What do companies’ 10-Q filings reveal about the state of the macro economy and do specific accounting variables contain particularly relevant information? To address these questions, we analyze the lead-lag patterns of more than twenty accounting variables in relation to aggregate economic activity. We develop new daily corporate account business activity indices that aggregate firm-level accounting information while controlling for shifts in the composition of announcers and reducing firm-specific noise. Our new indices show that firm liquidity becomes significantly lower while corporate debt grows significantly faster several months prior to recessions, and thus can be used as leading indicators. Conversely, operations, earnings, and profitability measures tend to be significantly lower after recessions, suggesting they are mostly lagging, pro-cyclical indicators of economic activity.
This paper presents a comparative analysis evaluating the accuracy of Large Language Models (LLMs) against traditional macro time series forecasting approaches. In recent times, LLMs have surged in popularity for forecasting due to their ability to capture intricate patterns in data and quickly adapt across very different domains. However, their effectiveness in forecasting macroeconomic time series data compared to conventional methods remains an area of interest. To address this, we conduct a rigorous evaluation of LLMs against traditional macro forecasting methods, using as common ground the FRED-MD database. Our findings provide valuable insights into the strengths and limitations of LLMs in forecasting macroeconomic time series, shedding light on their applicability in real-world scenarios.
Conditional forecasts, i.e. projections of a set of variables of interest on the future paths of some other variables, are used routinely by empirical macroeconomists in a number of applied settings. In spite of this, the existing algorithms used to generate conditional forecasts tend to be very computationally intensive, especially when working with large Vector Autoregressions or when multiple linear equality and inequality constraints are imposed at once. We introduce a novel precision-based sampler that is fast, scales well, and yields conditional forecasts from linear equality and inequality constraints. We show in a simulation study that the proposed method produces forecasts that are identical to those from the existing algorithms but in a fraction of the time. We then illustrate the performance of our method in a large Bayesian Vector Autoregression where we simultaneously impose a mix of linear equality and inequality constraints on the future trajectories of key US macroeconomic indicators over the 2020–2022 period.
We estimate the term structure of cash flow risk and its price of risk for the most prominent equity anomalies, at different frequencies, by directly modeling the dividend growth series instead of relying on a VAR-residual approach. We find the term structure of cash flow risk to be upward sloping for most anomaly portfolios. Moreover, the price of cash flow risk appears to be anomaly-specific – different anomalies tend to display heterogeneous sensitivity to cash flow news – and frequency-dependent – for a given anomaly, this sensitivity varies with the horizon at which portfolios are evaluated.
We use high-frequency data on firms’ dividend and buyback suspensions to estimate the effect on firm value from preserving cash during periods of financial market distress such as the Global Financial Crisis and the Covid-19 pandemic. Our results suggest that saving one percent in cash by suspending dividends is associated with a 2.5 percent increase in firm value. New dynamic tests based on the sequencing of firms’ financing decisions suggest that firm behavior was more in line with the Myers and Majluf (1984) pecking order theory during the pandemic than during the Global Financial Crisis.
Pettenuzzo, D., Timmermann,
A., Sabbatucci,
R. (2023), Payout suspensions during the Covid-19 pandemic,
Economics Letters, 224:111024
[Published
version]
Pettenuzzo, D., Timmermann,
A., Sabbatucci,
R. (2023), Dividend Suspensions and Cash Flows During the Covid-19
Pandemic: A Dynamic Econometric Model, Journal of
Econometrics, 235:1522–1541
[Published
version] [Working
paper]
Pettenuzzo, D., Timmermann,
A., Yong, S.
(2022), Corrigendum to “Predictability of stock returns and asset
allocation under structural breaks” [J. Econometrics 164 (2011) 60–78],
Journal of Econometrics, 227: 513-517
[Published
version]
Korobilis,
D., Pettenuzzo, D. (2020) Machine Learning Econometrics: Bayesian
Algorithms and Methods, Oxford Research Encyclopedia: Economics
and Finance
[Published
version] [Working
paper]
Pettenuzzo, D., Timmermann,
A., Sabbatucci,
R. (2020) Cash Flow News and Stock Price Dynamics, Journal
of Finance, 75: 2221-2270
[Published version]
[Working paper] [Online
Appendix]
Carvalho,
C., Fisher,
J., Pettenuzzo, D. (2020) Optimal Asset Allocation with Multivariate
Bayesian Dynamic Linear Models, Annals of Applied
Statistics, 14: 299-338
[Published
version] [Working
paper]
Pan,
Z., Pettenuzzo, D., Wang,
Y. (2020) Forecasting Stock Returns: A Predictor-constrained
Approach Journal of Empirical Finance, 55:
200-217
[Published
version] [Working
paper]
Korobilis,
D., Pettenuzzo, D. (2019) Adaptive Hierarchical Priors for
High-Dimensional Vector Autoregressions, Journal of
Econometrics, 212: 241-271
[Published
version] [Working
paper]
Koop, G.,
Korobilis,
D. , Pettenuzzo, D. (2019) Bayesian Compressed Vector
Autoregressions, Journal of Econometrics, 210:
135-154
[Published
version] [Working
paper] [Online
Appendix]
Gargano,
A., Pettenuzzo, D., Timmermann,
A. (2019) Bond Return Predictability: Economic Value and Links to
the Macroeconomy, Management Science, 65: 508-540
[Published
version] [Working
paper] [Online
Appendix]
Metaxoglou,
K., Pettenuzzo, D., Smith,
A. (2019) Option-Implied Equity Premium Predictions via Entropic
Tilting, Journal of Financial Econometrics, 17:
559-586
[Published
version] [Working
paper] [Online
Appendix]
Pettenuzzo, D., Timmermann,
A. (2017) Forecasting Macroeconomic Variables Under Model
Instability, Journal of Business and Economic
Statistics, 35: 183-201
[Published
version] [Working
paper] [Online
Appendix]
Pettenuzzo, D., Timmermann,
A., Valkanov, R.
(2016) A MIDAS Approach to Modeling First and Second Moment Dynamics,
Journal of Econometrics, 193: 315-334
[Published
version] [Working
paper]
Pettenuzzo, D., Ravazzolo, F. (2016) Optimal
Potfolio Choice under Decision-Based Model Combinations, Journal
of Applied Econometrics, 31: 1312-1332
[Published
version] [Working
paper] [Online
Appendix]
Pettenuzzo, D., Timmermann,
A., Valkanov, R.
(2014) Forecasting Stock Returns under Economic Constraints,
Journal of Financial Economics, 114: 517-553
[Published
version] [Working
paper]
Pettenuzzo, D., White,
H. (2014) Granger Causality, Exogeneity, Cointegration, and Economic
Policy Analysis, Journal of Econometrics, 178:
316-330
[Published
version] [Working
paper]
Pettenuzzo, D., Timmermann,
A. (2011) Predictability of Stock Returns and Asset Allocation under
Structural Breaks, Journal of Econometrics, 164:
60-78
[Published
version] [Working
paper] [Matlab
codes]
Pesaran,
H., Pettenuzzo, D., Timmermann,
A. (2007) Learning, Structural Instability, and Present Value
Calculations, Econometric Reviews, 26: 253-288
[Published
version] [Working
paper]
Pesaran,
H., Pettenuzzo, D., Timmermann,
A. (2006) Forecasting Time Series subject to Structural Breaks,
Review of Economic Studies, 73: 1057-1084
[Published
version] [Working
paper] [Matlab
codes]