Publications related to Financial Risk Management with Machine Learning

Floor Holder Detection and End of Speaker Turn Prediction in Meetings

We propose a novel fully automatic framework to detect which meeting participant is currently holding the conversational floor and when the current speaker turn is going to finish. Two sets of experiments were conducted on a large collection of multiparty ...

ISCA2010

Network Simplification: the Gaussian N-Relay Diamond Network

Christina Fragouli, Ayfer Özgür Aydin

We consider the Gaussian

N

-relay diamond network, where a source wants to communicate to a destination through a layer of

N

-relay nodes. We investigate the following question: What fraction of the capacity can we maintain by using only

k

out of the $ ...

2010

The Dark Side of Outside Directors: Do They Quit When They are Most Needed?

Rüdiger Fahlenbrach

Outside directors have incentives to resign to protect their reputation or to avoid an increase in their workload when they anticipate that the firm on whose board they sit will perform poorly or disclose adverse news. We call these incentives the dark sid ...

Fisher College of Business2010

A combined neural network/gradient-based approach for the identification of constitutive model parameters using self-boring pressuremeter tests

Laurent Vulliet, Rafal Filip Obrzud

This paper presents a numerical procedure of material parameter identification for the coupled hydromechanical boundary value problem (BVP) of the self-boring pressuremeter test (SBPT) in clay. First, the neural network (NN) technique is applied to obtain ...

2009

Optimization framework for calibration of constitutive models enhanced by neural networks

Laurent Vulliet, Rafal Filip Obrzud

A two-level procedure designed for the estimation of constitutive model parameters is presented in this paper. The neural network (NN) approach at the first level is applied to achieve the first approximation of parameters. This technique is used to avoid ...

2009

Evolutionary Advantages of Neuromodulated Plasticity in Dynamic, Reward- based Scenarios

Dario Floreano, Claudio Mattiussi, Peter Dürr, Andrea Soltoggio

memory in biological neural networks. Similarly, artificial neural networks could benefit from modulatory dynamics when facing certain types of learning problem. Here we test this hypothesis by introducing modulatory neurons to enhance or dampen neural pla ...

MIT Press2008

Neuroevolution: from architectures to learning

Dario Floreano, Claudio Mattiussi, Peter Dürr

Artificial neural networks are applied to many real-world problems, ranging from pattern classification to robot control. In order to design a neural network for a particular task, the choice of an architecture (including the choice of a neuron model), and ...

2008

Mill-cut: a neural network system for the prediction of thermo-mechanical loads induced in end-milling operations

Paul Xirouchakis, Jitender Kumar Rai, Louis Villedieu

This paper presents the design and implementation issues of a generalized system called mill-cut, developed for the prediction of cutting forces and temperature in end-milling operations. Based on an ANN approach, mill-cut predicts all the three components ...

Springer-Verlag2008

Stock returns in mergers and acquisitions

Erwan Morellec

This paper develops a real options framework to analyze the behavior of stock returns in mergers and acquisitions. In this framework, the timing and terms of takeovers are endogenous and result from value-maximizing decisions. The implications of the model ...

2008

A Short Introduction to Correlation Markets

Pierre Collin Dufresne

This short note gives a short overview of correlation markets and was prepared for the "Roundtable Discussion on Default Risk Correlation Models" given at the inaugural SoFiE-Conference in June 2008. ...

2008

Financial Risk Management with Machine Learning

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Floor Holder Detection and End of Speaker Turn Prediction in Meetings

Network Simplification: the Gaussian N-Relay Diamond Network

The Dark Side of Outside Directors: Do They Quit When They are Most Needed?

A combined neural network/gradient-based approach for the identification of constitutive model parameters using self-boring pressuremeter tests

Optimization framework for calibration of constitutive models enhanced by neural networks

Evolutionary Advantages of Neuromodulated Plasticity in Dynamic, Reward- based Scenarios

Neuroevolution: from architectures to learning

Mill-cut: a neural network system for the prediction of thermo-mechanical loads induced in end-milling operations

Stock returns in mergers and acquisitions

A Short Introduction to Correlation Markets

Floor Holder Detection and End of Speaker Turn Prediction in Meetings

Network Simplification: the Gaussian N-Relay Diamond Network

The Dark Side of Outside Directors: Do They Quit When They are Most Needed?

Neuroevolution: from architectures to learning

Mill-cut: a neural network system for the prediction of thermo-mechanical loads induced in end-milling operations

Optimization framework for calibration of constitutive models enhanced by neural networks

A Short Introduction to Correlation Markets

Stock returns in mergers and acquisitions

A combined neural network/gradient-based approach for the identification of constitutive model parameters using self-boring pressuremeter tests

Evolutionary Advantages of Neuromodulated Plasticity in Dynamic, Reward- based Scenarios