{"blocks":[{"key":"fm750","text":" ","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"8sm6i","text":" 讲了什么?","type":"unstyled","depth":0,"inlineStyleRanges":[{"offset":0,"length":6,"style":"BOLD"}],"entityRanges":[],"data":{}},{"key":"5aqti","text":" 我们首先一起来看CFA一二级完全相同的这个章节：Fintech in Investment Management。这个章节总共分成七个section。","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"bcgg5","text":" Section1～2 总体介绍&什么是Fintech","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"4e0bm","text":" Section 1：总体介绍。","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"2j533","text":" Section 2：什么是Fintech?","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"4ai88","text":" 我们为什么要学习Fintech呢?Fintech是金融(Finance)与科技(technology)碰撞的火花。它使用大数据、人工智能、机器学习来衡量投资机会、优化投资组合和规避风险。现在它不仅被量化投资者使用，连基本层面的投资者也在使用这些工具和技术做投资决策。此外，它还影响了投资顾问服务和财务记录保管，可以说是改变了金融这个行业提供服务的方式。Fintech覆盖的领域主要包括：大型数据集分析、以人工智能为例的分析工具、自动交易、智能顾问以及财务记录保管。总的来说，它在实务中抛头露面的频率越来越高，所以我们应该去了解它。","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"ealhd","text":">>>更多CFA相关问题点击与老师在线交流<<<","type":"unstyled","depth":0,"inlineStyleRanges":[{"offset":0,"length":24,"style":"BOLD"}],"entityRanges":[{"offset":0,"length":24,"key":0}],"data":{}},{"key":"dr62s","text":" Section3~5 介绍了Fintech领域常见的名词","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"3f9a","text":" Section 3：大数据","type":"unstyled","depth":0,"inlineStyleRanges":[{"offset":0,"length":15,"style":"BOLD"}],"entityRanges":[],"data":{}},{"key":"3nvcd","text":" 除了解释大数据这个概念以及它的性质，书上还详细讲解了大数据的来源以及它面临的挑战。","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"3qmq0","text":" Section 4：高等分析工具：人工智能和机器学习","type":"unstyled","depth":0,"inlineStyleRanges":[{"offset":0,"length":28,"style":"BOLD"}],"entityRanges":[],"data":{}},{"key":"80slj","text":" 人工智能技术的目标是使计算机系统表现出与人类相当、或是优于人类的认知能力和决策能力。而机器学习最初是人工智能的一小部分，由于发展前景促使机器学习成为研究的前沿，可以说是“青出于蓝而胜于蓝”。这部分用大量笔墨介绍机器学习，并介绍了机器学习的两个类型：监督学习和无监督学习，由此可见机器学习的重要性。","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"53io","text":" Section 5：数据科学：从大数据中提取数据","type":"unstyled","depth":0,"inlineStyleRanges":[{"offset":0,"length":26,"style":"BOLD"}],"entityRanges":[],"data":{}},{"key":"5cms6","text":" 这部分内容简洁，共有两个知识点：处理数据的方法和数据形象化。","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"c011d","text":" Section6~7是Fintech的应用","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"76qil","text":" Section 6：Fintech在投资管理中的一些应用","type":"unstyled","depth":0,"inlineStyleRanges":[{"offset":0,"length":29,"style":"BOLD"}],"entityRanges":[],"data":{}},{"key":"cv47e","text":" 总共包含四种应用，分别是文本分析与自然语言处理(NLP)，智能顾问，风险分析和算法交易。","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"913vv","text":" Section 7：分布式账本技术","type":"unstyled","depth":0,"inlineStyleRanges":[{"offset":0,"length":19,"style":"BOLD"}],"entityRanges":[],"data":{}},{"key":"454pi","text":" 分布式账本技术这个名词大家可能比较陌生，但说到“区块链”，是前段时间非常火的一个词，区块链就是一种分布式账本技术。这部分的内容分成两点：无需授权网络与需授权网络，以及在投资管理中的应用。无需授权网络的典型例子“比特币”又是大家如雷贯耳的，这就是分布式账本技术的应用之一：加密货币。