scholarly journals Crypto Exchanges and Credit Risk: Modeling and Forecasting the Probability of Closure

2021 ◽  
Vol 14 (11) ◽  
pp. 516
Author(s):  
Dean Fantazzini ◽  
Raffaella Calabrese
Keyword(s):  
Machine Learning ◽  
Credit Risk ◽  
Credit Scoring ◽  
Machine Learning Techniques ◽  
Risk Modeling ◽  
Learning Techniques ◽  
Modeling And Forecasting ◽  
Out Of Sample ◽  
Significant Covariates ◽  
Unique Dataset

While there is increasing interest in crypto assets, the credit risk of these exchanges is still relatively unexplored. To fill this gap, we considered a unique dataset of 144 exchanges, active from the first quarter of 2018 to the first quarter of 2021. We analyzed the determinants surrounding the decision to close an exchange using credit scoring and machine learning techniques. Cybersecurity grades, having a public developer team, the age of the exchange, and the number of available traded cryptocurrencies are the main significant covariates across different model specifications. Both in-sample and out-of-sample analyzes confirm these findings. These results are robust in regard to the inclusion of additional variables, considering the country of registration of these exchanges and whether they are centralized or decentralized.

scholarly journals Credit Risk Assessment using Machine Learning Techniques

2019 ◽  
Vol 9 (1) ◽  
pp. 3482-3486
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Random Forest ◽  
Credit Risk ◽  
Banking Sector ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Credit Risk Assessment ◽  
Learning Techniques ◽  
Cart Algorithm

Analysis of credit scoring is an effective credit risk assessment technique, which is one of the major research fields in the banking sector. Machine learning has a variety of applications in the banking sector and it has been widely used for data analysis. Modern techniques such as machine learning have provided a self-regulating process to analyze the data using classification techniques. The classification method is a supervised learning process in which the computer learns from the input data provided and makes use of this information to classify the new dataset. This research paper presents a comparison of various machine learning techniques used to evaluate the credit risk. A credit transaction that needs to be accepted or rejected is trained and implemented on the dataset using different machine learning algorithms. The techniques are implemented on the German credit dataset taken from UCI repository which has 1000 instances and 21 attributes, depending on which the transactions are either accepted or rejected. This paper compares algorithms such as Support Vector Network, Neural Network, Logistic Regression, Naive Bayes, Random Forest, and Classification and Regression Trees (CART) algorithm and the results obtained show that Random Forest algorithm was able to predict credit risk with higher accuracy

scholarly journals Credit Risk Modeling: Combining Classification And Regression Algorithms to Predict Expected Loss

2014 ◽  
Vol 8 (4) ◽  
pp. 4-10
Author(s):  
Tim Kreienkamp ◽  
Andrey Kateshov
Keyword(s):  
Credit Risk ◽  
Risk Model ◽  
Machine Learning Techniques ◽  
Risk Modeling ◽  
Expected Loss ◽  
Financial Industry ◽  
Loan Default ◽  
Loss Given Default ◽  
Learning Techniques ◽  
Selection Algorithms

Credit risk assessment is of paramount importance in the financial industry. Machine learning techniques have been used successfully over the last two decades to predict the probability of loan default (PD). This way, credit decisions can be automated and risk can be reduced significantly. In the more recent parts, intensified regulatory requirements led to the need to include another parameter – loss given default (LGD), the share of the loan which cannot be recovered in case of loan default – in risk models. We aim to build a unified credit risk model by estimating both parameters jointly to estimate expected loss. A large, highdimensional, real world dataset is used to benchmark several combinations of classification, regression and feature selection algorithms. The results indicate that non-linear techniques work especially well to model expected loss.

scholarly journals Credit Scoring using Machine Learning Techniques

2017 ◽  
Vol 161 (11) ◽  
pp. 1-4 ◽  
Author(s):  
Sunil Bhatia ◽  
Pratik Sharma ◽  
Rohit Burman ◽  
Santosh Hazari ◽  
Rupali Hande
Keyword(s):  
Machine Learning ◽  
Credit Scoring ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals The Effects of Geopolitical Uncertainty in Forecasting Financial Markets: A Machine Learning Approach

Algorithms ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 1 ◽  
Author(s):  
Vasilios Plakandaras ◽  
Periklis Gogas ◽  
Theophilos Papadimitriou
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Relevant Literature ◽  
Emerging Countries ◽  
Machine Learning Techniques ◽  
Economic Agents ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Out Of Sample ◽  
Terrorist Acts

