Abstract

Prediction of future stock markets has long been, and will continue to be a relevant topic. However, predicting markets is one of the most challenging areas to work with due to the unpredictability of the market. The extent to which markets can be predicted is a debated subject that has not yet been answered.

A common approach is to use machine learning in combination with historical data to predict future stock prices. In this report, a classical machine learning method, LSTM, will be applied to nickel product prices to predict future product prices. The data used is provided by the company Harald Pihl, which has been trading various metals since the early 1900s. As a comparative material, the method is also applied to data on the nickel futures market.

The results conclude that a larger number of data points are required for the prediction of nickel products to generate a credible result. In addition to this, there is a significant variation in the quality of the results depending on the dataset being used. The difference in results is due, among other things, to the number of data points, fluctuations in the dataset, and the regularity of the dataset.