A. Agarwal, I. Daumé, H. Gerber, and S. , Learning multiple tasks using manifold regularization, Advances in Neural Information Processing Systems, pp.46-54, 2010.

R. K. Ando and T. Zhang, A framework for learning predictive structures from multiple tasks and unlabeled data, Journal of Machine Learning Research, vol.6, pp.1817-1853, 2005.

A. Argyriou, C. A. Micchelli, and M. Pontil, Learning Convex Combinations of Continuously Parameterized Basic Kernels, Proceedings of the Eighteenth Conference on Learning Theory, pp.338-352, 2005.
DOI : 10.1007/11503415_23

A. Argyriou, T. Evgeniou, and M. Pontil, Convex multitask feature learning, Machine Learning, pp.243-272, 2008.

A. Argyriou, A. Maurer, and M. Pontil, An Algorithm for Transfer Learning in a Heterogeneous Environment, European Conference on Machine Learning, 2008.
DOI : 10.1007/978-3-540-87479-9_23

A. Argyriou, C. A. Micchelli, and M. Pontil, When is there a representer theorem? Vector versus matrix regularizers, Journal of Machine Learning Research, vol.10, pp.2507-2529, 2009.

B. Bakker and T. Heskes, Task clustering and gating for bayesian multi?task learning, Journal of Machine Learning Research, vol.4, pp.83-99, 2003.

L. Baldassarre, J. Morales, A. Argyriou, and M. Pontil, A general framework for structured sparsity via proximal optimization, International Conference on Artificial Intelligence and Statistics, pp.82-90, 2012.

H. H. Bauschke and P. L. Combettes, Convex Analysis and Monotone Operator Theory in Hilbert Spaces, CMS Books in Mathematics, 2011.
URL : https://hal.archives-ouvertes.fr/hal-01517477

J. Baxter, A Model of Inductive Bias Learning, Journal of Artificial Intelligence Research, vol.12, pp.149-198, 2000.
DOI : 10.1613/jair.731

S. Ben-david and R. Schuller, Exploiting Task Relatedness for Multiple Task Learning, Proceedings of the Sixteenth Annual Conference on Learning Theory, pp.567-580, 2003.
DOI : 10.1007/978-3-540-45167-9_41

A. Caponnetto, C. A. Micchelli, M. Pontil, Y. , and Y. , Universal multi-task kernels, The Journal of Machine Learning Research, vol.9, pp.1615-1646, 2008.

R. Caruana, Multi?task learning, Machine Learning, pp.41-75, 1997.

O. Chapelle, P. Shivaswamy, S. Vadrevu, K. Weinberger, Y. Zhang et al., Boosted multi-task learning, Machine Learning, vol.8, issue.1, pp.149-173, 2011.
DOI : 10.1145/1341531.1341544

T. Evgeniou, C. A. Micchelli, and M. Pontil, Learning multiple tasks with kernel methods, Journal of Machine Learning Research, vol.6, pp.615-637, 2005.

M. Fazel, H. Hindi, and S. P. Boyd, A rank minimization heuristic with application to minimum order system approximation, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148), pp.4734-4739, 2001.
DOI : 10.1109/ACC.2001.945730

L. Jacob, F. Bach, and J. Vert, Clustered multi-task learning: a convex formulation, Advances in Neural Information Processing Systems 21, pp.745-752, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00320573

Z. Kang, K. Grauman, S. , and F. , Learning with whom to share in multi-task feature learning, Proceedings of the 28th International Conference on Machine Learning, pp.521-528, 2011.

G. S. Kimeldorf and G. Wahba, A Correspondence Between Bayesian Estimation on Stochastic Processes and Smoothing by Splines, The Annals of Mathematical Statistics, vol.41, issue.2, pp.495-502, 1970.
DOI : 10.1214/aoms/1177697089

A. Kumar, I. Daumé, and H. , Learning task grouping and overlap in multi-task learning, Proceedings of the 29th International Conference on Machine Learning, pp.1383-1390, 2012.

G. R. Lanckriet, N. Cristianini, P. Bartlett, L. Ghaoui, J. El et al., Learning the kernel matrix with semi-definite programming, Journal of Machine Learning Research, vol.5, pp.27-72, 2004.

