Neutrosophic soft set decision making for stock trending analysis

In this paper, we point out a major issue of stock market regarding trending scenario of trades where data exactness, accuracy of expressing data and uncertainty of values (closing point of the day) are lacked. We use neutrosophic soft sets (NSS) consisting of three factors (True, Uncertainty and False) to deal with exact state of data in several directions. A new approach based on NSS is proposed for stock value prediction based on real data from last 7 years. It calculates the stock price based on the factors like “open”, “high”, “low” and “adjacent close”. The highest score value retrieved from the score function is used to determine which opening price and high price decide the closing price from the above mentioned four factors.