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Stock Market Prediction Using Prescriptive Analytics

EasyChair Preprint no. 5298

8 pagesDate: April 8, 2021


Stock market prediction is a system used to determine the pattern of flow of stocks. The system implements graphs statistical reports and updates the current news report about every individual stocks. These are made with predictive analytics so far. As of new inheritance of the system, in order to provide real-time solution, the system is newly designed using prescriptive analytics. This system provides suggestions and solutions to the user regarding stock exchange. Stock market plays a vital role in every investor. Each user invest money at their own risk. This risk factor determines the strength and professionalism of the user in stock market. Most of the common people think that stock trading is complicated and risky. The technology in stock market had turned everything into systematized prediction.

Initially, the user’s holdings are analyzed and all the loss stocks are sorted out separately. Then these stocks are compared with Global stocks and the system returns the best stocks at the same holdings price. The user is given an option to swap these stocks to the current notch stocks. This process can help a user to stop loss being faced.

To provide real-time effective solution to any stock market user and to take the prediction analysis to the next level using prescriptive analytics. Even though, the existing systems predicts stock using various methods and algorithm. We can make our system to easily fit into any prediction system.

Keyphrases: Decision Tree, decision-making algorithm, deep learning, LSTM, machine learning, Prescriptive analytic, Random Forest, Real Time Solution, real-time, stock market, Stock Market Prediction, Stock Marketing

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {N Meenakshi and A Kumaresan and R Nishanth and R Kishore Kumar and A Jone},
  title = {Stock Market Prediction Using Prescriptive Analytics},
  howpublished = {EasyChair Preprint no. 5298},

  year = {EasyChair, 2021}}
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