还有一个常见的应用叫做支付标记化(Tokenization)，这个在银行工作的同学可能听过，是一种对账户敏感信息进行保护的技术。","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"c377p","text":" Fintech总结","type":"unstyled","depth":0,"inlineStyleRanges":[{"offset":0,"length":11,"style":"BOLD"}],"entityRanges":[],"data":{}},{"key":"78g82","text":" 总的来说，这一章节Fintech的内容主要以名词解释为主，相关的方法和最前沿的应用也都是热点，可以结合实务来理解。正文部分没有计算，没有案例分析，也没有例题。","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"13irn","text":"Machine Learning","type":"unstyled","depth":0,"inlineStyleRanges":[{"offset":0,"length":16,"style":"BOLD"}],"entityRanges":[],"data":{}},{"key":"24uck","text":" 接下来我们再看CFA二级R8的section 7 Machine Learning，共分为以下五个小节:","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"6mub7","text":" 7.1 数据分析学的重点关注","type":"unstyled","depth":0,"inlineStyleRanges":[{"offset":0,"length":15,"style":"BOLD"}],"entityRanges":[],"data":{}},{"key":"cvlqg","text":" 这一节讲解了数据分析学的六大重点，有助于后面机器学习的讨论。","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"9vd7p","text":" 7.2 什么是机器学习?","type":"unstyled","depth":0,"inlineStyleRanges":[{"offset":0,"length":13,"style":"BOLD"}],"entityRanges":[],"data":{}},{"key":"e82l6","text":" 这一节正式开始介绍什么是机器学习，主要讲解了机器学习的定义以及它的三个组成元素TPE: Task, Performance measure and Experience.","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"7jd7v","text":"7.3 机器学习的类型","type":"unstyled","depth":0,"inlineStyleRanges":[{"offset":0,"length":11,"style":"BOLD"}],"entityRanges":[],"data":{}},{"key":"ftj29","text":" 这一节讲解了机器学习的类型，主要分为Supervised learning(监督式学习)和Unsupervised learning(非监督式学习)。这也是CFA考试大纲中新增的三个知识点的其中一个，需要重点掌握这两种类型的定义及特点。这一节协会给出了4道例题，均为概念题，不涉及计算，难度系数不大，所以大家只要掌握相关的概念即可。","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"6h4tk","text":"(小钻石)7.4 机器学习算法","type":"unstyled","depth":0,"inlineStyleRanges":[{"offset":0,"length":15,"style":"BOLD"}],"entityRanges":[],"data":{}},{"key":"drh15","text":" 这一节主要讲解了机器学习的算法，具体细分如下图所示：","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"fdipn","text":" 其中Penalized Regression、CART和Random Forests主要用于Supervised learning; Clustering Algorithms和Dimension Reduction主要用于Unsupervised learning; Neural Networks比较特殊，通常用于Supervised learning，但在reinforcement learning(which can be unsupervised)中也十分重要，所以处于中间位置。这也是考纲中新增的三个知识点的其中一个，并且协会给出了6道例题，主要还是考察这几种算法的概念，同样难度系数不大，同学们可以放心~","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"e234r","text":" 7.5 监督式机器学习: 训练","type":"unstyled","depth":0,"inlineStyleRanges":[{"offset":0,"length":16,"style":"BOLD"}],"entityRanges":[],"data":{}},{"key":"6lu1o","text":" 这一节主要讲解了model training，从而帮助我们更好的理解机器学习以及model training跟其他一些多元线性回归模型的区别。我们需要重点掌握Training的五大步骤，这也是新增的三个知识点中的最后一个。