nuts and sectoral loans default chart of turkey: graphical data-mining analysis with r software -...

FATMA ÇINAR, MBA, Capital Markets Board of Turkeye-mail: fatma.cinar@spk.gov.tr @fatma_cinar_ftm, @TRUserGroup

KUTLU MERİH, PhD e-mail: kutmerih@gmail.com @cortexien https://www.riskonomi.com

35th National Operations Research and Industrial Engineering Congress (ORIE 2015) 09-11,September, 2015,Middle East

Technical University, Ankara, Turkey.

SECTORAL LOANS DEFAULT CHART OF SECTORAL LOANS DEFAULT CHART OF TURKEYTURKEY

Wednesday, September 02, 2015

A baloon emerging on the importance of Big DataBig Data is not large data, Big Data is complex data.Strategical datasets may not big but quite complex

What is big data?

How Big Data Humour is big!

Monday, May 1, 2023

Purpose of the study

• We aim to decipher this complexity of datasets With advanced graphical packages of R software

• Our Technique is Graphical Datamining

• To Analyse various credit and financial situation of EnergyLoans and EnergyLoanDefaults of some of the cities and regions of Turkey.

• Our Dataset will be FINTURK of BRSA

Monday, May 1, 2023

Agenda

• Case study: BRSA NUTS and Sectoral Loans Default Chart of Turkey

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 BRSAPeriod: 2012-2014 AccountsDataset are factorized according to city, year, sector and region factors. Graphical Datamining applied on this factorized data.

*BRSA: Banking Regulations and Supervisison Agency

Monday, May 1, 2023

Action

• Real Time Interactive Data Management for

• Effect and Response Analysis

Technique: • Graphical Datamining using #ggplot2

Graphical Package of #R Software

Monday, May 1, 2023

Sectoral Loans Dataset Graphics Data-Mining Analysis

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"

Monday, May 1, 2023

NUTS of Turkey (Nomenclature of Territorial Units for Statistics, NUTS)

NUTS-1:12 Region of Turkey

• MEDITERRANEAN• SOUTHEAST ANATOLIA• EAGEAN REGION• NORTHEAST ANATOLIA• MIDDLE ANATOLIA• WEST BLACK SEA• WEST ANATOLIA• EAST BLACK SEA• WEST MARMARA• MIDDLE EAST ANATOLIA• ISTANBUL• EAST MARMARA

•NUTS-1: 12 Regions•NUTS-2: 26 Subregions•NUTS-3: 81 Provinces

Monday, May 1, 2023

(Nomenclature of Territorial Units for Statistics, NUTS)

Monday, May 1, 2023

Monday, May 1, 2023

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

Monday, May 1, 2023

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 NUTS Credit Loans FINTURK data

SOURCE OF THE DATA

What is ggplot2 and Grammar of Graphics ?

Grammer of graphics represents and abstraction of graphics ideas/objects

Think ‘verb’, ‘noun’, ‘adjective’ for graphics Allows for a ‘theory’ of graphics on which to build

new graphics and graphics ogjects ‘Shorten the distence from mind to page’ Created by Hadley WICKHAM of Rice University @hadleywickham

Monday, May 1, 2023

ggplot2 Graphics Package

• How to create basic plots (xyplot, scatterplots, histograms, baloon, facet, density and violin) using ggplot() function of ggplot2 package

• Setting vs. mapping• How to add extra variables with aesthetics (like

color, shape, and size) or faceting

• http://docs.ggplot2.org/current/

Monday, May 1, 2023

Grammer of Graphics ?

‘In brief, the grammer tells us that a statistical graphic is a mapping from data to aesthetic attributes (color, shape, size) of geometric object (point, lines, bars).The plot may also contain stastistical transformations of data and drawn on a specific coordinate system’

Hadley Wickham

Monday, May 1, 2023

Styles of Graphs

• We apply four types off ggplot2 graphical styles with ggplot2 geoms

1. Scatterplot with geom_point()2. Densityplot with geom_density()3. Violinplot with geom_violin()4. Smooth with geom_smooth()and facetplot with facet_grid()

Monday, May 1, 2023

Description of Baloon

Graphs

Baloon graphs of ggplot2 package can show us

3-dimensional relations distributed according 1-3

factors in scatterplot form.

With this type 2-dimensional numerical relations

can be represented under effect of 3rd numerical

value.

