Examines US equity risk premiums amid COVID-19.
problem Analyzing equity risk premiums during the pandemic.
method Not specified in the abstract.
result Not specified in the abstract.
On-device research index
arXiv research
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.
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48 results for “COVID-19 pandemic”
Study nationwide measures' impact on COVID-19 using models and machine learning.
problem Analyzing the impact of nationwide COVID-19 measures.
method Compartmental model and machine learning tools.
result Comparison of deterministic model and machine learning forecasts.
Study shows how cryptocurrency market skewness and kurtosis interact during pandemic.
problem Understanding the dynamics of cryptocurrency markets during the pandemic.
method Examined skewness and kurtosis interactions in cryptocurrency market data.
result More observations cluster around extremes during pandemic, indicating volatile behavior.
Study on excess mortality in Germany during 2020-21.
problem Analyzing excess mortality during the pandemic in Germany.
method Empirical study using official death counts.
result Provided conclusions for insurance businesses.
Paper uses machine learning and SIR models to predict COVID-19 cases.
problem Predicting the spread of COVID-19 cases for control measures.
method Machine learning and SIR models (deterministic and stochastic) with numerical approximations.
result Predictions help in finding concrete actions to control the pandemic.
Study shows how market efficiency changes during the pandemic.
problem Understanding market efficiency during the pandemic.
method Applied time-varying vector autoregression model.
result Market efficiency changes over time and can be improved by enhanced linkages.
Study applies financial models to predict COVID-19 pandemic.
problem Predicting the spread of COVID-19 using financial market models.
method Implemented ARIMAX and Cox-Ingersoll-Ross (CIR) models, using Euler-Maruyama and Milstein methods for CIR*.
result CIR* framework provides accurate forecasts for pandemics.
Study evaluates stock price forecasting models during the pandemic.
problem Forecasting stock prices during the Covid-19 pandemic.
method Four models (Long-Short Term Memory, XGBoost, Autoregression, Last Value) were tested on stock prices of Facebook, Amazon, Tesla, Google, and Apple.
result Autoregression and Last Value models outperform other models due to strong correlation between prices.
Study examines how COVID-19 affected stock and crypto market efficiency.
problem Impact of COVID-19 on market efficiency of different asset classes.
method Analysis of price returns, absolute returns, and volatility increments in stock and cryptocurrency markets.
result Market efficiency varied by asset class and market, with some time series showing gradual decline over time.
Study shows post-COVID commodity futures returns and volatility changed for different products.
problem Analyzing how the pandemic affected Chinese commodity futures markets.
method Empirical analysis of commodity futures returns and cointegration before and after the pandemic.
result Post-COVID, some commodity futures returns increased significantly, while others saw higher volatility.
Study uses Bayesian regression to analyze consumer behavior changes in restaurants post-COVID-19.
problem Impact of COVID-19 on consumer behavior in the restaurant industry.
method Bayesian regression with Hamiltonian Monte Carlo.
result Estimates change in consumer behavior before and after the pandemic.
Study shows how COVID-19 pandemic affected China's crude oil futures market efficiency.
problem Impact of COVID-19 on China's crude oil futures market efficiency.
method Multifractal analysis to compare market efficiency before and during the pandemic.
result Market efficiency of SC and its cross-correlations with other assets increased significantly after the outbreak of COVID-19.
The study uses stock market indicators to forecast COVID-19 cases.
problem Forecasting the spread of COVID-19 for resource allocation.
method Reinterpreting daily cases as candlesticks and applying stock market indicators.
result The stock market indicators show statistical significance in predicting COVID-19 cases.
Study shows COVID-19 cases increase stock market volatility in Pakistan.
problem Impact of COVID-19 on stock market volatility in Pakistan.
method Used vector autoregressive (VAR) model to analyze data from February 25, 2020 to December 7, 2020.
result A shock to total daily coronavirus cases in Pakistan leads to a significant increase in stock market volatility.
Study shows tweets about COVID-19 can predict stock market performance.
problem Understanding the impact of COVID-19 on stock markets.
method Text sentiment analysis of Twitter data to correlate tweets about COVID-19 with stock market performance.
result Strong relationship between COVID-19 sentiment and stock market performance can be predicted.
Stocks of more resilient firms outperformed during the pandemic, reflecting disaster risk.
problem The impact of social distancing on firms' operations and stock performance.
method Cross-sectional analysis of firms' resilience and stock performance, controlling for risk factors.
result Stocks of more resilient firms are expected to yield significantly lower returns than less resilient ones, reflecting disaster risk.
Study shows COVID-19 increases stock market crash risk in China.
problem Impact of COVID-19 on stock market crash risk in China.
method Estimated conditional skewness using GARCH-S model and constructed fear index from Baidu Index data.
result Conditional skewness reacts negatively to daily growth in total confirmed cases, indicating increased crash risk.
