Qiu, M. and Song, Y. (2016) Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model. PLoS ONE, 11, Article ID: e0155133.
https://doi.org/10.1371/journal.pone.0155133

has been cited by the following article:

• TITLE: Bull and Bear Dynamics of the Nigeria Stock Returns Transitory via Mingled Autoregressive Random Processes

  AUTHORS: Rasaki Olawale Olanrewaju, Anthony Gichuhi Waititu, Lukman Abiodun Nafiu

  KEYWORDS: Autoregressive Random Processes, Extreme-Value-Distributions, Mingled, Poisson, Stock Returns

  JOURNAL NAME: Open Journal of Statistics, Vol.11 No.5, October 19, 2021

  ABSTRACT: This paper expounds the nitty-gritty of stock returns transitory, periodical behavior of its markets’ demands and cyclical-like tenure-changing of number of the stocks sold. Mingling of autoregressive random processes via Poisson and Extreme-Value-Distributions (Fréchet, Gumbel, and Weibull) error terms were designed, generalized and imitated to capture stylized traits of k-serial tenures (ability to handle cycles), Markov transitional mixing weights, switching of mingling autoregressive processes and full range shape changing predictive distributions (multimodalities) that are usually caused by large fluctuations (outliers) and long-memory in stock returns. The Poisson and Extreme-Value-Distributions Mingled Autoregressive (PMA and EVDs) models were applied to a monthly number of stocks sold in Nigeria from 1960 to 2020. It was deduced that fitted Gumbel-MAR (2:1, 1) outstripped other linear models as well as best fitted among the Poisson and Extreme-Value-Distributions Mingled autoregressive models subjected to the discrete monthly stocks sold series.