Machine Learning Techniques for Share Price Forecasting

Forecasting the stock market on the financial and commodity markets is a huge problem for investors, businesses, and speculators, but it could be fruitful if done well. Financial time series, however, are among the most noisy and difficult to predict signals because of the stock market’s basic swings, nonlinear behaviour, complexity, adaptability, nonparametricity, and uncertainty. Corporate policies, political changes, general economic conditions, and trader expectations are just a few of the macroeconomic elements in finance that have an impact on stock market movements. As a result, predicting changes in financial market values can be difficult.

The stock forecasting model typically uses machine learning methods such as support vector machines (SVM), neural networks (NN), models that combine them with other algorithms, and long short-term memory (LSTM) as a deep learning tool. There have been numerous studies using artificial neural networks (ANNs) in time-series modelling and forecasting. ANNs have been used successfully in forecasting studies encompassing a wide range of domains.

Any machine learning method would find it extremely challenging because the network would need to learn a variety of things that change over time. The LSTM network performed well despite this, which is a significant advantage.

We may say that time series stock price predictions using MLP and LSTM models are both effective. SVM still need improvement for prediction applications, contrary to earlier studies that suggested it is a substitute model for financial timeseries forecasting. The more training cases we use,the better results we get in forecasting time series.

Future research is expected to contribute to the literature by comparing MLP and LSTM models with other large-scale data collected from diverse sectors and other methodologies used in time series forecasting applications. This will help determine how effective MLP and LSTM models are. We will develop a number of significant models for forecasting financial time series based on the results of these new studies.

Building numerous machine learning models for various variables, similar to the model employed but using a more advanced method to combine the data, could be one feasible solution for the data limits.

For the final price projections, combine various machine learning models into one.