Study shows economic policy uncertainty increases stock market crash risk during pandemic.
problem Impact of economic policy uncertainty on stock market crashes during the pandemic.
method Used GARCH-S model to estimate daily skewness as a proxy for crash risk, analyzed data from US stock market.
result Significantly negative correlation between economic policy uncertainty and stock market crash risk, stronger during pandemic.
An evolutionary game model analyzes e-commerce and traditional retail trends during the pandemic.
problem Understanding the dynamics between e-commerce and traditional retail during the pandemic.
method Developed an evolutionary game model to study consumer-producer interactions on e-commerce platforms.
result Investment in logistics and warehouses in e-commerce led to faster delivery and consumer trends.
Analysis of the COVID-19 pandemic by SIR model and machine learning technics for forecastingq-bio.PE
The SIR model and machine learning predict pandemic inflection and end times.
problem Forecasting the end of the COVID-19 pandemic.
method SIR model and machine learning techniques.
result Predicted end times for various countries.
Study uses machine learning to analyze Twitter sentiments about COVID-19.
problem Examining public concerns and sentiments about COVID-19 from Twitter.
method Machine learning (Latent Dirichlet Allocation) to identify topics and sentiments.
result Identified 13 topics and categorized into five themes, revealing dominant fears and mixed feelings.
AICov: An Integrative Deep Learning Framework for COVID-19 Forecasting with Population Covariatescs.LG
AICov integrates population covariates for better COVID-19 forecasting.
problem Forecasting COVID-19 with broader social context.
method Integrative deep learning framework with LSTM and multiple data sources.
result Improved prediction of COVID-19 cases and deaths with population risk factors.
Study uses Google matrix analysis to show how COVID-19 changed international trade flows.
problem Impact of COVID-19 on international trade patterns.
method Google matrix analysis of World Trade Network (WTN), including PageRank, CheiRank, and reduced Google matrix.
result Significant changes in international trade flows due to the pandemic, affecting export and import balances.
Study examines Fed's pandemic communication strategies.
problem Analyzing Federal Reserve's communication during the COVID-19 pandemic.
method Sentiment analysis, topic modeling, comparative analysis of previous crises.
result Fed's communication during the pandemic focused on financial stability, market volatility, social welfare, and unconventional monetary policy.
Deep learning for COVID-19 diagnosis using CXR images with limited data.
problem Difficulty in collecting CXR data for deep learning due to the pandemic.
method Patch-based convolutional neural network with limited trainable parameters.
result Achieves state-of-the-art performance and interpretable saliency maps.
Analyzes COVID-19 data to predict mortality, forecast spread, and optimize resource allocation.
problem Challenges in patient triage, treatment, and care management during the pandemic.
method Integrated four-step approach combining descriptive, predictive, and prescriptive analytics.
result Optimized resource allocation and informed policy decisions.
Bitcoin fails to prove safe haven status during pandemic.
problem Determining if Bitcoin is a reliable safe haven asset during crises.
method Quantile correlations of Bitcoin with S&P500, VIX, and gold.
result Gold is a better safe haven during crises, not Bitcoin.
Model predicts increased social unrest during COVID-19 using social media data.
problem Detecting rising conflict potential in societies during pandemics.
method Neural implicit motive pattern recognition from social media texts.
result Significant increase in conflict indicators during the pandemic.
Algorithm improves vanilla option pricing accuracy during and before COVID-19.
problem Improving vanilla option pricing accuracy during and before the pandemic.
method Combinational Mutation Strategy of Differential Evolution (CmDE) algorithm for bi-objective optimization.
result Algorithm approximates real market vanilla option prices more accurately than Black-Scholes.
Collectivistic countries influence Bitcoin returns more than individualistic ones during the pandemic.
problem The impact of public attention to COVID-19 on Bitcoin returns.
method Rolling and recursive-evolving algorithms to account for timing and estimation bias.
result Collectivistic countries have stronger causal impacts on Bitcoin returns.
Classification Aware Neural Topic Model and its Application on a New COVID-19 Disinformation Corpuscs.LG
Developed a neural topic model for classifying COVID-19 disinformation.
problem Tackles the challenge of disinformation during the COVID-19 pandemic.
method Classification-aware neural topic model (CANTM) for COVID-19 disinformation.
result Demonstrated the effectiveness of CANTM in classifying COVID-19 disinformation.
Study uses social media to analyze COVID-19 impact.
problem Understanding the global impact of COVID-19.
method Machine learning and linguistic tools to analyze social media posts.
result Automatic detection of positive reports of COVID-19.
