Author(s)

V. Romanov, O. Naletova, E. Pantileeva & A. Federyakov

Abstract

This work consists of two parts. The first is devoted to stock market simulation and is based on the model described by Li and Rosser. New factors were introduced in the mentioned model. \“Bad” or \“good” news arose at each moment. The model has a memory, which determines the rate of forgetting the news. In such a way, the news background is being formed by addition of decaying news intensities. The news background modifies the fundamental value of current market price according to the logistic law. The insiders at moment t-1 know the prices at the moment t. The market simulation by means of the proposed model shows that, in case of \“good” news, the stock-market prices are rising, and in the case of \“bad” news, the prices are falling. Moreover, the parameter that determines the news-forgetting rate changes the picture of rising and falling prices. The model also shows, that the effect of insiders activity depends on the return volume extracted, and when insiders’ return approaches some crucial value, the fundamental value v abruptly falls down, and further with the increasing insiders’ pressure the market explodes. In the second part of this work, we are considering the possibility for noise-trader to use market states recognition such as \“bullish”, \“bearish”, \“cyclic” or \“stable”, instead of using MACD-strategy. With this aim in mind, we applied Kohonen neural net and nonsupervised learning chart pattern recognition. Keywords: market analysis, news influence, stock market, neural nets, market dynamics, simulation, insiders’ role.

Keywords

market analysis, news influence, stock market, neural nets, market dynamics, simulation, insiders’ role.