Study predicts US stock market will continue to fall post-COVID-19.
problem Analyzing the recovery trend of the US stock market post-COVID-19.
method Used Deep Learning, Neuro Network, and Time-series analysis on S&P 500, Nasdaq 100, and Dow Jones Industrial Average data.
result LSTM model predicts US stock market will continue to fall post-COVID-19.
Analyzes Indian chemical industry post-Covid.
problem Global uncertainty impacts chemical industry performance.
method Fundamental analysis of key players and trends.
result Various geopolitical and macroeconomic trends shape industry performance.
DeCom predicts post-COVID RSV timing and intensity with NPI consideration.
problem Predicting RSV timing and intensity post-COVID with NPI impact.
method Deep coupled tensor factorization machine (DeCom) leveraging tensor factorization and residual modeling.
result DeCom achieves up to 46% lower RMSE and 49% lower MAE compared to baselines.
Study shows house buyers in Christchurch value earthquake risk differently based on time since 2011 quake.
problem Understanding how house buyers' perception of earthquake risk changes over time.
method Used a hedonic price model to analyze house prices in Christchurch over three periods.
result Buyers value earthquake risk differently based on the time since the 2011 Christchurch earthquake.
Robinhood users react strongly to overnight price changes and big losers, trading quickly after extreme losses.
problem Understanding trading behavior of Robinhood users, especially in high-frequency trading scenarios.
method Analyzed intraday and overnight price changes, focusing on big losers and gainers.
result Robinhood users react more to overnight price changes and big losers, trading quickly after extreme losses.
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.
Research predicts rice prices in Banda Aceh post-COVID using ARIMA models.
problem Forecasting rice prices in Banda Aceh post-COVID-19.
method Used LOCF imputation for missing data and auto-ARIMA for forecasting.
result ARIMA model (0,0,5) best for all rice qualities, showing price decline and then stability.
A winning method for day-ahead electricity demand forecasting during and after the COVID-19 pandemic.
problem Day-ahead electricity demand forecasting during and after the COVID-19 pandemic.
method Online forecast combination of multiple point prediction models with a holiday adjustment procedure and smoothed Bernstein Online Aggregation (BOA).
result Excellent forecasting performance, particularly due to the holiday adjustment procedure and fully adaptive smoothed BOA approach.
State-space models win a forecasting competition for unstable data.
problem Forecasting electricity demand during the post-covid period.
method Adapting state-space models to balance time-series adaptability and machine learning complexity.
result State-space models provide a better compromise between adaptability and accuracy for non-stationary data.
Study reviews Bachelier model for negative oil prices post-COVID.
problem Negative oil futures prices due to COVID-19.
method Literature review and model comparison.
result Connects Black-Scholes and Bachelier models for option pricing.
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.
Framework quantifies financial NLP robustness under regime shifts.
problem Semantic and causal drift in financial news narratives.
method Four metrics: FCAS, PCS, TSV, NLICS.
result Transformer models are more affected by semantic drift.
AI monitors social distancing and masks at manufacturing plants.
problem Ensuring safety of workers during post-COVID production.
method Computer vision and AI techniques for social distancing and mask detection.
result Real-time alerts prevent violations of social distancing and mask-wearing.
New core inflation measure predicts future headline inflation.
problem Creating a better measure of core inflation for timely policy decisions.
method Assemblage Regression, a nonnegative ridge regression that optimizes subcomponent weights.
result Significant improvements in forecasting medium-term inflation developments.
Study on cryptocurrency trading patterns using multifractal analysis.
problem Lack of systematic study on temporal structure of cryptocurrency trading.
method Multifractal detrended cross-correlation analysis of price returns, trades, and volume.
result All analyzed quantities exhibit multifractal structure, both univariate and bivariate.
Novel OTT method for cryptocurrency trading offers high annualized profit.
problem Quantifying and exploiting trading opportunities in cryptocurrency markets.
method Bi-objective convex optimization for balancing profit and risk.
result Annualized profit of 15.49% in cryptocurrency market from 2020 to 2022.
Study explores financial market linkages between Japan and US markets.
problem Inconsistency in empirical studies regarding financial market causal linkages.
method Causal discovery methods including VAR-LiNGAM and LPCMCI with domain knowledge.
result VAR-LiNGAM reveals causal influences among financial markets, while LPCMCI identifies potential latent confounders.
Paper proposes SERT model for US stock pricing, outperforming standard models during market shocks.
problem Capturing patterns of temporal sparsity in asset pricing during market fluctuations.
method Introduces SERT model based on pre-trained Transformer, compares with standard models in three periods.
result SERT model achieves highest out-of-sample R2 (11.94\% and 11.47\%) during extreme market fluctuations. This paper analyzes ETFs with Taiwan exposure, finding heavy tails and asymmetric volatility.
problem Heavy tails and asymmetric volatility in Taiwan-related ETFs.
method Tail-risk diagnostics, asymmetric volatility modeling, and portfolio optimization under mean--variance and CVaR criteria.
result CVaR optimization produces more concentrated allocations, favoring SMH during the post-COVID AI-driven expansion.
Forecasting stock market decline and recovery post-COVID-19.
problem Analyzing exogenous risk's impact on stock markets.
method Two case studies using historical data and stochastic fluctuations.
result 85% accuracy in predicting S&P500 index decline and recovery.
Study shows houses appreciated more during pandemic due to speculation, not just price uncertainty.
problem Impact of COVID-19 on house prices and speculation.
method Quasi-experimental design, unit-level matching, multivariate difference-in-difference regression.
result Properties listed for sale appreciated an additional 1% per month after pandemic onset, with an excess annual growth of 12.7 percentage points.
Hybrid model combines PCA and RNN for better aerospace stock price prediction.
problem Challenges in predicting stock prices of aerospace companies due to market uncertainty and complexity.
method Combination of Principal Component Analysis (PCA) and Recurrent Neural Networks (RNN).
result PCA improves both accuracy and efficiency of stock price prediction.
Study examines asset pricing using various attention models, finding global self-attention and sliding window sparse attention models perform well.
problem Traditional asset pricing models miss temporal dependency and short memory issues.
method Investigates RNN attention models with various attention mechanisms for large-cap US stocks.
result Global self-attention and sliding window sparse attention models outperform in deriving returns and hedging risks, especially during the pandemic.
Study analyzes impacts of COVID-19 on French forestry sector, finds mixed results in supply chain.
problem Impact of COVID-19 on forestry sector supply chain and future opportunities.
method Integrated methodology combining Material Flow Analysis and Wood Product Model.
result Significant disruptions and shifts in wood production, highlighting resilience and vulnerabilities.