An approach to securitisation in Europe NPLs- machine learning model field lab project Nova SBE | moody’s analytics

As a consulting project, we were proposed to develop a neural network (NN) to predict mortgage states in one year, based on the paper ‘Deep Learning for Mortgage Risk’ by Justin A. Sirignano, Apaar Sadhwani, Kay Giesecke (2018). We developed a neural network model with the aim of being able to capture the relationships between the different variables, with respect to each other and to the response variable (the loan status in 12 months), better than traditional classification methods, such as logistic regressions, which constitute the benchmark set. Data was provided by Moody’s, relating borrower, property and loan/financing characteristics for several mortgages over several periods in time (over 350 thousand mortgages). The purpose of our model is to predict the probabilities to transition to different states at a certain point in time. The best results were obtained with a 10 layer, 500 nodes per layer network. The model can identify a large portion of defaults. At the cost, however, of a general overestimation of the default rate over the years. The capability of identifying loans that will be in arrears is also acceptable, with, again, an overestimation of the verified rate. Variables relating to borrower characteristics and history as well as financing are found to be the most significant

Estimation of Default Probabilities with Support Vector Machines

Predicting default probabilities is important for firms and banks to operate successfully and to estimate their specific risks. There are many reasons to use nonlinear techniques for predicting bankruptcy from financial ratios. Here we propose the so called Support Vector Machine (SVM) to estimate default probabilities of German firms. Our analysis is based on the Creditreform database. The results reveal that the most important eight predictors related to bankruptcy for these German firms belong to the ratios of activity, profitability, liquidity, leverage and the percentage of incremental inventories. Based on the performance measures, the SVM tool can predict a firms default risk and identify the insolvent firm more accurately than the benchmark logit model. The sensitivity investigation and a corresponding visualization tool reveal that the classifying ability of SVM appears to be superior over a wide range of the SVM parameters. Based on the nonparametric Nadaraya-Watson estimator, the expected returns predicted by the SVM for regression have a significant positive linear relationship with the risk scores obtained for classification. This evidence is stronger than empirical results for the CAPM based on a linear regression and confirms that higher risks need to be compensated by higher potential returns.Support Vector Machine, Bankruptcy, Default Probabilities Prediction, Expected Profitability, CAPM.

Effects of Investor Sentiment Using Social Media on Corporate Financial Distress

The mainstream quantitative models in the finance literature have been ineffective in detecting possible bankruptcies during the 2007 to 2009 financial crisis. Coinciding with the same period, various researchers suggested that sentiments in social media can predict future events. The purpose of the study was to examine the relationship between investor sentiment within the social media and the financial distress of firms Grounded on the social amplification of risk framework that shows the media as an amplified channel for risk events, the central hypothesis of the study was that investor sentiments in the social media could predict t he level of financial distress of firms. Third quarter 2014 financial data and 66,038 public postings in the social media website Twitter were collected for 5,787 publicly held firms in the United States for this study. The Spearman rank correlation was applied using Altman Z-Score for measuring financial distress levels in corporate firms and Stanford natural language processing algorithm for detecting sentiment levels in the social media. The findings from the study suggested a non-significant relationship between investor sentiments in the social media and corporate financial distress, and, hence, did not support the research hypothesis. However, the model developed in this study for analyzing investor sentiments and corporate distress in firms is both original and extensible for future research and is also accessible as a low-cost solution for financial market sentiment analysis

A Survey of Systemic Risk Analytics

We provide a survey of 31 quantitative measures of systemic risk in the economics and finance literature, chosen to span key themes and issues in systemic risk measurement and management. We motivate these measures from the supervisory, research, and data perspectives in the main text and present concise definitions of each risk measure—including required inputs, expected outputs, and data requirements—in an extensive Supplemental Appendix. To encourage experimentation and innovation among as broad an audience as possible, we have developed an open-source Matlab® library for most of the analytics surveyed, which, once tested, will be accessible through the Office of Financial Research (OFR) at http://www.treasury.gov/initiatives/wsr/ofr/Pages/default.aspx.United States. Dept. of the Treasury. Office of Financial ResearchMassachusetts Institute of Technology. Laboratory for Financial EngineeringNational Science Foundation (U.S.) (Grant ECCS-1027905

