Venice used 'helicopter money' to subsidize during famine and plague, but it caused instability.
problem Subsidizing inhabitants during containment policies while preventing long-term debt increase.
method Net-worth helicopter money strategy, equivalent to monetary expansion generating losses to the issuer.
result The strategy caused much monetary instability and had to be quickly reversed.
Forecasting US stock market indices during COVID-19 using machine learning models.
problem Predicting stock market behavior during the pandemic.
method Used Random Forest and LSTM models on historical stock prices.
result Improved accuracy in forecasting stock market returns.
The paper finds that bear markets cause recessions and bull markets cause expansions, with bull markets having a stronger causal effect.
problem Understanding the asymmetric causal relationships between market conditions and economic cycles.
method Asymmetric causality tests using partial sums of positive and negative market components, with bootstrap simulations and leverage adjustments.
result Bear markets cause recessions and bull markets cause expansions, with bull markets having a stronger causal effect.
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.
I examine global recessions as a cascade phenomenon. In other words, how recessions arising in one or more countries might percolate across a network of connected economies. A heterogeneous agent based model is set up in which the agents are Western economies. A country has a probability of entering a recession in any …
ML models predict stock prices poorly during recessions.
problem Predicting stock prices during economic downturns.
method Examined S&P 500 index, analyzed performance during recessions and expansions.
result ML models perform better during expansions, not due to ML methods but effective monetary policies.
Machine learning models predict US economic recessions using Treasury term spreads.
problem Predicting US economic recessions using Treasury term spreads.
method Gradient Boosting and Random Forest methods trained with SHapley Additive exPlanations (SHAP) framework.
result 3 month to 6 month Treasury term spread is the most relevant for predicting US economic recession.
We show that a simple and intuitive three-parameter equation fits remarkably well the evolution of the gross domestic product (GDP) in current and constant dollars of many countries during times of recession and recovery. We then argue that this equation is the response function of the economy to isolated shocks, hence…
Following findings by Ormerod and Mounfield, Wright rises the problem whether a power or an exponential law describes the distribution of occurrences of economic recession periods. In order to clarify the controversy a different set of GDP data is hereby examined. The conclusion about a power law distribution of recess…
Machine learning fails to improve recession prediction with yield spread.
problem Improving recession prediction using yield spread selection.
method Machine learning algorithm to identify best maturity pair and coefficients.
result Machine learning does not significantly improve prediction of recession.
Neural networks predict US recessions with SHAP method.
problem Forecasting US recessions using machine learning.
method Long short-term memory (LSTM) and gated recurrent unit (GRU) models compared to linear models. SHAP method applied for interpretation.
result Neural networks can capture business cycle asymmetries and nonlinearities.
Financial planners helped preserve and increase household net financial assets during the Great Recession.
problem Impact of financial planners on household net financial assets during the Great Recession.
method Utilized 2007-2009 Survey of Consumer Finances (SCF) panel dataset, analyzed 3,862 respondents.
result Starting to use a financial planner during the Great Recession had a positive impact on preserving and increasing household net financial assets.
A two-variable model is developed to forecast the probability of recession in the U.S. economy. Like many others, the model uses data a year or more old to explain movements of a dichotomous dependent variable for recession. The innovation of the present effort is the introduction of a confidence variable, which appear…
We introduce a novel application of Support Vector Machines (SVM), an important Machine Learning algorithm, to determine the beginning and end of recessions in real time. Nowcasting, "forecasting" a condition about the present time because the full information about it is not available until later, is key for recession…
Research shows franchised fast food companies' stock prices decline more during recessions.
problem Impact of recession on franchised fast food companies' stock prices.
method Analyzed stock price data with Weibull distribution.
result Recessions have a more severe impact on franchised fast food companies' stock prices.
Ormerod and Mounfield analysed GDP data of 17 leading capitalist economies from 1870 to 1994 and concluded that the frequency of the duration of recessions is consistent with a power-law. But in fact the data is consistent with an exponential (Boltzmann-Gibbs) law.
