Aspects Regarding a Deep Understanding of the Prediction for Stock Market Movements

Xuemei Hu

A chapter in Investment Strategies - New Advances and Challenges from IntechOpen

Abstract: It is an important puzzle in the financial market to predict stock return movement direction. In this chapter, we not only propose (group) penalized logistic regression with multiple indicators to predict up- or downtrends, but also propose group penalized trinomial logit regression with multiple indicator groups to predict stock return movement direction: uptrends, sideways trends and downtrends. For the former, we construct the corresponding coordinate descent (CD) algorithm to complete variable selection and obtain parameter estimator, and introduce two-class confusion matrix, Receiver Operating Characteristic (ROC) and the area under a ROC curve (AUC) to assess two-class prediction performance. For the latter, we develop a rapidly convergent group coordinate descent (GCD) algorithm to simultaneously complete group selection and group estimation, introduce the relatively optimal Bayes classifiers to identify class indexes, and finally adopt three-class confusion matrix, Kappa, PDI, ROC surface and hypervolume under the ROC manifold (HUM) to assess three-class prediction performance.

Keywords: technical indicators; stock return movement direction; coordinate descent algorithm; group coordinate descent algorithm; prediction accuracy; G-LASSO/G-SCAD/G-MCP estimators (search for similar items in EconPapers)
JEL-codes: M21 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:ito:pchaps:312139

DOI: 10.5772/intechopen.115081

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