RANKING SYSTEMIC RISKS IN BANK NETWORKS

Transcription

1 Assocaton for Informaton Systems AIS Electronc Lbrary (AISeL) ICIS 200 Proceedngs Internatonal Conference on Informaton Systems (ICIS) 200 RAKIG SYSTEIC RISKS I BAK ETWORKS Danng Hu Cty Unversty of Hong Kong, dannghu@ctyu.edu.h J. Leon Zhao Cty Unversty of Hong Kong, lzhao@ctyu.edu.h Zhmn Hua Cty Unversty of Hong Kong, zmhua2@student.ctyu.edu.h Follow ths and addtonal wors at: Recommended Ctaton Hu, Danng; Zhao, J. Leon; and Hua, Zhmn, "RAKIG SYSTEIC RISKS I BAK ETWORKS" (200). ICIS 200 Proceedngs Ths materal s brought to you by the Internatonal Conference on Informaton Systems (ICIS) at AIS Electronc Lbrary (AISeL). It has been accepted for ncluson n ICIS 200 Proceedngs by an authorzed admnstrator of AIS Electronc Lbrary (AISeL). For more nformaton, please contact elbrary@asnet.org.

2 RAKIG SYSTEIC RISKS I BAK ETWORKS Danng Hu Department of Informaton Systems, Cty Unversty of Hong Kong, Kowloon, Hong Kong SAR, Chna dannghu@ctyu.edu.h Research-n-Progress Zhmn Hua Department of Informaton Systems, Cty Unversty of Hong Kong, Kowloon, Hong Kong SAR, Chna zmhua2@student.ctyu.edu.h Abstract J. Leon Zhao Department of Informaton Systems, Cty Unversty of Hong Kong, Kowloon, Hong Kong SAR, Chna lzhao@ctyu.edu.h The recent global fnancal tsunam (2007 present) has swped the whole world wth dsastrous consequences, leadng to the banruptcy of maor bans, trllons of dollars n economc rescues, and maor natonal economc falures. Unle prevous fnancal crss, one maor drver of ths crss s the contagous falures of bans through a networ of nterban payments and correlated ban portfolos of fnancal products. The rs that the falure of a sngle ban can cause a cascadng falure of other connected bans and may potentally brng down the entre banng system (networ), s defned as systemc rs. Exstng ban rs management research focused on the causes and mpacts of systemc rs but largely gnored the montorng and preventve mechansm. In ths study, we developed a networ-based algorthm to ran systemc rs of bans and fnancal products n the ban networ. In addton, we extended the networ model of banng system developed n prevous systemc rs research and adopted ths model n the evaluaton experment of our proposed algorthm. Ths algorthm may provde an effectve mechansm for staeholders n the banng ndustry to montor and reduce systemc rs and thereby prevent system-wde breadown n ban networs. Keywords: Contagous Ban Falure, System Rss, Ranng Algorthm, etwor Analyss Thrty Frst Internatonal Conference on Informaton Systems, St. Lous 200

3 Knowledge anagement and Busness Intellgence Introducton The recent global fnancal tsunam (2007 present) has been consdered by many economsts as the worst fnancal crss snce the Great Depresson n the 930s (Bullard et al. 2009; Pendery 2009). It has resulted n the collapse of maor fnancal nsttutons le Lehman Brothers, downturns of stoc marets around the world, and even breadowns of fnancal systems n several countres le Greece and Iceland. Although the mmedate cause or trgger for ths crss s the burst of the U.S. housng bubble and the followng lqudty shortfall of maor bans, the mechansm that spreads and magnfes ther mpacts s the largely nterconnected U.S. ban networ (Harrngton 2009; arose et al. 200). In ths networ, bans are connected wth each other through nterban payments and correlated ban portfolos (.e. ownng the same fnancal product(s) such as IB stoc) (Elsnger et al. 2006). A ban s solvency largely depends on the nterban payments t receves from other bans n the networ and the value of ts portfolo of fnancal products. As such, the value of a ban depends on the fnancal health of the relevant ban networ. Therefore, correlaton n nterban payments and ban portfolo values can result n contagon of nsolvency between bans n ths networ n a domno effect. To llustrate how ths contagon of nsolvency lead to ban falures, Fgure shows a smple ban networ contanng three bans A, B and C, and two fnancal products (e.g., securtes, bonds or fnancal dervatves) X and Y. A sold lne n the fgure represents an nterban payment relatonshp between two bans whle a dotted lne represents an ownershp relatonshp between a ban and a fnancal product. We use an out-ln to represent an ownershp relatonshp between a ban and a fnancal product because only the default of a fnancal product has an mpact on an owner ban, not the other way around. evertheless, ths smple ban networ llustrates nterban payment relatonshps among bans and ther nterdependences due to holdng of same fnancal products. As Fgure shows, Ban A has a payment relatonshp wth Ban