SSRN Author: Daniel Alexandre BlochDaniel Alexandre Bloch SSRN Content
https://www.ssrn.com/author=802495
https://www.ssrn.com/rss/en-usTue, 09 Jul 2019 01:13:44 GMTeditor@ssrn.com (Editor)Tue, 09 Jul 2019 01:13:44 GMTwebmaster@ssrn.com (WebMaster)SSRN RSS Generator 1.0REVISION: Machine Learning: Models And AlgorithmsThis textbook introduces the mathematical models and algorithms utilised in machine learning, covering supervised and unsupervised learning as well as reinforcement learning: In supervised learning we present ensemble models, artificial neural networks, deep neural networks, recurrent neural networks and associative reservoir computing, while in unsupervised learning we present radial basis function (RBF), recurrent RBF networks amongst others. In both cases we give examples for chaotic series prediction as well as financial time series. We then introduce the learning theory and formalise supervised learning in the cases of neural networks and recurrent neural networks. We present statistical inference and graph theory in order to introduce probabilistic networks. Using stochastic control theory and dynamic programming we introduce reinforcement learning (RL) and deep RL, presenting various algorithms for learning the optimal policy. Then, we introduce constrained optimisation and ...
https://www.ssrn.com/abstract=3307566
https://www.ssrn.com/1804602.htmlMon, 08 Jul 2019 08:50:52 GMTREVISION: Machine Learning: Models And AlgorithmsThis textbook introduces the mathematical models and algorithms utilised in machine learning, covering supervised and unsupervised learning as well as reinforcement learning: In supervised learning we present ensemble models, artificial neural networks, deep neural networks, recurrent neural networks and associative reservoir computing, while in unsupervised learning we present radial basis function (RBF), recurrent RBF networks amongst others. In both cases we give examples for chaotic series prediction as well as financial time series. We then introduce the learning theory and formalise supervised learning in the cases of neural networks and recurrent neural networks. We present statistical inference and graph theory in order to introduce probabilistic networks. Using stochastic control theory and dynamic programming we introduce reinforcement learning (RL) and deep RL, presenting various algorithms for learning the optimal policy. Then, we introduce constrained optimisation and ...
https://www.ssrn.com/abstract=3307566
https://www.ssrn.com/1801026.htmlMon, 24 Jun 2019 22:53:17 GMTREVISION: Machine Learning: Models And AlgorithmsThis textbook introduces the mathematical models and algorithms utilised in machine learning, covering supervised and unsupervised learning as well as reinforcement learning: In supervised learning we present ensemble models, artificial neural networks, deep neural networks, recurrent neural networks and associative reservoir computing, while in unsupervised learning we present radial basis function (RBF), recurrent RBF networks amongst others. In both cases we give examples for chaotic series prediction as well as financial time series. We then introduce the learning theory and formalise supervised learning in the cases of neural networks and recurrent neural networks. We present statistical inference and graph theory in order to introduce probabilistic networks. Using stochastic control theory and dynamic programming we introduce reinforcement learning (RL) and deep RL, presenting various algorithms for learning the optimal policy. Then, we introduce constrained optimisation and ...
https://www.ssrn.com/abstract=3307566
https://www.ssrn.com/1799871.htmlThu, 20 Jun 2019 14:58:40 GMTREVISION: Machine Learning: Models And AlgorithmsThis textbook introduces the mathematical models and algorithms utilised in machine learning, covering supervised and unsupervised learning as well as reinforcement learning: In supervised learning we present ensemble models, artificial neural networks, deep neural networks, recurrent neural networks and associative reservoir computing, while in unsupervised learning we present radial basis function (RBF), recurrent RBF networks amongst others. In both cases we give examples for chaotic series prediction as well as financial time series. We then introduce the learning theory and formalise supervised learning in the cases of neural networks and recurrent neural networks. We present statistical inference and graph theory in order to introduce probabilistic networks. Using stochastic control theory and dynamic programming we introduce reinforcement learning (RL) and deep RL, presenting various algorithms for learning the optimal policy. Then, we introduce constrained optimisation and ...
