Abstract
Forecasting future price is not an easy task due to wide impact variables, nonlinear, and complexity in financial market. Fundamental analysis plays an essential role in price valuation when financial analyst, investors, and institutional traders were on their ways to evaluate current stock prices. Industrial Revolution 4.0 (IR4.0) creates a trend of evolution of artificial intelligence (AI) in financial technology field. The machine learning (ML) become one of the famous techniques used by the investors to forecast the movement of financial derivative’s price. Therefore, this study aims to determine the optimal model in forecasting the S&P 500 market index price. The models used are Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and Random Forest (RF). The duration of data used is 10 years which is from January 1st 2011 to December 31st 2021, which included the weekends and public holidays. The data was split into training set and testing set. Training set is fitted into the models to obtain the optimal combination of parameter to gain the accuracy and reliability of result. After getting the appropriate parameters for each model, testing phase was carried out to determine the optimal model by using the error metrics which are mean absolute percentage error (MAPE), root mean squared error (RMSE), and mean absolute error (MAE). As a results, ANN is the best optimal model in predicting the S&P 500 adjusted close process with the lowest error metrics and highest accuracy compared to LSTM and RF. The findings of this study can be used by individual and institution to forecast the future price changes of stock market and indexes. This can help the users to have a better prediction on future price so that they make an appropriate decision in the investment process.
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Huang, L.G., Wei, C.K., Izni, N.A., Fang, L.Y., Lyn, T.S., Saruchi, S.A. (2023). The Statistical Impact of Artificial Intelligence Towards the Price Change of Financial Instrument. In: Abdullah, M.A., et al. Advances in Intelligent Manufacturing and Mechatronics. Lecture Notes in Electrical Engineering, vol 988. Springer, Singapore. https://doi.org/10.1007/978-981-19-8703-8_25
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DOI: https://doi.org/10.1007/978-981-19-8703-8_25
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