Prediction of Closing Stock Prices Using the Artificial Neural Network in the Market for Alternative Investment (MAI) of the Stock Exchange of Thailand (SET)

Chaysiri, Rujira; Ngauv, Chanrathanak

doi:10.1007/978-3-030-62509-2_28

Cited by 1 publication

(1 citation statement)

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“…On-the-go stock purchases and sales can be made through the use of a downloadable app. Seeds, StockBit, OctaFx, and a great number of additional applications, just like them, are all examples of stock programs for mobile devices [2], [3]. Investing in stocks comes with both positives and negatives that should be considered.…”

Section: Introductionmentioning

confidence: 99%

Comparing neural network with linear Regression for stock market prediction

Kurniawan,

Arif,

Nugroho

et al. 2023

Bull.Socinf.The.App

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There are both gains and losses possible in stock market investing. Brokerage firms' stock investments carry a higher risk of loss since their stock prices are not being tracked or analyzed, which might be problematic for businesses seeking investors or individuals. Thanks to progress in information and communication technologies, investors may now easily collect and analyze stock market data to determine whether to buy or sell. Implementing machine learning algorithms in data mining to obtain information close to the truth from the desired objective will make it easier for an individual or group of investors to make stock trades. In this study, we test hypotheses on the performance of a financial services firm's stock using various machine learning and regression techniques. The relative error for the neural network method is only 0.72 percentage points, while it is 0.78 percentage points for the Linear Regression. More training cycles must be applied to the Algortima neural network to achieve more accurate results.

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Section: Introductionmentioning

confidence: 99%