J. Liu, S. Ji, Y. , and J. , SLEP: Sparse Learning with Efficient Projections, 2009.

A. Maurer, The Rademacher Complexity of Linear Transformation Classes, Proceedings of the 19th Annual Conference on Learning Theory (COLT), volume 4005 of LNAI, pp.65-78, 2006.
DOI : 10.1007/11776420_8

L. Mirsky and . John-von-neumann, Monatshefte f ur Mathematik, pp.303-306, 1975.

A. Rakotomamonjy, F. Bach, S. Canu, Y. Grandvalet, and . Simplemkl, Journal of Machine Learning Research, vol.9, pp.2491-2521, 2008.

N. Srebro and S. Ben-david, Learning Bounds for Support Vector Machines with Learned Kernels, Proceedings of the Nineteenth Conference on Learning Theory, pp.169-183, 2006.
DOI : 10.1007/11776420_15

N. Srebro, J. D. Rennie, and T. S. Jaakkola, Maximum-margin matrix factorization, Advances in Neural Information Processing Systems, pp.1329-1336, 2005.

Y. Ying and C. Campbell, Generalization bounds for learning the kernel, Proceedings of the 22nd Annual Conference on Learning Theory, 2009.

B. Annexe, P. Recommandations, and . Les, qui suivent montrent un exemple de recommandations concrètes issues de la méthode présentée dans cette thèse Elles sont publiées sous le nom de produit PRISMS, par la Recherche Quantitative d'Exane BNP Paribas. Il s'agit l'édition de juin 2013 de la série mensuelle " Market Risk report

N. Bibliographie-[-ac01-]-robert-almgren and . Chriss, Optimal execution of portfolio transactions, Journal of Risk, vol.3, pp.5-40, 2001.

P. Artzner, F. Delbaen, J. Eber, and D. Heath, Coherent Measures of Risk, Mathematical Finance, vol.9, issue.3, pp.203-228, 1999.
DOI : 10.1111/1467-9965.00068

H. [. Agarwal, I. Daumé, and S. Gerber, Learning multiple tasks using manifold regularization, Advances in Neural Information Processing Systems, pp.46-54, 2010.

T. [. Argyriou, M. Evgeniou, and . Pontil, Convex multi-task feature learning, Machine Learning, pp.243-272, 2008.

C. [. Argyriou, M. Micchelli, and . Pontil, Learning Convex Combinations of Continuously Parameterized Basic Kernels, Proceedings of the Eighteenth Conference on Learning Theory, pp.338-352, 2005.
DOI : 10.1007/11503415_23

A. [. Argyriou, M. Maurer, and . Pontil, An Algorithm for Transfer Learning in a Heterogeneous Environment, European Conference on Machine Learning, 2008.
DOI : 10.1007/978-3-540-87479-9_23

J. Audibert and A. B. Tsybakov, Fast learning rates for plug-in classifiers under the margin condition, 2005.
URL : https://hal.archives-ouvertes.fr/hal-00005882

T. [. Ando and . Zhang, A framework for learning predictive structures from multiple tasks and unlabeled data, Journal of Machine Learning Research, vol.6, pp.1817-1853, 2005.

L. Bachelier, Th??orie de la sp??culation, Annales scientifiques de l'??cole normale sup??rieure, vol.17, 1900.
DOI : 10.24033/asens.476

J. Baxter, A Model of Inductive Bias Learning, Journal of Artificial Intelligence Research, vol.12, pp.149-198, 2000.
DOI : 10.1613/jair.731

E. [. Béchu, J. Bertrand, and . Nebenzahl, L'analyse technique, Bauschke and P. L. Combettes. Convex Analysis and Monotone Operator Theory in Hilbert Spaces. CMS Books in Mathematics, 2008.

G. Biau and L. Devroye, On the layered nearest neighbour estimate, the bagged nearest neighbour estimate and the random forest method in regression and classification, Journal of Multivariate Analysis, vol.101, issue.10, pp.2499-2518, 2010.
DOI : 10.1016/j.jmva.2010.06.019

URL : https://hal.archives-ouvertes.fr/hal-00559811

G. Biau, L. Devroye, and G. Lugosi, Consistency of random forests and other averaging classifiers, Journal of Machine Learning Research, vol.9, pp.2015-2033, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00355368

[. Ben-david and R. Schuller, Exploiting Task Relatedness for Multiple Task Learning, Proceedings of the Sixteenth Annual Conference on Learning Theory, pp.567-580, 2003.
DOI : 10.1007/978-3-540-45167-9_41

O. Bousquet and A. Elisseeff, Stability and Generalization, Journal of Machine Learning Research, vol.2, pp.499-526, 2002.

