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Journal Article
The random neural network in price predictions
2022
Overview
Everybody likes to make a good prediction, in particular, when some sort of personal investment is involved in terms of finance, energy or time. The difficulty is to make a prediction that optimises the reward obtained from the original contribution; this is even more important when investments are the core service offered by a business or pension fund. The complexity of finance is that the human predictor may have other interests or bias than the human investor, the trust between predictor and investor will never be completely established as the investor will never know if the predictor has generated, intentionally or unintentionally, the optimum possible reward. This article presents the random neural network (RNN) in a recurrent configuration for the 4th industrial revolution on a Fintech application; the RNN is proposed to make predictions on time series data, specifically, prices. The biological model inspired by the brain structure and neural interconnections make predictions entirely on previous data from the time series rather than predictions based on several uncorrelated inputs. The model is validated against the property, stock and Fintech market: (1) UK property prices, (2) stock market indice prices, (3) cryptocurrency prices. Experimental results show that the proposed method makes accurate predictions on different investment portfolios. The prediction accuracy of the proposed RNN model is compared against long short-term memory (LSTM) and linear regression (LR) models.
Publisher
Springer London,Springer Nature B.V
Subject
/ Biological models (mathematics)
/ Computational Biology/Bioinformatics
/ Computational Science and Engineering
/ Data Mining and Knowledge Discovery
/ Finance
/ Image Processing and Computer Vision
/ Prices
/ Pricing
/ Probability and Statistics in Computer Science
/ Special Issue on Advances of Neural Computing in the era of 4th Industrial Revolution
