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Fatma ÇINAR, MBA Capital Markets Board of Turkeye-mail: [email protected] [email protected]
@fatma_cinar_ftm @DataLabTRC. Coşkun KÜÇÜKÖZMEN, PhD e-mail: [email protected]
@ckucukozmen @RiskLabTurkeyKutlu MERİH, PhD e-mail: [email protected]
[email protected] @cortexien www.datalabtr.com
https://www.riskonomi.com
RISK MANAGEMENT WITH DATA VISULISATION
MORTGAGE LOANS DEFAULT CHART OF TURKEY
RISK REPORT (abridged)
Description of The Report
This a abridged Sample Report of Defaulted Mortgage Loans for the 12 NUTS Sectors and 81 cities of Turkey
with 2012-2014 time spanCovers only Whole Turkey and West
Anatolia RegionReal Report covers all the 12 NUTS
Regions and 81 Cities of TurkeyWith 2010-2015 time span
Purpose of the Report• With this study we investigate NUTS 12
Regions credit loans performations by Graphical Datamining Analysis technique with a suitable software developed by us.
• This technique is submitted various OR and Finance congress
• Dataset are factorized according to cities and years, sectorals and financial periods factors.
• Sample Report Covers Periods: 2012-2014 accounts.
Source of The Data• We downloaded FINTURK dataset from
the site of BRSA and anotated it by NUTS factors.
• Our software read this data from an excel file with the name of “dataset”
• From now on “dataset” means our improvised NUTS Credit Loans FINTURK data
Description of The Analytics• Data: BRSA* and NUTS of Turkey
(Nomenclature of Territorial Units for Statistics, NUTS)• Dataset: NUTS Region Investment Promotion
and 3 account period Graphical Datamining Analysis of FINTURK of BRSA
• Period: 2012-2014 Accounts• Dataset are factorized according to year, sector,
quarter and region factors. • Graphical Datamining and Data Visualisation
applied on this factorized data.
*BRSA: Banking Regulations and Supervisison Agency
Fields : names(dataset)• names(dataset)• [1] "NYEAR" "SYEAR" "QUARTERS" • [4] "CITY" "CITYCODE" "NREGION" • [7] "REGION" "NUTS3CODE" "NUTS2CODE" • [10] "NUTS1CODE" "TRNUTS1REGION" "NUTS1REGION" • [13] "TRGROUP" "SECTORAL" "CASHLOANS" • [16] "NONCASHLOANS" "TOTALCASHLOANS" "AUTO" • [19] "MORTGAGE" "OVERDRAFTACCOUNT" "CREDITCARDS" • [22] "FOOD" "BUILDING" "MINERALS " • [25] "FINANCIAL" "TEXTILE" "WHOSESALE " • [28] "TOURISM" "AGRICULTURE" "ENERGY" • [31] "MARITIME" "OTHERCONSUMER" "DEFRECEIVABLE" • [34] "DEFCREDITCARDS" "DEFAUTO" "DEFMORTGAGE" • [37] "DEFOTHERCONSUMER" "DEFFOOD" "DEFBUILDING" • [40] "DEFMINERALS" "DEFFINANCIAL" "DEFTEXTILE" • [43] "DEFWHOLESALE " "DEFTOURISM" "DEFAGRICULTURE" • [46] "DEFENERGY" "DEFMARITIME" "NONCASHFOOD" • [49] "NONCAHBUILDING" "NONCASHMINERALS" "NONFINANCIAL" • [52] "NONCASHTEXTILE" "NONCASHWHOLESALE " "NONCASHTOURISM" • [55] "NONCASHAGRICULTURE“ "NONCASHENERGY" "NONCASHMARITIME"
NUTS of Turkey (Nomenclature of Territorial Units for Statistics, NUTS)
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NUTS-1:12 Regions of Turkey
• NUTS-1: 12 Regions• NUTS-2: 26 Subregions• NUTS-3: 81 Provinces
1. MEDITERRANEAN2. SOUTHEAST ANATOLIA3. EAGEAN REGION4. NORTHEAST ANATOLIA5. MIDDLE ANATOLIA6. WEST BLACK SEA7. WEST ANATOLIA8. EAST BLACK SEA9. WEST MARMARA10. MIDDLE EAST ANATOLIA11. ISTANBUL12. EAST MARMARA
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İstanbul Region
West Marmara
Region
Aegean Region
East Marmara
West Anatolia Region
Mediterranean Region
Anatolia Region
West Black Sea Region