B, and Ban A owns a certan share of Fnancal Product X. In ths networ, a contagous falure may happen as followngs. A sudden decrease n the maret value of fnancal product X may cause Ban A to default on ts payment oblgatons to Ban B. Such a default along wth the decreased value of X may n turn lead Ban B to fal on ts payment oblgatons to Ban C. A default by C may, n turn, have a maor feedbac effect on A and potentally banrupt A. Ths example llustrates the rs of domno collapsng n a banng system, startng wth the default of a sngle ban through a contagous falure process n a ban networ. Such type of rs s often termed as systemc rs (Bullard et al. 2009; arose et al. 200). Fnancal Products: X Y Ownershp lns Bans: A B C Interban Payment lns Fgure. An Illustraton of Ban etwor In a ban networ, systemc rs refers to the rss assocated wth the nterlnages and nterdependences among bans, where the falure of a sngle ban or a group of bans can cause a cascadng falure of other connected bans, whch may potentally brng down the entre banng system (networ). However, exstng research on banng regulaton and montorng manly focused on the rs at the level of the ndvdual ban (Elsnger et al. 2006) but largely overlooed the systemc rs. ew models and methods that can dscover hdden systemc rss n the ban networ are needed to prevent the potental buldup of systemc rss that may cause the breadown of the whole banng system. 2 Thrty Frst Internatonal Conference on Informaton Systems, St. Lous 200

4 Hu et. al. / Ranng Systemc Rss n Ban etwors Therefore, t s mportant to tae a system-wde and networ perspectve to model and analyze systemc rss, and devse effectve mechansms to montor, manage and prevent such type of rss. The man modelng challenge, dentfed by Elsnger et al. (2006), s to effectvely capture the two maor sources of systemc rss: ) correlated ban nvestment portfolos that may result n smultaneous nsolvency of multple bans due to negatve shocs n fnancal marets; 2)Bans may default on ther nterban payments towards other bans and thus cause them to default or even banrupt trggerng a domno effect. Elsnger et al. (2006) mproved a networ-based nterban payment model proposed n Esenberg and oe (200) and adopted smulaton method to study these two sources of systemc rss n Austran banng system. However, ther analyss focused on smulatng the system-wde mpacts of potental contagous default events on the bans n varous stress scenaros. There s a lac of systematc approach to model and analyze the level of systemc rs assocated wth each ban and fnancal product n the ban networ. Such approach s partcular useful for fnancal regulators and other staeholders when devsng strateges or mechansms to prevent or mtgate systemc rss. For nstance, before or durng the breadown of the banng system, t would be crucal to now whch bans to save (nect captal) and whch toxc fnancal assets to deal wth frst, n order to stablze the whole banng system and eep ts solvency. To address ths problem, frst we modfed Elsnger s nterban payment model by ncorporatng the other maor source of systemc rs correlated ban portfolos. Based on ths model, we buld a ban networ model n whch nodes are bans and fnancal products, whle lns are nterban payment (between bans) and ownershp relatonshps (between bans and fnancal products).we then develop a networ-based algorthm based on the HITS algorthm (Klenberg 999) to model and ran the systemc rs assocated wth each ban and fnancal product. At last, we propose to use smulaton methods to evaluate the effectveness of our ranng algorthm n future research. We plan to smulate dfferent stress scenaros usng real-world data on the U.S. banng system. We clam three contrbutons n ths research. Frstly, based on Elsnger s nterban payment model, we developed a ban networ model whch ncorporates the nterrelatonshps between bans and fnancal products they own. Ths model better represents the real-world banng system and provdes the bass for studyng systemc rss assocated wth correlated portfolos of fnancal products. Secondly, we developed a networ-based ranng algorthm that can be used to ran bans accordng ther systemc rss. Ths nformaton s useful to ban regulators that need to mae decsons on what bans to save frst n order to prevent a possble system-wde breadown. The ranng result can also help ndvdual bans to dentfy fnancal products assocated wth hgh systemc rss that may has sgnfcant negatve mpacts on ther nvestment portfolos durng fnancal crss. Thrdly, ths research contrbutes to the lterature of fnancal rs management by studyng systemc fnancal rss n the banng sector wth a networbased approach. The remander