J. [. Breiman and . Friedman, Predicting Multivariate Responses in Multiple Linear Regression, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.59, issue.1, pp.3-54, 1997.
DOI : 10.1111/1467-9868.00054

L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and Regression Trees, 1984.

B. Bakker and T. Heskes, Task clustering and gating for bayesian multi?task learning, Journal of Machine Learning Research, vol.4, pp.83-99, 2003.

G. Biau, Analysis of a random forests model, Journal of Machine Learning Research, vol.13, pp.1063-1095, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00704947

D. S. Broomhead and G. P. King, Extracting qualitative dynamics from experimental data, Physica D: Nonlinear Phenomena, vol.20, issue.2-3, pp.217-236, 1986.
DOI : 10.1016/0167-2789(86)90031-X

F. Black and R. Litterman, Global Portfolio Optimization, Financial Analysts Journal, vol.48, issue.5, 1992.
DOI : 10.2469/faj.v48.n5.28

J. Bruna and S. Mallat, Classification with scattering operators, CVPR 2011, 2010.
DOI : 10.1109/CVPR.2011.5995635

J. [. Baldassarre, A. Morales, M. Argyriou, and . Pontil, A general framework for structured sparsity via proximal optimization, International Conference on Artificial Intelligence and Statistics, pp.82-90, 2012.

M. Basseville and I. V. Nikiforov, Detection of abrupt changes : theory and application, 1993.
URL : https://hal.archives-ouvertes.fr/hal-00008518

T. Bollerslev, Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics, vol.31, issue.3, pp.307-327, 1986.
DOI : 10.1016/0304-4076(86)90063-1

M. Bertero and E. R. Pike, Resolution in Diffraction-limited Imaging, a Singular Value Analysis, Optica Acta: International Journal of Optics, vol.55, issue.6, pp.727-746, 1982.
DOI : 10.1080/713820704

L. Breiman, Bagging predictors, Machine Learning, vol.10, issue.2, pp.123-140, 1996.
DOI : 10.2307/1403680

L. Breiman, Some infinity theory for predictor ensembles, 2000.

L. Breiman, Random forests, Machine Learning, pp.5-32, 2001.

F. Black and M. Scholes, The pricing of ooption and corporate liabilities, Journal of Political Economy, vol.81, issue.3, 1973.

A. [. Basak and . Shapiro, Value-at-Risk-Based Risk Management: Optimal Policies and Asset Prices, Review of Financial Studies, vol.23, issue.2, pp.371-405, 2001.
DOI : 10.2307/2296146

G. Blanchard, C. Schäfer, and Y. Rozenholc, Learning Theory, chapter Oracle Bounds and Exact Algorithm for Dyadic Classification Trees, pp.378-392, 2004.

L. Peter, M. Bartlett, and . Traskin, AdaBoost is Consistent, Journal of Machine Learning Research, vol.8, pp.2347-2368, 2007.

L. Peter, M. H. Bartlett, and . Wegkamp, Classification with a reject option using a hinge loss, Journal of Machine Learning Research, vol.9, pp.1823-1840, 2008.

J. [. Brown and . Zidek, Adaptive Multivariate Ridge Regression. The Annals of Statistics, pp.64-74, 1980.

]. R. Car97 and . Caruana, Multi?task learning, Machine Learning, pp.41-75, 1997.

V. Chandola, A. Banerjee, and V. Kumar, Anomaly detection, ACM Computing Surveys, vol.41, issue.3, 2009.
DOI : 10.1145/1541880.1541882

N. Chapados, Sequential Machine Learning Approach for Portfolio Management, 2009.

S. Ciss, Forêts uniformément aléatoires et détection des irrégularités aux cotisations sociales, 2014.

C. [. Caponnetto, M. Micchelli, Y. Pontil, and . Ying, Universal multi-task kernels, The Journal of Machine Learning Research, vol.9, pp.1615-1646, 2008.

. Csv-+-11-]-o, P. Chapelle, S. Shivaswamy, K. Vadrevu, Y. Weinberger et al., Boosted multi-task learning, Machine learning, vol.85, issue.12, pp.149-173, 2011.

R. Ronald, M. Coifman, and . Victor-wickerhauser, Entropy-based algorithms for best basis selection, IEEE Transactions on Information Theory, 1992.

