A new hedging hypothesis regarding prediction interval formation in stock price forecasting

Abstract

We propose and test a simple hedging hypothesis for prediction interval formation in stock price forecasting. In the presence of uncertainty, forecasters hedge their forecasts by adjusting the bounds of the prediction interval in a way that reflects their forecast of the average forecast of others. This hypothesis suggests a positive relationship between the belief wedge, defined as the difference between the subject's forecast of the average forecast of others and the subject's own point forecast, and the asymmetry of the prediction interval. Empirical support for the hedging hypothesis is drawn from two in‐class surveys, an experiment, and a large survey of professional analysts' forecasts of future stock prices.

An ensemble model for stock index prediction based on media attention and emotional causal inference

“…Dash et al (2019) introduced a weighted classifier integration model, employing TOPSIS and crowd search algorithms, which surpassed single classifiers and other integrated models in predictive efficacy. Recent studies have also identified industry‐specific asymmetries in stock price prediction efficacy with investor sentiment (Jin et al, 2020) and introduced new frameworks for prediction interval formation (Zhu et al, 2022) and dynamic, scenario‐driven prediction techniques (Thesia et al, 2022).…”

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“…Besides, they are prone to external shocks like other markets [ 15 , 16 ]. Despite these, the return & volatility of cryptocurrency markets, like the stock markets, are predictable given the right factors and methods [ [17] , [18] , [19] ].…”