Modelling A.I. in Economics

Is NASDAQ stock prediction possible with machine learning? (Forecast)

ABSTRACT

Predicting stock prices using machine learning, including for NASDAQ, is an active area of research and many different algorithms and techniques have been used for this task. Some common approaches include using linear regression, time series forecasting methods such as ARIMA, and more advanced machine learning algorithms such as neural networks, decision trees, and support vector machines.


The goal of these approaches is to build a model that can accurately predict future stock prices based on historical data and other relevant features such as economic indicators, company performance, and market sentiment.


While there is no guarantee that any particular machine learning algorithm will provide accurate predictions, research has shown that some algorithms, such as neural networks, can perform well in predicting stock prices.


However, it's important to keep in mind that predicting stock prices is a challenging task and no algorithm is perfect. It's important to carefully evaluate the quality of the data used to train the model, the choice of features and hyperparameters used, and to avoid overfitting the model to the training data.


In addition, it's important to seek the advice of a professional financial advisor before making any investment decisions. Predictions made using machine learning should be treated as just one piece of information in a broader analysis of the market and the specific stocks being considered


Premium

  • Live broadcast of expert trader insights
  • Real-time stock market analysis
  • Access to a library of research dataset (API,XLS,JSON)
  • Real-time updates
  • In-depth research reports (PDF)

Login
This project is licensed under the license; additional terms may apply.