https://www.ssrn.com/abstract=3307566
https://www.ssrn.com/1794359.htmlWed, 05 Jun 2019 21:34:45 GMTREVISION: Machine Learning: Models And AlgorithmsThis textbook introduces the mathematical models and algorithms utilised in machine learning, covering supervised and unsupervised learning as well as reinforcement learning: In supervised learning we present ensemble models, artificial neural networks, deep neural networks, recurrent neural networks and associative reservoir computing, while in unsupervised learning we present radial basis function (RBF), recurrent RBF networks amongst others. In both cases we give examples for chaotic series prediction as well as financial time series. We then introduce the learning theory and formalise supervised learning in the cases of neural networks and recurrent neural networks. We present statistical inference and graph theory in order to introduce probabilistic networks. Using stochastic control theory and dynamic programming we introduce reinforcement learning (RL) and deep RL, presenting various algorithms for learning the optimal policy. Then, we introduce constrained optimisation and ...
https://www.ssrn.com/abstract=3307566
https://www.ssrn.com/1791701.htmlWed, 29 May 2019 11:06:56 GMTREVISION: Machine Learning: Models And AlgorithmsThis textbook introduces the mathematical models and algorithms utilised in machine learning, covering supervised and unsupervised learning as well as reinforcement learning: In supervised learning we present ensemble models, artificial neural networks, deep neural networks, recurrent neural networks and associative reservoir computing, while in unsupervised learning we present radial basis function (RBF), recurrent RBF networks amongst others. In both cases we give examples for chaotic series prediction as well as financial time series. We then introduce the learning theory and formalise supervised learning in the cases of neural networks and recurrent neural networks. We present statistical inference and graph theory in order to introduce probabilistic networks. Using stochastic control theory and dynamic programming we introduce reinforcement learning (RL) and deep RL, presenting various algorithms for learning the optimal policy. Then, we introduce constrained optimisation and ...
https://www.ssrn.com/abstract=3307566
https://www.ssrn.com/1789560.htmlTue, 21 May 2019 00:02:32 GMTREVISION: Machine Learning: Models And AlgorithmsThis textbook introduces the mathematical models and algorithms utilised in machine learning, covering supervised and unsupervised learning as well as reinforcement learning: In supervised learning we present ensemble models, artificial neural networks, deep neural networks, recurrent neural networks and associative reservoir computing, while in unsupervised learning we present radial basis function (RBF), recurrent RBF networks amongst others. In both cases we give examples for chaotic series prediction as well as financial time series. We then introduce the learning theory and formalise supervised learning in the cases of neural networks and recurrent neural networks. We present statistical inference and graph theory in order to introduce probabilistic networks. Using stochastic control theory and dynamic programming we introduce reinforcement learning (RL) and deep RL, presenting various algorithms for learning the optimal policy. Then, we introduce constrained optimisation and ...
https://www.ssrn.com/abstract=3307566
https://www.ssrn.com/1788639.htmlThu, 16 May 2019 15:52:16 GMTREVISION: Machine Learning: Models And AlgorithmsThis textbook introduces the mathematical models and algorithms utilised in machine learning, covering supervised and unsupervised learning as well as reinforcement learning: In supervised learning we present ensemble models, artificial neural networks, deep neural networks, recurrent neural networks and associative reservoir computing, while in unsupervised learning we present radial basis function (RBF), recurrent RBF networks amongst others. In both cases we give examples for chaotic series prediction as well as financial time series. We then introduce the learning theory and formalise supervised learning in the cases of neural networks and recurrent neural networks. We present statistical inference and graph theory in order to introduce probabilistic networks. Using stochastic control theory and dynamic programming we introduce reinforcement learning (RL) and deep RL, presenting various algorithms for learning the optimal policy. Then, we introduce constrained optimisation and ...
