August 2022
Intermediate to advanced
394 pages
12h 32m
Chinese
Content preview from 金融人工智能：用Python实现AI量化交易
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78
第 4 章
数据驱动的金融学
如果说人工智能是新的电力，那么大数据就是为发电机提供动力的石油。
——李开复，
2018
年
如今，分析师对卫星图像和信用卡数据等非传统信息进行筛选，或者使用机器学
习和自然语言处理等人工智能技术从经济数据和财报电话会议记录等传统来源获
得新的见解。
——
Robin W
igglesworth
，
2019
年
本章讨论了
数据驱动的金融学
的核心方面。在本书中，数据驱动的金融学被理解为主要由
数据驱动并从数据中获得见解的金融学（理论、模型、应用等）。
4.1
节讨论了科学方法
，其中给出了应该指导科学探索的一套公认的原则。
4.2
节介绍了金
融计量经济学和相关主题。
4.3
节
阐明了今天哪些类型的（金融）数据是可用的，以及通
过可编程
API
可
以获得的数据质量和数据量。
4.4
节重新审视了第
3
章的规范性理论，并
基于真实的金融时间序列数据进行了分析。
4.5
节同样基于真实的金融数据
，揭穿了金融
模型和理论中最常见的两个假设：
正态分布收益率
和
线性关系
。
4.1
科学方法
科学方法
是指指导任何科学项目的一套公认的原则，维基百科对科学方法的定义如下：
科学方法是一种获取知识的经验性方法，早在
17
世纪就成了科学发展的特征
。
它涉及仔细的观察，对观察结果应用严格的怀疑主义，因为认知假设会扭曲人们
对观察结果的解释。它包括根据这些观察，通过归纳，提出假设；对从假设中得
出的推论进行实验和测试；以及根据实验结果精炼（或消除）假设。这些是科学
方法的原则，与适用于所有科学实体的一系列明确的应用步骤是不同的。
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