Applied Mathematics & Information Sciences

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This paper proposes a way to forecast the future closing price of small sized companies by using geometric Brownian motion. Forecasting is restricted to short term investment because most of the investors aim to gain profit in short period of time. This study focusses on small sized companies because the asset prices are lower, hence the asset are affordable for all level of investors. But, to choose the suitable counters to invest is difficult and with the uncertainty of market prices, it will lead to the decline of the investor’s confidence level. Therefore, forecasting future closing price is essential. In this paper, we suggest that geometric Brownian motion which involves randomness, volatility and drift can be used to forecast a maximum of two week investment closing prices. This method is accurately proven by the lower value of the Mean Absolute Percentage Error (MAPE). In addition, the uses of data is also investigated and found that one week data is enough to forecast the share prices using geometric Brownian motion.

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