A. Damodaran, Equity Risk Premiums (ERP): Determinants, Estimation and Implications, SSRN Electronic Journal
DOI : 10.2139/ssrn.1274967

I. Daubechies, Orthonormal bases of compactly supported wavelets, Communications on Pure and Applied Mathematics, vol.34, issue.7, pp.909-996, 1988.
DOI : 10.1007/978-3-642-61987-8

G. Thomas and . Dietterich, Ensemble methods in machine learning, Proceedings of the First International Workshop on Multiple Classifier Systems, pp.1-15, 2000.

L. David and . Donoho, Cart and best-ortho-basis : a connection. The Annals of Statistics, pp.1870-1911, 1997.

P. Embrechts, C. Klüppelberg, and T. Mikosch, Modelling of extremal events in insurance and finance, ZOR Zeitschrift f???r Operations Research Mathematical Methods of Operations Research, vol.73, issue.1, 1997.
DOI : 10.1007/978-3-662-02847-6

C. [. Evgeniou, M. Micchelli, and . Pontil, Learning multiple tasks with kernel methods, Journal of Machine Learning Research, vol.6, pp.615-637, 2005.

R. F. Engle, Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation, Econometrica, vol.50, issue.4, pp.987-1007, 1982.
DOI : 10.2307/1912773

J. Estrada, Mean-semivariance behavior : A note, Finance Letters, vol.1, pp.9-14, 2003.

E. F. Fama, Efficient Capital Markets: A Review of Theory and Empirical Work, The Journal of Finance, vol.25, issue.2, 1970.
DOI : 10.2307/2325486

T. Fu, F. Chung, R. Luk, and C. Ng, Representing financial time series based on data point importance, Engineering Applications of Artificial Intelligence, vol.21, issue.2, pp.277-300, 2008.
DOI : 10.1016/j.engappai.2007.04.009

[. Fu, F. Chung, and C. Ng, Financial time series segmentation based on specialized binary tree representation, Conference on Data Mining, 2006.

J. Baptiste-faddoul, B. Chidlovskii, and F. Torre, Learning Multiple Tasks with Boosted Decision Trees

F. Eugene, K. R. Fama, and . French, Common risk factors in the returns on stocks and bonds, Journal of Financial Economics, vol.33, pp.3-56, 1993.

F. Eugene, K. R. Fama, and . French, The equity premium, The Journal of Finance, vol.57, issue.2, pp.637-659, 2002.

L. Favre and J. Galeano, Mean-Modified Value-at-Risk Optimization with Hedge Funds, The Journal of Alternative Investments, vol.5, issue.2, pp.21-25, 2002.
DOI : 10.3905/jai.2002.319052

H. Jerome, P. Friedman, and . Hall, On bagging and nonlinear estimation, 1999.

M. Fazel, H. Hindi, and S. P. Boyd, A rank minimization heuristic with application to minimum order system approximation, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148), pp.4734-4739, 2001.
DOI : 10.1109/ACC.2001.945730

J. Friedman, T. Hastie, and R. Tibshirani, Additive logistic regression : a statistical view of boosting. The Annals of Statistics, pp.337-407, 2000.
DOI : 10.1214/aos/1016120463

URL : https://doi.org/10.1214/aos/1016120463

Y. Freund, R. Iyer, R. E. Schapire, and Y. Singer, An efficient boosting algorithm for combining preferences, The Journal of Machine Learning Research, vol.4, pp.933-969, 2003.

Y. Freund, Boosting a Weak Learning Algorithm by Majority, Information and Computation, vol.121, issue.2, pp.256-285, 1995.
DOI : 10.1006/inco.1995.1136

Y. Freund and R. E. Schapire, A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting, Journal of Computer and System Sciences, vol.55, issue.1, pp.119-139, 1997.
DOI : 10.1006/jcss.1997.1504

S. Gey and É. Nedelec, Nonlinear Estimation and Classification, chapter Risk Bounds for CART Regression Trees, pp.369-379, 2003.

N. Golyandina, V. Nekrutkin, and A. Zhiglavsky, Analysis of Time Series Structure -SSA and Related Techniques, 2001.

A. A. Gaivoronski and G. Pflug, Value-at-risk in portfolio optimization: properties and computational approach, The Journal of Risk, vol.7, issue.2, 2005.
DOI : 10.21314/JOR.2005.106

URL : http://homepage.univie.ac.at/georg.pflug/science/technicalreports/GaivoronskiPflugNew.pdf

H. William and . Greene, Econometric Analysis, 2007.

D. Martin-green and J. A. Swets, Signal detection theory and psychophysics, 1966.

N. Golyandina and K. D. Usevich, 2D-Extension of Singular Spectrum Analysis: Algorithm and Elements of Theory, Matrix Methods : Theory, Algorithms, Applications World Scientific, pp.449-473, 2010.
DOI : 10.1142/9789812836021_0029

J. D. Hamilton, A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle, Econometrica, vol.57, issue.2, pp.357-384, 1989.
DOI : 10.2307/1912559

B. Hanczar and E. R. Dougherty, Classification with reject option in gene expression data, Bioinformatics, vol.347, issue.25, pp.1889-1895, 2008.
DOI : 10.1056/NEJMoa021967

URL : https://academic.oup.com/bioinformatics/article-pdf/24/17/1889/732268/btn349.pdf

T. Hancock, T. Jiang, M. Li, and J. Tromp, Lower bound on learning decision lists and trees, Informational Computing, vol.126, issue.2, pp.114-122, 1996.
DOI : 10.1007/3-540-59042-0_102

W. Huang, Y. Nakamori, and S. Wang, Forecasting stock market movement direction with support vector machine, Computers & Operations Research, vol.32, issue.10, pp.2513-2522, 2005.
DOI : 10.1016/j.cor.2004.03.016

L. Hyafil and R. L. Rivest, Constructing optimal binary decision trees is NP-complete, Information Processing Letters, vol.5, issue.1, 1976.
DOI : 10.1016/0020-0190(76)90095-8

P. Hall and R. J. Samworth, Properties of bagged nearest neighbour classifiers, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.49, issue.3, pp.363-379, 2005.
DOI : 10.1109/18.796368

URL : http://www.statslab.cam.ac.uk/~rjs57/FinalBaggingJRSSB.pdf

R. David, S. Hardoon, J. Szedmak, and . Shawe-taylor, Canonical correlation analysis ; An overview with application to learning methods, 2003.

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, 2001.

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning : Data Mining, Inference, and Prediction -Second Edition, 2009.

J. Hull and A. White, Pricing Interest-Rate-Derivative Securities, Review of Financial Studies, vol.6, issue.4, pp.573-592, 1990.
DOI : 10.1016/0304-405X(77)90016-2

R. Herbei and M. H. Wegkamp, Classification with reject option, Canadian Journal of Statistics, vol.33, issue.4, pp.709-721, 2006.
DOI : 10.1007/978-1-4757-2545-2

K. Ito, Stochastic integral, Proceedings of the Imperial Academy, pp.519-524, 1944.

F. [. Jacob, J. Bach, and . Vert, Clustered multi-task learning : a convex formulation, Advances in Neural Information Processing Systems 21, pp.745-752, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00320573

H. Jin, H. Markowitz, and X. Y. Zhou, A NOTE ON SEMIVARIANCE, Mathematical Finance, vol.29, issue.1, pp.53-61, 2006.
DOI : 10.1007/s002450010003

R. Jpmorgan, RiskMetrics -Technical Document, 1996.

B. Kégl, Robust Regression by Boosting the Median, 16th Conference on Computational Learning Theory, pp.258-272, 2003.
DOI : 10.1007/978-3-540-45167-9_20

T. Kimoto, K. Asakawa, M. Yoda, and M. Takeoka, Stock market prediction system with modular neural networks, 1990 IJCNN International Joint Conference on Neural Networks, pp.1-6, 1990.
DOI : 10.1109/IJCNN.1990.137535

E. Keogh, S. Chu, D. Hart, and M. Pazzani, Segmenting time series : A survey and novel approach. Data mining in time series database, pp.1-22, 2004.

A. Kumar, H. Daumé, and I. , Learning task grouping and overlap in multi-task learning, Proceedings of the 29th International Conference on Machine Learning, pp.1383-1390, 2012.

L. Karamitopoulos and G. Evangelidis, Current trends in time series representation, Panhellenic Conference in Informatics (PCI), 2007.

M. G. Kendall, A new measure of rank correlation, Biometrika, vol.30, 1938.

Z. Kang, K. Grauman, and F. Sha, Learning with whom to share in multi-task feature learning, Proceedings of the 28th International Conference on Machine Learning, pp.521-528, 2011.

