When a company releases earnings results or makes announcements, a sectoral wide lead-lag effect from the stock on the entire system may occur. To improve the estimation of a system experiencing system-wide lead-lag effects from a single asset in the presence of short time series, we introduce a model for Large-scale Influencer Structures in Vector AutoRegressions (LISAR). We study the asymptotic properties of the estimator and validate its performance in extensive synthetic data experiments. We study the performance of the LISAR model on high-frequency data for the constituents of the S&P100, separated by sectors. We find the LISAR model to significantly outperform on up to 14.7% of the days in terms of forecasting accuracy. Trading strategies with signals derived from the LISAR model achieved up to 60% excess return compared to other strategies. We show in this study, that in the presence of influencer structures within a sector, the LISAR model, compared to alternative models, provides higher accuracy, better forecasting results, and improves the understanding of market movements and sectoral structures.

Simon Trimborn is an Assistant Professor of Econometrics and Data Science at the Amsterdam School of Economics, University of Amsterdam. Before joining the UvA, he held academic positions at City University of Hong Kong and National University of Singapore following my PhD studies at the Humboldt-University at Berlin (Humboldt-Universität zu Berlin), where he defended his PhD thesis on the topic of “Statistics of Digital Finance”. His work focuses on high dimensional data analysis for time series data with which he tackles specific problems arising in social media, cryptocurrency, blockchain and finance. These studies appear in journals such as Journal of Financial Econometrics, Journal of Empirical Finance, R Journal, among others. He serves on the editorial board of the journals Digital Finance, Statistics, Statistical Methods and Applications, as well as Annual Review of FinTech. He is part of the Scientific Board of the CRIX index and a member of the AI4FinTech initiative at the UvA.