{"id":12142,"date":"2021-03-07T05:03:20","date_gmt":"2021-03-07T05:03:20","guid":{"rendered":"https:\/\/analystprep.com\/study-notes\/?p=12142"},"modified":"2026-01-24T17:09:06","modified_gmt":"2026-01-24T17:09:06","slug":"neural-networks-nns-deep-learning-nets-dlns-reinforcement-learning-rl","status":"publish","type":"post","link":"https:\/\/analystprep.com\/study-notes\/cfa-level-2\/quantitative-method\/neural-networks-nns-deep-learning-nets-dlns-reinforcement-learning-rl\/","title":{"rendered":"Neural Networks (NNs), Deep Learning Nets (DLNs), and Reinforcement Learning (RL)"},"content":{"rendered":"<p><script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"QAPage\",\n  \"mainEntity\": {\n    \"@type\": \"Question\",\n    \"name\": \"Applications of Neural Networks and Deep Learning in Investment Management\",\n    \"text\": \"An analyst with a background in machine learning has been thinking about the applications of neural networks (NNs) and deep learning nets (DLNs) to investment management.\\n\\nWhich statement is most likely to be incorrect?\\n\\nA. NNs and DLNs are applicable in developing single variable ordinary least squares regression models.\\n\\nB. NNs and DLNs are well-suited for image and speech recognition and natural language processing.\\n\\nC. NNs and DLNs are applicable in modeling non-linearity and complex interactions among many features.\",\n    \"answerCount\": 3,\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"NNs and DLNs are applicable in developing single variable ordinary least squares regression models.\",\n      \"explanation\": \"Neural networks and deep learning networks are designed to model complex, non-linear relationships involving many interacting features. Single-variable ordinary least squares regression models are simple linear models and do not require the complexity of NNs or DLNs. These techniques are instead well suited to tasks such as image and speech recognition, natural language processing, and modeling non-linear interactions across large feature sets.\"\n    }\n  }\n}\n<\/script><\/p>\n<p><script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"VideoObject\",\n  \"@id\": \"https:\/\/analystprep.com\/cfa-level-2\/videos\/machine-learning-module-6\",\n  \"name\": \"Machine Learning (2025 Level II CFA\u00ae Exam \u2013 Quantitative Methods \u2013 Module 6)\",\n  \"description\": \"This CFA Level II Quantitative Methods video introduces machine learning models used in financial analysis, predictive modeling, and portfolio construction. Topics include supervised learning (regression, classification, ensemble learning, random forests) and unsupervised learning (PCA, k-means, hierarchical clustering).\",\n  \"uploadDate\": \"2021-12-25\",\n  \"thumbnailUrl\": \"https:\/\/img.youtube.com\/vi\/JOpNoBlW_0Q\/maxresdefault.jpg\",\n  \"contentUrl\": \"https:\/\/www.youtube.com\/watch?v=JOpNoBlW_0Q\",\n  \"embedUrl\": \"https:\/\/www.youtube.com\/embed\/JOpNoBlW_0Q\",\n  \"duration\": \"PT1H00M18S\",\n  \"publisher\": {\n    \"@type\": \"Organization\",\n    \"name\": \"AnalystPrep\",\n    \"url\": \"https:\/\/analystprep.com\/\",\n    \"logo\": {\n      \"@type\": \"ImageObject\",\n      \"url\": \"https:\/\/analystprep.com\/wp-content\/uploads\/analystprep-logo.png\"\n    }\n  }\n}\n<\/script><br \/>\n<script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"ImageObject\",\n  \"@id\": \"https:\/\/analystprep.com\/study-notes\/images\/frame-of-neural-network\",\n  \"url\": \"https:\/\/analystprep.com\/study-notes\/wp-content\/uploads\/2019\/09\/page-67.jpg\",\n  \"contentUrl\": \"https:\/\/analystprep.com\/study-notes\/wp-content\/uploads\/2019\/09\/page-67.jpg\",\n  \"caption\": \"Frame of a neural network\",\n  \"width\": 1440,\n  \"height\": 1415,\n  \"copyrightNotice\": \"\u00a9 2024 AnalystPrep\",\n  \"acquireLicensePage\": \"https:\/\/analystprep.com\/license-info\",\n  \"creditText\": \"AnalystPrep Design Team\",\n  \"creator\": {\n    \"@type\": \"Organization\",\n    \"name\": \"AnalystPrep\",\n    \"url\": \"https:\/\/analystprep.com\/\"\n  }\n}\n<\/script><\/p>\n<p><script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"ImageObject\",\n  \"@id\": \"https:\/\/analystprep.com\/study-notes\/images\/rl-scenario\",\n  \"url\": \"https:\/\/analystprep.com\/study-notes\/wp-content\/uploads\/2021\/03\/Img_27.jpg\",\n  \"contentUrl\": \"https:\/\/analystprep.com\/study-notes\/wp-content\/uploads\/2021\/03\/Img_27.jpg\",\n  \"caption\": \"Reinforcement learning (RL) scenario\",\n  \"width\": 1590,\n  \"height\": 1051,\n  \"copyrightNotice\": \"\u00a9 2024 AnalystPrep\",\n  \"acquireLicensePage\": \"https:\/\/analystprep.com\/license-info\",\n  \"creditText\": \"AnalystPrep Design Team\",\n  \"creator\": {\n    \"@type\": \"Organization\",\n    \"name\": \"AnalystPrep\",\n    \"url\": \"https:\/\/analystprep.com\/\"\n  }\n}\n<\/script><\/p>\n<p><iframe loading=\"lazy\" width=\"560\" height=\"315\" src=\"https:\/\/www.youtube.com\/embed\/JOpNoBlW_0Q?si=44QbhXibxxfiEmO4\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><\/p>\n<p>Neural networks, deep learning nets, and reinforcement learning are sophisticated algorithms that handle complex tasks with non-linearity and interactions between large numbers of feature inputs. Some of these complicated tasks include image classification, speech recognition, and face recognition. We describe these algorithms here.<\/p>\n<h2>Neural Networks<\/h2>\n<p>Neural networks, also are known as artificial neural networks, or ANNs, consist of nodes connected by links. ANNs are non-linear statistical ML algorithms modeled after the human brain. They display a complicated relationship between the inputs and outputs to discover a new pattern.<\/p>\n<p>ANNs have three crucial layers which include input layers, hidden layers, and the output layer, as demonstrated in the figure below:<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/analystprep.com\/study-notes\/wp-content\/uploads\/2019\/09\/page-67.jpg\" alt=\"Frame of a Neural Network\" \/><\/p>\n<p>The above figure is a neural network with an input layer consisting of 3 features, one hidden layer made up of 4 hidden nodes, and 2 output nodes. Learning takes place in the hidden layer nodes, each of which consists of a summation operator and an activation function.<\/p>\n<p>Once the hidden node receives the four input values, the summation operator calculates the weighted sum of the input. The activation function then transforms this output into the appropriate result. In other words, the node\u2019s activation function operates like a light dimmer switch, which decreases or increases the strength of the (total net) input.<\/p>\n<h2>Deep Learning Nets<\/h2>\n<p>A deep learning net (DLN) is a neural network (NN) with many hidden layers at least 3 but often more than 20. NNs and DLNs have applications to a wide variety of complicated tasks characterized by nonlinearities and interactions among features, particularly pattern recognition problems such as credit card fraud detection and character and image recognition.<\/p>\n<h2>Reinforcement Learning<\/h2>\n<p>Reinforcement learning (RL) is an algorithm that involves the agent performing actions that maximize its rewards over time, based on environmental constraints. With supervised learning, the training data has the answer, so the algorithm is trained with the correct answer; RL does not have direct labeled data for each observation.<\/p>\n<p>The RL algorithm observes its environment, learns by testing new actions, and reuses its previous experiences. It learns from repeated trials and errors. The best solution for the RL algorithm depends on the maximum reward. A virtual trader can apply RL in financial markets to maximize his profits.