这一节没有例题。","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"2g1ma","text":" Machine Learning总结：","type":"unstyled","depth":0,"inlineStyleRanges":[{"offset":0,"length":20,"style":"BOLD"}],"entityRanges":[],"data":{}},{"key":"7jocd","text":" 总的来说，这几个知识点考纲要求的都是“describe”，原版书中考察的也都为概念题，不会涉及计算，所以对于这部分新增的内容大家不必过于担忧，只要掌握相关的概念即可。","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"5vqd5","text":" CFA三级道德&权益的Fintech","type":"unstyled","depth":0,"inlineStyleRanges":[{"offset":0,"length":19,"style":"BOLD"}],"entityRanges":[],"data":{}},{"key":"1tb8d","text":" 最后，我们来看三级道德和权益中的相关内容。","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"f27gc","text":" CFA三级涉及到的Fintech内容非常少。","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"bdub9","text":" 在道德新增的reading 5 Overview of the Asset Management Industry and Portfolio Management中，提到了近年来资管行业的发展趋势，包括大数据及智能投顾，这两部分涉及篇幅不大，都是从了解宏观层面进行的讲解。其中大数据部分介绍了近些年各类数据呈井喷式出现的现象，以及介绍了资管行业也在利用这些数据帮助投资。","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"139oc","text":" 这样的数据分为两大类：社交媒体数据(Social media data)和图像传感数据(Imagery and sensor data)。而智能投顾(Robo-Advisers)部分，从概念及应用大体介绍了这一概念。介绍了这一领域拥有较好的发展前景，并且从行业发展角度解释了为什么智能投顾有较好的前景。","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"bibmp","text":" 在权益投资reading 28 reading中，书中在权益的主动投资方法下引入了量化投资策略的介绍，对原有内容进行了系统性的扩充和完善。具体来说，新增了量化投资策略的介绍、其与传统投资的对比、基本面分析法的陷阱等。考纲的要求仅为“describe”，因此只需定性了解即可。","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"93gsr","text":" 怎么考?","type":"unstyled","depth":0,"inlineStyleRanges":[{"offset":0,"length":6,"style":"BOLD"}],"entityRanges":[],"data":{}},{"key":"1aued","text":" 每次协会改考纲，大家最担心的还是CFA考试会怎么考。尤其像这种新增的章节，没有往年的模考题，甚至有的时候原版书上既没有例题也没有课后题，大家学起来心里没有底。","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"8m3ei","text":" 对于一级R43、二级R6这两个相同内容的章节，协会是比较良心的，虽然正文没有例题，但是有课后题。这就给了我们练习的机会。这些课后题也能反映正式考试时协会的出题方向。对于二级R8新增的section7，协会在正文部分给了10道例题供大家参考，其中4道考察机器学习的类型，6道考察机器学习的算法类型，虽然没有相应的课后题，但这些例题恰恰与考纲中的知识点相对应，与课后题的地位同等重要。","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"6fmbj","text":" CFA三级道德和权益部分出现的Fintech内容均属于了解性质的知识点，文中没有对应的例题也没有相应的课后题。从考纲角度来看，道德部分没有单独为这部分列考点，权益部分也仅占了一个小知识点。因此新增知识难度不大，了解概念即可。","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"2uimc","text":" 总的来说，这些题目考察的全部是定性的内容，重点是对名词的理解以及它们的一些性质。这与考纲完全吻合，因为LOS的要求全部是“describe”。","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"5q1n1","text":" 没有数字，没有公式，也没有复杂的案例分析，所以难度系数不会太高，大家只要熟悉掌握Fintech和Machine Learning相关的概念，顺利拿下这部分的考题应该没有问题。","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"f3uo8","text":" 总的来说，Fintech部分新增的内容不难，大家可以放心。这部分大家好好学，其实机会很难得，第一次Fintech有相对系统一点的教材涉及，虽然内容不多，但也很值得反复推敲了。而且金程也会继续深挖其中的内容，希望给大家多拓展点知识的。","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"bl6nn","text":" 现在考纲明确了，其实二级也会涵盖一级所涉及到的Fintech内容，各位12月备考CFA一级的小伙伴安心准备考试吧，千万不要为了明年重新学Fintech故意考不过噢。","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"7e6pv","text":"CFA考试推荐：史上最全CFA大礼包点击限时免费获取：https://jinshuju.net/f/SeKxWF ","type":"unstyled","depth":0,"inlineStyleRanges":[{"offset":0,"length":56,"style":"BOLD"}],"entityRanges":[{"offset":27,"length":29,"key":1}],"data":{}}],"entityMap":{"0":{"type":"LINK","mutability":"MUTABLE","data":{"href":"https://chat7622.talk99.cn/chat/chat/p.do?_server=0&encrypt=1&c=20000653&f=10050794&g=10056807&refer=cfa#lccfawl0822 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