We test the implications of anchoring bias associated with forecast earnings per share (FEPS) for forecast errors, earnings surprises, stock returns, and stock splits. We find that analysts make optimistic (pessimistic) forecasts when a firm's FEPS is lower (higher) than the industry median....

In this paper, we study whether firm intangible information affects analyst behavior. We find direct evidence that when analysts make more judgment-intensive decisions, such as issuing stock recommendations, they overweight intangible information, leading to overreaction to intangible...

This research paper aim to examine the profitability of various kinds of oscillator used in technical analysis on market index of NSE (National Stock Exchange) S&P CNX NIFTY 50 during 2004-2014. We have selected the most commonly used three oscillators i.e., Stochastic oscillator, RSI Oscillator...

We measure the information content of monthly analyst consensus forecasts for one-year-forward earnings per share (EPS) based on two well-established price discovery measures drawn from the area of market micro-structure research. Employing a 36-year sample of large US companies listed in the...

We examine article, author and firm characteristics of investment articles published by non-professional analysts on the social media investment platform Seeking Alpha from 2006 to 2020 leading to visible market value changes. We show that there are differences between articles followed by stock...

Earning forecasts disclosed by financial analysts are known to be overly optimistic. Since an investor relies on their expertise, the question arises whether he would take analyst recommendations at face value or instead structure consultation with differently upward-biased analysts in a way...

We find that financial analysts provide more thorough forecasts when firms’ institutional investors are distracted (i.e., when firms are neglected). We establish the causality of the effect by identifying exogenous shocks leading to institutional investor distraction following Kempf, Manconi,...

We introduce a flexible utility-based empirical approach to directly determine asset allocation decisions between risky and risk-free assets. This is in contrast to the commonly used two-step approach where least squares optimal statistical equity premium predictions are first constructed to...

We propose a statistical model of differences in beliefs in which heterogeneous investors are represented as different machine learning model specifications. Each investor forms return forecasts from their own specific model using data inputs that are available to all investors. We measure...