E. Keogh and S. Kasetty, On the need for time series data mining benchmarks, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '02, pp.349-371, 2003.
DOI : 10.1145/775047.775062

[. King, C. I. Plosser, J. H. Stock, and M. Watson, Stochastic trends and economic fluctuations, 1992.
DOI : 10.3386/w2229

A. Khaleghi and D. Ryabko, Locating changes in highly dependent data with unknown number of change points, Advances in Neural Information Processing Systems, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00765436

A. Khaleghi and D. Ryabko, Nonparametric multiple change point estimation in highly dependent time series, Proc. 24th International Conf. on Algorithmic Learning Theory (ALT'13), 2013.
URL : https://hal.archives-ouvertes.fr/hal-00913250

A. Khaleghi and D. Ryabko, Asymptotically consistent estimation of the number of change points in highly dependent time series, pp.539-547, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01026583

R. L. Karandikar and M. Vidyasagar, Probably approximately correct learning with beta mixing input sequences, 2004.

D. Kocev, C. Vens, and J. Struyf, Ensembles of Multi-Objective Decision Trees, pp.624-631, 2007.
DOI : 10.1007/978-3-540-74958-5_61

G. S. Kimeldorf and G. Wahba, A Correspondence Between Bayesian Estimation on Stochastic Processes and Smoothing by Splines, The Annals of Mathematical Statistics, vol.41, issue.2, pp.495-502, 1970.
DOI : 10.1214/aoms/1177697089

S. Kim, P. Eric, and . Xing, Tree-Guided Group Lasso for Multi-Task Regression with Structured Sparsity, pp.1-14, 2010.

G. R. Lanckriet, N. Cristianini, P. Bartlett, L. Ghaoui, and M. I. Jordan, Learning the kernel matrix with semi-definite programming, Journal of Machine Learning Research, vol.5, pp.27-72, 2004.

J. Lintner, The valuation of risk assets and the selection of risky investments in stock protfolios and capital budgets. The review of economics and statistics, pp.13-37, 1965.

J. Liu, S. Ji, and J. Ye, SLEP : Sparse Learning with Efficient Projections, 2009.

C. Aurélie, S. R. Lozano, R. E. Kulkarni, and . Schapire, Convergence and Consistency of Regularized Boosting Algorithms with Stationary ? -Mixing Observations, Advances in Neural Information Processing Systems, pp.819-826, 2005.

D. Lamberton and B. Lapeyre, Introduction au Calcul Stochastique Appliqué à la Finance, 2012.

S. Laruelle, C. Lehalle, and G. Pagès, Optimal Split of Orders Across Liquidity Pools: A Stochastic Algorithm Approach, SIAM Journal on Financial Mathematics, vol.2, issue.1, pp.1042-1076, 2011.
DOI : 10.1137/090780596

URL : https://hal.archives-ouvertes.fr/hal-00707851

A. W. Lo, H. Mamaysky, and J. Wang, Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation, The Journal of Finance, vol.40, issue.4, pp.1705-1765, 2000.
DOI : 10.1111/j.1540-6261.1985.tb05000.x

]. K. Lptvdg09, M. Lounici, A. Pontil, S. Tsybakov, and . Van-de-geer, Taking advantage of sparsity in multi-task learning, Proc. of the 22nd Annual Conference on Learning Theory (COLT), 2009.

G. Lugosi and N. Vayatis, On the bayes-risk consistency of regularized boosting methods. The Annals of Statistics, pp.30-55, 2004.
URL : https://hal.archives-ouvertes.fr/hal-00102140

N. Mahler, Machine Learning Methods for Discrete Multi-scale Flows : Application to Finance, 2012.

G. Burton and . Malkiel, A Random Walk Down Wall Street, 1996.

S. Mallat, Une exploration des signaux en ondelettes. Les Éditions de l'École Polytechnique, 2000.

M. Harry and . Markowitz, Portfolio selection harry markowitz, The Journal of Finance, vol.7, issue.1, pp.77-91, 1952.

M. Harry and . Markowitz, Portfolio Selection -Efficient Diversification of Investment, 1959.

]. A. Mau06 and . Maurer, The Rademacher complexity of linear transformation classes, Proceedings of the 19th Annual Conference on Learning Theory (COLT), volume 4005 of LNAI, pp.65-78, 2006.

]. A. Mcn99 and . Mcneil, Extreme value theory for risk managers, Departement Mathematik ETH Zentrum, 1999.

