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Seminari de Finances Quantitatives: László Györfi

Tema: Seminari de Finances Quantitatives / Seminar on Quantitative

Quan
23/06/2016 des de/d' 11:30" (Europe/Madrid / UTC200)
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Benvolguts,

Us informem que la propera sessió del seminari de finances quantitatives es celebrarà dijous 23 de juny a les 11:30 al CRM (aula C1/028). 
· Speaker: László Györfi, Budapest University of Technology and Economics 
· Títol: Empirical growth optimal portfolio selections 
· Abstract: This talk provides a survey of discrete time, multi period, sequential investment strategies for financial markets. Under memoryless assumption on the underlying process generating the asset prices the Best Constantly Rebalanced Portfolio is studied, called log-optimal portfolio, which achieves the maximal asymptotic average growth rate. For generalized dynamic portfolio selection, when asset prices are generated by a stationary and ergodic process, growth optimal empirical strategies are shown, where some principles of nonparametric regression estimation and of machine learning aggregation are applied. The empirical performance of the methods is illustrated for NYSE data. 
Més informació a: http://www.crm.cat/en/Research/Networks/QuantitativeFinance/Pages/Seminar.aspx 

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Dear all,

We inform you that the next session of the Seminar on Quantitative Finances will take place on Thursday, June 23 at 11:30am at the CRM (room C1/028). 

· Speaker: László Györfi, Budapest University of Technology and Economics 
· Title: Empirical growth optimal portfolio selections 
· Abstract: ‎This talk provides a survey of discrete time, multi period, sequential investment strategies for financial markets. Under memoryless assumption on the underlying process generating the asset prices the Best Constantly Rebalanced Portfolio is studied, called log-optimal portfolio, which achieves the maximal asymptotic average growth rate. For generalized dynamic portfolio selection, when asset prices are generated by a stationary and ergodic process, growth optimal empirical strategies are shown, where some principles of nonparametric regression estimation and of machine learning aggregation are applied. The empirical performance of the methods is illustrated for NYSE data. 
For further information: http://www.crm.cat/en/Research/Networks/QuantitativeFinance/Pages/Seminar.aspx