Abstract
The study of decision-making is an intellectual discipline; mathematics, sociology, psychology, economics, political science, artificial intelligence, neuroscience and physics. Conventional decision theory tells us what choice of behavior should be made if we follow certain axioms. Scientific curiosity instructs us to reconsider beyond any area in which we have defined ourselves. We design the intertwining of brain, genetics, phylogenetics, and artificial and neural networks in financial trading to find the best combinations of parameter values in financial trading, incorporating them into ANN models for stock selection and trader identification. The purpose and goal of the paper is to make financial decisions in the intertwining of the brain, genetics, phylogenetics and artificial neural networks, focusing on opening new foundations, giving insights into the foundation rock that lies beneath that soil. Science seeks basic natural laws. Mathematics seeks new theorems to build on old ones. Engineering builds systems to address human needs. The three disciplines are interdependent, but different and yet Claude Shannon simultaneously makes a central contribution to all three disciplines, this was the guiding idea of our work (finance, neuroscience, artificial intelligence).
Highlights
Conventional decision theory only tells us which behavioral choice should be made if we follow certain axioms
The problem is that the fMRI studies are very tricky to do, and what you have to realize is that when you see a pretty picture of which parts of the brain are lighting up, that’s a composite
If we look briefly at the decision-making process rational, intuitive, or emotional understanding the functionality of the human brain is key to improving the quality of human decision-making
Summary
Conventional decision theory only tells us which behavioral choice should be made if we follow certain axioms. How the brain can “filter” information by “learning that certain pieces of information are more important than others in order to change behavior appropriately.” (Sohal, 2019). The prefrontal cortex plays an important role in this process and can determine which information to pay attention to and which to ignore It makes decisions based on signals from other parts of the brain, such as the hippocampus, where “anxiety neurons” reside. In a study by Neural Networks in Finance and Investing, Robert Trippi and Efraim Turban gathered a “stellar collection” of articles by industry and academics experts on the applications of neural networks in this important arena They discuss the successes and failures of neural networks, as well as identify the huge unrealized potential of neural networks in a number of specialized areas of financial decisionmaking. Some chapters discuss how to use neural networks to predict stock markets, commodity trading, bond and mortgage risk assessment, bankruptcy prediction, and investment strategy implementation
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