We examine how the structure of the world trade network has been shaped by globalization and recessions over the last 40 years. We show that by treating the world trade network as an evolving system, theory predicts the trade network is more sensitive to evolutionary shocks and recovers more slowly from them now than i…
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.
An original method, assuming potential and kinetic energy for prices and conservation of their sum is developed for forecasting exchanges. Connections with power law are shown. Semiempirical applications on S&P500, DJIA, and NASDAQ predict a coming recession in them. An emerging market, Istanbul Stock Exchange index IS…
New method interprets machine learning forecasts as historical analogies.
problem Interpreting machine learning predictions as a sum of predictor contributions.
method Expressing predictions as a linear combination of in-sample values with weights based on pairwise proximity scores.
result The approach provides sparser interpretations in settings with many regressors and little training data.
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 shows how business cycle affects dividend payout based on managerial stock incentives.
problem Impact of managerial stock incentives on dividend payout policy during business cycles.
method Using S&P 1500 companies data from 2000-2018, analyzing full sample and recession periods.
result Negative relationship between managerial stock options and dividend payouts, significant for medium-sized companies.
New method improves stock return prediction in non-stationary markets.
problem Tackles the challenge of predicting stock returns in non-stationary environments.
method Jointly optimizes model class and training window size using a tournament procedure.
result Consistently outperforms standard benchmarks by 14-23% in out-of-sample R2. The theorems we proved describe the structure of economic equilibrium in the exchange economy model. We have studied the structure of property vectors under given structure of demand vectors at which given price vector is equilibrium one. On this ground, we describe the general structure of the equilibrium state and gi…
Paper tackles pandemic resource allocation challenges.
problem Shortages of medical resources during pandemics.
method Risk management approach, focusing on spatio-temporal competitions.
result New strategies for optimal stockpiling and allocation balancing resource competition.
Post-pandemic, work patterns shifted with fewer days in offices and a new midweek mountain.
problem Shift in work patterns and integration of personal and professional life.
method Behavioral analysis using mobile geolocation records.
result Significant decline in office-based workdays and emergence of a new midweek mountain.
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 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.
Paper forecasts recession indicators using yield spread models.
problem Forecasting the leading indicator of a recession using yield spread.
method Applied econometric time series and machine learning models to forecast yield spread.
result Parsimonious univariate ARIMA model outperforms richly parameterized VAR method.
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.
Even at the beginning of 2008, the economic recession of 2008/09 was not being predicted. The failure to predict recessions is a persistent theme in economic forecasting. The Survey of Professional Forecasters (SPF) provides data on predictions made for the growth of total output, GDP, in the United States for one, two…
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.
Bayesian econometrics improves nowcasting during pandemics.
problem Improving nowcasting during extreme economic events like pandemics.
method Bayesian econometric methods using non-parametric mixed frequency VARs with additive regression trees.
result Significant improvements in nowcasting performance compared to linear models.
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.
A non-Bayesian time-varying model is developed by introducing the concept of the degree of market efficiency that varies over time. This model may be seen as a reflection of the idea that continuous technological progress alters the trading environment over time. With new methodologies and a new measure of the degree o…
Paper forecasts tax revenues in Bulgaria during pandemic.
problem Forecasting tax revenues during pandemic.
method Model based on IMF recommendations, using 1995-2019 data.
result Pandemic negatively impacts tax revenues, but econometrics can still produce forecasts.
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.
The American economy can be thought of as a highly connected random network in terms of both its technological and informational connections. The cumulative size of economic recessions, the fall in output from peak to trough, is analysed for the US economy 1900-2002. A least squares fit of an exponential relationship b…
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.
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.
Study examines how pandemic anxiety affects financial market trust.
problem Anxiety during pandemic and trust in financial markets.
method Used Google search volume and stock market data to create mood indicators.
result Different clusters of countries and markets in terms of pessimism and optimism emerged.
Are expansions and recessions more likely to end as their magnitude increases? In this paper we apply parametric hazard models to investigate this issue in a sample of 16 countries from 1881 to 2000. For the total sample we find evidence of positive magnitude dependence for recessions, while for expansions we are not a…
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.
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.
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 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.
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.