Monday, May 1, 2023

Monday, May 1, 2023

library(ggplot2)ds<-ggplot(dataset)ae<-aes(log10(ENERGY), log10(DEFENERGY), size=DEFRECEIVABLE,color=SECTORAL)gs<-geom_point()ps<-ds+ae+gsprint(ps)

ggplot2 commands for scatterplot graphs

NUTS Regions Log10 Energy Vs Log10 Default Energy,Baloon Defreceivable Explained by Sectoral and Year Factors Grid

Graphics

Eagean Regions Log10 Energy Vs Log10 Default Energy,Baloon Defreceivable Explained by City, Sectoral and Year Factors Grid

Graphics

Monday, May 1, 2023

Eagean Regions Log10 Energy Vs Log10 Default Energy,Baloon Defreceivable Explained by Sectoral and Year Factors Grid

Graphics

İzmir Province Log10 Energy Vs Log10 Default Energy,Baloon Defreceivable Explained by City, Year and Sectoral Factors Grid

Graphics

İzmir Province Log10 Energy Vs Log10 Default Energy, Baloon Defreceivable Explained by Quarters, Years and Sectoral

Factors Grid Graphics

Monday, May 1, 2023

• 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

• Logarithmic scale leads a more stable density formations for financial data.

Description of Density

Graphs

Monday, May 1, 2023

library(ggplot2)ds<-ggplot(dataset)ae<-aes(log10(DEFENERGY), color=SECTORAL)gd<-geom_density(alpha=0.5)pd<-ds+ae+gdprint(pd)

ggplot2 commands for density graphs

NUTS Regions Log10 Default Energy, Explained by Years Factors Density Graphic by Log10DefEnergy

Eagean Regions and Province Log10 Default Energy, Explained by City Factors

Density Graphic by Log10 DefEnergy

Monday, May 1, 2023

Eagean Regions and Provinces Log10 Default Energy, Explained by Years Factors

Density Graphic of DefEnergy

İzmir Province Log10 Default Energy, Explained by Quarters Factors

Density Graphic by DefEnergy

Monday, May 1, 2023

Description of Violin Graphs

• Violin Graphs can be seen as two-dimensional density graphs

• Violin Graphs are very important for Risk Analysis of financial Data

• Through the mean of X-axis Y-density graph occurs with mirror copy

• Usually 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

Monday, May 1, 2023

library(ggplot2)ds<-ggplot(dataset)ae<-aes(log10(ENERGY), log10(DEFENERGY), color=SECTORAL)gv<-geom_violin(alpha=0.5)pv<-ds+ae+gvprint(pv)

ggplot2 commands for violin graphs

NUTS Regions Log10 Default Energy, Explained by Years Factors

Violin Graphic

Monday, May 1, 2023NUTS Regions Log10 Energy, Explained by Sectoral Factors

Violin Graphic

Eagean Regions and Province Log10 Default Energy,Explained by City Factors

Violin Graphic

Monday, May 1, 2023

Eagean Regions and Province Log10 Energy, Explained by Years Factors Violin Graphic

Monday, May 1, 2023

İzmir Province Log10 Default Energy, Explained by Sectoral Factors

Violin Graphic

Monday, May 1, 2023

2 by 2 COMBINED DENSITY AND VIOLIN

GRAPHICS

Monday, May 1, 2023

NUTS Regions Log10 Energy Vs Log10 Default Energy Explained by Years Factors Density/Violin Graphics

Eagean Regions and Province Log10 Energy Vs Log10 Default Energy, Baloon Defreceivable Explained by City Factors

Density/Violin Graphics

Monday, May 1, 2023

Eagean Regions and Province Log10 Energy Vs Log10 Default Energy, Explained by Years Factors

Density/Violin Graphics

Monday, May 1, 2023İzmir Province Log10 Energy Vs Log10 Default Energy,Explained

by Quarters Factors Density/Violin Graphics

Monday, May 1, 2023

Description of

PowerLaw Graphs

Power law distributions are usually used to model data whose frequency of an event varies as a power of some attribute of that event.

A numeric constant giving the power-law exponent and describes a scale free rate of change

Our POWERLAW ANALYSIS DataMining technique basicly a straigt line smoothing of double logatithmic data

This technique especially powerful on describing Risk Profiles of financial data

Our procedure for analyzing the data will follow the procedure in the paper: POWERLAW

Monday, May 1, 2023

library(ggplot2)ds<-ggplot(dataset)ae<-aes(log10(ENERGY), log10(DEFENERGY), color=SECTORAL)gp<-geom_point()ss<-stat_smooth(method = "lm", formula = y ~ x, size = 2)pl<-ds+ae+gp+ssprint(pl)

ggplot2 commands for Power Law Smooth graphs

Nuts Regions Log10 Energy Vs Log10 Default Energy, Baloon Defreceivable Explained by Sectoral Factors