https://www.ssrn.com/abstract=3307566
https://www.ssrn.com/1787728.htmlMon, 13 May 2019 22:13:41 GMTREVISION: Machine Learning: Models And AlgorithmsThis textbook introduces the mathematical models and algorithms utilised in machine learning, covering supervised and unsupervised learning as well as reinforcement learning: In supervised learning we present ensemble models, artificial neural networks, deep neural networks, recurrent neural networks and associative reservoir computing, while in unsupervised learning we present radial basis function (RBF), recurrent RBF networks amongst others. In both cases we give examples for chaotic series prediction as well as financial time series. We then introduce the learning theory and formalise supervised learning in the cases of neural networks and recurrent neural networks. We present statistical inference and graph theory in order to introduce probabilistic networks. Using stochastic control theory and dynamic programming we introduce reinforcement learning (RL) and deep RL, presenting various algorithms for learning the optimal policy. Then, we introduce constrained optimisation and ...
https://www.ssrn.com/abstract=3307566
https://www.ssrn.com/1786472.htmlWed, 08 May 2019 21:56:53 GMTREVISION: Machine Learning: Models And AlgorithmsThis textbook introduces the mathematical models and algorithms utilised in machine learning, covering supervised and unsupervised learning as well as reinforcement learning: In supervised learning we present ensemble models, artificial neural networks, recurrent neural networks and associative reservoir computing, while in unsupervised learning we present radial basis function (RBF), recurrent RBF networks amongst others. In both cases we give examples for chaotic series prediction as well as financial time series. We then introduce the learning theory and formalise supervised learning in the cases of neural networks and recurrent neural networks. We present statistical inference and graph theory in order to introduce probabilistic networks. Using stochastic control theory and dynamic programming we introduce reinforcement learning (RL) and deep RL, presenting various algorithms for learning the optimal policy. Then, we introduce constrained optimisation and global search ...
https://www.ssrn.com/abstract=3307566
https://www.ssrn.com/1782186.htmlWed, 24 Apr 2019 04:19:49 GMTREVISION: Machine Learning: Models And AlgorithmsThis textbook introduces the mathematical models and algorithms utilised in machine learning, covering supervised and unsupervised learning as well as reinforcement learning: In supervised learning we present ensemble models, artificial neural networks, recurrent neural networks and associative reservoir computing, while in unsupervised learning we present radial basis function (RBF), recurrent RBF networks amongst others. In both cases we give examples for chaotic series prediction as well as financial time series. We then introduce the learning theory and formalise supervised learning in the cases of neural networks and recurrent neural networks. Using stochastic control theory and dynamic programming we introduce reinforcement learning (RL) and deep RL, presenting various algorithms for learning the optimal policy. Then, we introduce constrained optimisation and global search optimisation, and discuss the use of machine learning for solving some continuous and discrete ...
https://www.ssrn.com/abstract=3307566
https://www.ssrn.com/1769822.htmlSun, 10 Mar 2019 18:30:19 GMTREVISION: Machine Learning: Models And AlgorithmsThis textbook introduces the mathematical models and algorithms utilised in machine learning, covering supervised and unsupervised learning as well as reinforcement learning: In supervised learning we present ensemble models, artificial neural networks, recurrent neural networks and associative reservoir computing, while in unsupervised learning we present radial basis function (RBF), recurrent RBF networks amongst others. In both cases we give examples for chaotic series prediction as well as financial time series. We then introduce the learning theory and formalise supervised learning in the cases of neural networks and recurrent neural networks. Using stochastic control theory and dynamic programming we introduce reinforcement learning (RL) and deep RL, presenting various algorithms for learning the optimal policy. Then, we introduce constrained optimisation and global search optimisation, and discuss the use of machine learning for solving some continuous and discrete ...
https://www.ssrn.com/abstract=3307566
https://www.ssrn.com/1768259.htmlMon, 04 Mar 2019 18:44:25 GMTREVISION: Machine Learning: Models And AlgorithmsThis textbook introduces the mathematical models and algorithms utilised in machine learning, covering supervised and unsupervised learning as well as reinforcement learning: In supervised learning we present ensemble models, artificial neural networks, recurrent neural networks and associative reservoir computing, while in unsupervised learning we present radial basis function (RBF), recurrent RBF networks amongst others. In both cases we give examples for chaotic series prediction as well as financial time series. We then introduce the learning theory and formalise supervised learning in the cases of neural networks and recurrent neural networks. Using stochastic control theory and dynamic programming we introduce reinforcement learning (RL) and deep RL, presenting various algorithms for learning the optimal policy. Then, we introduce constrained optimisation and global search optimisation, and discuss the use of machine learning for solving some continuous and discrete ...