of ths paper s organzed as follows. In the next secton, we revew related studes. The thrd secton ntroduces a mathematcal model of banng system based on Elsnger s nterban payment model (2006). The fourth secton descrbes the proposed networ-based ranng algorthm. Then we outlne a plan to evaluate the effectveness of our proposed algorthm usng smulaton technques. Fnally, we dscuss the possble mplcatons and potental contrbutons of our research. Related Studes Our study proposes to use a busness ntellgence approach for managng systemc rs n the ban networs. In ths secton, we revew relevant studes n busness ntellgence and ban rs management, as well as the networ-based ranng algorthm related to the systemc rs ranng algorthm developed n our research. Busness Intellgence and Ban Rs anagement Varous busness Intellgence (BI) technques have been employed to manage ban rs, mostly focusng on predctons of ban falures. These studes often adopt Artfcal Intellgence and data mnng technques on realworld banng data, amng to dscover the causes of ban falures and successfully predct potental falures. These technques nclude eural etwors (), Support Vector achnes (SV), Bayesan etwors (B), and other common classfcaton algorthms. Tam et al. (992) has demonstrated the effectveness of eural etwors technque n ban falure predctons. A fuzzy support vector machne method for credt rs assessment n banng ndustry s proposed n Wang et al. (2005). Sarar and Srram (200) have appled Bayesan etwors models for early warnng of ban falures. In addton, Data mnng methods have also been used n the research about ant- Thrty Frst Internatonal Conference on Informaton Systems, St. Lous 200 3

5 Knowledge anagement and Busness Intellgence money launderng (John 2004; Zhongfe et al. 2003). In general, these busness ntellgence technques can provde novel nsghts dfferent from tradtonal fnancal rs management technques for decson maers n the banng ndustry to acheve better rs measurement, assessment and mtgaton. However, there are manly two ssues for exstng research usng busness ntellgence technques on ban rs management. Frstly, the analyses n these studes manly focus at the level of ndvdual bans or nsttutons but largely overloo the systemc rs. Secondly, relevant wth the prevous ssue, the relatonal (networ) data such as nterban payment s rarely studed. Thus networ analyss technques or networ-based ranng algorthm s rarely used for ban rs management. Our research ams to address these two ssues by ) adoptng a networ perspectve to model systemc rs assocated wth nterban payments and correlated ban portfolos, and 2) developng networ-based ranng algorthm for systemc rs mtgaton n ban networs. etwor-based Ranng Algorthms Wth the fast development of dgtal communcaton networs (e.g., World Wde Web) n the past decades, varous networ-based algorthms have been developed to ran the relatve mportance of nodes n these networs. For nstance, these algorthms are wdely adopted n modern web search engnes le Google and Yahoo whch effectvely search and ran the web pages on the World Wde Web. ost of such algorthms utlze the networ structural nformaton such as degree (.e., the number of lns a node has) for ranng node mportance. Two most wdely used algorthms are Google s Pageran (Brn and Page 998) and the HITS algorthm developed by Klenberg (999). Other networ-based ranng algorthms nclude CLEVER (Charabart et al. 998) proect n IB, SALSA (Lempel and oran 200), as well as TrustRan (Gyöngy et al. 2004). However, these algorthms are manly used n nformaton retreval, but have rarely been used n other areas. Our systemc rs ranng algorthm s based on the HITS algorthm (Klenberg 999) whch s desgned to ran the mportance of ndvdual web pages on the Internet. The HITS algorthm calculates two scores for each webpage the authorty score and the hub score. The authorty score measures the value of the content for the web page, whle the hub score estmates the value of the web page s lns to other pages. Authorty and hub scores are defned n terms of one another n a mutual recurson. An authorty score s computed as the sum of the scaled hub scores that pont to that page. A hub score s the sum of the scaled authorty scores of the pages t ponts to. Both scores for each web page are calculated multple tmes and normalzed n multple teratons untl they converge. A etwor-based odel of Banng System In ths research, we develop a networ-based model of banng system manly for analyzng systemc rss and evaluatng the effectveness of the proposed ranng algorthm. We use ths model along wth real-world banrelated data to smulate the mpacts of varous economc shocs