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Financial Informatics –V: Financial Knowledge Based Systems

Financial Informatics –V: Financial Knowledge Based Systems. Khurshid Ahmad, Professor of Computer Science, Department of Computer Science Trinity College, Dublin-2, IRELAND November 17 th , 2008. https://www.cs.tcd.ie/Khurshid.Ahmad/Teaching.html. 1. Knowledge Based Systems.

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Financial Informatics –V: Financial Knowledge Based Systems

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  1. Financial Informatics –V:Financial Knowledge Based Systems Khurshid Ahmad, Professor of Computer Science, Department of Computer Science Trinity College, Dublin-2, IRELAND November 17th, 2008. https://www.cs.tcd.ie/Khurshid.Ahmad/Teaching.html 1

  2. Knowledge Based Systems A computer program which, with its associated data, embodies organised knowledge concerning some specific area of human activity. Such a system is expected to perform competently, skilfully and in a cost-effective manner; it may be thought of as a computer program which mimics the performance of a human expert.

  3. Frame-based Expert System:Financial Statement Analysis Financial statement analysis is critical for finance management; the analysis involves utilizes financial ratios and, in turn, is used for making investment decisions and financial control. Values used in calculating financial ratios are taken from the balance sheet, income statement, cash flow statement and (rarely) statement of retained earnings. The traditional approach to analyzing financial statements mainly relies on the complicated and laborious manual analysis and process in which financial consultants, accountants, and bankers are involved. Weissor Shiue, Sheng-Tun Li, Kuan-Ju Chen (2008). A frame knowledge system for managing financial decision knowledge. Expert Systems with Applications 35 (2008) 1068–1079.

  4. Frame-based Expert System:Financial Statement Analysis A frame-based system for financial statement analysis has been proposed by Shiue et al (2007). The authors claim that ‘decomposing and structuring of knowledge by financial analysis experts’ into a frame-based knowledge representation helps to encapsulate the knowledge experts as objects. ‘Inheritance between objects is generated and evolved in terms of the degree of knowledge abstraction and generalization.’ (Shiue et al 2007:1069). Weissor Shiue, Sheng-Tun Li, Kuan-Ju Chen (2008). A frame knowledge system for managing financial decision knowledge. Expert Systems with Applications Vol 35 (2008) 1068–1079.

  5. Frame-based Expert System:Engineering the knowledge of Financial Statement Analysis Weissor Shiue, Sheng-Tun Li, Kuan-Ju Chen (2008). A frame knowledge system for managing financial decision knowledge. Expert Systems with Applications Vol 35 (2008) 1068–1079.

  6. Frame-based Expert System:Heuristics for compiling financial statements When it comes to analysis of short-term liquidity, it can be considered to start by analyzing the current ratio. A good current ratio indicates that the current assets of the company can fully cover its current liabilities, therefore the short-term liquidity should also be good. However, it is by no means completely so. Even if the current ratio is not so good, the company still may not be facing an immediate financial crisis due to its good short-term payment ability, which represents the ability of generating enough cash inflow in time to cover cash outflow. Weissor Shiue, Sheng-Tun Li, Kuan-Ju Chen (2008). A frame knowledge system for managing financial decision knowledge. Expert Systems with Applications Vol 35 (2008) 1068–1079.

  7. Frame-based Expert System:Heuristics for compiling financial statements In general, long-term solvency analysis begins by studying a firm’s financial structure. It is processed by evaluating stockholders’ equity to asset ratio because stockholders’ equity belong to the firm, not funds from outside, and will be retained in the firm no matter the economy is good or bad, i.e. the firm has no obligation to pay out. In other words, more stockholders’ equity means that the firm has more ability to stand against a bad economic situation and the financial risk is relatively lower for investors and creditors, thus the long-term solvency will be rated as good. Weissor Shiue, Sheng-Tun Li, Kuan-Ju Chen (2008). A frame knowledge system for managing financial decision knowledge. Expert Systems with Applications Vol 35 (2008) 1068–1079.

  8. Frame-based Expert System:An ontology of financial statements Shiue et al 2007:1071 Weissor Shiue, Sheng-Tun Li, Kuan-Ju Chen (2008). A frame knowledge system for managing financial decision knowledge. Expert Systems with Applications Vol 35 (2008) 1068–1079.

  9. Frame-based Expert System:A frame-based representation of liquidity Weissor Shiue, Sheng-Tun Li, Kuan-Ju Chen (2008). A frame knowledge system for managing financial decision knowledge. Expert Systems with Applications Vol 35 (2008) 1068–1079.

  10. Frame-based Expert System:Rules for computing liquidity • IF CURRENT RATIO is bad • & NET OPERATING CYCLE is bad • THEN short-term liquidity is rated as bad • IF NET OPERATING CYCLE is bad • & SALES GROWTH RATE is bad • THEN short-term liquidity is rated as fair. Weissor Shiue, Sheng-Tun Li, Kuan-Ju Chen (2008). A frame knowledge system for managing financial decision knowledge. Expert Systems with Applications Vol 35 (2008) 1068–1079.

  11. Frame-based Expert System:Rules for computing liquidity 13 basic financial ratios were rated as five qualitative categorizations following the qualitative criteria: very bad, bad, fair, good, and very good. Then taking CR, NOC, and S for instance, the expert rated the 13 financial ratios from level 1 to level 5 according : Weissor Shiue, Sheng-Tun Li, Kuan-Ju Chen (2008). A frame knowledge system for managing financial decision knowledge. Expert Systems with Applications Vol 35 (2008) 1068–1079.

