A Review of Artificial Neural Networks Application to Stock Market Predictions

The purpose of this paper is to review artificial neural network applications used in the field of stock price forecasting. The field of stock price forecasting has increasingly grown to be an important subject matter for researchers, everyday investors and practitioners in the finance domain as it aids financial decision making. This study brings to attention some of the neural network applications used in stock price forecasting focusing on application comparisons on different stock market data and the gaps that can be worked on in the foreseeable future. This work makes an introduction of neural network applications to those novels in the field of artificial intelligence. Keywords: Neural Networks, Forecasting Stock Price. Financial Markets, Complexity, Error Measures, Decision Makin

A Quantum based Evolutionary Algorithm for Stock Index and Bitcoin Price Forecasting

Quantum computing has emerged as a new dimension with various applications in different fields like robotic, cryptography, uncertainty modeling etc. On the other hand, nature inspired techniques are playing vital role in solving complex problems through evolutionary approach. While evolutionary approaches are good to solve stochastic problems in unbounded search space, predicting uncertain and ambiguous problems in real life is of immense importance. With improved forecasting accuracy many unforeseen events can be managed well. In this paper a novel algorithm for Fuzzy Time Series (FTS) prediction by using Quantum concepts is proposed in this paper. Quantum Evolutionary Algorithm (QEA) is used along with fuzzy logic for prediction of time series data. QEA is applied on interval lengths for finding out optimized lengths of intervals producing best forecasting accuracy. The algorithm is applied for forecasting Taiwan Futures Exchange (TIAFEX) index as well as for Bitcoin crypto currency time series data as a new approach. Model results were compared with many preceding algorithms

The Importance of Quantum Information in the Stock Market and Financial Decision Making in Conditions of Radical Uncertainty

The Universe is a coin that’s already been flipped, heads or tails predetermined: all we’re doing is uncovering it the ‘paradox’ is only a conflict between reality and your feeling of what reality ‘ought to be’.Richard FeynmanThe aim of the research takes place through two parallel directions. The first is gaining an understanding of the applicability of quantum mechanics/quantum physics to human decision-making processes in the stock market with quantum information as a decision-making lever, and the second direction is neuroscience and artificial intelligence using postulates analogous to the postulates of quantum mechanics and radical uncertainty in conditions of radical uncertainty.The world of radical uncertainty (radical uncertainty is based on the knowledge of quantum mechanics from the claim that there is no causal certainty). it is everywhere in our world. "Radical uncertainty is characterized by vagueness, ignorance, indeterminacy, ambiguity and lack of information. He prefers to create 'mysteries' rather than 'puzzles' with defined solutions. Mysteries are ill-defined problems in which action is required, but the future is uncertain, the consequences unpredictable, and disagreement inevitable. "How should we make decisions in these circumstances?" (J. Kay and M. King, 2020), while "uncertainty and ambiguity are at the very core of the stock market. "Narratives are the currency of uncertainty" (N. Mangee, 2022)

The Importance of Quantum Information in the Stock Market and Financial Decision Making in Conditions of Radical Uncertainty

The Universe is a coin that’s already been flipped, heads or tails predetermined: all we’re doing is uncovering it the ‘paradox’ is only a conflict between reality and your feeling of what reality ‘ought to be’.Richard FeynmanThe aim of the research takes place through two parallel directions. The first is gaining an understanding of the applicability of quantum mechanics/quantum physics to human decision-making processes in the stock market with quantum information as a decision-making lever, and the second direction is neuroscience and artificial intelligence using postulates analogous to the postulates of quantum mechanics and radical uncertainty in conditions of radical uncertainty.The world of radical uncertainty (radical uncertainty is based on the knowledge of quantum mechanics from the claim that there is no causal certainty). it is everywhere in our world. "Radical uncertainty is characterized by vagueness, ignorance, indeterminacy, ambiguity and lack of information. He prefers to create 'mysteries' rather than 'puzzles' with defined solutions. Mysteries are ill-defined problems in which action is required, but the future is uncertain, the consequences unpredictable, and disagreement inevitable. "How should we make decisions in these circumstances?" (J. Kay and M. King, 2020), while "uncertainty and ambiguity are at the very core of the stock market. "Narratives are the currency of uncertainty" (N. Mangee, 2022)