on bans and the fnancal products they own. We also smulate how the proposed algorthm can be used to reduce contagous falures n the ban networ under these shocs. Our model s manly based on the banng system model developed by Esenberg and oe (200) and extended by Elsnger et al. (2006). We modfy t to nclude the systemc rss orgnated from the correlated ban portfolos. Consderng a set of = {,.., } bans, each ban has a value e whch represents the value of ths ban s total assets. The total value of a ban s the sum of ts total assets e, the value of ths ban s portfolo of fnancal products, and the value of all nterban payments t receved, mnus ts nterban payment labltes towards other bans. If the total value of a ban becomes negatve, the ban s nsolvent. Therefore, there are three components n the total value of a ban total assets of the ban, the ban s portfolo of fnancal products, the nterban payments recevable from other bans or nterban labltes. We then descrbe our model of banng system n the followng three sub sectons. Correlated Ban Portfolos The maor dfference between our model and the model developed by Elsnger et al. (2006) s the ncluson of the correlated ban portfolo component. Elsnger et al. (2006) ndentfed that one of the maor sources of systemc rss s the shared holdngs of the same fnancal product(s) n dfferent ban portfolos, causng correlated changes 4 Thrty Frst Internatonal Conference on Informaton Systems, St. Lous 200

6 Hu et. al. / Ranng Systemc Rss n Ban etwors n ban portfolo values when the prces of these shared fnancal products change. For nstance, the burst of housng bubble has caused maor bans to suffer heavy losses n many asset-baced securtes (ABS) they own such as ortgage Baced Securtes (BS), Collateralzed Debt Oblgatons (CDO) (Krahnen and Wlde 2006) and Credt Default Swaps (CDS) (arose et al. 200). Such loss n correlated ban portfolos may largely reduce the payment abltes of these bans smultaneously, causng ther nsolvency. However, Elsnger et al. (2006) dd not explctly nclude ths systemc rs component and evaluate ts mpacts n ther model of banng system. In our modfed model of banng system, we develop and nclude a correlated ban portfolo component to represent ths source of systemc rs. We defne b as the value of a ban s nvestment on a portfolo of fnancal products = {,..., } on the observaton day. Ths value s calculated as b = = R V where R s fnancal product s maret closng prce at the observaton day and at the end of that day. Interban Payment V s the volume the ban held To model nterban payments, we use a matrx L, n whch l represents ban s payment oblgaton towards ban and represents the set of all bans n the banng system. Therefore, the value of ban s total oblgatons towards the rest of the banng system can be denoted as d = = l [ 0, ] by normalzng the entres by the total oblgaton d :. We then defne a new matrx π l = / d f ( d > 0) 0 otherwse Clearng Payment Vector Then the banng system can be descrbed as the sum of the three aforementoned components total assets e, correlated ban portfolo b, and the nterban payment oblgatons d. Followng Esenberg and oe (200) and Elsnger et al. (2006), we then defne a clearng payment vector p as p = = R V + d 0 = π p + e f f f d = > = R V = R V + R V + = + = π p = π p + e π p Ths payment vector p s used to represent total nterban payments made by ban to the rest of the banng system under the clearng mechansm. It has lmted lablty and proportonal sharng n case of banruptcy. Usng ths payment vector, we can easly dentfy the nsolvent bans n the system f p + e d + e p < d < 0 0. In that case, the recovery rate wll be ( / ). ore specfcally speang, a ban defaults and becomes nsolvent f d = + = R V π p + e d < 0. A contagous default may happen on Ban n one of the two followng scenaros: Thrty Frst Internatonal Conference on Informaton Systems, St. Lous 200 5

7 Knowledge anagement and Busness Intellgence One or more bans are not be able to eep ther payment promses to Ban, causng s nsolvency. Usng our + = = model to explan, that s V d + e d 0 + = = R π, but R V π p + e d < 0. The defaults of one or more correlated fnancal products n ban s portfolo can reduce the portfolo value b. At the same tme, they can also reduce the payment abltes of bans that have oblgatons to. Together these = = two effects may cause ban become nsolvent..e., R V π d + e d 0, but + = = ' R V π p + e d < 0, where that become default. + ' R denotes the maret closng prce of fnancal product To summarze, ths model of the banng system s developed for smulatng the contagous defaults of bans under varous stress scenaros and how our ranng