CAST: using neural networks to improve trading systems based on technical analysis by means of the RSI financial indicator

Stock price predictions have been a field of study from several points of view including, among others, artificial intelligence and expert systems. For short term predictions, the technical indicator relative strength indicator (RSI) has been published in many papers and used worldwide. CAST is presented in this paper. CAST can be seen as a set of solutions for calculating the RSI using arti ficial intelligence techniques. The improvement is based on the use of feedforward neural networks to calculate the RSI in a more accurate way, which we call the iRSI. This new tool will be used in two sce narios. In the first, it will predict a market in our case, the Spanish IBEX 35 stock market. In the second, it will predict single company values pertaining to the IBEX 35. The results are very encouraging and reveal that the CAST can predict the given market as a whole along with individual stock pertaining to the IBEX 35 index.This work is supported by the Spanish Ministry of Industry, Tourism, and Commerce under the EUREKA project SITIO (TSI- 020400-2009-148), SONAR2 (TSI-020100-2008-665), INNOVA 3.0 (TSI-020100-2009-612) and GO2 (TSI-020400-2009-127).Publicad

Close to the metal: Towards a material political economy of the epistemology of computation

This paper investigates the role of the materiality of computation in two domains: blockchain technologies and artificial intelligence (AI). Although historically designed as parallel computing accelerators for image rendering and videogames, graphics processing units (GPUs) have been instrumental in the explosion of both cryptoasset mining and machine learning models. The political economy associated with video games and Bitcoin and Ethereum mining provided a staggering growth in performance and energy efficiency and this, in turn, fostered a change in the epistemological understanding of AI: from rules-based or symbolic AI towards the matrix multiplications underpinning connectionism, machine learning and neural nets. Combining a material political economy of markets with a material epistemology of science, the article shows that there is no clear-cut division between software and hardware, between instructions and tools, and between frameworks of thought and the material and economic conditions of possibility of thought itself. As the microchip shortage and the growing geopolitical relevance of the hardware and semiconductor supply chain come to the fore, the paper invites social scientists to engage more closely with the materialities and hardware architectures of ‘virtual’ algorithms and software

Investment Performance of Machine Learning: Analysis of S&P 500 Index

This study aims to explore the prediction of S&P 500 stock price movement and conduct an analysis of its investment performance. Based on the S&P 500 index, the study compares three machine learning models: ANN, SVM, and Random Forest. With a performance evaluation of S&P 500 index historical data spanning from 2014 to 2018, we find: (1) By overall performance measures, machine learning models outperform benchmark market index. (2) By risk-adjusted measures, the empirical results suggest that Random Forest generates the best performance, followed by SVM and ANN. Keywords: ANN, SVM, Random Forest, Machine Learning, Investment Performance JEL Classifications: C11; C15; C53; G17 DOI: https://doi.org/10.32479/ijefi.892