https://www.ssrn.com/abstract=3307566
https://www.ssrn.com/1765471.htmlThu, 21 Feb 2019 11:03:13 GMTREVISION: Machine Learning: Models And AlgorithmsThis textbook introduces the mathematical models and algorithms utilised in machine learning, covering supervised and unsupervised learning as well as reinforcement learning: In supervised learning we present ensemble models, artificial neural networks, recurrent neural networks and associative reservoir computing, while in unsupervised learning we present radial basis function (RBF), recurrent RBF networks amongst others. In both cases we give examples for chaotic series prediction as well as financial time series. We then introduce the learning theory and formalise supervised learning in the cases of neural networks and recurrent neural networks. Using stochastic control theory and dynamic programming we introduce reinforcement learning (RL) and deep RL, presenting various algorithms for learning the optimal policy. Then, we introduce constrained optimisation and global search optimisation, and discuss the use of machine learning for solving some continuous and discrete ...
https://www.ssrn.com/abstract=3307566
https://www.ssrn.com/1764155.htmlFri, 15 Feb 2019 00:14:15 GMTREVISION: Machine Learning: Models And AlgorithmsThis textbook introduces the mathematical models and algorithms utilised in machine learning, covering supervised and unsupervised learning as well as reinforcement learning: In supervised learning we present ensemble models, artificial neural networks, recurrent neural networks and associative reservoir computing, while in unsupervised learning we present radial basis function (RBF), recurrent RBF networks amongst others. In both cases we give examples for chaotic series prediction as well as financial time series. We then introduce the learning theory and formalise supervised learning in the cases of neural networks and recurrent neural networks. Using stochastic control theory and dynamic programming we introduce reinforcement learning (RL) and deep RL, presenting various algorithms for learning the optimal policy. Then, we introduce constrained optimisation and global search optimisation, and discuss the use of machine learning for solving some continuous and discrete ...
https://www.ssrn.com/abstract=3307566
https://www.ssrn.com/1762482.htmlSun, 10 Feb 2019 09:52:02 GMTREVISION: Machine Learning: Models And AlgorithmsThis textbook introduces the mathematical models and algorithms utilised in machine learning, covering supervised and unsupervised learning as well as reinforcement learning: In supervised learning we present ensemble models, artificial neural networks, recurrent neural networks and associative reservoir computing, while in unsupervised learning we present radial basis function (RBF), recurrent RBF networks amongst others. In both cases we give examples for chaotic series prediction as well as financial time series. We then introduce the learning theory and formalise supervised learning in the cases of neural networks and recurrent neural networks. Using stochastic control theory and dynamic programming we introduce reinforcement learning (RL) and deep RL, presenting various algorithms for learning the optimal policy. Then, we introduce constrained optimisation and global search optimisation, and discuss the use of machine learning for solving some continuous and discrete ...
https://www.ssrn.com/abstract=3307566
https://www.ssrn.com/1761019.htmlTue, 05 Feb 2019 07:55:09 GMTREVISION: Machine Learning: Models And AlgorithmsThis textbook introduces the mathematical models and algorithms utilised in machine learning, covering supervised and unsupervised learning as well as reinforcement learning: In supervised learning we present ensemble models, artificial neural networks, recurrent neural networks and associative reservoir computing, while in unsupervised learning we present radial basis function (RBF), recurrent RBF networks amongst others. In both cases we give examples for chaotic series prediction as well as financial time series. We then introduce the learning theory and formalise supervised learning in the cases of neural networks and recurrent neural networks. Using stochastic control theory and dynamic programming we introduce reinforcement learning (RL) and deep RL, presenting various algorithms for learning the optimal policy. Then, we introduce constrained optimisation and discuss the use of machine learning for solving some continuous and discrete optimisation problems. Finally, we apply ...
