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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/applying-data-analytics-business-in-finance
课程评论：没有评论
课程名称：金融数据分析应用 课程概述：本课程介绍金融分析的概述，学习在实际情况下为什么、何时和如何应用金融分析。学员将探索分析时间序列数据的技术，以及如何评估现代投资组合理论中的风险与收益权衡。虽然大多数内容将集中在公司股票的价格、收益和风险上，但分析技术也可以应用于其他领域。课程最后简要介绍算法交易。 完成本课程后，学员将能够理解时间序列数据、进行预测并确定估计的有效性。此外，学员还将能够利用实际股票价格数据创建资产组合，同时优化风险与收益。理解金融数据是分析师、经理或顾问的重要技能。 课程大纲： 1. 课程介绍：介绍金融分析技术，学习应用金融分析的时间和情况，探索时间序列数据分析的技术以及现代投资组合理论中风险与收益的权衡。 2. 模块1：金融分析与时间序列数据的介绍：概述金融分析，学习实际应用的时机与方法，分析公司股票的价格、收益和风险，并了解如何进行算法交易。 3. 模块2：绩效测量与霍尔特-温特斯模型：介绍分析时间序列数据的分析方法，构建预测模型以支持决策，学习预测性能测量、移动平均、指数平滑方法和霍尔特-温特斯方法。 4. 模块3：平稳性与ARIMA模型：学习分析时间序列数据的第一步——平稳性，判断时间序列是否平稳的方法，以及如何将非平稳数据转化为平稳数据，研习基本预测模型ARIMA，并使用R构建ARIMA预测模型。 5. 模块4：现代投资组合理论与算法交易简介：介绍现代投资组合理论的一些基本测量，理解风险与收益的平衡，评估投资组合。
Name:Course Introduction
Description:In this course, we will introduce a number of financial analytic techniques. You will learn why, when, and how to apply financial analytics in real-world situations. We will explore techniques to analyze time series data and how to evaluate the risk-reward trade off expounded in modern portfolio theory. While most of the focus will be on the prices, returns, and risks of corporate stocks, the analytical techniques can be leveraged in other domains. Finally, a short introduction to algorithmic trading concludes the course.
Name:Module 1: Introduction to Financial Analytics and Time Series Data
Description:In this module, we will introduce an overview of financial analytics. Students will learn why, when, and how to apply financial analytics in real-world situations. We will explore techniques to analyze time series data and how to evaluate the risk-reward trade off expounded in modern portfolio theory. While most of our focus will be on the prices, returns, and risks of corporate stocks, the analytical techniques can be leveraged in other domains. Finally, a short introduction to algorithmic trading concludes the course.
Name:Module 2: Performance Measures and Holt-Winters Model
Description:We will introduce analytical methods to analyze time series data to build forecasting models and support decision-making. Students will learn how to analyze financial data that is usually presented as time series data. Topics include forecasting performance measures, moving average, exponential smoothing methods, and the Holt-Winters method.
Name:Module 3: Stationarity and ARIMA Model
Description:In this module, we will begin with stationarity, the first and necessary step in analyzing time series data. Students will learn how to identify if a time series is stationary or not and know how to make nonstationary data become stationary. Next, we will study a basic forecasting model: ARIMA. Students will learn how to build an ARIMA forecasting model using R.
Name:Module 4: Modern Portfolio Theory and Intro to Algorithmic Trading
Description:We will introduce some basic measurements of modern portfolio theory. Students will understand about risk and returns, how to balance them, and how to evaluate an investment portfolio.
This course introduces an overview of financial analytics. You will learn why, when, and how to apply financial analytics in real-world situations. You will explore techniques to analyze time series data and how to evaluate the risk-reward trade off expounded in modern portfolio theory. While most of the focus will be on the prices, returns, and risk of corporate stocks, the analytical techniques can be leverages in other domains. Finally, a short introduction to algorithmic trading concludes the course. After completing this course, you should be able to understand time series data, create forecasts, and determine the efficacy of the estimates. Also, you will be able to create a portfolio of assets using actual stock price data while optimizing risk and reward. Understanding financial data is an important skill as an analyst, manager, or consultant.
