knowledge based systems - trinity college dublin · 2014-03-04 · 3 5 frame-based expert system:...

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Financial Informatics –V:Financial Knowledge Based

Systems

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Khurshid Ahmad, Professor of Computer Science,Department of Computer Science

Trinity College,Dublin-2, IRELAND

March 2014.https://www.cs.tcd.ie/Khurshid.Ahmad/Teaching.html

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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.

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Frame-based Expert System: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 35 (2008) 1068–1079.

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.

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Frame-based Expert System: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.

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).

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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.

6Weissor Shiue, Sheng-Tun Li, Kuan-Ju Chen (2008). A frame knowledge system for managing financial decision


Frame-based Expert System:Heuristics for compiling financial statements

When it comes to the 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.

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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.



Frame-based Expert System:An ontology of financial statements

Shiue et al 2007:1071

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Frame-based Expert System:A frame-based representation of liquidity



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Frame-based Expert System:Rules for computing liquidity

(1) IF CURRENT RATIO is bad& NET OPERATING CYCLE is badTHEN short-term liquidity is rated as bad

(2) IF NET OPERATING CYCLE is bad& SALES GROWTH RATE is badTHEN short-term liquidity is rated as fair.



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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 :



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O� Good

X� Fair

∆ � Bad



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Frame-based Expert System:Systems Architecture



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.

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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.

16Jerzy 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

Case Based Systems:Credit Risk Assessment

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17Jerzy Stefanowski; Szymon Wilk (2001). Evaluating business credit risk by means of approach-integrating decision rules and Casebased


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

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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


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Case Based Systems:Heuristics for Credit Risk Assessment under Polish Practice



Group Liabilities Repayment Forecast

I None Excellent

II Sub-standard;

reserve/ write-down

20% of the loan

Expect delays of 1-3 months

III Questionable; reserve/

write-down 50% of

the loan

Expect delays of 3-6 months

IV Poor; reserve/ write-

down 100%

Expect delays of longer than

6 months; perhaps

Bankrupt; In liquidation

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Case Based Systems:Refined heuristics for Credit Risk Assessment under Polish Practice



Group Liabilities

I a Best borrowers

I b Good borrowers who will

be moved to the ‘best’ list

I c Average borrowers with

some risk

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Case Based Systems:Cases for Credit Risk Assessment under Polish Practice



The training set – large number of good risks

Class Number of case studies

Total %

I-A 58 64.4

I-B 11 12.2

I-C 7 7.8

II 5 5.6

III 9 10

TOTAL 90 100

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Case Based Systems:Evaluation



The testing results

Class Avg. # Rules Accuracy

Min Max Min Max

IA 28 48 67 72

IB 42 111 73 71

II 19 479 77 71

III 58 144 78 80

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Case Based Systems:Automated Trading Systems

Kearns, Michael., and Luis Ortiz (2003). The Penn-Lehman Automated Trading Project. IEEE INTELLIGENT

SYSTEMS. (NOVEMBER/DECEMBER 2003), pp 22-31

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.

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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.

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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!

knowledge based systems - trinity college dublin · 2014-03-04 · 3 5 frame-based expert system:...

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