Share Price Prediction using Machine Learning Technique

Jeevan, B; Naresh, E; Kumar, Bagesh; Kambli, Prashanth

doi:10.1109/cimca.2018.8739647

Cited by 19 publications

(6 citation statements)

References 4 publications

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“…Despite the dataset size in [9], the experiment showed satisfying results with the least error of 0.006 % and a maximum of 3.9% in the predictions, however, a larger dataset could be employed for better accuracy. The model proposed in [18] delivered predictions that were very close to that of the actual values. The authors hereby concluded ML algorithms to be the best approach for forecasting stock market prices.…”

Section: Challenges In Existing Mechanismssupporting

confidence: 57%

“…The paper "Share Price Prediction using Machine Learning Technique" [18] represented the stock price in the form of a time series and avoided the complications endured by the model in the training process. The paper used normalised data and a Recurrent Neural Network model for making the predictions that predicted values that were very close to the actual ones and thus, the author's considered machine learning algorithms best for forecasting the stock prices.…”

Section: Time Series Analysismentioning

confidence: 99%

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Machine Learning Approaches in Stock Price Prediction: A Systematic Review

Soni

Tewari

Krishnan

2022

J. Phys.: Conf. Ser.

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Prediction of stock prices is one of the most researched topics and gathers interest from academia and the industry alike. With the emergence of Artificial Intelligence, various algorithms have been employed in order to predict the equity market movement. The combined application of statistics and machine learning algorithms have been designed either for predicting the opening price of the stock the very next day or understanding the long term market in the future. This paper explores the different techniques that are used in the prediction of share prices from traditional machine learning and deep learning methods to neural networks and graph-based approaches. It draws a detailed analysis of the techniques employed in predicting the stock prices as well as explores the challenges entailed along with the future scope of work in the domain.

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Section: Challenges In Existing Mechanismssupporting

confidence: 57%

Section: Time Series Analysismentioning

confidence: 99%

Machine Learning Approaches in Stock Price Prediction: A Systematic Review

Soni

Tewari

Krishnan

2022

J. Phys.: Conf. Ser.

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“…The study by Yichuan Xu and Vlado Keselj IEEE (2019) uses the attention based Lstm variant for the prediction.Their approach involves combining of finance tweets sentiment and stock technical indicators to gains better performance from this modified LSTM.Accuracy of this model is 56% but future improvements could increase the number of percentage. Mudinas, Zhang, and Levene (2019) classified sentiments from news and tweets into eight categories (such as fear and anger) [4]. Only a small number of sentimental emotions were somewhat correlated with subsequent stock movements.…”

Section: Literature Surveymentioning

confidence: 99%

Web Application for Company Stock Price Prediction

Hitesh Chaudhari,

Deepesh Bhede,

Saurabh Thorat

et al. 2024

Int Res J Adv Engg Mgt

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This study contributes to predicting stock prices. In this paper, we have provided a novel machine learning and deep learning technique for stock prediction, which capitalizes on the complex emotional patterns of investors that can be extracted from Twitter data. By joining two separate datasets, such as Twitter sentiments and the associated stock prices, we have applied complex algorithms and evaluated this method, which classifies a user’s sentiment into a positive and negative one. It was possible to address the complexity of investor behavior within the highly unstable stock market environment. One of the most important problems that are encountered by developers is gaining the possibility to properly capture stock price fluctuations in the temporal dimension. It implies the necessity to utilize such reliable evaluation metrics as precision-recall factors for classification purpose and root mean squared error for regression purpose. by using these metrics, the performance of the predictive model can be evaluated in detail, which allows to draw meaningful permanent conclusions within the context of how well it reflects the complex relationship between investor sentiment and market dynamics. The results of the evaluation conducted as part of the process of validating the proposed hypothesis show that, indeed, the predictive accuracy improves significantly from the use of machine learning and deep learning algorithms if paired with Twitter users’ emotional sentiment data. This combination enhances the predictive capacity of the model and provides meaningful insights into how the changes in stock prices are impacted by the sentiment of investors. Moreover, in the process of this study, the explanation and detail consideration of the algorithms for feature engineering and data processing were provided, which stands crucial for increasing the predictive model’s accuracy The study has a valuable implication for both the financial analysis and technical spheres due to the resolution of these methods and presents a model for further research activities in stock prediction. To conclude, this interdisciplinary strategy that combines sentiment analysis and stock prediction not only advances our understanding of the investor behavior but also brings the opportunity for novel applications in the emerging field at the juncture of technology and finance. As a result, the current research sheds light on the intricate connection between the dynamics of the stock market and investor sentiment, thus offering new insights that can better inform predictive modeling research in the financial industry.

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“…The paper [6] represented the inventory fee in the form of a time series and avoided the headaches continued via the version within the schooling process. The paper used normalized records and an intermittent Neural network model for making the prognostications that prognosticated values that have been veritably near the authentic bones and therefore, the writer's taken into consideration device mastering algorithms elegant for vaticinating the stock prices.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning's Algorithm Profoundly Impacts Predicting the Share Market Stock's Price

-¹

2022

IJFMR

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The stock market is a type of trading where an investor invests their money in a company’s share. In the stock market, an investor with fast and accurate decisions can make money hand over fist. But the inconsistent nature of the stock market makes the decision critical and investment risky. To sort out it, the researcher has developed an Artificial intelligence and machine learning model with the lowest error percentage to forecast the stock price. The author discusses the importance and benefits of forecasting in the share market. The main aim of this paper is to explore the various technologies used to predict the share market stocks. The report also highlights the factors why prediction is not 100% accurate and whether it is possible or not.

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Share Price Prediction using Machine Learning Technique

Cited by 19 publications

References 4 publications

Machine Learning Approaches in Stock Price Prediction: A Systematic Review

Machine Learning Approaches in Stock Price Prediction: A Systematic Review

Web Application for Company Stock Price Prediction

Machine Learning's Algorithm Profoundly Impacts Predicting the Share Market Stock's Price

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