How can we predict transportation stock prices using artificial intelligence? Findings from experiments with Long Short-Term Memory based algorithms

•Predict transport stock prices using LSTM with different activations and optimizers.•Compare LSTM performance across different activations and optimizers.•Evaluate multiple transportation stocks to identify best-performing models.•Implement optimized LSTM models in a web-based application for real-...

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Vydáno v:International journal of information management data insights Ročník 4; číslo 2; s. 100293
Hlavní autoři: Kristiyanti, Dinar Ajeng, Pramudya, Willibrordus Bayu Nova, Sanjaya, Samuel Ady
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.11.2024
Elsevier
Témata:
Activation
Long short-term memory
Optimizer
Prediction
Transportation stock
Long short-term memory
Transportation stock
Activation
Optimizer
Prediction
ISSN:2667-0968, 2667-0968
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Shrnutí:•Predict transport stock prices using LSTM with different activations and optimizers.•Compare LSTM performance across different activations and optimizers.•Evaluate multiple transportation stocks to identify best-performing models.•Implement optimized LSTM models in a web-based application for real-time stock prediction. Inflation growth in Indonesia and other countries impacts the currency value and investors' purchasing power, particularly in the transportation sector. This research explores the impact of inflation growth in Indonesia and comparable nations on currency valuation and the purchasing power of investors, with a focus on the transportation sector. Data collection was carried out from April to October 2023 by scraping stock data from several transportation stocks such as: AKSI.JK, CMPP.JK, SAFE.JK, SMDR.JK, TMAS.JK, and WEHA. The research primarily aims to forecast stock prices in Indonesia's transportation sector, utilizing data mining techniques within the Cross Industry Standard Process for Data Mining (CRISP-DM) framework, which includes stages such as business understanding, data preparation, modeling, evaluation, and deployment. It employs the Long Short-Term Memory (LSTM) algorithm, assessing different hyperparameter activation functions (linear, ReLU, sigmoid, tanh) and optimizers (ADAM, ADAGRAD, NADAM, RMSPROP, ADADELTA, SGD, ADAMAX) to refine prediction accuracy. Findings demonstrate the ReLU activation function and ADAM optimizer's effectiveness, highlighted by evaluation metrics such as Mean Absolute Error (MAE) of 0.0092918, Mean Absolute Percentage Error (MAPE) of 0.06422, and R-Squared of 96 %. The study notably identifies significant growth in Temas (TMAS.JK) stock from April to October 2023, surpassing other sector stocks. Additionally, a web-based application for predicting transportation stock prices has been developed, facilitating user inputs like ticker, activation-optimizer choice, and date range.
ISSN:2667-0968
2667-0968
DOI:10.1016/j.jjimei.2024.100293