  12. Frame-based Expert System:Rules for computing liquidity O Good X Fair Δ  Bad Weissor Shiue, Sheng-Tun Li, Kuan-Ju Chen (2008). A frame knowledge system for managing financial decision knowledge. Expert Systems with Applications Vol 35 (2008) 1068–1079.

  13. Frame-based Expert System:Systems Architecture Weissor Shiue, Sheng-Tun Li, Kuan-Ju Chen (2008). A frame knowledge system for managing financial decision knowledge. Expert Systems with Applications Vol 35 (2008) 1068–1079.

  14. Frame-based Expert System:Evaluation A total of 567 companies (quoted on the Taiwan Stock Exchange) were chosen from different industry sectors: cement, food , plastic, textiles, electricals, chemicals, glass, papermaking, steel, rubber, automobiles, electronics, construction, transportation, travel, department store and others. Financial sector was excluded. Weissor Shiue, Sheng-Tun Li, Kuan-Ju Chen (2008). A frame knowledge system for managing financial decision knowledge. Expert Systems with Applications Vol 35 (2008) 1068–1079.

  15. Frame-based Expert System:Evaluation 50% of the companies were selected at random and the data used to test the system and compared with experts’ opinion. The KBS constructed based on the expert’s knowledge had a misclassification error of 13.4%, which stands for the inconsistency between the system and the expert’s knowledge. Weissor Shiue, Sheng-Tun Li, Kuan-Ju Chen (2008). A frame knowledge system for managing financial decision knowledge. Expert Systems with Applications Vol 35 (2008) 1068–1079.

  16. Case Based Systems:Credit Risk Assessment Jerzy Stefanowski; Szymon Wilk (2001). Evaluating business credit risk by means of approach-integrating decision rules and Casebased Learning.International Journal of Intelligent Systems in Accounting, Finance and Manag...Jun 2001; 10, 2; ABI/INFORM Global pg. 97

  17. Case Based Systems:Credit Risk Assessment The case based system does not require explicit coding of rules: each training example is rendered as a case frame and given an overall credit score. The system is then tested with unknown examples: if the systems fails to correctly identify the unknown case, then this case is included in the knowledge base of the system Jerzy Stefanowski; Szymon Wilk (2001). Evaluating business credit risk by means of approach-integrating decision rules and Casebased Learning.International Journal of Intelligent Systems in Accounting, Finance and Manag...Jun 2001; 10, 2; ABI/INFORM Global pg. 97

  18. Case Based Systems:An Ontology for Credit Risk Assessment under Polish Practice Jerzy Stefanowski; Szymon Wilk (2001). Evaluating business credit risk by means of approach-integrating decision rules and Casebased Learning.International Journal of Intelligent Systems in Accounting, Finance and Manag...Jun 2001; 10, 2; ABI/INFORM Global pg. 97

  19. Case Based Systems:Heuristics for Credit Risk Assessment under Polish Practice Jerzy Stefanowski; Szymon Wilk (2001). Evaluating business credit risk by means of approach-integrating decision rules and Casebased Learning.International Journal of Intelligent Systems in Accounting, Finance and Manag...Jun 2001; 10, 2; ABI/INFORM Global pg. 97

  20. Case Based Systems:Refined heuristics for Credit Risk Assessment under Polish Practice Jerzy Stefanowski; Szymon Wilk (2001). Evaluating business credit risk by means of approach-integrating decision rules and Casebased Learning.International Journal of Intelligent Systems in Accounting, Finance and Manag...Jun 2001; 10, 2; ABI/INFORM Global pg. 97

  21. Case Based Systems:Cases for Credit Risk Assessment under Polish Practice The training set – large number of good risks Jerzy Stefanowski; Szymon Wilk (2001). Evaluating business credit risk by means of approach-integrating decision rules and Casebased Learning.International Journal of Intelligent Systems in Accounting, Finance and Manag...Jun 2001; 10, 2; ABI/INFORM Global pg. 97

  22. Case Based Systems:Evaluation The testing results Jerzy Stefanowski; Szymon Wilk (2001). Evaluating business credit risk by means of approach-integrating decision rules and Casebased Learning.International Journal of Intelligent Systems in Accounting, Finance and Manag...Jun 2001; 10, 2; ABI/INFORM Global pg. 97

  23. Case Based Systems:Automated Trading Systems The Penn-Lehman Automated Trading Project is a broad investigation of algorithms and strategies for automated trading in financial markets. The PLAT Project’s centerpiece is the Penn Exchange Simulator (PXS), a software simulator for automated stock trading that merges automated client orders for shares with real-world, real-time order data. PXS automatically computes client profits and losses, volumes traded, simulator and external prices, and other quantities of interest. Kearns, Michael., and Luis Ortiz (2003). The Penn-Lehman Automated Trading Project. IEEE INTELLIGENT SYSTEMS. (NOVEMBER/DECEMBER 2003), pp 22-31

  24. Case Based Systems:Automated Trading Systems The PLAT project has demonstrated the strength of case-based reasoning systems in its ability to learn aspects of the microstructure of NASDAQ market transactions. Especially the dealing over its electronic cross-over network. A case based system learns to compute the spread – difference between bid and ask prices- and makes appropriate buy/sell decisions. Kearns, Michael., and Luis Ortiz (2003). The Penn-Lehman Automated Trading Project. IEEE INTELLIGENT SYSTEMS. (NOVEMBER/DECEMBER 2003), pp 22-31

  25. Case Based Systems:Automated Trading Systems The case based system competed against system were mainly conventional algorithmic systems. The trial lasted over a good trading stretch and the case based system won! Kearns, Michael., and Luis Ortiz (2003). The Penn-Lehman Automated Trading Project. IEEE INTELLIGENT SYSTEMS. (NOVEMBER/DECEMBER 2003), pp 22-31

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