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Stock price forecasting, which is an important topic in finance and
economics, has prodded the enthusiasm of specialists throughout the years to
develop models for better forecasts. Stock market is a key factor of monetary
markets and signs of economic growth. Therefore, differ-ent forecasting models
have been explored in literature for the stock data prediction. In this article,
classical ARIMA model is mentioned and then Fuzzy Auto Regressive Integrated
Moving Average (FARIMA) model is proposed as a new model. These two models
are used for forecasting the stock exchange market of Attock cement Pakistan
limited. Due to vague natures of stock data and parameter, fuzzy least square
method is used in proposed FARIMA model. FARIMA model is based on the
possibil-ity of success. These possibilities are defined by linguistic term, such as
very low, low, average, high, and very high. This model makes it possible
for decision makers to forecast the best based on fewer observations than
the ARIMA model. Finally, comparison between the proposed model and
ARIMA shows that the proposed model has better performance than ARIMA by
using different criteria, such as mean squared error, mean ab-solute percentage
error, and mean absolute deviation.
Saima Mustaf, Sophia Siddique. (2020) Improved Fuzzy Forecasting Model for Stock Exchange Market, Punjab University Journal of Mathematics, Volume 52 , Issue No.1.
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