An important ingredient in economic policy planning both in the public or the private sector is risk management. In economics and finance, risk manifests through many forms and it is subject to the sector that it entails (financial, fiscal, international, etc.). An under-investigated form is the risk stemming from geopolitical events, such as wars, political tensions, and conflicts. In contrast, the effects of terrorist acts have been thoroughly examined in the relevant literature. In this paper, we examine the potential ability of geopolitical risk of 14 emerging countries to forecast several assets: oil prices, exchange rates, national stock indices, and the price of gold. In doing so, we build forecasting models that are based on machine learning techniques and evaluate the associated out-of-sample forecasting error in various horizons from one to twenty-four months ahead. Our empirical findings suggest that geopolitical events in emerging countries are of little importance to the global economy, since their effect on the assets examined is mainly transitory and only of regional importance. In contrast, gold prices seem to be affected by fluctuation in geopolitical risk. This finding may be justified by the nature of investments in gold, in that they are typically used by economic agents to hedge risk.

scholarly journals Use of Machine Learning Techniques to Create a Credit Score Model for Airtime Loans

2020 ◽  
Vol 13 (8) ◽  
pp. 180 ◽  
Author(s):  
Bernard Dushimimana ◽  
Yvonne Wambui ◽  
Timothy Lubega ◽  
Patrick E. McSharry
Keyword(s):  
Machine Learning ◽  
Credit Scoring ◽  
Machine Learning Techniques ◽  
Microfinance Institutions ◽  
Credit Score ◽  
Learning Techniques ◽  
Machine Learning Model ◽  
Default Rates ◽  
Default Rate ◽  
Score Model

Airtime lending default rates are typically lower than those experienced by banks and microfinance institutions (MFIs) but are likely to grow as the service is offered more widely. In this paper, credit scoring techniques are reviewed, and that knowledge is built upon to create an appropriate machine learning model for airtime lending. Over three million loans belonging to more than 41 thousand customers with a repayment period of three months are analysed. Logistic Regression, Decision Trees and Random Forest are evaluated for their ability to classify defaulters using several cross-validation approaches and the latter model performed best. When the default rate is below 2%, it is better to offer everyone a loan. For higher default rates, the model substantially enhances profitability. The model quadruples the tolerable level of default rate for breaking even from 8% to 32%. Nonlinear classification models offer considerable potential for credit scoring, coping with higher levels of default and therefore allowing for larger volumes of customers.

Optimization algorithms and investment portfolio analytics with machine learning techniques under time-varying liquidity constraints

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mazin A.M. Al Janabi
Keyword(s):  
Machine Learning ◽  
Liquidity Constraints ◽  
Market Risk ◽  
Machine Learning Techniques ◽  
Time Varying ◽  
Risk Modeling ◽  
Investment Portfolio ◽  
Content Type ◽  
Learning Techniques ◽  
Portfolio Managers

Purpose This paper aims to examine from commodity portfolio managers’ perspective the performance of liquidity adjusted risk modeling in assessing the market risk parameters of a large commodity portfolio and in obtaining efficient and coherent portfolios under different market circumstances. Design/methodology/approach The implemented market risk modeling algorithm and investment portfolio analytics using reinforcement machine learning techniques can simultaneously handle risk-return characteristics of commodity investments under regular and crisis market settings besides considering the particular effects of the time-varying liquidity constraints of the multiple-asset commodity portfolios. Findings In particular, the paper implements a robust machine learning method to commodity optimal portfolio selection and within a liquidity-adjusted value-at-risk (LVaR) framework. In addition, the paper explains how the adapted LVaR modeling algorithms can be used by a commodity trading unit in a dynamic asset allocation framework for estimating risk exposure, assessing risk reduction alternates and creating efficient and coherent market portfolios. Originality/value The optimization parameters subject to meaningful operational and financial constraints, investment portfolio analytics and empirical results can have important practical uses and applications for commodity portfolio managers particularly in the wake of the 2007–2009 global financial crisis. In addition, the recommended reinforcement machine learning optimization algorithms can aid in solving some real-world dilemmas under stressed and adverse market conditions (e.g. illiquidity, switching in correlations factors signs, nonlinear and non-normal distribution of assets’ returns) and can have key applications in machine learning, expert systems, smart financial functions, internet of things (IoT) and financial technology (FinTech) in big data ecosystems.

Sign in / Sign up

Export Citation Format

Share Document