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.
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.
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.
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.
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.
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.
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.
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 …
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.
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.
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.
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.
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…
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.
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.
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.
Among other macroeconomic indicators, the monthly release of U.S. unemployment rate figures in the Employment Situation report by the U.S. Bureau of Labour Statistics gets a lot of media attention and strongly affects the stock markets. I investigate whether a profitable investment strategy can be constructed by predic…
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.
Improved volatility forecasts for U.S. stocks using social media and news data.
problem Challenges in forecasting equity market volatility due to infrequency and variability of macroeconomic announcements.
method Estimating public attention and sentiment towards scheduled macroeconomic variables using various data sources and machine learning.
result Significant improvement in volatility forecasts for U.S. stocks, up to 14.99% on average.
Paper improves asset allocation using machine learning for regime detection.
problem Improving asset allocation strategies in uncertain economic conditions.
method Machine learning for regime detection, modified k-means algorithm, portfolio optimization.
result Significant portfolio performance improvements over traditional benchmarks.
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…
Model predicts operational risk using HMMs with economic covariates.
problem Predicting operational risk losses with time-dependent structures and economic covariates.
method Hidden Markov Models extended to multivariate observations with an auxiliary economic variable.
result Calibration results show relevance of including economic covariates.
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.
A model simulates how different types of traders react to macroeconomic news.
problem Understanding how various market participants respond to macroeconomic surprises.
method Developed a calibrated data generation process (DGP) with four trader archetypes and a Monte Carlo simulation.
result Higher information and lower risk-averse traders take larger positions and achieve higher average wealth.
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 …
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.
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.
The study finds that low frequency macroeconomic variables are more important for short-term electricity price forecasting.
problem Improving short-term forecasting of daily electricity prices using macroeconomic variables.
method Developed a Bayesian reverse unrestricted MIDAS model to account for frequency mismatch.
result Inclusion of macroeconomic low frequency variables improves short-term forecasts more than using only surveys or industrial production data.
Study shows diverse data sources improve cryptocurrency forecasting models.
problem Improving cryptocurrency market forecasting accuracy.
method Integrating various data types, including on-chain metrics, traditional indices, and macroeconomic indicators.
result Data source diversity significantly enhances forecasting model performance.
We extend the exploration regarding dynamical approach of macroeconomic variables by tackling systematically expenditure using Statistical Physics models (for the first time to the best of our knowledge). Also, using polynomial distribution which characterizes the behavior of dynamical systems in certain situations, we…
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.
KLD token adjusts supply based on macroeconomic debt index, creating deflationary effect.
problem Managing deflationary pressures in digital assets.
method Debt-indexed supply adjustments linked to macroeconomic data.
result Deflationary mechanism strengthens as debt rises.
Transformer model with mixed-frequency data improves stock volatility prediction.
problem Improving stock volatility prediction using mixed-frequency data.
method Transformer model trained on mixed-frequency data (GARCH-MIDAS model for frequency alignment).
result Transformer model reduces mean square error from 1.00 to 0.86.
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.
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.
Develops a statistical model for SOFR term structure in incomplete markets.
problem Incomplete liquidity and completeness in SOFR derivatives market.
method Statistical model incorporating macroeconomic factors and jumps in SOFR rates.
result Model is well-suited for risk management and derivatives pricing.
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…
Tabular Q-learning outperforms advanced RL methods in monetary policy.
problem Dynamic setting of short-term interest rates to stabilize inflation and unemployment under uncertain macroeconomic conditions.
method Discrete-action Markov Decision Process with tabular Q-learning, SARSA, Actor-Critic, Deep Q-Networks, Bayesian Q-learning, POMDP formulations.
result Standard tabular Q-learning achieved the best performance (-615.13 +- 309.58 mean return) compared to advanced RL methods and traditional policy rules.
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…
Study predicts bond yields using machine learning and ultimate forward rates.
problem Forecasting bond yields using ultimate forward rates.
method Applied de Kort-Vellekooptype methodology for UFR estimation, used linear and nonlinear machine learning techniques.
result Nonlinear machine learning models outperform linear models in bond yield forecasting.
Clusters asset classes to identify lead-lag relationships in market regimes.
problem Understanding lead-lag relationships between different asset classes.
method Defining macroeconomic regimes by clustering indices and investigating lead-lag relationships.
result Unravels market features and highlights informative market trends or risks.
This paper uses NARX neural networks for macroeconomic forecasting and goal setting.
problem Improving accuracy in macroeconomic forecasting and goal setting.
method Literature review and construction of specific NARX neural networks for macroeconomic indicators.
result NARX neural networks can be trained to make accurate predictions for macroeconomic indicators and national goals.
New framework detects time-varying economic persistence.
problem Time-varying persistence in economic shocks.
method Localized regression techniques to identify evolving heterogeneity.
result Substantial persistence variations align with macroeconomic events.
LGB+ improves macroeconomic forecasting by combining linear and tree models.
problem Efficiency in small samples for forecasting with mixed linear and nonlinear dynamics.
method LGB+ is a boosting procedure that evaluates both tree and linear candidates at each step, advancing only the winner. It decomposes forecasts into linear and nonlinear contributions.
result LGB+ delivers strong gains for targets with pronounced autoregressive dynamics or mixed signals.
We study association between macroeconomic news and stock market returns using the statistical theory of copulas, and a new comprehensive measure of news based on the indexing of news wires. We find the impact of economic news on equity returns to be nonlinear and asymmetric. In particular, controlling for economic con…
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…