Quantum computation, a paradigm shift in computer science, has the potential to profoundly affect research in economics and finance. This special issue brings together a subset of papers that were presented at the conference. Quantum advantage has been demonstrated for payment systems and the first three papers cover such applications. The next set of papers cover asset pricing applications. The final one covers quantum data auction models.

This paper develops the asymptotic theory for high-dimensional panel data regressions in settings with cross-sectionally dependent errors driven by common shocks. We consider a factor-augmented sparse-group LASSO estimator that combines MIDAS aggregation with latent factors. The estimator can take advantage of the mixed-frequency group structure in the time-series dimension. Theory shows that it can outperform the standard LASSO estimator both for prediction and estimation while allowing for cross-sectional dependence.

In physics the famous von Neumann measurement model assumes that - apart from measurement noise - the data is aligned with the model. In economics and finance, models are prone to specification errors. Decision theory has offered insights into statistical inference given uncertainty in the data generating process. Quantum behavioral models have resolved longstanding paradoxes facing decision theory by allowing candidate decisions to behave like quantum mechanical states in superposition. We introduce quantum decision-theoretic foundations of ambiguity and model/parameter uncertainty to deal with model selection and specification errors. We provide an illustrative example involving asset pricing models.

Classical shadows are an efficient method for constructing an approximate classical description of a quantum state using very few measurements. In the paper we propose to enhance classical shadow methods using bootstrap resampling methods. We apply nonparametric bootstrapping to assess the variability and accuracy of estimators by repeatedly sampling with replacement from the observed data, i.e.\ in our case the classical shadow measurements. We show that the bootstrap distributions are very different from the Gaussian approximations. Likewise, the theoretical error bounds are not tight compared to the bootstrap percentiles. Finally, we suggest using resampling tools to make risk assessments.

Modern empirical analysis often relies on high-dimensional panel datasets with non-negligible cross-sectional and time-series correlations. Factor models are natural for capturing such dependencies. A tensor factor model describes the d-dimensional panel as a sum of a reduced rank component and an idiosyncratic noise, generalizing traditional factor models for two-dimensional panels. We consider a tensor factor model corresponding to the notion of a reduced multilinear rank of a tensor. We show that for a strong factor model, a simple tensor principal component analysis algorithm is optimal for estimating factors and loadings. When the factors are weak, the convergence rate of simple TPCA can be improved with alternating least-squares iterations. We also provide inferential results for factors and loadings and propose the first test to select the number of factors. The new tools are applied to the problem of imputing missing values in a multidimensional panel of firm characteristics.

Stochastic volatility models are the backbone of financial engineering. We study both continuous time diffusions as well as discrete time models. We propose two novel approaches to estimating stochastic volatility diffusions, one using Quantum-Inspired Classical Hidden Markov Models (HMM) and the other using Quantum Hidden Markov Models. In both cases we have approximate likelihood functions and filtering algorithms that are easy to compute. We show that the non-asymptotic bounds for the quantum HMM are tighter compared to those with classical model estimates.

We present a classical enhancement to improve the accuracy of the Hybrid variant (Hybrid HHL) of the quantum algorithm for solving linear systems of equations proposed by Harrow, Hassidim, and Lloyd (HHL). We achieve this by using higher precision quantum estimates of the eigenvalues relevant to the linear system, and a new classical step to guide the eigenvalue inversion part of Hybrid HHL. We show that eigenvalue estimates with just two extra bits of precision result in tighter error bounds for our Enhanced Hybrid HHL compared to HHL. Our enhancement reduces the error of Hybrid HHL by an average of 57 percent on an ideal quantum processor for a representative sample of 2x2 systems. On IBM Torino and IonQ Aria-1 hardware, we see that the error of Enhanced Hybrid HHL is on average 13 percent and 20 percent (respectively) less than that of HHL for the same set of systems.

Factor analysis is a widely used tool to summarize high dimensional panel data via a small dimensional set of latent factors. Many applications in finance and macroeconomics, are often focused on observable factors with an economic interpretation. The objective of this paper is to provide a test to answer a question which naturally comes up in discussions regarding latent versus observable factors: do latent and observable factors span the same space? We derive asymptotic properties of a formal test and propose a bootstrap version with improved small sample properties. We find empirical evidence for a small number of factors common between a small number of traditional Fama-French risk factors - or returns on a few stocks (i.e. "magnificent'' 5 or 7) - and large panels of US, North American and international portfolio returns.