algorthm can help reduce systemc rs n the ban networ. In addton, the desgn of the ranng algorthm has also utlzed representatons developed n ths model. A etwor-based Algorthm for Ranng Systemc Rss Exstng rs management research focused on econometrc analyss of maret and credt rss. Although there are several studes tang a networ perspectve to study the systemc rss n the banng systems (Esenberg and oe 200; Elsnger et al. 2006; arose et al. 200; ay and Arnamnpathy 200), no effectve mechansms or algorthms have been proposed to measure and manage such rss. In ths study, we developed a algorthm that s based on the famous HITS algorthm (Klenberg 999) to ran systemc rss for each ban and fnancal product n the ban networ. Although the HITS algorthm s ntally desgned to measure the mportance of web pages n the densely nterconnected Would Wde Web, the underlyng mechansm can be appled n many other domans. The mportance (of a web page) s a characterstc that cannot be drectly measured. HITS algorthm s essentally usng the lns a web page receved from other web pages to quantfy and measure ts mportance. The assumpton s that, f a web page A provdes a hyperln that connects to web page B, A s actually offerng ts recognton to enhance the mportance of web page B through ths hyperln. Therefore, the authorty score of the web page A calculated usng HITS s actually measurng the overall mpacts of recogntons offered by all web pages that lns to A. The hub score s measurng the overall mpacts of recogntons A offered to the web pages t lns to. We developed a networ-based algorthm usng the same underlyng mechansm of HITS algorthm to measure and then ran the systemc rss assocated wth bans manly for three reasons Frstly, le mportance of web pages, the systemc rs assocated wth bans cannot be quantfed and drectly measured. Secondly, the systemc rs assocated wth a ban s a type of exogenous rs that orgnates from the possble defaults of bans that have nterban payment oblgatons wth A and also from the fnancal products owned by A. Thrdly, the systemc rss manly exsts n a densely connected ban networ and ther mpacts are manly transmtted through the nterban payment relatonshps and the ownershp (wth fnancal products) relatonshps. We modfed HITS algorthm to ran systemc rss n ban networs whch are sgnfcantly dfferent from ranng web page mportance. Frst, there are two types of nodes n the ban networ bans and fnancal products. Thus we need to calculate both authorty and hub scores for these two types of nodes. Second, snce the possble defaults of fnancal products may cause the nsolvency of bans, not the other way around, the fnancal product nodes only have out-lns. Accordngly, n our algorthm, fnancal product nodes wll only have hub scores. On the other hand, the default of bans may cause the nsolvency of each other through nterban payment lns. Therefore, ban nodes wll have both n and out- lns, along wth the authorty and hub scores. easurng Systemc Rs Assocated wth Fnancal Product odes We frstly am to measure the systemc rs assocated wth the fnancal product nodes from the bans perspectve. We manly focused on two factors: The lelhood of the default of a fnancal product owned by a ban. Ths s often lned to ratng scores provded by credt agences. We denote the probablty of the default of a fnancal product as f ( ). 6 Thrty Frst Internatonal Conference on Informaton Systems, St. Lous 200

8 Hu et. al. / Ranng Systemc Rss n Ban etwors The total mpacts/losses caused by the default of a fnancal product on ts owner ban. We denote that usng R V p, where R s the change n the prce of the product caused by the default event. / Together these two factors determne the expected mpacts (possble loss) of the default of a fnancal product on ts owner ban as f ( R V / p ). easurng Systemc Rs Assocated wth Interban Payments Then we am to measure the systemc rss assocated wth the nterban payments n the ban networ. We propose IP as a measure of the expected mpacts (possble loss) caused by the default of an ndvdual ban on one of ts debtor bans through the nterban payment lns. That s IP = C( l / p ), where C s the average default rate for all bans n the ban networ durng a certan tme perod. Ranng Algorthm Desgn We then ntroduce the desgn our networ-based ranng algorthm. Smlar to HITS, our algorthm has an teratve process to update both hub score and authorty score for the two types of nodes. One thng to note s that fnancal product nodes only have out-lns whle ban nodes have both n- and out-lns. Thus the fnancal product nodes only have hub scores (FPHUB) and ban nodes have both authorty (BAKAU) and hub scores (BAKHUB). In the ntal step, for each ban, we calculate the coeffcent for the hub score of the fnancal product t owns as α = f ( R V / p ) as explaned n the prevous secton. Then we set the coeffcent for the ban s hub score to ban as β = IP = C( l / p ). In ths step, we are weghtng the n-lns of ban nodes based on the expected mpacts of the two sources for these lns. Second, we start to update ban s authorty score BAKAU as: BAKAU = α FPHUB + β BAKHUB () where s the set of fnancal product owned by ban, and s the set of bans that have payment oblgatons to. Smlar to the HITS algorthm, ntally both FPHUB and BAKHUB are set to one. Ths update process means that the authorty score that ran ban s systemc rs s from the systemc rss assocated wth s portfolo of fnancal products, and all the bans that may affect s cash flow through possble defaults of nterban payments. The coeffcents are the possble mpacts of these two systemc rs sources on ban. Thrd, we update fnancal product s hub score as FPHUB. Ths score represents the overall systemc rs fnancal product may has (the possble loss of ts default) on the whole ban networ/system through ts owner bans. Therefore, t s calculated as where = H FPHUB S BAKAU (2) S s the percentage of the product held by ban, H s the set of bans that hold. Fourth, we update Ban s hub score as BAKHUB. Ths score represents Ban s systemc rs (the possble loss of ts default) on the whole ban networ through the bans t has payment oblgatons. Therefore, t s calculated as = ' BAKHUB OP BAKAU (3) Thrty Frst Internatonal Conference on Informaton Systems, St. Lous 200 7

9 Knowledge anagement and Busness Intellgence where OP s the percentage of the ban s total ncomng payments ban s payment occupes, and of bans that has payment oblgatons. ' s the set One thng to note s that for each teraton of ths algorthm we normalze these three scores le the orgnal HITS algorthm does. Then we repeatedly execute the three steps untl these three scores converge. The convergence proof of our algorthm s smlar wth the proof for the weghted HITS algorthm n Bharat and Henznger s paper (998). To explan ths proof, we frst unfy the representatons of ban nodes and fnancal product nodes by denotng AU = [BAKAU,0] and HUB= [ BAKHUB, FPHUB] as the unfed authorty score and hub score B A vector, respectvely. Then the authorty-weght matrx of our algorthm s AW =, where B = β 0 0 and A = α n formula () and B s the transpose of matrx B. The computaton of the authorty score can be OP 0 =. Smlarly, the hub-weght matrx s = S 0 OP B OP A HUB = HW AU. We defne D= HW AW = S B S A wrtten as AU AW HUB the hub score can be wrtten as HW and the computaton of n whch all entres are postve. Followng the Lemma n (Bharat et al. 998), after teratons, the hub socre vector HUB whch equals to D Z wll converge (Golub and Van Loan 996), where Z s a vector wth each coordnate equal to. The authorty score wll converge for the same reason. The converged hub scores for each fnancal product and ban are our ranng scores for measurng ther systemc rss. Evaluaton of the Proposed Algorthm through Smulaton To assess the effectveness of ths networ-based ranng algorthm, we frst plan to use a smulaton approach to generate stress scenaros n whch the ban networ undergoes varous types of maor economc shocs. We also smulate how the contagous ban falures happen through the two sources of systemc rss modeled prevously. In ths smulaton, t s assumed that there are two tme ponts: t = 0, the observaton day, and t =, the payment clearng date, when all nterban payments are settled accordng to the clearng vector p we defned before. At t = 0, the portfolo holdngs of fnancal products b for each ban are observed. In addton, the nterban payments among bans are modeled as a matrx L. The remanng value of ban assets s represented as e. Both the values of b and e s exposed to varous maret and credt rs such as the sudden drop of prces for maor fnancal products bans held. Accordng the clearng payment vector p defned before, the value of p depends on the realzaton of such rs factors. To generate a stress scenaro, we draw a realzaton of these rs factors usng realworld data such as nterest rates and currency exchange rates, and revalue b and e for each ban to estmate ts new value of p. After generatng varous possble scenaros of contagous defaults of bans, we focus on evaluatng the effectveness of our proposed ranng algorthm. We wll apply ths algorthm to the data n each generated scenaro for calculatng and ranng the systemc rss assocate wth each ban and fnancal product. The output s a lst of raned bans and fnancal products based on the level of systemc rss they have. We then devse strateges based on ths lst to reduce the systemc rs n the ban networ (e.g., prevent bans to purchase fnancal products wth hgh systemc rs). We then compare the number and the rate of default bans (caused by contagous falures) between the orgnal scenaro and the one whch use the algorthm to mtgate the systemc rss. For multple scenaros, f the numbers of default bans n orgnal scenaros are consstently and sgnfcantly larger than the ones n the scenaros that adopted our ranng algorthm. Then the algorthm wll be proven to be effectve n reducng systemc rss n ban networs. 8 Thrty Frst Internatonal Conference on Informaton Systems, St. Lous 200

