Using autoregressive modelling and machine learning for stock market prediction and trading
AffiliationUniversity of Bedfordshire
MetadataShow full item record
Other TitlesThird International Congress on Information and Communication Technology
AbstractInvestors raise profit from stock market by maximising gains and minimising loses. The profit is difficult to raise because of the volatile nature of stock market prices. Predictive modelling allows investors to make informed decisions. In this paper, we compare four forecasting models: autoregressive integrated moving average (ARIMA), vector autoregression (VAR), long short-term memory (LSTM) and nonlinear autoregressive Exogenous (NARX). The results of predictive modelling are analysed and compared in terms of prediction accuracy. The research aims to develop a new profitable trading strategy. Our findings are: (i) the NARX model has provided accurate short-term predictions but failed long forecasts, and (ii) the VAR model can form a good trend line required for trading. Thus, the profitable trading strategy can combine the machine learning predictive modelling and technical analysis.
CitationHushani P (2019) 'Using autoregressive modelling and machine learning for stock market prediction and trading', Third International Congress on Information and Communication Technology ICICT 2018 - London, Springer.
TypeConference papers, meetings and proceedings