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Hello, I'm Zurab Kirkitadze

  • Welcome to my personal website. I am zurab kirkitadze doctor of business administration. i finished all 3 steps of higher education in business administration. i also passed internship at national bank of georgia. i have published 8 scientific articles as in georgia and also in foreign countries. have work experience and i am active learner. i defended my phd dissertation in foreign direct investment, specifically effects of FDI strategie on host country economy (on the case of georgia)

My Education

  • 2011-2020 Doctoral student of Tbilisi state university
  • Faculty of Economics and Business, program of business administration
  • module of international business
  • 2011-2012 Intern
  • National bank of Georgia
  • Department of macroeconomics and statistics
  • Monetary policy division
  • 2009-2011 Master of American University for humanities
  • faculty of business administration (English master’s program of business administration)
  • 2007-2009 Caucasus school of business for incomplete period
  • Student on master’s degree course of business administration
  • 2001-2005 Bachelor of American university for humanities
  • Faculty of business administration (English Bachelor’s program of business administration)

My Skills

  • Microsoft office programs Excel-very well, Word-well, Powerpoint-well
  • Eviews 12-very well
  • SPSS 23-very well
  • Stata 12-very well
  • R-Normal
  • Python-Normal
  • Apex ERP-Accounting Modulus-Normal
  • Tableau Public-Normal
  • Microsoft Power BI-normal
  • VBA-normal (know how to program code by searching in relevant sources)
  • SQL-Normal
  • MongoDB-Normal
  • A/B testing
  • DAX-normal
  • IFRS standards – Normally
  • Basic knowledge of HTML, javascript and CSS (i created 6 static websites)
  • Bayesian estimation of DSGE using python
  • I am also versed in the following methods of credit risk assessment:
  • 1. Variance and consequently standard deviation
  • 2. Copula method with R and Python
  • 3. Fuzzy set method
  • 4. Value at risk assessment by Monte Carlo simulation in Python
  • 5. Various classification methods such as linear discriminant analysis with R
  • 6. Various classification methods in Python: AdaBoost Classifier, Decision Tree Classifier, Quadratic Discriminant Analysis, GaussianNB, Gaussian Process Classifier, KNeighbors Classifier, MLP Classifier, Random Forest Classifier and svm
  • 7. Logistic regression and for small sample exact logistic regression with Eviews, SPSS or STATA
  • 8. Fuzzy logic model in MATLAB and Python
  • 9. Machine learning and deep learning methods, such as neural networks (such as Multilayer Perceptron and Radial Basis Function)
  • 10. Survival analysis/Cox regression to evaluate credit risk, survival analysis in STATA and SPSS that is to find what time period passes from giving credit to customer to default event.
  • 11. Sentiment analysis to find emotions in texts sent from borrowers to lenders
  • 12. Quantile regression to find how default probability varies according to various levels of income for example.
  • 13. Ensemble methods that is averaging results derived from various models to find output that is close to each model that is finding result that won’t be too different from almost practically correct result
  • 14. Stress testing that is creation of econometric model to see how undesirable condition of borrower characteristics, industry-specific risks, and macroeconomic indicators will influence credit risk.
  • 15. Time series forecasting methods such as ARIMA, Exponential smoothing, Seasonal decomposition, gradient boosting, MLP regression, Random forest, Support Vector Machines (SVM), Simple Moving Average.
  • 16. BERT classifier and the Passive Aggressive classifier that are text classifiers in order to determine the veracity of promises made by borrowers.
  • 17. Panel data regression to evaluate how borrowers behaved over time in order to evaluate behavior of credit portfolio that is to determine generally what factors stimulated default and what factors lessened default event.
  • 18. Altman's Z-score, which predicts bankruptcy risk using financial ratios.
  • 19. Reinforcement learning (RL), specifically Q-learning to decide whether to grant credit or not in python.
  • 20. Credit Scoring Models
  • 21. IFRS 9 and the implications of CECL (Current Expected Credit Loss)
  • 22. Behavioral Scoring Models
  • 23. Causal Inference: Understanding methods for establishing causality in my analyses, which can help in understanding the impact of interventions on default rates.
  • 24. SHapley Additive exPlanations (SHAP) that helps explain the output of machine learning models by assigning each feature an importance value for a particular prediction.
  • 25. Finding Nash equilibrium under context of game theory when players are lenders and borrowers
  • 26. ARCH/GARCH to forecast variance/volatility for example when dependent variable is absolute percentage change in profit/income of borrower to forecast variance/volatility of profit/income and this way find out uncertainty in future period income/profit of borrower. conclusion is it that default probability is higher when future forecasted income/profit is characterized by higher variance/volatility. Also knowledge of ARCH/GARCH variations.
  • 27. XGBoost for Binary Classification and fine-tuning the XGBoost model using Grid Search. Also LightGBM, Catboost and Adaboost
  • 28. Autoencoders that is used for anomaly detection, which can be useful in identifying fraudulent or unusual credit behavior patterns.
  • 29. Factor Analysis that is used for uncovering latent variables that might explain the default risk of a borrower
  • 30. Recurrent Neural Networks (RNNs) or Long Short-Term Memory (LSTM) networks that could be beneficial for modeling sequential data (e.g., borrower payment history over time)
  • 31. Mean reversion model to model the interest rate dynamics
  • 32. Metrics such as Sharpe Ratio to assess risk-adjusted return in credit portfolios.
  • 33. Financial analysis using financial coefficients from financial statements
  • 34. Linear Programming (LP) for optimizing a credit portfolio with python
  • 35. Bayesian Logistic Regression to predict whether borrower will default
  • 36. Techniques like Generative Adversarial Networks (GANs) to generate synthetic data for training models when data is scarce.
  • 37. Genetic algorithm for credit portfolio optimization
  • 38. Knowledge of credit rating migration matrix including how to construct it.
  • 39. Model Drift and Concept Drift: Techniques for monitoring the performance of credit risk models over time, detecting if and when they start to underperform due to changes in borrower behavior, market conditions, or economic factors.
  • 40. Credit pricing model that is what interest rate to charge according to probability of default
  • 41. Multilevel classifier in Python for credit risk prediction
  • 42. Evaluation of market risk influence on credit risk
  • 43. Default probability by Merton model
  • 44. Calculation of expected loss assuming that borrower will use real options (expanding, contracting or abandoning). I can create binomial tree of present value without real options and also can create binomial tree of present value taking into consideration real options in excel and Python
  • 45. Model validation that is testing how model performs on unseen data that is on test data and backtesting using confusion matrix and accuracy and other measures
  • 46. Basel regulations that give guidelines and requirements how to assess and manage credit risk
  • 47. Credit portfolio diversification and concentration
  • 48. what effect does GDP growth, interest rate and inflation have on credit risk
  • 49. Assesing the liquidity of borrower
  • 50. Assuring that incorrect model output does not discriminate towards good borrowers
  • 51. GDPR
  • 52. Kalman filter to derive more accurate estimate of default probability
  • .

