Classifying of VN-Index Bullishness by Bayesian Inference

Abstract

Decision making in stock market is a movement in which investors gather information and carry out complex analysis to select options, based on market variations and investor's preferences. This involves the facts of risk of return, appreciating or depreciating of stock markets in value and dynamic circumstances. We present a design to study and discover bear and bull markets from macroeconomic variables in a probabilistic manner to assist the decision-making process. Features such as return, risk, simple, and exponential moving average are represented as flexible time series. The learning method that involves conditional dependence of stock variables and inference is described by the base of Bayesian theorem. We highlight our learning method using an actual case study with a consistent stock portfolio optimization. The case study addresses a set of selected stock symbols of VN-index and the logical method is illustrated by significant rates of accuracy over a variation of types of stock symbols.