Application of the Cellular Automata Model Based on the Stock Market
Haiqi Wang, Wang, Wenxv, Pang, He · ASME Press eBooks · 2010
In current literature academics and practitioners test different models such as autoregressive and multivariate structures, neuronal-networks, genetic, algorithms, and technical analysis to forecast stock returns, This quest gears our motivation to undertake the evaluation of testing a fuzzy approach known as Cellular Automata (CA) looking to explore an alternative model to solve the complex problem of forecasting stock returns. Hence we employ the cellular automata method (CA) to forecast stock returns given the capacity problems believing that Forecasting future price fluctuations truly represent a high level of complexity. I present a new cellular automation (CA) model for simulating the complex system of stock markets. And use it to solve complex problem of forecasting stock returns. We empirically assess the method of CA studding six stocks BIOM, CVM, FMTI, HSKA, IG and PRCS traded in the Biotechnological sector of the USA equity market and find in favor of the CA approach achieving forecasting capacity ranging between 61.7% for the case of FMTI and 68.1% for both, CVM and IG. Overall we outperform the Buy-and-hold strategy and interestingly for the case of the stocks FMTI, HSKA and PRCS we get less standard deviation.