Nature inspired computational intelligence for financial contagion modelling

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Financial contagion refers to a scenario in which small shocks, which initially affect only a few financial institutions or a particular region of the economy, spread to the rest of the financial sector and other countries whose economies were previously healthy. This resembles the “transmission” of a medical disease. Financial contagion happens both at domestic level and international level. At domestic level, usually the failure of a domestic bank or financial intermediary triggers transmission by defaulting on inter-bank liabilities, selling assets in a fire sale, and undermining confidence in similar banks. An example of this phenomenon is the failure of Lehman Brothers and the subsequent turmoil in the US financial markets. International financial contagion happens in both advanced economies and developing economies, and is the transmission of financial crises across financial markets. Within the current globalise financial system, with large volumes of cash flow and cross-regional operations of large banks and hedge funds, financial contagion usually happens simultaneously among both domestic institutions and across countries. There is no conclusive definition of financial contagion, most research papers study contagion by analyzing the change in the variance-covariance matrix during the period of market turmoil. King and Wadhwani (1990) first test the correlations between the US, UK and Japan, during the US stock market crash of 1987. Boyer (1997) finds significant increases in correlation during financial crises, and reinforces a definition of financial contagion as a correlation changing during the crash period. Forbes and Rigobon (2002) give a definition of financial contagion. In their work, the term interdependence is used as the alternative to contagion. They claim that for the period they study, there is no contagion but only interdependence. Interdependence leads to common price movements during periods both of stability and turmoil. In the past two decades, many studies (e.g. Kaminsky et at., 1998; Kaminsky 1999) have developed early warning systems focused on the origins of financial crises rather than on financial contagion. Further authors (e.g. Forbes and Rigobon, 2002; Caporale et al, 2005), on the other hand, have focused on studying contagion or interdependence. In this thesis, an overall mechanism is proposed that simulates characteristics of propagating crisis through contagion. Within that scope, a new co-evolutionary market model is developed, where some of the technical traders change their behaviour during crisis to transform into herd traders making their decisions based on market sentiment rather than underlying strategies or factors. The thesis focuses on the transformation of market interdependence into contagion and on the contagion effects. The author first build a multi-national platform to allow different type of players to trade implementing their own rules and considering information from the domestic and a foreign market. Traders’ strategies and the performance of the simulated domestic market are trained using historical prices on both markets, and optimizing artificial market’s parameters through immune - particle swarm optimization techniques (I-PSO). The author also introduces a mechanism contributing to the transformation of technical into herd traders. A generalized auto-regressive conditional heteroscedasticity - copula (GARCH-copula) is further applied to calculate the tail dependence between the affected market and the origin of the crisis, and that parameter is used in the fitness function for selecting the best solutions within the evolving population of possible model parameters, and therefore in the optimization criteria for contagion simulation. The overall model is also applied in predictive mode, where the author optimize in the pre-crisis period using data from the domestic market and the crisis-origin foreign market, and predict in the crisis period using data from the foreign market and predicting the affected domestic market

Methodologies for innovation and best practices in Industry 4.0 for SMEs

Today, cyber physical systems are transforming the way in which industries operate, we call this Industry 4.0 or the fourth industrial revolution. Industry 4.0 involves the use of technologies such as Cloud Computing, Edge Computing, Internet of Things, Robotics and most of all Big Data. Big Data are the very basis of the Industry 4.0 paradigm, because they can provide crucial information on all the processes that take place within manufacturing (which helps optimize processes and prevent downtime), as well as provide information about the employees (performance, individual needs, safety in the workplace) as well as clients/customers (their needs and wants, trends, opinions) which helps businesses become competitive and expand on the international market. Current processing capabilities thanks to technologies such as Internet of Things, Cloud Computing and Edge Computing, mean that data can be processed much faster and with greater security. The implementation of Artificial Intelligence techniques, such as Machine Learning, can enable technologies, can help machines take certain decisions autonomously, or help humans make decisions much faster. Furthermore, data can be used to feed predictive models which can help businesses and manufacturers anticipate future changes and needs, address problems before they cause tangible harm

Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting

More accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated. Examples include seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. These forecasting models have resulted in an over-reliance on the use of informal judgment and higher expenses when lacking the ability to determine data characteristics and patterns. The hybridization of optimization methods and superior evolutionary algorithms can provide important improvements via good parameter determinations in the optimization process, which is of great assistance to actions taken by energy decision-makers. This book aimed to attract researchers with an interest in the research areas described above. Specifically, it sought contributions to the development of any hybrid optimization methods (e.g., quadratic programming techniques, chaotic mapping, fuzzy inference theory, quantum computing, etc.) with advanced algorithms (e.g., genetic algorithms, ant colony optimization, particle swarm optimization algorithm, etc.) that have superior capabilities over the traditional optimization approaches to overcome some embedded drawbacks, and the application of these advanced hybrid approaches to significantly improve forecasting accuracy