Hint: these are not the Fama-French 3 factors and they are not even spanned by the Fama-French 5 factors. More importantly, they feature superior out-of-sample pricing performance compared to standard asset pricing models. What is "common" about these factors? We identify the factor space common between individual stocks and sorted portfolios - neither affected by time-varying betas nor by the sorting characteristics.

The paper studies the nowcasting of Euro area Gross Domestic Product (GDP) growth using mixed data sampling machine learning panel data regressions with both standard macro releases and daily news data. Using a panel of 19 Euro area countries, we investigate whether directly nowcasting the Euro area aggregate is better than weighted individual country nowcasts. Our results highlight the importance of the information from small- and medium-sized countries, particularly when including the COVID-19 pandemic period. The empirical analysis is supplemented by studying the so-called Big Four – France, Germany, Italy, and Spain – and the value added of news data when official statistics are lagging. From a theoretical perspective, we formally show that the aggregation of individual components forecasted with pooled panel data regressions is superior to direct aggregate forecasting due to lower estimation error.

Quantum computers are not yet capable of providing computational advantages for practical stochastic diffusion models commonly used by financial analysts. In this paper, we introduce a class of stochastic processes that are both realistic in terms of mimicking financial market risks and more amenable to potential quantum computational advantages. The models we study are based on a regime-switching volatility model driven by a Markov chain with observable states. We investigate algorithms to estimate credit risk and option pricing on a gate-based quantum computer. These models bring us closer to realistic market settings, thus bringing quantum computing closer to the realm of practical applications. We demonstrate the accuracy of the state preparation circuits outlined for each model using the IBM_torino and Honeywell H1 processors.

The paper uses structured machine learning regressions for nowcasting with panel data consisting of series sampled at different frequencies. Motivated by the problem of predicting corporate earnings for a large cross-section of firms with macroeconomic, financial, and news time series sampled at different frequencies, we focus on the sparse-group LASSO regularization which can take advantage of the mixed frequency time series panel data structures. Our empirical results show the superior performance of our machine learning panel data regression models over analysts' predictions, forecast combinations, firm-specific time series regression models, and standard machine learning methods.

This paper surveys the recent advances in machine learning method for economic forecasting. The survey covers the following topics: nowcasting, textual data, panel and tensor data, high-dimensional Granger causality tests, time series cross-validation, classification with economic losses.

We estimate unsmoothed private equity net asset values (NAVs) at weekly frequency for individual funds. Using simulations and large samples of buyout and venture funds, we show that our method yields superior estimates of NAVs relative to simple approaches based on extrapolation of reported NAVs. The market beta of an average buyout (venture) fund is around 1.0 (1.4), and the total risk is 33% (4%) per year. The risk-return profile of the funds varies significantly over time and across funds. Risk-taking and reporting quality appear to persist by manager.

We revisit in-sample asymptotic analysis extensively used in the realized volatility literature. We show that there are gains to be made in estimating current realized volatility from considering realizations in prior periods. The weighting schemes also relate to Kalman-Bucy filters, although our approach is non-Gaussian and model-free. We derive theoretical results for a broad class of processes pertaining to volatility, higher moments, and leverage. The paper also contains a Monte Carlo simulation study showing the benefits of across-sample combinations.

Quantum computers are not yet up to the task of providing computational advantages for practical stochastic diffusion models commonly used by financial analysts. In this paper we introduce a class of stochastic processes that are both realistic in terms of mimicking financial market risks as well as more amenable to potential quantum computational advantages. The type of models we study are based on a regime switching volatility model driven by a Markov chain with observable states. The basic model features a Geometric Brownian Motion with drift and volatility parameters determined by the finite states of a Markov chain. We study algorithms to estimate credit risk and option pricing on a gate-based quantum computer. These models bring us closer to realistic market settings, and therefore quantum computing closer the realm of practical applications.