Analysis of various machine learning algorithm and hybrid model for stock market prediction using python

Vazirani, Sahil; Sharma, Abhishek; Sharma, Pavika

doi:10.1109/icstcee49637.2020.9276859

Cited by 20 publications

(10 citation statements)

References 9 publications

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“…This gives the evaluation of the stock, larger P/E ratio means stock price is overrated and going to fall anytime thus the proposed model chose the lower rating and vice versa. ROI: Stands for Return On Investment, this can be calculated as ROI ¼ ððProfit from each share Ã Number of shareÞ = InvestmentÞ Ã 100 (5) This is a percentage quantity if the Return on Investment is more, then the better rating is given to the stock.…”

Section: Modified Recommendation Systemmentioning

confidence: 99%

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Holt-Winters Algorithm to Predict the Stock Value Using Recurrent Neural Network

Mohan¹,

Raja²,

Velmurugan³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Prediction of stock market value is highly risky because it is based on the concept of Time Series forecasting system that can be used for investments in a safe environment with minimized chances of loss. The proposed model uses a real time dataset of fifteen Stocks as input into the system and based on the data, predicts or forecast future stock prices of different companies belonging to different sectors. The dataset includes approximately fifteen companies from different sectors and forecasts their results based on which the user can decide whether to invest in the particular company or not; the forecasting is done for the next quarter. Our model uses 3 main concepts for forecasting results. The first one is for stocks that show periodic change throughout the season, the 'Holt-Winters Triple Exponential Smoothing'. 3 basic things taken into conclusion by this algorithm are Base Level, Trend Level and Seasoning Factor. The value of all these are calculated by us and then decomposition of all these factors is done by the Holt-Winters Algorithm. The second concept is 'Recurrent Neural Network'. The specific model of recurrent neural network that is being used is Long-Short Term Memory and it's the same as the Normal Neural Network, the only difference is that each intermediate cell is a memory cell and retails its value till the next feedback loop. The third concept is Recommendation System which filters and predict the rating based on the different factors.

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Section: Modified Recommendation Systemmentioning

confidence: 99%

“…The system with the help of Machine Learning recognizes the previous patterns and tries to suggest the future stock price prediction. Various algorithms that are being used to achieve this goal are Holt-Winters Algorithm, Recurrent Neural Network and Recommendation System to suggest the best stock market investment [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Holt-Winters Algorithm to Predict the Stock Value Using Recurrent Neural Network

Mohan¹,

Raja²,

Velmurugan³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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“…In the paper [7], author have used two methods to predict the price which first used a single algorithm and another a hybrid model and by using Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Square Error (RMSE). In this paper the error between predicted price and real price is calculated.…”

Section: Literature Reviewmentioning

confidence: 99%

Stock Price Prediction Using Machine Learning algorithm with Web Interface (GUI)

Mittal¹,

Joshi²,

Savani³

2022

Artificial Intelligence and Communication Technologies

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Predicting the stock market trend is difficult due to the stock market’s high level of uncertainty. Many approaches for stock price and market prediction have been developed in the past. Different algorithms such as Long Short-Term Memory (LSTM), Support Vector Machine (SVM), Linear regression, and Decision trees are utilized in this study to forecast the future closing price of stocks for a specific firm. In this paper, the accuracy of these mentioned algorithms is also being compared. A seven-day study is also performed, illustrating the actual and anticipated closing prices for two companies using different algorithms. The outcome of the previous forecast is compared to the other algorithms, and a final algorithm is chosen based on it. A final model is created by taking many technical parameters in consideration in order to have more precise anticipated the price. Based on the final model, a 10-days examination of the actual and anticipated closing prices for ten different firms is also performed. Finally, A web application-based Graphical User Interface (GUI) was built using the Stream-lit library. The GUI includes features such as a forecast graph, a graph for historical data, and a company selection as well as the start and the end date.

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“…The basic idea behind this approach is to test the "stock tree" with a series of variables [7]. It first needs to define the variables: in this case, this work brought up with 𝑇 = 2 for indicating a two-period model; the other variables are u, d, R, S0, and K, which follows the same definition from the start of this investigation.…”

Section: Mechanism and Variables Of The Python Approachmentioning

confidence: 99%

Investigation on the Mechanism of American Put Option Pricing under Binomial Consideration

Li¹,

Li²,

Shi³

2022

Proceedings of the 2022 2nd International Conference on Enterprise Management and Economic Development (ICEMED 2022)

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American options are style of options which allows the holder to exercise their rights at any time before and including the expiration date. Different from European options, that only allows the holder to exercise their right at the expiration date, American options seems to bring the holders with more freedom. There are two common types of options: calls and puts. In this investigation, we will be focusing on American put options specifically. Puts give the buyer the right, but not the obligation for selling the underlying asset at the strike price in the contract. Moreover, options may have their own pricing theory, that is a model to estimate the value of options by assigning a price, which is known as premium. With the theory, American option holders could make the decision of holding the option or not. Therefore, the pricing model of an American option seems to be significant and important for the final decision.

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Analysis of various machine learning algorithm and hybrid model for stock market prediction using python

Cited by 20 publications

References 9 publications

Holt-Winters Algorithm to Predict the Stock Value Using Recurrent Neural Network

Holt-Winters Algorithm to Predict the Stock Value Using Recurrent Neural Network

Stock Price Prediction Using Machine Learning algorithm with Web Interface (GUI)

Investigation on the Mechanism of American Put Option Pricing under Binomial Consideration

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