Abstract
Deep learning has transformed many fields includ-ing computer vision, self-driving cars, product recommendations, behaviour analysis, natural language processing (NLP), and medicine, to name a few. The financial sector is no surprise where the use of deep learning has produced one of the most lucrative applications. This research proposes a novel fintech machine learning method that uses Transformer neural networks for stock price predictions. Transformers are relatively new and while have been applied for NLP and computer vision, they have not been explored much with time-series data. In our method, self-attention mechanisms are utilized to learn nonlinear patterns and dynamics from time-series data with high volatility and nonlinearity. The model makes predictions about closing prices for the next trading day by taking into account various stock price inputs. We used pricing data from the Saudi Stock Exchange (Tadawul) to develop this model. We validated our model using four error evaluation metrics. The applicability and usefulness of our model to fintech are demonstrated by its ability to predict closing prices with a probability above 90%. To the best of our knowledge, this is the first work where transformer networks are used for stock price prediction. Our work is expected to make significant advancements in fintech and other fields depending on time-series forecasting.
Highlights
We have come a long way in developing our societies, improving and optimising every task and thing we do, and artificial intelligence (AI) is at the heart of these endeavours [1], [2]
Many works have been reported on the use of AI for the financial sector, such as the use of multilayer perceptrons (MLP) for NSADA stock index [16], the use of stacked autoencoders for US stock forecasting [17], and the use of Long short-term memory network (LSTM) to predict the closing prices of iShares MSCI United Kingdom index [18]
The vision transformer (ViT) is among the first attempts to apply the outstanding performance of Transformers [22] to image classification tasks rather than natural language processing
Summary
We have come a long way in developing our societies, improving and optimising every task and thing we do, and artificial intelligence (AI) is at the heart of these endeavours [1], [2]. Practitioners should learn how to select specific trends, seasonal components, and other data components manually, especially for financial data series with highly nonlinear and fluctuating data These drawbacks have limited their applications in advanced large-scale time-series prediction tasks. An array of deep neural network architectures has been applied to time-series models to understand trends and patterns by learning from ground truth data. Long short-term memory (LSTM) models have been proposed to improve the standard RNN model for time series analysis. We propose a novel fintech machine learning method that uses Transformer neural networks for stock price predictions. We propose a novel predictive Transformer based model with divided time series data into patches for predicating future value.
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