We propose a novel multi-period bilateral clearing framework, where the level of systemic risk is mitigated through an optimal bailout allocation strategy. The interbank liability network evolves stochastically over time, with default events having a persistent impact on the balance sheet structure of the network. The optimal bailout policy and associated clearing payments are recovered as the ...

A growing empirical literature documents that social communication a ects individual trading behavior and market trading patterns in nancial markets. Motivated by this evidence, I develop an asset pricing model a la Kyle (1985) in which agents communicate information in social networks prior to trading. In particular, an agent who is more con dent in her private information puts greater weight ...

IntroductIon Soft computing represents that area of computing adapted from the physical sciences. Artificial intelligence (AI) techniques within this realm attempt to solve problems by applying physical laws and processes. This style of computing is particularly tolerant of imprecision and uncertainty , making the approach attractive to those researching within " noisy " realms, where the signa...

We simulate daily trading of straddles on financial indexes. The straddles are traded based on predictions of daily volatility differences in the indexes. The main predictive models studied are recurrent neural nets (RNN). Such applications have often been studied in isolation. However, due to the special character of daily financial time-series, it is difficult to make full use of RNN represen...

Neural networks are good at classification, forecasting and recognition. They are also good candidates of financial forecasting tools. Forecasting is often used in the decision making process. Neural network training is an art. Trading based on neural network outputs, or trading strategy is also an art. We will discuss a seven-step neural network forecasting model building approach in this arti...

We present here a brief summary of the various possible applications of network theory in the field of finance. Since we want to characterize different systems by means of simple and universal features, graph theory could represent a rather powerful methodology. In the following we report our activity in three different subfields, namely the board and director networks, the networks formed by p...

Service-oriented architecture (SOA) is recognized as an enabler to overcome limitations of traditional monolithic application architectures such as efficiently adapting to changing business requirements and external partners with individual systems. However, services within any SOA require broadly accepted service definitions and a systematic management after the design stage. Thus, ownership o...

The study of Artificial Neural Networks derives from first trials to translate in mathematical models the principles of biological “processing”. An Artificial Neural Network deals with generating, in the fastest times, an implicit and predictive model of the evolution of a system. In particular, it derives from experience its ability to be able to recognize some behaviours or situations and to ...

Time changes of return, inefficiency studies performed and presence of effective factors on share return rate are caused development modern and intelligent methods in estimation and evaluation of share return in stock companies. Aim of this research is prediction of return using financial variables with artificial neural network approach. Therefore, the statistical population of this study incl...

This paper presents a novel approach to financial time series analysis and prediction. It is mainly devoted to the problem of forecasting university facility and administrative cost recovery. However, it can also be used in other areas of a similar nature. The methodology incorporates a two-stage hybrid mechanism for selection of prediction-relevant features and for forecasting based on this se...