Dynamics, behaviours, and anomaly persistence in cryptocurrencies and equities surrounding COVID-19q-fin.ST
Study shows similarities and differences in crypto and equity dynamics during pandemic.
problem Comparing cryptocurrency and equity market dynamics during the pandemic.
method New methodologies applied to study cryptocurrency and equity market dynamics, including recently introduced methods for trajectory and anomaly analysis.
result Cryptocurrencies exhibit stronger collective dynamics and correlation, while equities show greater persistence in anomalies over time.
The study analyzes sentiment of European tweets during the pandemic.
problem Understanding public sentiment during the COVID-19 pandemic.
method Cross-language sentiment analysis of multilingual tweets using neural networks and sentence embeddings.
result Sentiment analysis reveals that lockdown announcements correlate with a deterioration of mood, which recovers quickly.
The abstract discusses open data resources for studying and controlling the spread of COVID-19.
problem Understanding and controlling the spread of COVID-19.
method Identification and description of open data resources and data-driven methodologies.
result Identification of variables and open data resources for analyzing COVID-19.
LSTM predicts COVID-19 growth patterns from global data.
problem Forecasting the spread of COVID-19 using big data and machine learning.
method Used multivariate long short-term memory (LSTM) to learn correlations over time.
result LSTM outperformed RNN in predicting COVID-19 growth with lower validation error.
LSTMs outperform DFM in nowcasting COVID-19 economic variables.
problem Timely estimation of macroeconomic variables during the pandemic.
method Comparison of LSTM and DFM performance on three variables (export values, volumes, and services exports).
result LSTMs outperformed DFM in two-thirds of variable/quarter combinations.
New algorithm tracks COVID-19 outbreak phases.
problem Decision-making in pandemic data.
method Developed a new algorithm (BLLR) based on decision theory.
result Demonstrated ability to track different phases of the COVID-19 outbreak.
CPAS uses machine learning to plan hospital resources for COVID-19.
problem Forecasting hospital resource demands during the COVID-19 pandemic.
method Combining machine learning algorithms with diverse data sources.
result CPAS successfully managed hospital resource planning in the UK.
Hidden Markov Model helps track asymptomatic carriers in pandemic.
problem Tracking spread of asymptomatic carriers (super-spreaders) during pandemic.
method Applied Hidden Markov Model to analyze COVID-19 data.
result Better assessment of spread extent for calibrated interventions.
Deep learning models predict COVID-19 spread.
problem Predicting the spread of COVID-19 to mitigate its impact.
method Proposed DSPM and NRM models trained on 19.53M cases.
result Superior prediction performance of proposed models.
Study shows insurance industry in North Macedonia declined 10% due to COVID-19.
problem Impact of COVID-19 on insurance industry activity.
method Seasonal autoregressive models and data analysis for 11 insurance classes.
result Insurance activity in North Macedonia decreased by more than 10% during the pandemic.
Machine learning improves economic forecasting during the pandemic.
problem Forecasting economic downturns during the COVID-19 pandemic.
method Use of machine learning methods to capture nonlinearity in macroeconomic data.
result Some nonlinear ML methods can extrapolate and improve forecasting accuracy.
Study examines cryptocurrency behavior during and after the pandemic.
problem Impact of the pandemic on cryptocurrency long-term memory and volatility.
method Used wavelet-based Hurst exponent analysis on eleven important coins.
result Long-term memory of returns mildly affected during pandemic, but volatility suffered temporary impact.
Study examines how COVID-19 intensified demand variability in U.S. supply chains.
problem The amplification of demand variability (Bullwhip Effect) in supply chains during the pandemic.
method Extensive industry-level data analysis using traditional and advanced empirical techniques.
result COVID-19 significantly amplified the Bullwhip Effect across different U.S. industries.
Study analyzes crude oil futures markets using visibility graphs to understand their structure and dynamics.
problem Understanding the structure and dynamics of crude oil futures markets during global challenges.
method Visibility graph analysis of daily and high-frequency data.
result Crude oil futures markets exhibit small-world properties and assortative mixing, with unique sensitivities to global disruptions.
Graph neural networks predict future COVID-19 cases based on human mobility.
problem Predicting future COVID-19 cases using human mobility data.
method Created a graph with regions as nodes and human mobility as edge weights. Used graph neural networks to capture diffusion patterns and transfer learning for limited data.
result Graph neural networks outperform traditional methods in predicting future cases.
Bayesian model detects sudden changes in stock market correlations during pandemic.
problem Capturing sudden structural changes in financial dependence during global events.
method Develops a Bayesian multivariate stochastic volatility model based on time-varying graphs.
result Captures abrupt changes in dependence structure across US stock portfolios.