Deep Prediction of Investor Interest: a Supervised Clustering Approach

We propose a novel deep learning architecture suitable for the prediction of investor interest for a given asset in a given timeframe. This architecture performs both investor clustering and modelling at the same time. We first verify its superior performance on a simulated scenario inspired by real data and then apply it to a large proprietary database from BNP Paribas Corporate and Institutional Banking.

[1]  Geoffrey E. Hinton,et al.  Adaptive Mixtures of Local Experts , 1991, Neural Computation.

[2]  James Bennett,et al.  The Netflix Prize , 2007 .

[3]  Kaiming He,et al.  Focal Loss for Dense Object Detection , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[4]  Tie-Yan Liu,et al.  LightGBM: A Highly Efficient Gradient Boosting Decision Tree , 2017, NIPS.

[5]  Peter I. Frazier,et al.  A Tutorial on Bayesian Optimization , 2018, ArXiv.

[6]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[7]  Justin A. Sirignano,et al.  Universal features of price formation in financial markets: perspectives from deep learning , 2018, Machine Learning and AI in Finance.

[8]  Xin Yao,et al.  Simultaneous training of negatively correlated neural networks in an ensemble , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[9]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[10]  Alexandre Arenas,et al.  Mapping individual behavior in financial markets: synchronization and anticipation , 2019, EPJ Data Science.

[11]  Y. Nesterov.  A method for solving the convex programming problem with convergence rate O(1/k^2) , 1983 .

[12]  Geoffrey E. Hinton,et al.  Lookahead Optimizer: k steps forward, 1 step back , 2019, NeurIPS.

[13]  Joseph N. Wilson,et al.  Twenty Years of Mixture of Experts , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[14]  Leland McInnes,et al.  UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction , 2018, ArXiv.

[15]  Juho Kanniainen,et al.  Multilayer Aggregation with Statistical Validation: Application to Investor Networks , 2017, Scientific Reports.

[16]  Geoffrey E. Hinton,et al.  On the importance of initialization and momentum in deep learning , 2013, ICML.

[17]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[18]  Timothy Dozat,et al.  Incorporating Nesterov Momentum into Adam , 2016 .

[19]  Mark Goadrich,et al.  The relationship between Precision-Recall and ROC curves , 2006, ICML.

[20]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[21]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[22]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[23]  Damien Challet,et al.  Statistically validated lead-lag Networks and inventory Prediction in the Foreign Exchange Market , 2016, Adv. Complex Syst..

[24]  Yoshua Bengio,et al.  Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..

[25]  Geoffrey E. Hinton,et al.  Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer , 2017, ICLR.

[26]  Rosario N. Mantegna,et al.  Long-term ecology of investors in a financial market , 2018, Palgrave Communications.

[27]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .