Sharpening the Accuracy of Credit Scoring Models with Machine Learning Algorithms

AbstractThe big data revolution and recent advancements in computing power have increased the interest in credit scoring techniques based on artificial intelligence. This has found easy leverage in the fact that the accuracy of credit scoring models has a crucial impact on the profitability of lending institutions. In this chapter, we survey the most popular supervised credit scoring classification methods (and their combinations through ensemble methods) in an attempt to identify a superior classification technique in the light of the applied literature. There are at least three key insights that emerge from surveying the literature. First, as far as individual classifiers are concerned, linear classification methods often display a performance that is at least as good as that of machine learning methods. Second, ensemble methods tend to outperform individual classifiers. However, a dominant ensemble method cannot be easily identified in the empirical literature. Third, despite the possibility that machine learning techniques could fail to outperform linear classification methods when standard accuracy measures are considered, in the end they lead to significant cost savings compared to the financial implications of using different scoring models

Switching coefficients or automatic variable selection: an application in forecasting commodity returns

In this paper, we conduct a thorough investigation of the predictive ability of forward and backward stepwise regressions and hidden Markov models for the futures returns of several commodities. The predictive performance relative a standard AR(1) benchmark is assessed under both statistical and economic loss functions. We find that the evidence that either stepwise regressions or hidden Markov models may outperform the benchmark under standard statistical loss functions is rather weak and limited to low-volatility regimes. However, a mean-variance investor that adopts flexible forecasting models (especially stepwise predictive regressions) when building her portfolio, achieves large benefits in terms of realized Sharpe ratios and mean-variance utility compared to an investor employing AR(1) forecasts

The dynamics of returns predictability in cryptocurrency markets

In this paper, we take a forecasting perspective and compare the information content of a set of market risk factors, cryptocurrency-specific predictors, and sentiment variables for the returns of cryptocurrencies vs traditional asset classes. To this aim, we rely on a flexible dynamic econometric model that not only features time-varying coefficients, but also allows for the entire forecasting model to change over time to capture the time variation in the exposures of major digital currencies to the predictive variables. Besides, we investigate whether the inclusion of cryptocurrencies in an already diversified portfolio leads to additional economic gains. The main empirical results suggest that cryptocurrencies are not systematically predicted by stock market factors, precious metal commodities or supply factors. On the contrary, they display a time-varying but significant exposure to investors' attention. In addition, also because of a lack of predictability compared to traditional asset classes, cryptocurrencies lead to realized expected utility gains for a power utility investor

Decomposing Joint Distributions via Reweighting Functions: An Application to Intergenerational Economic Mobility

We propose a decomposition method that extends the traditional Oaxaca-Blinder decomposition to a continuous group membership setting that can be applied to any distributional measure of interest. This is achieved by reframing the problem as a decomposition of joint distributions: we decompose the difference between an empirical and a (hypothetical) independent joint distribution of membership index and an outcome of interest. Differences are divided into a composition effect and a structure effect. The method is based on the estimation of a counterfactual joint distribution via reweighting functions that can be caste into various distributional measures to investigate the drivers of the empirical relationship. We apply the method to U.S. intergenerational economic mobility and investigate multiple versions of the intergenerational elasticity of income (IGE): the traditional linear IGE, quantile regression counterparts, and a nonparametric IGE. Quantile results reveal a U-shaped effect which is primarily compositional in nature; nonparametric results indicate the composition effect is the main driver of the mean parental-offspring link at low levels of parental income while the structural effect is the main driver at high levels of parental income. Both of these effects are masked by the traditional IGE which implies an even 50-50 split between the composition and structure effect

Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice

Can Commodity-Specific Factors Improve the Forecasting Power of Macroeconomic Variables for Commodity Futures Returns

