Extreme Analyst Forecasts
Project Description
This project at the University of Liechtenstein's Chair of Sustainable Finance and Investments examines the predictive power of extreme analyst target prices for subsequent factor adjusted stock returns. We focus on the ratio of consensus target price to current price (TP/P) and deliberately study the tails of the distribution-exceptionally high or low TP/P values. Such extremes may arise from market (over )reactions, firm specific events, or behavioral biases.
We will systematically identify extremes, characterize their drivers, and evaluate their forecasting ability relative to middle range observations. The methodology combines portfolio sorts, empirical asset pricing regressions, and flexible non linear estimators. Findings will inform investors, asset managers, and regulators about the reliability of analyst signals and assess whether extreme targets can underpin robust, transaction cost aware investment rules.
We will systematically identify extremes, characterize their drivers, and evaluate their forecasting ability relative to middle range observations. The methodology combines portfolio sorts, empirical asset pricing regressions, and flexible non linear estimators. Findings will inform investors, asset managers, and regulators about the reliability of analyst signals and assess whether extreme targets can underpin robust, transaction cost aware investment rules.
Relevance to Liechtenstein
For Liechtenstein's private banking and asset management ecosystem, assessing the reliability of analyst signals is highly practical. The project clarifies whether, and under which conditions, extreme target prices provide actionable input for mandates and products-accounting for realistic trading frictions and risk constraints. It supports improvements in rule based investment processes, advisory use of research signals, and risk management (e.g., mitigating exuberance during stress periods). It also enhances evidence based education at the University of Liechtenstein and fosters knowledge transfer with local market participants.
