The Research of Case Based Reasoning Technology Applied to the Macro Financial Risk Analysis NI Yang1, WANG Ping*2, PANG Shan‐shan2 Institute of Finance, Shandong University of Finance and Economics, Jinan, China
1
School of Management, Hefei University of Technology, Hefei, China
2
354138205@qq.com Abstract With the global financial markets continuing to stride forward towards the liberalization and globalization , macro financial market environment is increasingly becoming more complex, thus leading to the increase of the factors that trigger financial risks, and that the risk management becomes more and more difficult. In this paper, the case based reasoning method is introduced into the field of macro financial risk management, completely analyzing the process of macro financial risks that is based on case based reasoning. First of all, the use of three tuples form realizes the description of the macro financial risk case; then, the article describes the correction of risk cases and the retrieval of risk cases; finally, this paper describes the learning strategies of the risk cases and the maintenance of the case library. This study provides a new research idea that can effectively predict and assess the macro financial risk events that may occur for the country and the financial institutions. Keywords Financial Risk; Risk Analysis; Case Based Reasoning; Case Revision; Case Maintenance
Introduction The so‐called risk is the possibility of loss of behavioral agent which is caused by the uncertainty of various results, under a certain condition and a certain period of time. Financial risk refers to the possibility of financial behavior results deviating from the expected results, and financial risk also is the financial results of uncertainty(Xu Feng,2007).Financial risk is the inevitable product of the market economy, which cannot be avoided. In general, the financial risk is divided into two levels: the macro level of financial risk and the micro level of financial risk. This paper focuses on the former. For the research on the technical analysis of the financial risk, there are about several methods. Since Harry Markowitz (1952) published the paper Portfolio selection, fluctuation analysis has become a very influential classical method measuring financial risk. The method assumes that the investment risk can be regarded as uncertainty of investment income, and this uncertainty can be measured by the variance or standard deviation in statistics. If the variance is greater, the corresponding risk is also greater. Variance should be provided with good statistical characteristics; therefore, this method is widely used to measure the risk of the portfolio of financial assets. Wave analysis method has advantages of simple calculation and easy to use, but this method can only describe the extent of the deviation from the expected return level, which fails to describe the direction of the deviation. In financial markets, there is the part of the proceeds of beyond the expected profit of the value, which people generally do not regard as a risk. In order to solve the defect, Harlow proposed the LPM (Lower Partial Moment) model (Wang Yi, 2012).The method believe that only income is lower than the target value when the risk occurs. Compared with the wave analysis method, the Harlow model is more consistent with the investorʹs psychological feeling. Since 1993,VaR concept was proposed by G30 members, and recommended to the banks, the use of which has attracted widely attention(Fang Xianli,2003).At present, VaR (Value at Risk) method has been widely used in the International Journal of Sociology Study, Vol. 3 No. 1‐June 2015 41 2328‐1685/15/01 041‐05, © 2015 DEStech Publications, Inc. doi: 10.12783/ijss.2015.03.008
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major global banking company and regulators. Since VaR technology provides a common language and a general framework for financial institutions in risk management and research, researchers have even predicted that the VaR method will replace the current asset liability management method and become the international standard of financial risk management. In recent years, the research of case based reasoning (Case‐Based Reasoning, CBR) and the development of its system receives peopleʹs universal attention.CBR got the historical memory of the source case with the target case tips, which is a strategy solving the target case by the instruction of the source case(Cao Lijun,2006). The main reason for the rise of CBR is the existence of many shortcomings of the traditional rule‐based system. For instance, the difficulty in acquiring knowledge and no memory for the treated problem lead to the low efficiency of reasoning, which also cause the failure of handling the exception of the case, therefore, the overall performance is more vulnerable, etc, while CBR can solve the above problems(Zhang Shuguang,2002). Since the reason of the emergence of the macro financial risks is complex, which is a kind of difficult problem with a lot of uncertain factors, it is difficult to directly analyze and calculate. This is a system engineering, which crosses multiple disciplines. In this paper, the case based reasoning method is introduced into the field of macro financial risk management. The article provides the study on the index system of macro financial risk, and analysis using CBR technology, which provides a new research idea for the country and the financial institutions to effectively predict and assess the macro financial risk events that may occur. Case Based Reasoning Technology Roger Schank(1982) published a book Dynamic Memory, which describes in detail the CBR most early research work, and provides the method of building CBR on the computer. His early thoughts were gradually implemented and applied. The basic principle of case based reasoning technology: the solution of problem of past cases are stored in the case library according to a certain way of organization. When the user input a new problem that has not been solved, first of all, the system will search the case database. If there is no similar case to the problem to be solved, it will return to the link of the description of the problem, in order to inspect and modify; If there are the cases which are consistent with the conditions, it will modify the cases which have the maximum similarity values, synthesize a number of similar cases and get the solution of the current problem. If the user is satisfied with the solution, it will output the result, improve the description of the problem, form the case of the problem, and study the case; Otherwise, it will return to the section of the description of the problem or the section of adjustment stage of the project, re obtained for the current problems, and once again obtain the certain solution of the current problem. The working process of the case based reasoning method is shown in figure 1.