Financial predictions using intelligent systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis presents a collection of practical techniques for analysing various market properties in order to design advanced self-evolving trading systems based on neural networks combined with a genetic algorithm optimisation approach. Nonlinear multivariate statistical models have gained increasing importance in financial time series analysis, as it is very hard to fmd statistically significant market inefficiencies using standard linear modes. Nonlinear models capture more of the underlying dynamics of these high dimensional noisy systems than traditional models, whilst at the same time making fewer restrictive assumptions about them. These adaptive trading systems can extract information about associated time varying processes that may not be readily captured by traditional models. In order to characterise the fmancial time series in terms of its dynamic nature, this research employs various methods such as fractal analysis, chaos theory and dynamical recurrence analysis. These techniques are used for evaluating whether markets are stochastic and deterministic or nonlinear and chaotic, and to discover regularities that are completely hidden in these time series and not detectable using conventional analysis. Particular emphasis is placed on examining the feasibility of prediction in fmancial time series and the analysis of extreme market events. The market's fractal structure and log-periodic oscillations, typical of periods before extreme events occur, are revealed through recurrence plots. Recurrence qualification analysis indicated a strong presence of structure, recurrence and determinism in the fmancial time series studied. Crucial fmancial time series transition periods were also detected. This research performs several tests on a large number of US and European stocks using methodologies inspired by both fundamental analysis and technical trading rules. Results from the tests show that profitable trading models utilising advanced nonlinear trading systems can be created after accounting for realistic transaction costs. The return achieved by applying the trading model to a portfolio of real price series differs significantly from that achieved by applying it to a randomly generated price series. In some cases, these models are compared against simpler alternative approaches to ensure that there is an added value in the use of these more complex models. The superior performance of multivariate nonlinear models is also demonstrated. The long-short trading strategies performed well in both bull and bear markets, as well as in a sideways market, showing a great degree of flexibility and adjustability to changing market conditions. Empirical evidence shows that information is not instantly incorporated into market pnces and supports the claim that the fmancial time series studied, for the periods analysed, are not entirely random. This research clearly shows that equity markets are partially inefficient and do not behave along lines dictated by the efficient market hypothesis

Predicting Nature of Default using Machine Learning Techniques

This paper presents machine learning techniques to help financial institutions model a loan’s nature of default and further incorporate nature of default in the prediction of loss given default models. Nature of default decribes the main reason why the lender puts the borrower in default. The comparison of different techniques show the decision tree approach as the best model, specifically the time it takes to default since a loan’s origination is the most important feature in distinguishing different default types. We find loans with longer time to default are more likely to emerge to bankruptcy; whereas loans defaulted shortly after origination are more likely to be sold at a discount, resulting in a material credit loss. We also find that trade finance loans are more likely to receive a specific provision or write-off from the lending bank when they default, possibly due to the significant decrease in collateral valuations when the company is in financial difficulty. The nature of default is also found to be a significant factor in predicting loss given default. The unique insight this paper provides, when compared to similar default and loss studies in the existing literature, lies with its specificity in the loan’s nature of default and its association with loss rates

On the Differential Analysis of Enterprise Valuation Methods as a Guideline for Unlisted Companies Assessment (II): Applying Machine-Learning Techniques for Unbiased Enterprise Value Assessment

The search for an unbiased company valuation method to reduce uncertainty, whether or not it is automatic, has been a relevant topic in social sciences and business development for decades. Many methods have been described in the literature, but consensus has not been reached. In the companion paper we aimed to review the assessment capabilities of traditional company valuation model, based on company’s intrinsic value using the Discounted Cash Flow (DCF). In this paper, we capitalized on the potential of exogenous information combined with Machine Learning (ML) techniques. To do so, we performed an extensive analysis to evaluate the predictive capabilities with up to 18 different ML techniques. Endogenous variables (features) related to value creation (DCF) were proved to be crucial elements for the models, while the incorporation of exogenous, industry/country specific ones, incrementally improves the ML performance. Bagging Trees, Supported Vector Machine Regression, Gaussian Process Regression methods consistently provided the best results. We concluded that an unbiased model can be created based on endogenous and exogenous information to build a reference framework, to price and benchmark Enterprise Value for valuation and credit risk assessment