PowerLaw Graphics

Monday, May 1, 2023

Nuts Regions Log10 Energy Vs Log10 Default Energy, Baloon Defreceivable Explained by Nuts1Regions Factors

PowerLaw Graphics

Eagean Regions and Province Log10 Energy Vs Log10 Default Energy, Baloon Defreceivable Explained by City Factors

PowerLaw Graphics

Monday, May 1, 2023

İzmir Province Log10 Energy Vs Log10 Default Energy, Baloon Defreceivable Explained by Years Factors

PowerLaw Graphics

Monday, May 1, 2023

İzmir Province Log10 Energy Vs Log10 Default Energy, Baloon Defreceivable Explained by City Factor

PowerLaw Graphics

Monday, May 1, 2023

İzmir Province Log10 Energy Vs Log10 Default Energy, Baloon Defreceivable Explained by Quarters Factors

PowerLaw Graphics

Monday, May 1, 2023

İzmir Province Log10 Energy Vs Log10 Default Energy, Baloon Defreceivable Explained by Sectoral Factors

PowerLaw Graphics

Monday, May 1, 2023

• 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.

• Here we investigate Energy versus default Energy balooned by Total Default Receivables according to region, year and period factors.

• Colors period, balloons Total Cash loans.

Description of Facet Graphs

library(ggplot2)ds<-ggplot(dataset)ae<-aes(log10(ENERGY), log10(DEFENERGY),

color=SECTORAL)gp<-geom_point()fs<-facet_grid(“factor1” ~ “facror2”)pf<-ds+ae+gp+fsprint(pf)

Monday, May 1, 2023

ggplot2 commands for facetting

Nuts Regions Log10 Energy Vs Log10 Default Energy, Baloon Defreceivable Explained by Nuts1Regions Factors

Facet Graphic

Monday, May 1, 2023

İzmir Province Regions Log10 Energy Vs Log10 Default Energy, Baloon Defreceivable Explained by Year and Sectoral Factors

Facet Graphic

Monday, May 1, 2023

Eagean Regions and Province Log10 Energy Vs Log10 Default Energy, Baloon Defreceivable Explained by City,Year and Sectoral Factors

Facet Graphic

With this study we investigate NUTS 12 Regions credit loans performations by Graphical Datamining Analysis technique with a suitable software developed by us.

Dataset are factorized according to cities and years, sectorals and financial periods factors.

Periods: 2012-2014 accounts. Graphical Datamining applied on this factorized

data and financial anomalies dedected 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

Monday, May 1, 2023

We would like to express our deep gratitude to;

Dr. C. Coşkun KÜÇÜKÖZMENfor their valuable contibutions,

Fatma CINARKutlu MERIH

Contact

@TRUserGroup@CORTEXIEN@Riskonometri@Riskonomi@datanalitik@Riskanalitigi@RiskLabTurkey@fatma_cinar_ftmtr.linkedin.com/in/fatmacinartr.linkedin.com/pub/kutlu-merihtr.linkedin.com/in/coskunkucukozmen

kutmerih@gmail.com

kutlu@merih.netcoskun.kucukozmen@ieu.edu.trhttp://www.ieu.edu.tr/tr coskunkucukozmen@gmail.comhttp://www.coskunkucukozmen.comfatma.cinar@spk.gov.tr

http://www.spk.gov.tr/

http://www.riskonomi.com

Monday, May 1, 2023

Küçüközmen, C. C. and Çınar F., (2014). “Modelling of Corporate Performance In Multi-Dimensional Complex Structured Organizations “CBBC” Management”, Submitted to the “2nd International Symposium on Chaos, Complexity and Leadership (ICCLS), December 17-19 at Middle East Technical University (METU), Ankara, Turkey.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.htmlKüçüközmen, 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. Merih, K. ve Çınar, F., (2013). “Modelling of Corporate Performance In Multi-Dimensional Complex Structured Organizations: “Cbbc” Approach”, Submitted to the EconAnadolu 2013: Anadolu International Conference in Economics III June 19-21, 2013, Eskişehir.  http://www.econanadolu.org/en/index.php/articles2013/3683Küçüközmen, 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.Pedroni M., and Bertrand Meyer (2009). “Object-oriented modeling of Object-Oriented Concepts”, ‘A Case Study in Structuring an Educational Domain’, Chair of Software Engineering, ETH Zurich, Switzerland. fmichela.pedroni|bertrand.meyerg@inf.ethz.chKüçüközmen, 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