https://www.ssrn.com/abstract=3307566
https://www.ssrn.com/1759216.htmlTue, 29 Jan 2019 14:04:53 GMTREVISION: Machine Learning: Models And AlgorithmsThis textbook introduces the mathematical models and algorithms utilised in machine learning, covering supervised and unsupervised learning as well as reinforcement learning: In supervised learning we present artificial neural networks, recurrent neural networks and associative reservoir computing, while in unsupervised learning we present radial basis function (RBF), recurrent RBF networks amongst others. In both cases we give examples for chaotic series prediction as well as financial time series. We then introduce the learning theory and formalise supervised learning in the cases of neural networks and recurrent neural networks. Using stochastic control theory and dynamic programming we introduce reinforcement learning (RL) and deep RL, presenting various algorithms for learning the optimal policy. Then, we introduce constrained optimisation and discuss the use of machine learning for solving some continuous and discrete optimisation problems. Finally, we apply supervised and ...
https://www.ssrn.com/abstract=3307566
https://www.ssrn.com/1757357.htmlMon, 21 Jan 2019 12:39:01 GMTREVISION: Machine Learning: Models And AlgorithmsThis textbook introduces the mathematical models and algorithms utilised in machine learning, covering supervised and unsupervised learning as well as reinforcement learning: In supervised learning we present artificial neural networks, recurrent neural networks and associative reservoir computing, while in unsupervised learning we present radial basis function (RBF), recurrent RBF networks amongst others. In both cases we give examples for chaotic series prediction as well as financial time series. We then introduce the learning theory and formalise supervised learning in the cases of neural networks and recurrent neural networks. Using stochastic control theory and dynamic programming we introduce reinforcement learning (RL) and deep RL, presenting various algorithms for learning the optimal policy. Then, we introduce constrained optimisation and discuss the use of machine learning for solving some continuous and discrete optimisation problems. Finally, we apply supervised and ...
https://www.ssrn.com/abstract=3307566
https://www.ssrn.com/1755103.htmlMon, 14 Jan 2019 06:24:43 GMTREVISION: Machine Learning: Models And AlgorithmsThis textbook introduces the mathematical models and algorithms utilised in machine learning, covering supervised and unsupervised learning as well as reinforcement learning: In supervised learning we present artificial neural networks, recurrent neural networks and associative reservoir computing, while in unsupervised learning we present radial basis function (RBF), recurrent RBF networks amongst others. In both cases we give examples for chaotic series prediction as well as financial time series. We then introduce the learning theory and formalise supervised learning in the cases of neural networks and recurrent neural networks. Using stochastic control theory and dynamic programming we introduce reinforcement learning (RL) and deep RL, presenting various algorithms for learning the optimal policy. Then, we introduce constrained optimisation and discuss the use of machine learning for solving some continuous and discrete optimisation problems. Finally, we apply supervised and ...
https://www.ssrn.com/abstract=3307566
https://www.ssrn.com/1753910.htmlThu, 10 Jan 2019 10:10:17 GMTREVISION: Machine Learning: Models And AlgorithmsThis textbook introduces the mathematical models and algorithms utilised in machine learning, covering supervised and unsupervised learning as well as reinforcement learning: In supervised learning we present artificial neural networks, recurrent neural networks and associative reservoir computing, while in unsupervised learning we present radial basis function (RBF), recurrent RBF networks amongst others. In both cases we give examples for chaotic series prediction as well as financial time series. We then introduce the learning theory and formalise supervised learning in the cases of neural networks and recurrent neural networks. Using stochastic control theory and dynamic programming we introduce reinforcement learning (RL) and deep RL, presenting various algorithms for learning the optimal policy. Then, we introduce constrained optimisation and discuss the use of machine learning for solving some continuous and discrete optimisation problems. Finally, we apply supervised and ...
https://www.ssrn.com/abstract=3307566
https://www.ssrn.com/1752610.htmlMon, 07 Jan 2019 14:17:02 GMTREVISION: Recipe for Quantitative Trading with Machine LearningOn one hand, financial time series are multifractal, thus exhibiting non-Gaussian distribution, the presence of extreme values (outliers), and long-range dependent dynamics. On the other hand, machine learning (ML) models are processes relying heavily on statistical models and methodologies, but treated as black box models due to their inability to explicitly know the relations established between explanatory variables (input) and dependent variables (output). However, when forecasting market returns, or generating autonomous patterns, it is crucial to know the statistical properties of the time series produced by the ML model under consideration.