<\/p>\n<p>A typical RL scenario is as shown in the figure below:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-14919\" src=\"https:\/\/analystprep.com\/study-notes\/wp-content\/uploads\/2021\/03\/Img_27.jpg\" alt=\"RL Scenario\" width=\"1590\" height=\"1051\" srcset=\"https:\/\/analystprep.com\/study-notes\/wp-content\/uploads\/2021\/03\/Img_27.jpg 1590w, https:\/\/analystprep.com\/study-notes\/wp-content\/uploads\/2021\/03\/Img_27-300x198.jpg 300w, https:\/\/analystprep.com\/study-notes\/wp-content\/uploads\/2021\/03\/Img_27-1024x677.jpg 1024w, https:\/\/analystprep.com\/study-notes\/wp-content\/uploads\/2021\/03\/Img_27-768x508.jpg 768w, https:\/\/analystprep.com\/study-notes\/wp-content\/uploads\/2021\/03\/Img_27-1536x1015.jpg 1536w, https:\/\/analystprep.com\/study-notes\/wp-content\/uploads\/2021\/03\/Img_27-400x264.jpg 400w\" sizes=\"auto, (max-width: 1590px) 100vw, 1590px\" \/>A limitation of the RL algorithm is that the parameters used may affect the speed of learning.<\/p>\n<blockquote>\n<h2>Question<\/h2>\n<p>An analyst with a background in machine learning has been thinking about the applications of neural networks (NNs) and deep learning nets (DLNs) to investment management.<\/p>\n<p>Which statement is <em>most likely<\/em> to be incorrect?<\/p>\n<p>\u00a0 \u00a0 \u00a0 \u00a0 A. NNs and DLNs are applicable in developing single variable ordinary least squares regression models.<\/p>\n<p>\u00a0 \u00a0 \u00a0 \u00a0 B. NNs and DLNs are well-suited for image and speech recognition and natural language processing.<\/p>\n<p>\u00a0 \u00a0 \u00a0 \u00a0 C. NNs and DLNs are applicable in modeling non-linearity and complex interactions among many features.<\/p>\n<h3>Solution<\/h3>\n<p><strong>The correct answer is A.<\/strong><\/p>\n<p>NNs and DLNs suitable for modeling highly complicated machine learning tasks. On the contrary, single variable ordinary least squares regression models are simple models.<\/p>\n<p><strong>B is incorrect. <\/strong>NNs and DLNs address highly sophisticated machine learning tasks, including image classification, face recognition, speech recognition, and natural language processing.<\/p>\n<p><strong>C is incorrect.<\/strong>\u00a0NNs and DLNs are well suited for modeling non-linearity and complex interactions among many features, which characterize complex tasks such as speech recognition.<\/p>\n<p>\n<strong>Note: This LOS will no longer be tested after the November 2025 exam.<\/strong><\/p>\n<\/blockquote>\n<p>Reading 6: Machine Learning<\/p>\n<p><em>LOS 6 (e) Describe neural networks, deep learning nets, and reinforcement learning<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Neural networks, deep learning nets, and reinforcement learning are sophisticated algorithms that handle complex tasks with non-linearity and interactions between large numbers of feature inputs. Some of these complicated tasks include image classification, speech recognition, and face recognition. We describe&#8230;<\/p>\n","protected":false},"author":5,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[102,229],"tags":[216,260,259,230,261],"class_list":["post-12142","post","type-post","status-publish","format-standard","hentry","category-cfa-level-2","category-quantitative-method","tag-cfa-level-2","tag-deep-learning-nets-dlns","tag-neural-networks-nns","tag-quantitative-method","tag-reinforcement-learning-rl","blog-post","no-post-thumbnail","animate"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Neural Networks, Deep Learning, and RL | CFA Level II<\/title>\n<meta name=\"description\" content=\"Learn how neural networks, deep learning nets, and reinforcement learning are applied in finance, including model structure, training, and common use cases.\" 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