10 Hu et. al. / Ranng Systemc Rss n Ban etwors Dataset In ths study, we plan to use data from the Ban Regulatory and the Ban Holdng Companes Databases n the Wharton Research Data Servces (WRDS). The Ban Regulatory Database contans accountng data for ban holdng companes, commercal bans, savngs bans, and savngs and loans nsttutons. The source data s from the requred regulatory forms fled for supervsng purposes. The Ban Holdng Companes Database collects fnancal data ncluded n the FRY-9 reports whch contan balance sheet, ncome nformaton, rs-based captal measures and addtonal supportng schedules. The nformaton n these two databases s manly used to generate stress scenaros n our smulatons of contagous falures n ban networs. Dscusson and Future Wor In summary, we developed a networ-based ranng algorthm to ran systemc rs assocated wth bans and fnancal products n the ban networs. In addton, we extended Elsnger s (2006) model of the banng system and plan to adopt t on real world banng data to smulate the effects of our proposed algorthm n reducng systemc rs (contagous falures) n the ban networ. Our proposed research has both theoretcal and practcal contrbutons. Theoretcally, t contrbutes to the research that ams to dscover the causes and mechansms of contagous ban falures n ban networs. It provdes a novel networ perspectve for researchers to study how system-wde ban falures happen caused by the falures of several ey bans or a maret freeze. Emprcally, our study ntend to provde a networ-based ranng mechansm for systemc rs assocated wth bans and fnancal products, amng to help staeholders n the banng ndustry to devse effectve strateges for reducng such systemc rs. The long term goal s to prevent system-wde breadown n the banng system by dentfyng bans and fnancal products wth hgh systemc rs and reducng such rs before total meltdown. Our future wor conssts of three parts. Frst, we need to collect data from the Ban Regulatory and the Ban Holdng Companes Databases n WRDS. Second, we wll construct the ban networ and generate varous stress scenaros for the banng system. Thrd, we then compare the numbers of contagous ban falures n orgnal scenaros and n scenaros that adopted our proposed networ-based algorthm for systemc rs mtgaton. We beleve our algorthm offers a new way to estmate systemc rs (contagous ban falures) n a ban networ under varous stress scenaros. References Bharat, K., and Henznger,.R "Improved Algorthms for Topc Dstllaton n a Hyperlned Envronment," n: Proceedngs of the 2st annual nternatonal AC SIGIR. elbourne, Australa: AC, pp Brn, S., and Page, L "The Anatomy of a Large-Scale Hypertextual Web Search Engne," Computer etwors and ISD Systems (30:-7), pp Bullard, J., eely, C.J., and Wheeloc, D.C "Systemc Rs and the Fnancal Crss: A Prmer," Federal Reserve Ban of St Lous Revew (9:5), Sep-Oct, pp Charabart, S., Dom, B., Raghavan, P., Raagopalan, S., Gbson, D., and Klenberg, J "Automatc Resource Complaton by Analyzng Hyperln Structure and Assocated Text," Computer etwors and ISD Systems (30:-7), pp Esenberg, L., and oe, T.H "Systemc Rs n Fnancal Systems," anagement Scence (47:2), Feb, pp Elsnger, H., Lehar, A., and Summer, "Rs Assessment for Banng Systems," anagement Scence (52:9), Sep, pp Golub, G., and Van Loan, C atrx Computatons (Johns Hopns Studes n athematcal Scences)(3rd Edton). The Johns Hopns Unversty Press. Gyöngy, Z., Garca-olna, H., and Pedersen, J "Combatng Web Spam wth Trustran," n: Proceedngs of the Thrteth nternatonal conference on Very large data bases - Volume 30. Toronto, Canada: VLDB Endowment, pp Harrngton, S.E "The Fnancal Crss, Systemc Rs, and the Future of Insurance Regulaton," Journal of Rs and Insurance (76:4), Dec, pp John, S.Z "Detectng oney Launderng and Terrorst Fnancng Va Data nng," Communcatons of the AC (47:5), pp Thrty Frst Internatonal Conference on Informaton Systems, St. Lous 200 9

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