East Black Sea Region
Northeast Anatolia Region
East Anatolia Region
Southeast
Anatolia
İstanbul (Subregion)
Tekirdağ (Subregion)
İzmir (Subregion)
Bursa (Subregion)
Ankara (Subregion)
Antalya (Subregion)
Kırıkkale (Subregion)
Zonguldak (Subregion)
Trabzon (Subregion)
Erzurum (Subregion)
Malatya (Subregion)
Gaziantep
(Subregion)
Edirne Aydın (Subregion) Eskişehir Konya
(Subregion) Isparta Aksaray Karabük Ordu Erzincan Elazığ Adıyaman
Kırlareli Denizli Bilecik Karaman Burdur Niğde Bartın Giresun Bayburt Bingöl Kilis
Balıkesir (Subregion) Muğla Kocaeli
(Subregion) Adana (Subregion) Nevşehir Kastamonu
(Subregion) Rize Ağrı (Subregion) Dersim
Şanlıurfa
(Subregion)
Çanakkale Manisa (Subregion) Sakarya Mersin Kırşehir Çankırı Artvin Kars Van
(Subregion)Diyarba
kır
A.Karahisar Düzce Hatay (Subregion)
Kayseri (Subregion) Sinop Gümüşhane Iğdır Muş
Mardin (Subreg
ion)
Kütahya Bolu Kahramanmaraş Sivas Samsun (Subregion) Ardahan Bitlis Batman
Uşak Yalova Osmaniye Yozgat Tokat Hakkari Şırnak
Çorum Siirt
Amasya
1 Province 5 Province 8 Province 8 Province 3 Province 8 Province 8 Province 10 Province 6 Province 7 Province 8 Province9
Province
Graphical DataMining Analysis with FINTURK Sectoral Loans Dataset
• Real Time Interactive Data Management for
• Effect and Response Analysis
Technique: • Graphical DataMining using #ggplot2
Graphical Package of #R Software• Graphical DataMining will be the
dominant anlysis technique of the future
Styles of Graphs• For brevity we apply three types off
ggplot2 graphical styles with ggplot2 geoms with this report1. Densityplot with geom_density()2. Violinplot with
geom_violin()3. Facetplot with facet_grid()
• Logarithmic scale leads a more stable density formations for financial data.
Description of Density Graphs• Density Graphs are the
continuous version of Histograms They plot a single numerical variable against their frequency.
• We can detect single or multiple peaks of density graphs and pinpoint the effective factors.
• On the other hand soperposing density graphs acording the factors with different colors provide us with information of the effect of the factors
Description of Violin Graphs• Violin Graphs can be
seen as two-dimensional density graphs
• Axis of the violin represents the median of the free variable
• Through the median of X-axis Y-density graph occurs with mirror copy
Mushroom, Pottery and Bottle Risk Profiles• Violin Graphs comes
with Mushroom, Pottery and Bottle formations
• Mushroom formation represents a risk concentration on hig order values of financial data
• Pottery means risk on the medium order
• and the bottle menas risk on the lower orders
Description of Power Law Graphs
• When double Log scale applied Power Law analysis is a by product
• LogY = a.LogX + b• a is the Risk
Measure • And it is the same
for every level of X and Y
• Power Law means that risk is scale free
Description of Facet Graphs• Facet graphs of
ggplot2 package can show us
3-dimensional graphs distributed according 3 factors in matrix form.
• In which we can see the anomalies occurs on which year and which region and which period.
Risk ProfilesGraphical Dataminig of the Profiles
of Default Mortgage Risks for Whole Turkey, 12 NUTS Regions
and 81 Cities(This version covers only Whole Turkey
and West Anatolia Region)
Risk Profiles for Whole Turkey
Here we investigate Mortgage Loans versus Default Mortgage
Loans for Whole Turkey according to region, year and period factors.
