Predicting Unlisted SMEs' Default: Incorporating Market Information on Accounting-based Models for Improved Accuracy

The risk associated with lending to small businesses has become more important since regulations started obliging banks to use separate procedures in assessing SMEs' credit worthiness. However, current accounting-based models for SMEs do not account for the impact of market information on default prediction. We fill this gap in the literature by introducing a hybrid default prediction model for unlisted SMEs that uses market information of listed SMEs (comparable approach) alongside existing accounting information of unlisted SMEs. Our results suggest that the accuracy of this default prediction modelling approach in the hold-out sample, during the period of the financial crisis 2007-09 and for the entire sample-period, improves considerably. We conclude that the proposed hybrid model is a good replacement for existing standard accounting-based methods on SMEs' default prediction

Application of Stationary Wavelet Support Vector Machines for the Prediction of Economic Recessions

This paper examines the efficiency of various approaches on the classification and prediction of economic expansion and recession periods in United Kingdom. Four approaches are applied. The first is discrete choice models using Logit and Probit regressions, while the second approach is a Markov Switching Regime (MSR) Model with Time-Varying Transition Probabilities. The third approach refers on Support Vector Machines (SVM), while the fourth approach proposed in this study is a Stationary Wavelet SVM modelling. The findings show that SW-SVM and MSR present the best forecasting performance, in the out-of sample period. In addition, the forecasts for period 2012-2015 are provided using all approaches

Comparing early warning systems for banking crises

Despite the extensive literature on prediction of banking crises by Early Warning Systems (EWS), their practical use by policy makers is limited, even in the international financial institutions. This is a paradox since the changing nature of banking risks as more economies liberalise and develop their financial systems, as well as ongoing innovation, makes the use of EWS for crisis prevention more necessary than ever. In this context, we assess the logit and signal extraction EWS for banking crises on a comprehensive common dataset. We suggest that logit is the most appropriate approach for global EWS and signal extraction for country specific EWS. Furthermore it is important to consider the policy maker s objectives when designing predictive models and setting related thresholds since there is a sharp trade-off between correctly calling crises and false alarms

The Outbreak of the Russian Banking Crisis

Russian banks have been strongly influenced by the worldwide financial crisis which started in the second half of 2008. This was caused by a combination of domestic, regional and international factors. We estimate an early warning model for the Russian crisis. We identified 47 Russian banks which failed after September 2008. Using the Bankscope data set, we show that balance sheet indicators were informative about possible failures of these banks as early as 2006. The early predictive indicators include especially equity, net interest revenues, return on average equity, net loans, and loan loss reserves

Modelling Credit Risk for SMEs in Saudi Arabia

The Saudi Government’s 2030 Vision directs local banks to increase and improve credit for the Small and Medium Enterprises (SMEs) of the economy (Jadwa, 2017). Banks are, however, still finding it difficult to provide credit for small businesses that meet Basel’s capital requirements. Most of the current credit-risk models only apply to large corporations with little constructed for SMEs applications (Altman and Sabato, 2007). This study fills this gap by focusing on the Saudi SMEs perspective. My empirical work constructs a bankruptcy prediction model based on logistic regressions that cover 14,727 firm-year observations for an 11-year period between 2001 and 2011. I use the first eight years data (2001-2008) to build the model and use it to predict the last three years (2009-2011) of the sample, i.e. conducting an out-of-sample test. This approach yields a highly accurate model with great prediction power, though the results are partially influenced by the external economic and geopolitical volatilities that took place during the period of 2009-2010 (the world financial crisis). To avoid making predictions in such a volatile period, I rebuild the model based on 2003-2010 data, and use it to predict the default events for 2011. The new model is highly consistent and accurate. My model suggests that, from an academic perspective, some key quantitative variables, such as gross profit margin, days inventory, revenues, days payable and age of the entity, have a significant power in predicting the default probability of an entity. I further price the risks of the SMEs by using a credit-risk pricing model similar to Bauer and Agarwal (2014), which enables us to determine the risk-return tradeoffs on Saudi’s SMEs

Estimating an SME investment gap and the contribution of financing frictions. ESRI WP589, March 2018

In this paper, we use firm-level survey data to explore the determinants of SME investment activity and the extent to which observed investment is in line with that suggested by economic fundamentals. In contrast to previous literature which has focused on whether investment gaps exist at a more aggregate level, we find evidence that for SMEs actual investment is below what would be expected given how companies are currently performing. The estimated magnitude of this investment gap is economically meaningful at just over 30 per cent in 2016. We explore the extent to which the gap is explained by financial market challenges such as access to finance, interest rates, and the availability of collateral. Financing frictions are found to account for a moderate share of the overall investment gap (between 10 per cent and 20 per cent of the gap)

Bankruptcy Prediction of Small and Medium Enterprises Using a Flexible Binary Generalized Extreme Value Model

We introduce a binary regression accounting-based model for bankruptcy prediction of small and medium enterprises (SMEs). The main advantage of the model lies in its predictive performance in identifying defaulted SMEs. Another advantage, which is especially relevant for banks, is that the relationship between the accounting characteristics of SMEs and response is not assumed a priori (e.g., linear, quadratic or cubic) and can be determined from the data. The proposed approach uses the quantile function of the generalized extreme value distribution as link function as well as smooth functions of accounting characteristics to flexibly model covariate effects. Therefore, the usual assumptions in scoring models of symmetric link function and linear or pre-specied covariate-response relationships are relaxed. Out-of-sample and out-of-time validation on Italian data shows that our proposal outperforms the commonly used (logistic) scoring model for different default horizons

European exchange trading funds trading with locally weighted support vector regression

In this paper, two different Locally Weighted Support Vector Regression (wSVR) algorithms are generated and applied to the task of forecasting and trading five European Exchange Traded Funds. The trading application covers the recent European Monetary Union debt crisis. The performance of the proposed models is benchmarked against traditional Support Vector Regression (SVR) models. The Radial Basis Function, the Wavelet and the Mahalanobis kernel are explored and tested as SVR kernels. Finally, a novel statistical SVR input selection procedure is introduced based on a principal component analysis and the Hansen, Lunde, and Nason (2011) model confidence test. The results demonstrate the superiority of the wSVR models over the traditional SVRs and of the v-SVR over the ε-SVR algorithms. We note that the performance of all models varies and considerably deteriorates in the peak of the debt crisis. In terms of the kernels, our results do not confirm the belief that the Radial Basis Function is the optimum choice for financial series