Analysis of Market Behavior Using Popular Digital Design Technical Indicators and Neural Network

George, Jossy P.; Nair, Akhil M.; Yathish, S.

doi:10.1007/978-981-16-2126-0_37

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…Therefore, it is challenging for investors to identify the up and down movements of stock prices. Most of the work considered Technical indicators statistics to identify the up and down movements of stock prices (Lee et al, 2022;Dhafer et al, 2022;George et al, 2022). Moreover, there are many technical indicators, but all are not relevant.…”

Section: Introductionmentioning

confidence: 99%

Stock Price Classification Based on Hybrid Feature Selection Method

Srivinay¹,

Manujakshi²,

Kabadi³

et al. 2023

Journal of Computer Science

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In recent years, investors and traders have used Technical Indicators (TIs) to forecast the stock market. An accurate classification model is required in the stock market to gain more profit. Selecting relevant TIs for the stock market remains a hot research topic. The proposed work aims to identify important technical indicators. Therefore, the proposed work considers a hybrid feature selection method to identify the relevant TIs. The hybrid feature selection combines two individual feature selection methods, such as Boruta and the Random Forest (RF) feature importance method. The work considers 20 TIs. The regression power of TIs is computed using the hybrid feature selection method. Using the hybrid feature selection method, the selected relevant TIs are given as input to the classification model, namely Naive Bayes (NB) and Deep Learning. The classification model aims to classify the stock price as up or down. The Hybrid Feature selection-based Deep Learning H2O model performs better than the hybrid feature selectionbased NB model in the experimental work. The accuracy of the hybrid feature selection of the Deep Learning H2O model is around 86 to 89%. The work considers the National Stock Exchange (NSE) in India for the experimental work.

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Section: Introductionmentioning

confidence: 99%

Stock Price Classification Based on Hybrid Feature Selection Method

Srivinay¹,

Manujakshi²,

Kabadi³

et al. 2023

Journal of Computer Science

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“…Ayankoya et al (2016) explore the neural network for daily white maize spot price forecasting on the Johannesburg Stock Exchange in South Africa during 2010–2015. George et al (2022) study the neural network for forecasts of the Nifty 50 index during 2011–2019. Moody et al (1998) research reinforcement learning for trading systems and portfolios and demonstrate predictabilities of the advanced method through simulations using the monthly S&P 500 stock index during 1970–1994.…”

Section: Introductionmentioning

confidence: 99%

Commodity price forecasting via neural networks for coffee, corn, cotton, oats, soybeans, soybean oil, sugar, and wheat

Zhang

2022

Intell Sys Acc Fin Mgmt

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Agricultural commodity price forecasting represents a key concern for market participants. We explore the usefulness of neural network modeling for forecasting problems in datasets of daily prices over periods of greater than 50 years for coffee, corn, cotton, oats, soybeans, soybean oil, sugar, and wheat. By investigating different model settings across the algorithm, delay, hidden neuron, and data-splitting ratio, we arrive at models leading to a decent performance for each commodity, with the overall relative root mean square error ranging from 1.70% to 3.19%. These results have small advantages over no-change models due to particular price adjustments in the prices considered here. Our results can be used on a standalone basis or combined with fundamental forecasts in forming perspectives of commodity price trends and conducting policy analysis. Our empirical framework should not be diffucult to implement, which is a critical consideration for many decision-makers and has the potential to be generalized for price forecasts of more commodities.

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