X and Y Scales used is Log10
Density Graphs of Mortgage Loans by Nuts Regions of Turkey
Faceted Density Graphics of Mortgage Loansby Quarters
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Violin Graphs of Defaulted Mortgage Loans by Nuts Regions of Turkey
Faceted Violin Graphics of Defaulted Mortgage Loans by Quarters
Wednesday, May 3, 2023
Power Law Risk Analysis of Mortgage Loansby Nuts Regions of Turkey
Wednesday, May 3, 2023
Faceted Power Law Analysis of Mortgage Loansby Quarters
Wednesday, May 3, 2023
Risk Profiles for W. Anatolia RegionHere we investigate Mortgage Loans versus Default Mortgage Loans for West Anatolian Region
according to cities, year and period factors.
X and Y Scales used is Log10
Density Graphs of Mortgage Loans by Cities of West Anatolia
Wednesday, May 3, 2023
Faceted Density Graphics of Mortgage Loansby Quarters
Wednesday, May 3, 2023
Violin Graphs of Defaulted Mortgage Loans by Cities of West Anatolia
Faceted Violin Graphics of Defaulted Mortgage Loans by Quarters
Wednesday, May 3, 2023
Power Law Risk Analysis of Mortgage Loansby Cities of West Anatolia
Wednesday, May 3, 2023
Faceted Power Law Analysis of Mortgage Loansby Quarters
Conclusion• Graphical Datamining applied on this
factorized data and financial anomalies detected acording to time and space factors.
• We observes apparently obvious differences of risk profiles affected by these factors
• It is quite clear that pictures tells more stories than numbers
• This is only an abridged version of the whole report for 12 Nuts Regions
Contact
@DataLabTR@GeoLabTR@TRUserGroup@CORTEXIEN@Riskonometri@Riskonomi@datanalitik@Riskanalitigi@RiskLabTurkey@fatma_cinar_ftmtr.linkedin.com/in/fatmacinartr.linkedin.com/pub/kutlu-merihtr.linkedin.com/in/coskunkucukozmen
www.datalabtr.com
[email protected]@ieu.edu.trhttp://www.ieu.edu.tr/tr [email protected]://[email protected]@spk.gov.tr
http://www.spk.gov.tr/
http://www.riskonomi.com
Resources• Küçüközmen, C. C. Ve Çınar F., (2014). “Finansal Karar Süreçlerinde
Grafik-Datamining Analizi”, TROUGBI/DW SIG, Nisan 2014 İstanbul, http://www.troug.org/?p=684
• Küçüközmen, C. C. ve Çınar F., (2014). “Görsel Veri Analizinde Devrim” Söyleşi, Ekonomik Çözüm, Temmuz 2014, http://ekonomik-cozum.com.tr/gorsel-veri-analizinde-devrim-mi.html.
• Küçüközmen, C. C. ve Merih K., (2014). “Görsel Teknikler Çağı" Söyleşi, Ekonomik Çözüm, Temmuz 2014, http://ekonomik-cozum.com.tr/gorsel-teknikler-cagi.html
• Küçüközmen, C. C. and Çınar F., (2014). “Banking Sector Analysis of Izmir Province: A Graphical Data Mining Approach”, Submitted to the 34th National Conference for Operations Research and Industrial Engineering (YAEM 2014), Görükle Campus of Uludağ University in Bursa, Turkey on 25-27 June 2014.
• Küçüközmen, C. C. and Çınar F., (2014). “New Sectoral Incentive System and Credit Defaults: Graphic-Data Mining Analysis”, Submitted to the ICEF 2014 Conference, Yıldız Technical University in İstanbul, Turkey on 08-09 Sep. 2014.
• Küçüközmen, C. C. and Çınar F., (2015). “Visual Anaysis of Electricity Demand Energy Dashboard Graphics” Submitted to the 5th Multinational Energy and Value Conference May 7-9, 2015 Kadir Has University in İstanbul, Turkey
• Merih, K. C. and Çınar F., (2015). “Sectoral Loans Default Chart of Turkey ”, Submitted to 35th National Operations Research and Industrial Engineering Congress (ORIE 2015) 09-11,September, 2015,Middle East Technical University, Ankara, Turkey
