Enhanced default risk models with SVM+

Default risk models have lately raised a great interest due to the recent world economic crisis. In spite of many advanced techniques that have extensively been proposed, no comprehensive method incorporating a holistic perspective has hitherto been considered. Thus, the existing models for bankruptcy prediction lack the whole coverage of contextual knowledge which may prevent the decision makers such as investors and financial analysts to take the right decisions. Recently, SVM+ provides a formal way to incorporate additional information (not only training data) onto the learning models improving generalization. In financial settings examples of such non-financial (though relevant) information are marketing reports, competitors landscape, economic environment, customers screening, industry trends, etc. By exploiting additional information able to improve classical inductive learning we propose a prediction model where data is naturally separated into several structured groups clustered by the size and annual turnover of the firms. Experimental results in the setting of a heterogeneous data set of French companies demonstrated that the proposed default risk model showed better predictability performance than the baseline SVM and multi-task learning with SVM.info:eu-repo/semantics/publishedVersio

Convex formulation for multi-task L1-, L2-, and LS-SVMs

Quite often a machine learning problem lends itself to be split in several well-defined subproblems, or tasks. The goal of Multi-Task Learning (MTL) is to leverage the joint learning of the problem from two different perspectives: on the one hand, a single, overall model, and on the other hand task-specific models. In this way, the found solution by MTL may be better than those of either the common or the task-specific models. Starting with the work of Evgeniou et al., support vector machines (SVMs) have lent themselves naturally to this approach. This paper proposes a convex formulation of MTL for the L1-, L2- and LS-SVM models that results in dual problems quite similar to the single-task ones, but with multi-task kernels; in turn, this makes possible to train the convex MTL models using standard solvers. As an alternative approach, the direct optimal combination of the already trained common and task-specific models can also be considered. In this paper, a procedure to compute the optimal combining parameter with respect to four different error functions is derived. As shown experimentally, the proposed convex MTL approach performs generally better than the alternative optimal convex combination, and both of them are better than the straight use of either common or task-specific modelsWith partial support from Spain’s grant TIN2016-76406-P. Work supported also by the UAM–ADIC Chair for Data Science and Machine Learning

A survey on multi-output regression

In recent years, a plethora of approaches have been proposed to deal with the increasingly challenging task of multi-output regression. This paper provides a survey on state-of-the-art multi-output regression methods, that are categorized as problem transformation and algorithm adaptation methods. In addition, we present the mostly used performance evaluation measures, publicly available data sets for multi-output regression real-world problems, as well as open-source software frameworks

Kernel methods for time series data

Kernel methods are powerful learning techniques with excellent generalization capability. This thesis develops three advanced approaches within the generic SVM framework in the application domain of time series data. The first contribution presents a new methodology for incorporating privileged information about the future evolution of time series, which is only available in the training phase. The task is prediction of the ordered categories of future time series movements. This is implemented by directly extending support vector ordinal regression with implicit constraints to leaning using privileged information paradigm. The second contribution demonstrates a novel methodology of constructing efficient kernels for time series classification problems. These kernels are constructed by representing each time series through a linear readout model from a high dimensional state space model with a fixed deterministically constructed dynamic part. Learning is then performed in the linear readout model space. Finally, in the same context, we introduce yet another novel time series kernel by co-learning the dynamic part and a global metric in the linear readout model space, encouraging time series from the same class to be represented by close model representations, while model representations of time series from different classes to be well-separated

Risk management of variable annuity portfolios using machine learning techniques

Variable annuities (VAs) are increasingly becoming popular insurance products in many developed countries which provide guaranteed forms of income depending on the performance of the equity market. Insurance companies often hold large VA portfolios and the associate valuation of such portfolios is a very time-consuming task. There have been several studies focusing on inventing techniques aimed at reducing the computational time including the selection of representative VA contracts and the use of a metamodel to estimate the values of all contracts in the portfolio. In this thesis, LASSO regression is used to select a set of representative scenarios after the representative contracts are chosen, which in turn allows for the set of representative contracts to expand without significant increase in computational load. The proposed approach leads to a remarkable improvement in the computational efficiency and accuracy of the metamodel. Stochastic reserving and calculation of capital requirement require VA providers to calculate risk measures such as Value at Risk and Conditional Tail Expectation. An emulation framework is proposed to calculate these risk measures by building a neural network to model the net liability of a VA contract at some given scenario. The surrogate model is faster at estimating net liability than the exact calculation. Efficiency is improved thanks to faster computing of net liability for any contract at any scenario in the Monte Carlo simulation. This approach can also be used to select scenarios where the estimated portfolio liabilities are in the top quantile. The true liabilities of the portfolio at these top-quantile scenarios can be computed which can then be used to compute the risk measures. This results in a reduction in computational time because the Monte Carlo method is performed on only a fraction of the original scenarios. As an equity-linked insurance products, VA is exposed to significant market risks due to the underlying assets in the mutual funds that its contributions are invested in. To hedge against these market risks, insurers need to construct a hedging portfolio consisting of the underlying assets whose hedge positions can be determined by the Greeks of the portfolio such as the partial dollar Deltas. For a large portfolio, the calculation of the Greeks using Monte Carlo simulation is very slow, so a metamodeling approach can be used to estimate the Greeks. Assuming that the mutual funds of the VA insurers is a mixture of major market indices, there is likely a dependence between the partial dollar Deltas of the portfolio on the market indices. This dependent relationship can be incorporated into the model using multi-output regression approaches and the resulting improvement in the effectiveness of the metamodel or the lack thereof will be studied in the thesis