Stock Price Prediction in Bursa Malaysia
Abstract
Investment in stock is a highly risky investment, it is because the existence of randomness in the
stock price. In lecture, usually we used Binomial model to price the stock. But, in real world, how do
we price the stock? Because the stock price is random, the volatility and drift is a crucial items to
behold. The main questions is how to calculate this volatility and drift, and the answer to the
question is the sample variance and the sample mean. At any time, the stock price will be either up or
down from the previous price. This is where we need a method or model to calculate parameters for
up-state and down-state for the stock price. And it will cover the volatility and the drift in an
embrace. The method we used in this paper is the Hull-White algorithm. Hull-White algorithm is to
find the parameters value of u and d for prediction to stock price. Using SPSS, we will run the data to
get the sample variance and sample mean. Then, using Maple 10, we calculate the u and d before
enter the value of u and d into programming C++.
stock price. In lecture, usually we used Binomial model to price the stock. But, in real world, how do
we price the stock? Because the stock price is random, the volatility and drift is a crucial items to
behold. The main questions is how to calculate this volatility and drift, and the answer to the
question is the sample variance and the sample mean. At any time, the stock price will be either up or
down from the previous price. This is where we need a method or model to calculate parameters for
up-state and down-state for the stock price. And it will cover the volatility and the drift in an
embrace. The method we used in this paper is the Hull-White algorithm. Hull-White algorithm is to
find the parameters value of u and d for prediction to stock price. Using SPSS, we will run the data to
get the sample variance and sample mean. Then, using Maple 10, we calculate the u and d before
enter the value of u and d into programming C++.
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PDF (Bahasa Indonesia)DOI: https://doi.org/10.29313/jstat.v7i1.949
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