book

Machine Learning for Financial Risk Management with Python

by Abdullah Karasan

December 2021

Intermediate to advanced

331 pages

8h 28m

English

O'Reilly Media, Inc.

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Conventions Used in This BookUsing Code ExamplesO’Reilly Online LearningHow to Contact UsAcknowledgements
RiskReturnRisk ManagementMain Financial RisksBig Financial CollapseInformation Asymmetry in Financial Risk ManagementAdverse SelectionMoral HazardConclusionReferences
Time Series ComponentsTrendSeasonalityCyclicalityResidualTime Series ModelsWhite NoiseMoving Average ModelAutoregressive ModelAutoregressive Integrated Moving Average ModelConclusionReferences
Recurrent Neural NetworksLong-Short Term MemoryConclusionReferences
ARCH ModelGARCH ModelGJR-GARCHEGARCHSupport Vector Regression: GARCHNeural NetworksThe Bayesian ApproachMarkov Chain Monte CarloMetropolis–HastingsConclusionReferences
Value at Risk (VaR)Variance-Covariance MethodThe Historical Simulation MethodThe Monte Carlo Simulation VaRDenoisingExpected ShortfallLiquidity-Augmented Expected ShortfallEffective CostConclusionReferences
Estimating the Credit RiskRisk BucketingProbability of Default Estimation with Logistic RegressionProbability of Default Estimation with the Bayesian ModelProbability of Default Estimation with Support Vector MachinesProbability of Default Estimation with Random ForestProbability of Default Estimation with Neural NetworkProbability of Default Estimation with Deep LearningConclusionReferences
Liquidity MeasuresVolume-Based Liquidity MeasuresTransaction Cost–Based Liquidity MeasuresPrice Impact–Based Liquidity MeasuresMarket Impact-Based Liquidity MeasuresGaussian Mixture ModelGaussian Mixture Copula ModelConclusionReferences

Getting Familiar with Fraud DataSupervised Learning Modeling for Fraud ExaminationCost-Based Fraud ExaminationSaving ScoreCost-Sensitive ModelingBayesian Minimum RiskUnsupervised Learning Modeling for Fraud ExaminationSelf-Organizing MapAutoencodersConclusionReferences
Stock Price Crash MeasuresMinimum Covariance DeterminantApplication of Minimum Covariance DeterminantLogistic Panel ApplicationConclusionReferences
Synthetic Data GenerationEvaluation of the Synthetic DataGenerating Synthetic DataA Brief Introduction to the Hidden Markov ModelFama-French Three-Factor Model Versus HMMConclusionReferences

Content preview from Machine Learning for Financial Risk Management with Python

Preface

AI and ML reflect the natural evolution of technology as increased computing power enables computers to sort through large data sets and crunch numbers to identify patterns and outliers.

BlackRock (2019)

Financial modeling has a long history with many successfully accomplished tasks, but at the same time it has been fiercely criticized due mainly to lack of flexibility and non-inclusiveness of the models. The 2007–2008 financial crisis fueled this debate as well as paved the way for innovations and different approaches in the field of financial modeling.

Of course, the financial crisis was not the only thing precipitating the growth of AI applications in finance. Two other drivers, data availability and increased computing power, have spurred the adoption of AI in finance and have intensified research in this area starting in the 1990s.

The Financial Stability Board (2017) stresses the validity of this fact:

Many applications, or use “cases,” of AI and machine learning already exist. The adoption of these use cases has been driven by both supply factors, such as technological advances and the availability of financial sector data and infrastructure, and by demand factors, such as profitability needs, competition with other firms, and the demands of financial regulation.

As a subbranch of financial modeling, financial risk management has been evolving with the adoption of AI in parallel with its ever-growing role in the financial decision-making process. In his celebrated ...