29,413 research outputs found
Comparison of Support Vector Machine and Back Propagation Neural Network in Evaluating the Enterprise Financial Distress
Recently, applying the novel data mining techniques for evaluating enterprise
financial distress has received much research alternation. Support Vector
Machine (SVM) and back propagation neural (BPN) network has been applied
successfully in many areas with excellent generalization results, such as rule
extraction, classification and evaluation. In this paper, a model based on SVM
with Gaussian RBF kernel is proposed here for enterprise financial distress
evaluation. BPN network is considered one of the simplest and are most general
methods used for supervised training of multilayered neural network. The
comparative results show that through the difference between the performance
measures is marginal; SVM gives higher precision and lower error rates.Comment: 13 pages, 1 figur
Duality for pathwise superhedging in continuous time
We provide a model-free pricing-hedging duality in continuous time. For a
frictionless market consisting of risky assets with continuous price
trajectories, we show that the purely analytic problem of finding the minimal
superhedging price of a path dependent European option has the same value as
the purely probabilistic problem of finding the supremum of the expectations of
the option over all martingale measures. The superhedging problem is formulated
with simple trading strategies, the claim is the limit inferior of continuous
functions, which allows for upper and lower semi-continuous claims, and
superhedging is required in the pathwise sense on a -compact sample
space of price trajectories. If the sample space is stable under stopping, the
probabilistic problem reduces to finding the supremum over all martingale
measures with compact support. As an application of the general results we
deduce dualities for Vovk's outer measure and semi-static superhedging with
finitely many securities
A neural network-based framework for financial model calibration
A data-driven approach called CaNN (Calibration Neural Network) is proposed
to calibrate financial asset price models using an Artificial Neural Network
(ANN). Determining optimal values of the model parameters is formulated as
training hidden neurons within a machine learning framework, based on available
financial option prices. The framework consists of two parts: a forward pass in
which we train the weights of the ANN off-line, valuing options under many
different asset model parameter settings; and a backward pass, in which we
evaluate the trained ANN-solver on-line, aiming to find the weights of the
neurons in the input layer. The rapid on-line learning of implied volatility by
ANNs, in combination with the use of an adapted parallel global optimization
method, tackles the computation bottleneck and provides a fast and reliable
technique for calibrating model parameters while avoiding, as much as possible,
getting stuck in local minima. Numerical experiments confirm that this
machine-learning framework can be employed to calibrate parameters of
high-dimensional stochastic volatility models efficiently and accurately.Comment: 34 pages, 9 figures, 11 table
Prediction of Banks Financial Distress
In this research we conduct a comprehensive review on the existing literature of
prediction techniques that have been used to assist on prediction of the bank distress.
We categorized the review results on the groups depending on the prediction techniques method,
our categorization started by firstly using time factors of the founded literature, so we mark the
literature founded in the period (1990-2010) as history of prediction techniques, and after this
period until 2013 as recent prediction techniques and then presented the strengths and
weaknesses of both. We came out by the fact that there was no specific type fit with all bank
distress issue although we found that intelligent hybrid techniques considered the most
candidates methods in term of accuracy and reputatio
An academic review: applications of data mining techniques in finance industry
With the development of Internet techniques, data volumes are doubling every two years, faster than predicted by Mooreâs Law. Big Data Analytics becomes particularly important for enterprise business. Modern computational technologies will provide effective tools to help understand hugely accumulated data and leverage this information to get insights into the finance industry. In order to get actionable insights into the business, data has become most valuable asset of financial organisations, as there are no physical products in finance industry to manufacture. This is where data mining techniques come to their rescue by allowing access to the right information at the right time. These techniques are used by the finance industry in various areas such as fraud detection, intelligent forecasting, credit rating, loan management, customer profiling, money laundering, marketing and prediction of price movements to name a few. This work aims to survey the research on data mining techniques applied to the finance industry from 2010 to 2015.The review finds that Stock prediction and Credit rating have received most attention of researchers, compared to Loan prediction, Money Laundering and Time Series prediction. Due to the dynamics, uncertainty and variety of data, nonlinear mapping techniques have been deeply studied than linear techniques. Also it has been proved that hybrid methods are more accurate in prediction, closely followed by Neural Network technique. This survey could provide a clue of applications of data mining techniques for finance industry, and a summary of methodologies for researchers in this area. Especially, it could provide a good vision of Data Mining Techniques in computational finance for beginners who want to work in the field of computational finance
An artificial neural network approach for assigning rating judgements to Italian Small Firms
Based on new regulations of Basel II Accord in 2004, banks and financial nstitutions have now the possibility to develop internal rating systems with the aim of correctly udging financial health status of firms. This study analyses the situation of Italian small firms that are difficult to judge because their economic and financial data are often not available. The intend of this work is to propose a simulation framework to give a rating judgements to firms presenting poor financial information. The model assigns a rating judgement that is a simulated counterpart of that done by Bureau van Dijk-K Finance (BvD). Assigning rating score to small firms with problem of poor availability of financial data is really problematic. Nevertheless, in Italy the majority of firms are small and there is not a law that requires to firms to deposit balance-sheet in a detailed form. For this reason the model proposed in this work is a three-layer framework that allows us to assign ating judgements to small enterprises using simple balance-sheet data.rating judgements, artificial neural networks, feature selection
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