My Work Experience

  • 2011 Jsc “charkhi-xazi”

    Administrator of Web page

    functions: Intermediation in creation of web page and running of web page

  • 2009-2010 MLM Technologys

    Sales manager

    Functions: sale of products, Attraction of people to work in this company

  • 2005-2007 Inspector, Tax Inspection of Tbilisi

    Functions: training of tax payers in tax code of Georgia, prevention of tax law violation, drawing up a statement of the case in case of Tax law violation, Delivering tax requests to tax payers

  • My pedagogical experience

    have small pedagogical experience. I was performing seminars with students in business administration, specifically I was preparing tests for students, was checking their exams and was asking them questions about materials that they studied on lectures. My seminars were evaluated with 100 points from 100 points.

Contact Me

If you'd like to get in touch, feel free to drop a message below!

Email: kirki_1205@yahoo.com

Mobile: +995 595 51 54 09

Address: Tbilisi, Georgia

Research

  • 2009-2011 Fund of Naumann, Strategic management activities and leadership style of Henry Ford
  • My research interests mainly focus on the following areas:
  • 1) Foreign Direct Investment (FDI) — Investigating the effects of FDI strategies on host country economies, with a particular focus on Georgia.
  • 2) Credit Risk Assessment — Developing and applying advanced statistical methods for credit risk analysis, such as machine learning techniques and econometric modeling.
  • 3) Data Science & Financial Modeling — Using machine learning algorithms and statistical models for financial forecasting and risk management.
  • 4) Data analysis
  • 5) Econometrics and machine learning

Publications

  • 1. Zurab Kirkitadze, REGRESSION ANALYSIS OF FDI BY USING ECONOMETRIC TESTS,TSU P. Gugushvili Institute of Economics, Proceedings, P.418-424
  • 2. Z. kirkitadze, Passed measures for attraction of FDI and provement of their consistency, Journal “New Economist” 2018, # 2-3, P.46-54
  • 3. Z. Kirkitadze, Analysis of FDI determinants in Georgia, Journal “Economists”, 2018 #2, P.146-156
  • 4. Z. Kirkitadze, POSITIVE EFFECTS OF FDI IN HOST COUNTRIES, Journal “Economics”, 2018, #12, p.36-42
  • 5. Zurab Kirkitadze. Discussion of passed measures for attraction of FDI and affirmation of their consistency. World economy and international economic relations. International Scientific Collection.Kiev. GUL, 2018. ISBN 978-611-01-1079-2. P. 24-29.
  • 6. Z. Kirkitadze, T. Shengelia, Y. Kozak. FDI motivation effects on Host countries. World economy and international economic relations. International Scientific Collection. Volume 3, Kiev. GUL, 2020. ISBN 978-611-01-1615-2. P. 15-20.
  • 7. T. Shengelia, Z. kirkitadze, The Post-Coronavirus Economy of the World and Georgia, Journal “Economics and Business”, 2020, #4
  • 8. T. Shengelia, Z. kirkitadze, HOW TO OVERCOME SOME IMPORTANT ECONOMIC IN GEORGIA WITH FDI. World economy and international economic relations. International Scientific Collection – Vol. 4. Kyiev, GUL 2021. ISBN 978-611-01-1865-1
  • .

My personal projects

  • links to my personal projects:
  • 1) https://www.kaggle.com/code/zurabkirkitadze/notebookead71f46ca