Prediction of the financial return of the paper sector with artificial neural networks

Abstract

The unknown nature of the future requires us to question our decisions and seek reliable methods. The artificial neural networks approach, which is one of the methods used to best predict the future and one that is important for decision making has been thought of, particularly in recent years, as a method with a high level of validity in the fields of economy and financial prediction. The Istanbul Stock Exchange (ISE), at which millions of national and international investors operate, is among the developed stock exchanges of the world. The ISE has the attributes of being appropriate for making predictions regarding financial returns, without any sector differentiation, as a whole. In this study, it was aimed to predict monthly stock yields of 14 different paper companies dealing with the ISE (Istanbul Stock Exchange) by using artificial neural network. Four different variables (the gold price, ISE daily trading volume, exchange rate purchase-sale average, and monthly deposit interest rates by utilizing) and 127 months data were used. Results show that the monthly stock yields of the paper sector can be predicted correctly to account for 95% of the variability of data with the artificial neural network model, and the average absolute percentage failure value was 6.85%.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 36 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.