The analysis and prediction of stock price

Tong, Xing; Sun, Yuan; Qian, Wang; Guo, Yu

doi:10.1109/grc.2013.6740438

Cited by 19 publications

(2 citation statements)

References 15 publications

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“…The fundamental idea of demand-and-supply economic policy serves as the foundation for this. For instance, the price of a particular company's share always falls when there is less demand for that company's stock [4,5].…”

Section: Introductionmentioning

confidence: 99%

Stock Price Prediction using ML algorithms

Gupta,

Tiwari,

Bhatnagar

et al. 2023

IJRASET

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Stock Price Prediction using ML( Machine Learning) helps to determine the unborn value of stocks of any fiscal means traded on an exchange. The entire generality of prognosticating stock prices is to gain significant gains by minimizing losses vaticinating the stock request price is always challenging for numerous business judges and experimenters. vaticination of the stock request plays a vital part in the stock business. The compass gradationally expanded. The time series model cannot contribute to the non-linear part of the stock data and is therefore hamstrung for the long term, and LSTM neural network makes better use of non-sequential data and has better use of sequence data. Useful information in the long term which makes the root mean square error of the vaticination result, the LSTM neural network needs lower than the time series model, showing LSTM is a better stock price soothsaying system. Machine Learning approach to predict or sense the behavior tracking of the stock market Sensex. Random Forest is the Machine Learning model implemented effectively in predicting the stock prices anddefining the activity between the exchanges the securities between the buyers and sellers.

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Section: Introductionmentioning

confidence: 99%

Stock Price Prediction using ML algorithms

Gupta,

Tiwari,

Bhatnagar

et al. 2023

IJRASET

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show abstract

“…Various methods/models (Hamed et al 2012), (Xing et al 2013) have been proposed to forecast the market movement with the utilization of ANN, probabilistic models, SVMs, Genetic Algorithm (GA) (Wang & Lin 2016) and other soft computing techniques (Srinivasan & Lakshmi 2018) . In this literature, an attempt will be made to analyse the different techniques from different domains such as ANN, SVM, GA and others (Rajput & Bobde 2017) .…”

Section: Introductionmentioning

confidence: 99%

Forecasting Share Prices Using Soft Computing Techniques

Ganesan

2019

ijsrtm

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Background: For a long time, there has been a trend of trading of shares. Brokerage firms and dealers buy/sell stocks for clients and companies. Their work is based on knowing how the share price of the company will react in the market. Market/ share price predictions are useful as the investor/broker can attempt to predict the output in order to maximize his dividends or minimize his losses. Methodology: R and Python tools are used to sort, segregate and process the data, and techniques/algorithms such as Genetic Algorithm, ARIMA, Artificial Neural Networks, and Linear Regression are used to forecast results of data. Along with the model data, external factors affecting share prices also be taken into account. Findings: For each of the applied algorithms, their results are compared and the difference in output with the real-time values has been observed and recorded. Implications: Using data mining techniques, an attempt is made to estimate a prediction model to help forecast share prices.

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Stock Market Prediction Using Machine Learning

Patil,

Pawar

2023

Techno-Societal 2022

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The analysis and prediction of stock price

Cited by 19 publications

References 15 publications

Stock Price Prediction using ML algorithms

Stock Price Prediction using ML algorithms

Forecasting Share Prices Using Soft Computing Techniques

Stock Market Prediction Using Machine Learning

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