STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Telecommunication, Switching Systems and Networks

Wall Street Prognosis

  1. Dhruv Wankhede
  2. Nishant Pathare
  3. Mohit Sajeevan
  4. Divyansh Pandey
  5. Chintan Jethva

Abstract

We all know that the stock market is volatile. There is so much turmoil and turbulence in the stock market that it is difficult to predict what will happen. The main purpose of the thematic debate is to predict the future stability of the market with probability coefficients. Investors are familiar with the adage “buy low, sell high” but it doesn't provide enough context to make sound investment decisions. Before investing in stocks, investors need to know how the stock market works. Investing in good stocks at the wrong time can be detrimental, while investing in mediocre stocks at the right time can be profitable. Financial investors today face these trading challenges because they do not understand which stocks to buy and which stocks to sell for optimal returns. Predicting the long-term value of stocks is relatively easier than making day-to-day predictions. This is because stocks fluctuate rapidly hourly based on world events. In the latest trend in stock market prediction technology, machine learning has been brought into the picture to deploy training sets and data models and make predictions. Machine Learning approaches have been used to forecast the closing price for a variety of firms in a variety of industries. The stock's open, close, high and low prices are utilized to create new variables that are used as model inputs.

Keywords

Support