History of Reasoning Methods and Artificial Intelligence Technologies with Finance

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Indirect Investment Securities

The case where an investor directly invests through buying and selling instruments has been discussed above briefly. Another type of investment exists, which is the indirect Investment. Indirect investment is when traders invest in an indirect manner through purchasing shares of investment companies.
• Mutual Funds, the concept of the mutual fund is based on holding a securities portfolio with a specified objective and policy. It gives to the investors an opportunity to access diversified portfolios of stocks, equities, bonds, money markets, and other securities.
Now that the most traded types of securities are introduced, it is important to gain a certain familiarity with types of financial markets.

Financial Markets

Financial markets are the centers of economic development or regression of a nation. It is the place where the different previously described securities are traded, and where borrowers and suppliers of funds meet. Suppliers are normally the parties that supply funds as an investment, whether it is individual investors, companies, firms, or corporations. Any party with a surplus fund dedicated for investment is a supplier. While borrowers simply (as their name indicated), are parties that borrow money from suppliers in different terms, as dept or investment loans, to professionally employ these funds for the intention of making profit. The main and more important factor of markets is the reputation. It directly and greatly affects the allocations of funds. As in financial securities there are different possibilities of categorizing markets. However the general categorization of financial markets is into two types, primary and secondary markets.

Primary Market

Also known as the Initial Public Offer, or the New Issue Market. This market involves new issues of securities, where they are initially sold. Hence, this market provides a direct flow of cash to the party issuing securities. It holds the burden of selling securities to the public. It acts as a main reservoir of funds raised from many entities like individual investors, financial companies, institutions, etc. This makes it the best place for corporate sectors to raise their funds. The role of a Primary market can be resumed into, investigation, underwriting and Distribution.

Quantitative and Qualitative Fundamental Factors

In fundamental analysis the factors that contribute in the analysis normally fall into two categories, the quantitative and qualitative factors. Similar to the regular definition of the two words, quantitative factors are the ones that are subject to numerical calculation normally anything concerning companies financial data, while qualitative factors are normally company aspects that are less tangible. Normally, each company has documents that are concerned with all its financial data. There are mainly measurable numeric characteristics of a certain business. Financial statements are considered to be the most important source of financial information of a company. Other than financial statements, there are many sources used to derive the quantitative factors. Examples of such sources are balance sheets, income statements, statements of cash flow, annual reports, footnotes, and much more. Fundamental analysts use these financial documents to determine certain measures and ratios for the quantitative part of the fundamental analysis [37]. Figure 2.1, lists all categorized quantitative and qualitative factors. Below is a brief introduction to each of the commonly used quantitative factors.
• Earnings Per Share (EPS), usually earnings are used to indicate the expected dividends and growth potential of a company. The EPS ratio is calculated through dividing the net earnings of a company to the number of issued shares of a company. For example, if a company reports a year net earning of 20 million USD, and has 10 million outstanding shares, then the EPS of that company becomes 2 USD per share. This ratio plays an important role in comparing earnings of different companies.

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Table of contents :

0.1 Overview of the Situation Understudy
0.2 Motivation and Contribution
0.3 Outline of the Dissertation
1 Portfolio Management 
1.1 Introduction
1.2 Financial Investment
1.3 Financial Securities
1.3.1 Direct Investment Securities
1.3.2 Indirect Investment Securities
1.4 Financial Markets
1.4.1 Primary Market
1.4.2 Secondary Market
1.5 Security Analysis
1.5.1 Fundamental Analysis
1.5.2 Technical Analysis
1.5.3 Efficient Market Theory
1.6 Portfolio Analysis
1.6.1 Diversification
1.7 Portfolio Selection
1.7.1 Modern Portfolio Theory (MPT)
1.7.2 Capital Asset Pricing Method
1.7.3 Arbitrage Pricing Model
1.8 Portfolio Evaluation
1.8.1 Sharpe’s Rule
1.8.2 Tranor Ratio
1.8.3 Jensen’s Alpha
1.8.4 Information Ratio
1.9 Conclusion
2 Fundamental and Technical Analysis, Introducing Technical Indicators
2.1 Introduction
2.2 Fundamental Analysis
2.2.1 Introducing Fundamental analysis
2.2.2 Quantitative and Qualitative Fundamental Factors
2.2.3 Information Evaluation
2.2.4 Strengths and Weaknesses of Fundamental Analysis
2.3 Technical analysis
2.3.1 History
2.3.2 Rational of Technical Analysis
2.3.3 Price Fields, Charts, and Patterns
2.4 Technical Indicators
2.4.1 Crossovers, Divergences, and Breakthroughs
2.4.2 Technical Indicators
2.5 Conclusion
3 History of Artificial Intelligence Technologies with Finance: The General Pre-processing Approach 
3.1 Introduction
3.2 A Logical Reflection
3.3 History of Reasoning Methods and Artificial Intelligence Technologies with Finance
3.3.1 Visual Technical Pattern Recognition Approaches in Finance
3.3.2 History of Fuzzy Systems, Genetic Algorithms, and Trading Rules with Finance
3.3.3 Hybrid Artificial Intelligence Systems in Finance
3.4 Possibility Theory
3.4.1 Assumptions of Possibility Theory
3.4.2 Possibility Theory with information Fusion and Uncertainty Handling
3.5 Conclusion
4 Hybrid Probability Possibility Indicators-Based Decision Support Approach 
4.1 Introduction
4.2 The General Data Pre-processing System
4.2.1 Technical Indicators Module
4.2.2 Probability Module
4.2.3 Transformation Module
4.3 Proposed Decision Fusion Support Systems (DSS)
4.3.1 Majority Vote Decision Support System
4.3.2 Non-weighted Possibility Fusion Decision Support System
4.3.3 Information Theory: Entropy, Relative Entropy, and Mutual Information
4.3.4 Weighted Possibility Fusion Decision Support System
4.3.5 Dynamically Weighted Possibility Fusion DSS
4.4 System Performance Evaluation and Analysis
4.4.1 Tests on Indices
4.4.2 Evaluation Criterion
4.4.3 Studied Time Horizon
4.4.4 Indicators Selection Process
4.4.5 Systems Performance Evaluation Results
4.4.6 Winning Dates Testing
4.5 Conclusion
5 Technical Indicators Learning for fusion with Bayesian Networks 
5.1 Introduction
5.2 Graph Theory
5.2.1 Basic Terminologies: Graphs, Nodes, Arcs
5.2.2 Structure of the Graph
5.3 Basics of Bayesian Networks
5.3.1 Concepts
5.3.2 Joint Probability Distribution
5.3.3 Conditional Independence
5.3.4 Markov’s Property and Conditional Probability
5.3.5 D-separation
5.4 Reasoning with Bayesian Networks
5.4.1 Inference
5.4.2 Structure Learning
5.4.3 Parameter Learning
5.5 Learning Bayesian Networks with the bnlearn Package in R
5.5.1 What is bnlearn and the Purpose Behind Using it
5.5.2 Available Algorithms
5.6 Technical Indicators fusion Approach Learned with Bayesian Networks
5.6.1 Structure learning with bnlearn
5.6.2 Parameter learning with bnlearn
5.6.3 Testing the Learned Networks
5.7 Conclusion
6 Conclusion 
List of Publications
Bibliography

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