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Economics, Financial Engineering and Business
Economics is the study of how people use resources efficiently and how they are produced, distributed, and consumed, and the purpose of economics will be to find the optimal creation and distribution of value in response to the scarcity of resources and lack of people’s needs.
The main fields of economics are macroeconomics and microeconomics, each of which studies the criteria by which individuals, firms, and nations make decisions about production, distribution, and consumption, as well as the functioning of markets and the international economy.
Financial engineering is the branch of economics that deals specifically with finance. Financial engineering is the study of the pricing, risk assessment, and portfolio management of financial markets and financial instruments using mathematical models and statistical methods, with the main objective of developing strategies and methods for financial institutions and companies to minimize risk and maximize profits.
In this economic and financial context, the activities of organizations and individuals that profit from providing goods and services are referred to as business. The purpose of business is for companies and sole proprietors to contribute to society and earn profits by providing goods and services. Important concepts in business include marketing, financial management, human resource management, and strategic decision making.
Relation to Artificial Intelligence Technology
The application of artificial intelligence technology to these problems has attracted particular attention in recent years. The following are examples of such applications.
The first example is economic forecasting using artificial intelligence technology. Specifically, the use of artificial intelligence in financial market forecasting can be used to predict trends in stock prices and foreign exchange rates.
The next example is big data analysis using artificial intelligence technology, which enables companies to analyze customer behavior and preferences to forecast product demand and understand customer needs, and to find the best way to provide products and services.
Furthermore, artificial intelligence technology can also help optimize labor and resources. This would be production planning and inventory management using artificial intelligence, which in turn are expected to improve productivity and reduce costs for firms.
Various topics related to this economics, financial engineering, and business are discussed here.
Various topics in economics and financial engineering and business
Behavioral economics will be the field of economics concerning when human behavior deviates from the assumptions of economic theory. While traditional economics assumes that people make rational decisions and seek to maximize their own self-interest, in reality, people behave in more complex and unpredictable ways. Behavioral economics aims to improve the design of markets and policies and enhance social welfare by understanding how people behave.
Behavioral economics is an interdisciplinary field that combines the disciplines of psychology, sociology, behavioral science, and economics. Typical areas of research include prospect theory, framing effects, loss aversion tendencies, temporal preferences, choice abundance, and the influence of social norms.
Philosophy is an approach to deep contemplation and theoretical reflection on a variety of questions. Philosophy has played an important role in transforming society by fostering logical thinking, problem-solving skills, and ethical judgment. Hiroki Azuma, a contemporary Japanese philosopher, has stated that one such movement by philosophy may be the evolution of modern IT.
Heuristics refers to expedient or heuristic methods used when there is a need to solve a problem or make a decision on an uncertain matter, but there are no clear clues to do so. Contrasted with heuristics are algorithms, which are methods that can be rigorously followed to obtain times.
Economic mathematics is the study of mathematical methods of analysis in economics. It refers to the application of mathematical methods to economics, the study of resource allocation and decision-making by society and individuals, in order to model and analyze mathematically in order to gain deeper insights.
Economic mathematics is used to model theories of supply and demand or by applying mathematical approaches to a variety of other economic problems, such as pricing, cost reduction, production optimization, investment, portfolio theory, and financial market theory.
Financial engineering (FE) is the discipline that applies mathematical and statistical methods and econometric tools to the theoretical analysis, design, valuation, and risk management of financial instruments and financial markets, with the main objective of modeling the prices and risks of financial instruments and developing efficient investment and risk management strategies.
The Black-Scholes model is a valuation model of option prices in financial engineering. This model was published by Fisher Black and Merton Scholes in 1973 and is used to calculate the prices of equity options and other derivative financial instruments. The model has been used to calculate prices of equity options and other financial derivatives, and has also been applied to pricing derivatives and predicting volatility.
- Fast & Slow How is your will determined?
- Predictably Inconvenient
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Applications
- Overview and Various Implementations of Bayesian Estimation
- Overview of Reinforcement Learning Techniques and Various Implementations
- Overview of Game Theory and Examples of Integration and Implementation with AI Technology
- Overview of Intelligence Augmentation (IA) and its application examples
- Overview of Causal Inference and Causal Search
- Overview of Support Vector Machines, examples of application, and various implementations
- Time Series Analysis with Prophet
- Time Series Data Analysis
- Application of State Space Model to Marketing
- Various Applications and Implementation Examples of Knowledge Graphs
- Overview of change detection techniques and implementation examples
- Overview of twitter recommendation algorithms
- Simulation, Data Science and Artificial Intelligence
- Sparse Modeling and Multivariate Analysis (8) Sparsity of Time Transitions
- About the various methods and implementations of Explainable Machine Learning
- Overview of Bayesian Structural Time Series Models and Examples of Applications and Implementations
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