N. Meinshausen, Quantile regression forests, Journal of Machine Learning Research, vol.7, pp.983-999, 2006.

C. Robert and . Merton, Theory of rational option pricing, The Bell Journal of Economics and Management Science, vol.4, issue.1, 1973.

C. Robert and . Merton, On the pricing of corporate debt : The risk structure of interest rates, The Journal of Finance, vol.29, pp.449-470, 1974.

C. Robert and . Merton, Continuous-Time Finance, 1992.

[. Meucci, Risk and Asset Allocation, 2005.
DOI : 10.1007/978-3-540-27904-4

Y. Meyer, Orthonormal wavelets, pp.21-37, 1989.
DOI : 10.1007/978-3-642-75988-8_2

L. Mirsky, A trace inequality of John von Neumann, Monatshefte f ur Mathematik, pp.303-306, 1975.
DOI : 10.1007/BF01647331

S. Mannor, R. Meir, and T. Zhang, The Consistency of Greedy Algorithms for Classification, Computational Learning Theory, vol.2375, pp.319-333, 2002.
DOI : 10.1007/3-540-45435-7_22

URL : http://www-ee.technion.ac.il/~rmeir/Publications/colt2002.pdf

J. Mossin, Equilibrium in a Capital Asset Market, Econometrica, vol.34, issue.4, pp.768-783, 1966.
DOI : 10.2307/1910098

M. Mohri and A. Rostamizadeh, Stability bounds for stationary ?-mixing and ?-mixing processes, Journal of Machine Learning Research, 2010.

É. Moulines and F. Roueff, Analyse des Séries Temporelles et Applications, Télécom ParisTech, 2010.

M. Markou and S. Singh, Novelty detection: a review???part 1: statistical approaches, Signal Processing, vol.83, issue.12, pp.2481-2497, 2003.
DOI : 10.1016/j.sigpro.2003.07.018

URL : http://www.dcs.ex.ac.uk/research/pann/pdf/pann_SS_086.PDF

M. Markou and S. Singh, Novelty detection: a review???part 2:, Signal Processing, vol.83, issue.12, pp.2499-2521, 2003.
DOI : 10.1016/j.sigpro.2003.07.019

A. B. Nobel, Analysis of a complexity-based pruning scheme for classification trees, IEEE Transactions on Information Theory, vol.48, issue.8, pp.2362-2368, 2002.
DOI : 10.1109/TIT.2002.800482

URL : http://www.stat.unc.edu/faculty/nobel/papers/prune.ps

C. Park and S. H. Irwin, The proftability of technical analysis : A review, 2004.
DOI : 10.2139/ssrn.603481

URL : http://www.farmdoc.uiuc.edu/agmas/reports/04_04/AgMAS04_04.pdf

P. Quiry and Y. L. Fur, , 2014.

J. R. , Q. , and R. L. Rivest, Inferring descision trees using the minimum description length principle, Information and Computation, vol.80, pp.227-248, 1989.

J. R. and Q. , Simplifying decision trees, International Journal of Man-Machine Studies, vol.27, issue.3, pp.221-234, 1987.
DOI : 10.1016/S0020-7373(87)80053-6

J. R. and Q. , C4.5 : Programs for Machine Learning, 1993.

J. B. Ramsey, Wavelets in Economics and Finance: Past and Future, Studies in Nonlinear Dynamics & Econometrics, vol.6, issue.3, 2002.
DOI : 10.2202/1558-3708.1090

A. Rakotomamonjy, F. Bach, S. Canu, Y. Grandvalet, and . Simplemkl, Journal of Machine Learning Research, vol.9, pp.2491-2521, 2008.

L. Rokach and O. Maimon, Top-Down Induction of Decision Trees Classifiers???A Survey, IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), vol.35, issue.4, pp.476-487, 2005.
DOI : 10.1109/TSMCC.2004.843247

T. Roncalli, Big data in asset management, 7th Financial Risks International Forum : Big Data in Finance and Insurance, 2014.

A. Stephen and . Ross, The arbitrage theory of capital asset pricing, Journal of Economic Theory, vol.13, pp.341-360, 1976.

