Where Has the Foreign Signal Gone?

Piyush Akimitsu; Ulrich Hounyo

Home/Archive/arXiv

arXiv · 2610.28175

Where Has the Foreign Signal Gone?

Piyush Akimitsu·Ulrich Hounyo

View original source ↗Read source document ↗Export metadata ↓

Abstract

We compare the relation between sentiment noise in the Twitter feed and losses for stocks of domestic and foreign firms. The mean quarterly domestic-foreign difference in the slope of next-quarter losses on measured noise is -1.40 (Newey-West t = -2.99). This slope is negative for 78 and 48 percent of the quarters for domestic and foreign stocks, respectively (block-bootstrap p = 0.016). Thus, this relationship is temporally more stable for domestic stocks. We use a noisy rational expectations model to explain these patterns through the composition of the contributor pool. The feeds for stocks of foreign firms have higher repeat-author concentration and a larger model-implied nonfundamental component than those for domestic firms. We interpret these differences through the producer-knowledge mismatch hypothesis. For foreign firms, the English-language contributor pool underrepresents the home-market investors who tend to possess informational advantages about them. Thus, the foreign signal is not gone but diluted.

Explore related subjects

econ.EM stat.ML

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Piyush Akimitsu, Ulrich Hounyo. 2026-10-20. Where Has the Foreign Signal Gone?. https://arxiv.org/abs/2610.28175

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Quasi-Score Matching Estimation for Spatial Autoregressive Model with Random Weights Matrix and Regressors

With the rapid advancements in technology for data collection, the application of the spatial autoregressive (SAR) model has become increasingly prevalent in real-world analysis, particularly when dealing with large datasets. However, the commonly used quasi-maximum likelihood estimation (QMLE) for the SAR model is not computationally scalable to handle the data with a large size. In addition, when establishing the asymptotic properties of the parameter estimators of the SAR model, both weights matrix and regressors are assumed to be nonstochastic in classical spatial econometrics, which is perhaps not realistic in real applications. Motivated by the machine learning literature, this paper proposes quasi-score matching estimation for the SAR model. This new estimation approach is developed based on the likelihood, but significantly reduces the computational complexity of the QMLE. The asymptotic properties of parameter estimators under the random weights matrix and regressors are established, which provides a new theoretical framework for the asymptotic inference of the SAR-type models. The usefulness of the quasi-score matching estimation and its asymptotic inference is illustrated via extensive simulation studies and a case study of an anti-conflict social network experiment for middle school students.

econ.EM↗

arXiv2026

A multiplier bootstrap score test for Tobit specification

In this paper, we propose a simple score-based specification test for the Tobit regression model, implemented through a computationally efficient multiplier bootstrap. We test correct specification through the conditional moment restriction that the generalized Tobit residual has zero conditional mean, and construct a feasible score-based empirical process using the integrated conditional moments approach, conditioning on the estimated index so that no smoothing or tuning parameter is required. We derive a first-order uniform representation of the feasible process, showing how its limiting behavior is affected by maximum-likelihood estimation of the structural parameter, and obtain the asymptotic properties of the corresponding Kolmogorov--Smirnov statistic under the null, fixed alternatives, and a sequence of local alternatives. Since the limiting null distribution is non-pivotal, $p$-values are obtained from a multiplier bootstrap that mimics the first-order representation and therefore captures the parameter estimation uncertainty. Monte Carlo experiments, including an unknown-scale design, an index-visible/index-invisible power study, and comparisons with existing specification tests, confirm accurate size and competitive power across a range of designs and covariate dimensions. Two empirical applications further demonstrate the practical utility of our approach.

econ.EM↗

arXiv2026

What Impulse Response Do Instrumental Variables Identify?

We study what local projection instrumental variable (LP-IV) regressions identify when an instrument is correlated with several structural shocks, such as defense and non-defense spending shocks. LP-IV identifies an instrument-specific affine combination of the component impulse responses, possibly with negative weights. Over-identified 2SLS or GMM yields another such combination, and the over-identification test can reject even with valid instruments. With as many instruments as shocks, the component responses are identified when each shock has a policy variable (e.g., sectoral spending) and its impact on the other policy variables is restricted. We illustrate with government spending and tax multipliers.

econ.EM↗