Forecasting the direction of the US stock market with dynamic binary probit models

114 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A Multiple-Criteria Approach for Forecasting Stock Price Direction : Nonlinear Probability Models with Application in S & P 500 Index (2016, methodology): “Nyberg [36] predicated index movement in a financial market using dynamic Probit model [37].”
Forecasting oil and stock returns with a Qual VAR using over 150 years of data (2017, unknown): “More recent studies followed, like Estrella and Mishkin (1998), Stock and Watson (2003), Rapach and Weber (2004) Nyberg (2011), and have been surveyed in detail in Erdogan et al., (2015), and Balcilar, Gupta and Wohar (forthcoming).”
A Random Forests Approach to Predicting Clean Energy Stock Prices (2021, unknown): “…addition, the existing literature on stock price predictability shows that predicting stock price direction is more successful than predicting actual stock prices (Basak et al. 2019; Leung et al. 2000; Nyberg 2011; Nyberg and Pönkä 2016; Pönkä 2016; Ballings et al. 2015; Lohrmann and Luukka 2019).”