The aim of this paper is to assess whether three well-known commodity-specific variables (basis, hedging pressure, and momentum) may improve the predictive power for commodity futures returns of models otherwise based on macroeconomic factors. We compute recursive, out-of-sample forecasts for the monthly returns of fifteen commodity futures, when estimation is based on a stepwise model selection approach under a probability-weighted regime-switching regression that identifies different volatility regimes. We systematically compare these forecasts with those produced by a simple AR(1) model that we use as a benchmark and we find that the inclusion of commodity-specific factors does not improve the forecasting power. We perform a back-testing exercise of a mean–variance investment strategy that exploits any predictability of the conditional risk premium of commodities, stocks, and bond returns, also consider transaction costs caused by portfolio rebalancing. The risk-adjusted performance of this strategy does not allow us to conclude that any forecasting approach outperforms the others. However, there is evidence that investment strategies based on commodity-specific predictors outperform the remaining strategies in the high-volatility state

Sharpening the Accuracy of Credit Scoring Models with Machine Learning Algorithms

The big data revolution and recent advancements in computing power have increased the interest in credit scoring techniques based on artificial intelligence. This has found easy leverage in the fact that the accuracy of credit scoring models has a crucial impact on the profitability of lending institutions. In this chapter,we survey themost popular supervised credit scoring classification methods (and their combinations through ensemble methods) in an attempt to identify a superior classification technique in the light of the applied literature. There are at least three key insights that emerge from surveying the literature. First, as far as individual classifiers are concerned, linear classification methods often display a performance that is at least as good as that of machine learning methods. Second, ensemble methods tend to outperform individual classifiers. However, a dominant ensemble method cannot be easily identified in the empirical literature. Third, despite the possibility that machine learning techniques could fail to outperform linear classification methods when standard accuracy measures are considered, in the end they lead to significant cost savings compared to the financial implications of using different scoring models

Forecasting and trading monetary policy effects on the riskless yield curve with regime switching Nelson–Siegel models

We use 1982–2014 data on the US riskless yield curve to show that regime switching dynamics in Nelson-Siegel factor models extended to encompass variables that summarize the state of monetary policy lead to superior predictive accuracy. Such spread in forecasting power turns out to be statistically significant even controlling for parameter uncertainty and sample variation. Exploiting regimes, we obtain evidence that the increase in predictive accuracy is stronger during the Great Financial Crisis, when monetary policy underwent a significant, sudden shift. Although more caution applies in comparisons to a naïve random walk benchmark over a few sub-samples and when transaction costs are accounted for, we report that the increase in predictive power owed to the combination of regimes and of variables that capture the stance of unconventional monetary policies is tradeable. We devise and test butterfly strategies that exploit the forecasts from the models and obtain evidence of risk-adjusted profits both per se and in comparisons to simpler models

Switching Coefficients or Automatic Variable Selection: An Application in Forecasting Commodity Returns

In this paper, we conduct a thorough investigation of the predictive ability of forward and backward stepwise regressions and hidden Markov models for the futures returns of several commodities. The predictive performance relative a standard AR(1) benchmark is assessed under both statistical and economic loss functions. We find that the evidence that either stepwise regressions or hidden Markov models may outperform the benchmark under standard statistical loss functions is rather weak and limited to low-volatility regimes. However, a mean-variance investor that adopts flexible forecasting models (especially stepwise predictive regressions) when building her portfolio, achieves large benefits in terms of realized Sharpe ratios and mean-variance utility compared to an investor employing AR(1) forecasts

Essentials of applied portfolio management

This book offers an essential introduction to modern portfolio theory. The book provides a number of simple, practical examples to allow the reader to apply the theoretical concepts presented in each chapter. A portion of such practical cases are worked out in Excel and made available through the book’s website. The book takes inspiration from Markowitz’s classical mean-variance, it then proceeds to develop modelling tools of increasing sophistication that eventually take into account the role played by generic risk-averse preferences. The book also explores a few advanced topics: the use of multi-factor asset pricing models and the role of background risks and human capital