News attention to financial intermediaries and crises predicts excess bond premium and macroeconomic movements.
problem Drivers of the excess bond premium (EBP).
method News attention to 180 topics captures up to 80% of EBP variation and forecasts macroeconomic movements.
result News attention to financial intermediaries and crises drives up the EBP and predicts macroeconomic downturns.
Model predicts stock prices using GAN and RoI Pooling.
problem Predicting stock prices influenced by macroeconomic factors.
method Markov Decision Process, GAN, RoI Pooling.
result Identifies macroeconomic factors' influence on stock prices.
Cryptocurrency forecasting model considers macro, sentiment, and technical indicators.
problem High price volatility in cryptocurrency markets.
method Dual-prediction mechanism incorporating macroeconomic fluctuations, technical indicators, and individual cryptocurrency price changes.
result The proposed model outperforms ten comparison methods in short-term cryptocurrency forecasting.
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…
Throughout economic history, the global economy has experienced recurring crises. The persistent recurrence of such economic crises calls for an understanding of their generic features rather than treating them as singular events. The global economic system is a highly complex system and can best be viewed in terms of …
The paper explores how macroeconomic variables' correlation structure changes over time and under different scenarios.
problem Understanding the changing correlation structure of macroeconomic variables.
method The paper uses a principal component based algorithm to perform unsupervised clustering on macroeconomic variables.
result The correlation structure of macroeconomic variables changes significantly during financial crises and under hypothetical scenarios.
This paper deals with the stability properties of a closed market, where capital and labour force are acting like a predator-prey system in population-dynamics. The spatial movement of the capital and labour force are taken into account by cross-diffusion effect. First, we are showing two possible ways for modeling thi…
The prediction of a stock market direction may serve as an early recommendation system for short-term investors and as an early financial distress warning system for long-term shareholders. Many stock prediction studies focus on using macroeconomic indicators, such as CPI and GDP, to train the prediction model. However…
The paper sets limits on the accuracy of macroeconomic forecasts based on statistical moments and trade volumes.
problem Uncertainty in predicting macroeconomic variables like prices and returns.
method Defines theoretical lower bounds of uncertainty and upper limits on forecast accuracy based on statistical moments and trade volumes.
result Accuracy of forecasts of probabilities of macroeconomic variables doesn't exceed Gaussian approximations.
Intense volatility in financial markets affect humans worldwide. Therefore, relatively accurate prediction of volatility is critical. We suggest that massive data sources resulting from human interaction with the Internet may offer a new perspective on the behavior of market participants in periods of large market move…
Since the beginning of the new millennium, stock markets went through every state from long-time troughs, trade suspensions to all-time highs. The literature on asset pricing hence assumes random processes to be underlying the movement of stock returns. Observed procyclicality and time-varying correlation of stock retu…
Complexity science offers new insights into macroeconomics and finance.
problem Insufficient understanding of economic and financial phenomena.
method Adopting complexity science to better understand complex systems.
result Complex system characteristics can benefit financial analysts, regulators, and policymakers.
The paper uses machine learning to forecast macroeconomic outcomes with high-dimensional data.
problem Forecasting the full conditional distribution of macroeconomic outcomes.
method Systematically integrating three key principles: high-dimensional data with regularization, rigorous out-of-sample validation, and incorporating nonlinearities.
result Regularization via shrinkage is essential to control model complexity, while nonlinearities yield limited improvements in predictive accuracy.
The study examines how market trade randomness influences price and return volatility.
problem The accuracy of predicting market-based volatilities and macroeconomic variables is limited.
method Analyzes time series of trade values and volumes, and develops econometric methodologies for predicting volatilities.
result Current macroeconomic models underestimate the accuracy of predicting market-based volatilities and macroeconomic variables.
We study how the phenomenon of contagion can take place in the network of the world's stock exchanges due to the behavioral trait "blindeness to small changes". On large scale individual, the delay in the collective response may significantly change the dynamics of the overall system. We explicitely insert a term descr…
The paper identifies key macroeconomic events affecting exchange rate volatility.
problem Understanding which macroeconomic events impact exchange rate volatility.
method Data-driven approach to select relevant macroeconomic events using sparsity-based methods.
result The identified macroeconomic events significantly impact exchange rate volatility.
Educational game on crypto investment helps students grasp macroeconomics.
problem Weak connections between microeconomic decision-making and macroeconomic concepts in classroom games.
method Design and study of an educational game on cryptocurrency investment.
result Engages students in understanding macroeconomics through incentivized individual investment decisions.