J. [. Renaud, F. Starck, and . Murtagh, Wavelet-based forecasting of short and long memory time series, 2002.

R. Tyrrell, R. , and S. Uryasev, Optimization of conditional valueat-risk, Journal of Risk, vol.2, pp.21-42, 2000.

M. Rubinstein, Markowitz's " portfolio selection " : A fifty-year retrospective. The Journal of Finance, LVII, 2002.

S. [. Srebro and . Ben-david, Learning Bounds for Support Vector Machines with Learned Kernels, Proceedings of the Nineteenth Conference on Learning Theory, pp.169-183, 2006.
DOI : 10.1007/11776420_15

R. E. Schapire, The strength of weak learnability, Machine Learning, pp.197-227, 1990.

R. E. Schapire, Theoretical Views of Boosting and Applications, Proceedings of the 10th International Conference on Algorithmic Learning Theory, pp.13-25, 1999.
DOI : 10.1007/3-540-46769-6_2

M. Sewell, Technical analysis, 2007.

F. William and . Sharpe, Capital asset prices : a theory of market equilibrium under conditions of risk, The Journal of Finance, vol.19, issue.3, pp.425-442, 1964.

F. William and . Sharpe, Mutual fund performance, The Journal of Business, vol.39, issue.1, pp.119-138, 1966.

I. Steinwart, D. Hush, and C. Scovel, Learning from dependent observations, Journal of Multivariate Analysis, vol.100, issue.1, pp.175-194, 2009.
DOI : 10.1016/j.jmva.2008.04.001

]. B. Sil05 and . Silverman, Functional Data Analysis, 2005.

Y. Sun and X. Lin, Regularization for stationary multivariate time series, Quantitative Finance, vol.101, issue.476, pp.573-586, 2012.
DOI : 10.1198/016214506000000735

M. Solnon, Apprentissage statistique multi-tâches, 2013.

J. [. Srebro, T. S. Rennie, and . Jaakkola, Maximum-margin matrix factorization, Advances in Neural Information Processing Systems 17, pp.1329-1336, 2005.

E. Robert, Y. Schapire, and . Singer, Improved Boosting Algorithms Using Confidence-rated Predictions, Machine Learning, pp.297-336, 1999.

I. Nicholas, R. Sapankevych, and . Sankar, Time series prediction using support vector machines : A survey, IEEE Computational Intelligence Magazine, vol.4, issue.2, pp.24-38, 2009.

J. Shadbolt and J. G. Taylor, Neural Networks and the Financial Markets : Predicting , Combining, and Portfolio Optimisation, 2002.
DOI : 10.1007/978-1-4471-0151-2

J. Tobin, Liquidity Preference as Behavior Towards Risk, The Review of Economic Studies, vol.25, issue.2, pp.65-86, 1958.
DOI : 10.2307/2296205

S. Ruey and . Tsay, Analysis of Financial Time Series, 2005.

C. M. Turner, R. Startz, and C. R. Nelson, A Markov model of heteroskedasticity, risk, and learning in the stock market, Journal of Financial Economics, vol.25, issue.1, pp.3-22, 1989.
DOI : 10.1016/0304-405X(89)90094-9

[. Uryasev, Conditional value-at-risk: optimization algorithms and applications, Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No.00TH8520), pp.49-57, 2000.
DOI : 10.1109/CIFER.2000.844598

N. Vayatis, Approches statistiques en apprentissage : boosting et ranking. Hdr, 2006.
URL : https://hal.archives-ouvertes.fr/tel-00120738

L. Ulrike-von, A tutorial on spectral clustering, Statistics and Computing, vol.17, issue.4, pp.395-416, 2007.

D. B. West, Introduction to Graph Theory, 2001.

P. Wilmott, Paul Wilmott Introduces Quantitative Finance, 2007.

Q. Wang, L. Zhang, M. Chi, and J. Guo, MTForest : Ensemble Decision Trees based on Multi-Task Learning, pp.122-126, 2008.

Y. Ying and C. Campbell, Generalization bounds for learning the kernel, Proceedings of the 22nd Annual Conference on Learning Theory, 2009.

B. Yu, Rates of convergence for empirical processes of stationary mixing sequences . The Annals of Probability, pp.94-116, 1994.

M. Yuan and M. H. Wegkamp, Classification methods with reject option based on convex risk minimization, Journal of Machine Learning Research, vol.11, pp.111-130, 2010.

H. Zantema and H. L. Bodlander, FINDING SMALL EQUIVALENT DECISION TREES IS HARD, International Journal of Foundations of Computer Science, vol.37, issue.02, 2000.
DOI : 10.1145/129617.129621

J. Zhou, J. Chen, and J. Ye, Multi-task Learning via Structural Regularization, 2012.