Taking into consideration these statistical characteristics, we present a recipe using technical indicators to forecast both market returns and their directions. We choose to reverse the causality and propose a solution consisting in deciding upon the framework by defining how the model should be specified before beginning to analyse the ...
https://www.ssrn.com/abstract=3232143
https://www.ssrn.com/1737414.htmlThu, 08 Nov 2018 21:25:30 GMTREVISION: Recipe for Quantitative Trading with Machine LearningOn one hand, financial time series are multifractal, thus exhibiting non-Gaussian distribution, the presence of extreme values (outliers), and long-range dependent dynamics. On the other hand, machine learning (ML) models are processes relying heavily on statistical models and methodologies, but treated as black box models due to their inability to explicitly know the relations established between explanatory variables (input) and dependent variables (output). However, when forecasting market returns, or generating autonomous patterns, it is crucial to know the statistical properties of the time series produced by the ML model under consideration.
Taking into consideration these statistical characteristics, we present a recipe using technical indicators to forecast both market returns and their directions. We choose to reverse the causality and propose a solution consisting in deciding upon the framework by defining how the model should be specified before beginning to analyse the ...
https://www.ssrn.com/abstract=3232143
https://www.ssrn.com/1732372.htmlSun, 21 Oct 2018 13:51:38 GMTREVISION: Recipe for Quantitative Trading with Machine LearningOn one hand, financial time series are multifractal, thus exhibiting non-Gaussian distribution, the presence of extreme values (outliers), and long-range dependent dynamics. On the other hand, machine learning (ML) models are processes relying heavily on statistical models and methodologies, but treated as black box models due to their inability to explicitly know the relations established between explanatory variables (input) and dependent variables (output). However, when forecasting market returns, or generating autonomous patterns, it is crucial to know the statistical properties of the time series produced by the ML model under consideration.
Taking into consideration these statistical characteristics, we present a recipe using technical indicators to forecast both market returns and their directions. We choose to reverse the causality and propose a solution consisting in deciding upon the framework by defining how the model should be specified before beginning to analyse the ...
https://www.ssrn.com/abstract=3232143
https://www.ssrn.com/1730398.htmlSat, 13 Oct 2018 12:02:49 GMTREVISION: Recipe for Quantitative Trading with Machine LearningOn one hand, financial time series are multifractal, thus exhibiting non-Gaussian distribution, the presence of extreme values (outliers), and long-range dependent dynamics. On the other hand, machine learning (ML) models are processes relying heavily on statistical models and methodologies, but treated as black box models due to their inability to explicitly know the relations established between explanatory variables (input) and dependent variables (output). However, when forecasting market returns, or generating autonomous patterns, it is crucial to know the statistical properties of the time series produced by the ML model under consideration.
Taking into consideration these statistical characteristics, we present a recipe using technical indicators to forecast both market returns and their directions. We choose to reverse the causality and propose a solution consisting in deciding upon the framework by defining how the model should be specified before beginning to analyse the ...
https://www.ssrn.com/abstract=3232143
https://www.ssrn.com/1728453.htmlSun, 07 Oct 2018 10:38:34 GMTREVISION: Recipe for Quantitative Trading with Machine LearningOn one hand, financial time series are multifractal, thus exhibiting non-Gaussian distribution, the presence of extreme values (outliers), and long-range dependent dynamics. On the other hand, machine learning (ML) models are processes relying heavily on statistical models and methodologies, but treated as black box models due to their inability to explicitly know the relations established between explanatory variables (input) and dependent variables (output). However, when forecasting market returns, or generating autonomous patterns, it is crucial to know the statistical properties of the time series produced by the ML model under consideration.
Taking into consideration these statistical characteristics, we present a recipe using technical indicators to forecast both market returns and their directions. We choose to reverse the causality and propose a solution consisting in deciding upon the framework by defining how the model should be specified before beginning to analyse the ...
https://www.ssrn.com/abstract=3232143
https://www.ssrn.com/1724993.htmlSat, 22 Sep 2018 17:42:23 GMT