Study finds macroeconomic indicators predict health workforce and infrastructure measures.
problem Evaluating the predictive value of macroeconomic indicators for public health targets.
method Examined multiple forecasting approaches including neural networks, generalized additive models, random forests, and time series models with exogenous indicators.
result Macroeconomic indicators provide consistent and reproducible predictive signals for health workforce and infrastructure measures, but less so for other targets.
Machine Learning improves macroeconomic forecasting by capturing nonlinearities.
problem Improving macroeconomic forecasting accuracy.
method Study four features (nonlinearities, regularization, cross-validation, loss function) in data-rich and data-poor environments.
result Nonlinearity is the key to improving forecasting accuracy.
The cluster analysis methods are used in order to perform a comparative study of 15 EU countries in relation with the fluctuations of some basic macroeconomic indicators. The statistical distances between countries are calculated for various moving time windows, and the time variation of the mean statistical distance i…
New method identifies uncertainty shocks in financial markets using revised VIX.
problem Traditional VIX fails to capture non-Gaussian, heavy-tailed asset returns.
method Fit a double-subordinated Normal Inverse Gaussian Levy process to S&P 500 option prices to construct a revised VIX.
result Revised VIX provides a more comprehensive measure of volatility reflecting extreme movements and heavy tails.
A neural network model tackles high-dimensional data with latent structures.
problem Modeling high-dimensional data with latent low-dimensional structures.
method Integrates PCA and Soft PCA layers into neural network architecture for factor modeling and non-linear transformations.
result Demonstrates improved performance in forecasting and nowcasting with real-world data.
Bayesian neural networks improve macroeconomic forecasting and model nonlinearities.
problem Handling small T, big K macroeconomic datasets with temporal dependence.
method Developed Bayesian neural networks with mixture activation functions, shrinkage priors, and stochastic volatility.
result BNNs produce precise density forecasts, often better than other methods.
Bank transactions help predict macroeconomic indexes faster and more accurately.
problem Lag in macroeconomic index availability and autoregressive models' limitations in complex scenarios.
method Use financial transactions data to estimate macroeconomic indexes using neural networks and smart sampling.
result Neural network approach outperforms baseline methods on hand-crafted features based on transactions.
Ensemble of MFESNs improves macroeconomic forecasting.
problem Improving accuracy in macroeconomic forecasting.
method Hedge and Follow-the-Leader schemes applied to MFESNs.
result Ensemble models outperform individual models in forecasting.
This study improves stock price prediction by incorporating anticipated macroeconomic policy changes.
problem Improving accuracy in stock price prediction.
method Incorporates future expected macroeconomic policy changes and historical stock prices.
result Our method outperforms conventional approaches with an RMSE of 1.61 compared to 1.75.
The study analyzes macroeconomic factors affecting copper futures volatility and long-term correlation with S&P 500.
problem Understanding the impact of macroeconomic variables on copper futures volatility and long-term correlation.
method Employed GARCH-MIDAS and DCC-MIDAS modeling frameworks to examine the influence of low-frequency macroeconomic variables on copper futures returns and long-term correlation with S&P 500.
result PPI is the most efficient macroeconomic variable impacting copper futures returns, and MIDAS filter improves model fitness and long-run relationship.
We show how random matrix theory can be applied to develop new algorithms to extract dynamic factors from macroeconomic time series. In particular, we consider a limit where the number of random variables N and the number of consecutive time measurements T are large but the ratio N / T is fixed. In this regime the unde…
HANET combines LSTM and attention mechanisms for better financial forecasting.
problem Lack of distinct macroeconomic regimes in financial datasets.
method Hierarchical Cross-Attention mechanism integrating long-run macro contexts with high-frequency market dynamics.
result HANET outperforms neural forecasters, especially during turbulent periods.
Study improves retail demand forecasting by integrating macroeconomic data.
problem Lack of accurate demand forecasting due to incomplete data.
method Enriched time series data with macroeconomic variables; compared regression and machine learning models.
result Improved accuracy in predicting retail demand through comprehensive data integration.
The major perspective of this paper is to provide more evidence into the empirical determinants of capital structure adjustment in different macroeconomics states by focusing and discussing the relative importance of firm-specific and macroeconomic characteristics from an alternative scope in U.S. This study extends th…
Firms delay write-downs for adverse macroeconomic and industry outcomes but not for firm-specific issues.
problem Timeliness of write-downs for adverse macroeconomic and industry outcomes versus firm-specific issues.
method Comparative analysis of write-downs driven by macroeconomic and industry outcomes versus firm-specific outcomes.
result Firms delay write-downs for adverse macroeconomic and industry outcomes but not for firm-specific issues.