FIGURE 1. THE WORK PROCESS OF CASE BASED REASONING.
In general, case‐based reasoning has the following steps (Fang Yuan, 2011; Sheng‐Tun Li, 2009):
retrieve: According to the input information about the problem to be resolved, retrieve similar cases in
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the case library;
Reuse: To obtain the solutions from the retrieved similar case ,and judge whether they conform to the requirements, if they meet, then reuse these solutions(or the combined solution of multiple solutions ),otherwise they need to be revised;
Revise: Revising the solution according to the similar cases, and make it suitable for solving the current problems, and get a new solution for the current programs;
Retain: Retaining the new case and its solution in the case library according to certain strategy, this is the learning method of CBR.
Case based reasoning as an important supplement of rule‐based reasoning technology has been noticed by artificial intelligence researchers. There are two main reasons for studying case‐based reasoning: one is to imitate the way of thinking of human reasoning, promote the development of cognitive science. The second is to establish efficient and feasible computer system (He Xiao,2005). Compared with the traditional expert system, the biggest advantage is that the dynamic knowledge base, namely through incremental learning to constantly increase and enrich the case library. The Application of Case Based Reasoning Technology in Macro Financial Risk Analysis To apply CBR technology in the macro financial risk, the first thing we must solve is the description and storage of various macro financial risk cases; the second is the matching of macro financial risk cases; the third is the correction of risk cases and learning strategies; finally is the maintenance technology of risk case library . The Description of Risk Cases The description of macro financial risk case is the description of macro financial risk events and their solutions; they can be expressed by three tuples as follows (Hu Yanqiang, 2006): Case = < problem, symptom, solution > The description of the three tuples includes three domains: the problem domain, symptoms describe domain, the solution domain. The problem domain is used for completely describing the characteristic information of the problem. The symptoms describe domain is the description of the problem domain in natural language. The solution domain is a description of the solution method used for macro financial risk case problems. There are many kinds of methods to express cases, such as script. Script recorded the specific information which has connected to a situation, namely the case is described as a series of characteristics which lead to a particular result. Simply, you can use the form of text or relational database. Express case can be in the form of a hierarchy, and other expressions, such as class, object oriented database, semantic network, neural network, frame structure, and so on. For complex form, a case can also be composed of many sub‐cases. There are many factors can cause macro financial risk and the risk formation process is complex. When analyzed by CBR technology, the first thing is to design a set of index system to describe macro financial risk. The abnormal changes of these indexes are said to appear a certain degree of financial risks. According to Chinese financial operation history, this paper chose the following test indicators: exchange rate, the index of stock market price, the index of fiscal balance, the index of foreign exchange balance, the index of balance of payments, foreign debt risk, capital adequacy ratio of financial institutions, degree of financial market foreign capital control, poor financial assets ratio, external dependency of capital project, etc. The Retrieval Process of Risk Cases The retrieval type of macro financial risk case search out the solution to the most similar problem from the case library, then revise it. The revised solution can solve the current problem. The retrieval of CBR need to achieve the following two goals: the retrieved cases must be as little as possible; the retrieved cases should be similar or related with the target case as far as possible. There are a variety of CBR retrieval strategies, the main retrieval methods are as follows(Fang Liyuan,2009): the nearest neighbor retrieval method, template retrieval, classification network model, inductive retrieval, neural network retrieval, fuzzy
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retrieval, rough set retrieval, and so on. Here are the deep retrieval methods based on financial knowledge and risk knowledge. This method gives the attributes different weights based on the attribute importance of characteristics in previous financial events and financial problems, so as to reflect the essence of the problem when matching macro financial risk case event. It is usually combined with the nearest neighbor method, and this is a special kind of retrieval based on financial knowledge. There is a class which combines the surface knowledge in financial field with the deep knowledge in financial risk field. The deep knowledge includes the causal model of the financial field and explains the results according to reasoning process and the principle of revising strategies. It avoids the retrieval of irrelevant case and even realizes the dynamic modification of index method in retrieval process. The Revision Process of Risk Case and the Retrieval Technology Based on Revision In financial risk CBR, case revisal stage is the central issue of CBR. Revisal techniques commonly used are: replacement method, conversion method, derived analogy method, multiple case synthesis method, case conversion and derivative analogy, modify knowledge, derived replay method based on the case, the correction method of genetic algorithm, constraint satisfaction method, and so on. Retrieval is vital for CBR systems, and we can take advantage of the characteristic similarity and revisal knowledge to guide the search process. The purpose of retrieving similar cases is to study the revisal knowledge. It can improve the learning efficiency of revisal knowledge, because these new knowledge can be used in retrieval and revisal stage. Therefore, we give a retrieval technology thinking based on revision according to the similarity.
