problem Improving portfolio selection through diverse information integration.
method Bayesian learning via Gaussian mixture model to harmonize historical and market data.
result The method enhances forecasting accuracy and robustness across various capital markets.
This paper studies an application of machine learning in extracting features from the historical market implied corporate bond yields. We consider an example of a hypothetical illiquid fixed income market. After choosing a surrogate liquid market, we apply the Denoising Autoencoder (DAE) algorithm to learn the features…
The paper evaluates criteria for selecting cryptocurrencies based on historical data.
problem High risk of cryptocurrencies due to volatility.
method Characterized returns and risks using historical data in short time windows (7 and 15 days). Analyzed the importance of criteria using various methods.
result Importance of criteria for selecting cryptocurrencies is analyzed and evaluated.
DBNs improve VaR forecasting compared to traditional models, but SVaR forecasts are conservative.
problem Forecasting VaR and SVaR using dynamic Bayesian networks.
method DBN framework applied to S&P 500 index returns, comparing to autoregressive models and historical simulation.
result DBNs achieve comparable VaR forecasting accuracy to historical simulation models, but SVaR forecasts remain conservative.
The paper explores using historical data to improve clinical trial analysis by optimizing covariate weights.
problem Limited covariates in small clinical trials reduce the effectiveness of analysis.
method Leverage historical data to pre-specify covariate weights as a composite covariate.
result A composite covariate improves the cost/benefit ratio and reduces overfitting in small clinical trials.
Study refines trend-following strategy to improve adaptability.
problem Challenges in practical implementation of historical trend-following strategies.
method Modifications to historical strategy, including T-bills exclusion, alternative allocations, industry exclusions, momentum signals, and Walk-Forward Analysis.
result Persistent challenges in adapting historical strategies to modern markets.
This paper reviews and compares deep generative models for financial time series and VaR.
problem Forecasting risk factor distribution in financial markets.
method Apply multiple deep generative models (CGAN, CWGAN, Diffusion, Signature WGAN) and propose new methods for conditional time series generation.
result Top performing models are Historical Simulation, GARCH, and CWGAN.
Study shows survivorship bias inflates returns in India's small-cap index.
problem Survivorship bias in emerging market small-cap indices.
method Reconstructing historical index composition through market capitalization ranking and comparing equal-weight portfolios of current constituents versus all historical members.
result Survivor-only backtesting overstates returns by 4.94 percentage points and Sharpe ratios by 0.097.
LLMs overestimate stock returns and are less accurate at predicting extreme outcomes.
problem Behavioral biases in LLMs' stock return forecasts.
method Comparison of LLM forecasts with crowd-sourced estimates and historical data.
result LLMs overestimate stock returns and are less accurate at predicting extreme outcomes.