In this paper we sketch some reflections on the pitfalls and inconsistencies of the research program - currently dominant among the profession - aimed at providing microfoundations to macroeconomics along a Walrasian perspective. We argue that such a methodological approach constitutes an unsatisfactory answer to a wel…
Payments data and machine learning improve nowcasting accuracy for macroeconomic indicators.
problem Lagged indicators in linear models are insufficient during crisis periods.
method Non-traditional payments data, nonlinear machine learning, and tailored cross-validation.
result Improved macroeconomic nowcasting accuracy up to 40% during crises.
We demonstrate using multi-layered networks, the existence of an empirical linkage between the dynamics of the financial network constructed from the market indices and the macroeconomic networks constructed from macroeconomic variables such as trade, foreign direct investments, etc. for several countries across the gl…
Expert system predicts credit card charge-offs using macroeconomic indicators.
problem Managing charge-off rates in the credit card industry.
method Developed an expert system using machine learning and macroeconomic indicators.
result Achieved mean squared error values of 1.15E-03 and 1.04E-03.
Researchers have constantly asked whether stock returns can be predicted by some macroeconomic data. However, it is known that macroeconomic data may exhibit nonstationarity and/or heavy tails, which complicates existing testing procedures for predictability. In this paper we propose novel empirical likelihood methods …
Masanao Aoki developed a new methodology for a basic problem of economics: deducing rigorously the macroeconomic dynamics as emerging from the interactions of many individual agents. This includes deduction of the fractal / intermittent fluctuations of macroeconomic quantities from the granularity of the mezo-economic …
Bayesian model uses simple functions to forecast macroeconomic data.
problem Forecasting large datasets in macroeconomics with complex nonlinear relationships.
method Sum of simple two-component location mixtures, logistic function threshold, conjugate priors.
result Accurate point and density forecasts in US macroeconomic aggregates.
Most of the analytical techniques used in the business cycle synchronisation literature rely upon the estimation of an empirical correlation matrix of time series data of macroeconomic aggregates, real GDP usually being the key variable. But the small number of available observations and small number of economies mean …
Interactive tools help teach economics online.
problem Teaching macroeconomic models online.
method Browser-based simulation toolkits.
result Intuitive approach to comparative statics.
A large class of trading strategies focus on opportunities offered by the yield curve. In particular, a set of yield curve trading strategies are based on the view that the yield curve mean-reverts. Based on these strategies' positive performance, a multiple pairs trading strategy on major currency pairs was implemente…
This study shows how monetary uncertainty affects stock market reactions to macroeconomic news.
problem Understanding stock market reactions to macroeconomic news under varying levels of monetary uncertainty.
method Decomposes stock market response into cash flow and risk-free rate channels, analyzing time-varying effects.
result High monetary uncertainty weakens the positive stock market response to macroeconomic news.
Transformations of macroeconomic data affect machine learning forecasts, especially with regularization and nonlinearity.
problem The impact of data transformations on machine learning forecasts in macroeconomic contexts.
method Review and propose new data transformations, empirically evaluate their effects, and compare traditional and moving average rotations.
result Traditional factors should almost always be included as predictors, and moving average rotations can provide important gains.
SMC analysis reveals key transient effects in macroeconomic ABM.
problem Analysis of complex ABMs is challenging and often relies on ad hoc methods.
method Statistical model checking (SMC) implemented through MultiVeStA.
result Clear contrast across parameter families in macro-financial and structural sweeps.
Deep learning improves macroeconomic forecasting and risk assessment.
problem Improving accuracy in macroeconomic forecasting and sovereign risk assessment.
method Nowcasting and forecasting using deep learning techniques.
result Deep learning methods outperform traditional econometric techniques in out-of-sample performance.
LSTM models improve macroeconomic forecasting with mixed frequency data.
problem Improving accuracy of macroeconomic forecasts using mixed frequency data.
method Adapted LSTM model to mixed frequency data, using U-MIDAS scheme.
result Proposed LSTM models outperform conventional MIDAS models in out-of-sample predictive performance.
Business cycles affect startup valuations, both directly and indirectly.
problem How do business cycles impact startup valuations?
method Structural Equation Model approach using a dataset of 1,089 venture capital investments.
result Business cycles impact startup valuations both directly and indirectly.