Enter the target case;
Extract features from the target case;
Select the modifiable and the most similar candidate case library;
Modifiable local estimate;
Modifiable global estimate;
Determine a similar source case.
This method has its unique advantages in many aspects, such as on the issue of ʺcase library swamp ʺ,it will greatly alleviate the occurrence of such events and improve the retrieval accuracy of financial risk similar events at the same time, that is beneficial to the overall performance of the case based reasoning system. Certainly, it can also be improved, that is quantitatively determine the easiest revised case, thus to minimize the overhead of revision. The Learning Strategies of Risk Case and the Maintenance of Case Library In the CBR system, learning strategy is that if the case library has the old case which the similarity is greater than a preset threshold, get the solution of new problems through revising the old case, and then add it to the case library, so as to realize the process of knowledge cumulating. In the field of macro financial risk, maybe there is no existing cases beyond the threshold in the case library, the way in this case is to reduce the threshold and retrieve a set of cases in lower similarity, then combine with the expert experience and knowledge in financial field, proper revise these set of cases, then obtain the solution of the new macro financial risk problem, and save it in the case library. In the macro financial risk case library, financial events happen all the time; the expansion of the case library is quickly along with the learning process. An operation which must be carried out is: case base maintenance (CBM). The solution is to delete the cases which are useless to the whole system performance, thus limiting the size of the case library. The swamping problem should be reasonably controlled in CBR, these deletion strategies are required to maintain the function of CBR system and the efficiency of CBR system. Conclusions Due to the complexity of macro market environment, the lack of relevant security conditions, the rapid change of international financial form and the financial system reform in our country, the financial field has to face the severe challenge of risk. In this paper, the case based reasoning method is applied in the field of national macro financial risk; it completely studies the process of macro financial risk analysis that is based on case based reasoning; this
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research will help to improve the management level of controlling the macro financial risk in our country. The next step is to implement the macro financial risk analysis system based on case based reasoning. REFERENCES
[1]
Xu Feng. The research on Chinaʹs financial risk prevention mechanism. Hefei University of Technology, 2007.
[2]
Harry Markowitz. Portfolio selection. The Journal of Finance, 7 (1952):77‐91.
[3]
Wang Yi, Chen Zhiping, Yang Li. The development of financial market risk measurement methods. Chinese Journal of Engineering Mathematics, 29(2012):1‐22.
[4]
Fang Xianli. The research on economic globalization and financial risk prevention. Fudan University, 2003.
[5]
Cao Lijun, Wang Xinggui, Qin Junqi. Fault forecast expert system based on integration of case and rule reasoning. Computer Engineering, 1 (2006):208‐210.
[6]
Zhang Shuguang. The application research of case retrieval and maintenance in case based reasoning. University of Science and Technology of China, 2002.
[7]
R.Schank. Dynamic memory: A theory of learning in computers and people. New York：Cambridge University Press, 1982.
[8]
Fang Yuan, Hu Daiping. The research of stock price manipulation judging system based on case based reasoning. Science Technology and Engineering, 5(2011):1147‐1151.
[9]
Sheng‐Tun Li, Hei‐Fong Ho. Predicting financial activity with evolutionary fuzzy case‐based reasoning. Expert Systems with Applications, 36(2009):411‐422.
[10] He Xiao. The research on the application of case‐based reasoning in the intelligent diagnosis system based on grey correlation. Huazhong University of Science and Technology, 2005. [11] Hu Yanqiang,Liu Zhongying. Methods of estimating finance operation risk based on case discursion. Hainan Finance, 12(2006):48‐50. [12] Fang Liyuan. The research and implementation of analog estimation method based on case based reasoning. National University of Defense Technology, 2009.