Data analytics and visualization in financial statement analysis

Group	Teacher	Department	Language
Year 3	Petya Platikanova	Economía, Finanzas y Contabilidad	ENG

Group	Teacher	Department	Language
Year 4	Petya Platikanova	Economía, Finanzas y Contabilidad	ENG

COURSE CONTRIBUTION TO PROGRAM

In recent years, the ability to effectively communicate financial information has become critical for professional developments in any finance-related discipline, from corporate finance to investment banking and consultancy. Advances in the data visualization tools enable us to better communicate complex relationships in financial data to insiders (such as managers, collaborators and colleagues) to outsiders (including current and prospective investors, regulatory authorities and creditors).

Similarly to the large volume of financial information produced on a daily basis, there are thousands of sources with recommendations how to present financial information with a convincing story-telling visualization and solid financial analysis. Despite this abundance, it is difficult to find a concise guide about data visualization and analysis in the area of finance and financial analysis specifically. Many sources contain effective solutions to data visualization in financial analysis but lack specific instructions and programming tools to implement them. This intensive course offers a practical approach to data visualization and analysis with real business examples and specific programming outcomes.

The course will lay out the foundations of data visualization and analysis using Python on the implementation side. Python is a leading programming language for data science. In the finance industry, Python is used to model risk exposure, analyze market trends, evaluate different investment options and make more informed decisions. In this course, we will introduce the foundations of Python programming that enable the data visualization of financial information relevant for both finance professionals and corporate leaders.

Course Learning Objectives

The objective of this course is expand your knowledge and reinforce your learning about working with data by using different resources.
- We will revise, reflect, and refine your skill and understanding about the challenges of working with data through practical exercises.
- We will challenge your existing approaches to creating and consuming visualizations, examine effective or ineffective visualization, and encourage you to have a more structured approach to data visualization.
- The course will increase your awareness of the possible approaches to visualizing data and hopefully help you become an informed user of data visualizations available in social media, press reports, etc. and often presented at work and outside work for decision making

CONTENT

1. Topic 1: Introduction to data visualization in financial statement analysis. Review and discussion of viz examples with financial data. Differentiation of effective and ineffective visualizations with financial data Objectives: a. Introduce the main advantages and disadvantages of data visualizations in financial statement analysis. b. Identify the key components of effective visualizations. c. Discuss the main difference between exploratory vs. explanatory visualizations. d. Recommend possible improvements for more effective visualizations.
2. Topic 2: Examine representative financial metrics discussed in corporate filings and other corporate documents. Objectives: a. Get familiar with corporate reports and its structure. b. Practice data analysis of financial indicators. c. Evaluate the information properties of exploratory and explanatory visualizations. d. Recommend possible improvements for more effective visualizations.
3. Topic 3: Practice data visualization. Discuss data selection to "drive" data reading in a certain direction - data selection bias (example: Airbnb IPO vs others). Objectives: a. Get familiar with a data visualization tool. b. Practice data analytical skills using specific examples (e.g., Airbnb IPO). c. Evaluate and select data to present a particular point of view (strong sell as compared to strong buy argument). d. Discuss possible data visualizations to effectively communicate a particular perspective.
4. Topic 4: Examine financial information and perform financial analysis. Select the main financial inputs to elaborate a strong buy or sell recommendation. Objectives: a. Examine the main financial inputs used in financial analysis, b. Identify patterns in the financial data. c. Discuss the main aspects of financial analysis using a real-world business example. d. Select financial inputs for data visualization in support of opposing recommendations (strong buy and strong sell recommendation)
5. Topic 5: Introduce Google Colab and examine easy-to-use Python resources for financial analysis and visualization. Objectives: a. Examine examples in Python related to: data upload, import of stock market data using for example Yahoo Finance API, sentiment analysis, along with respective data visualizations. b. Learn to modify or customize ready-made Python codes.

1. Topic 1: Introduction to data visualization in financial statement analysis. Review and discussion of viz examples with financial data. Differentiation of effective and ineffective visualizations with financial data

Objectives:
a. Introduce the main advantages and disadvantages of data visualizations in financial statement analysis.
b. Identify the key components of effective visualizations.
c. Discuss the main difference between exploratory vs. explanatory visualizations.
d. Recommend possible improvements for more effective visualizations.

2. Topic 2: Examine representative financial metrics discussed in corporate filings and other corporate documents.

Objectives: a. Get familiar with corporate reports and its structure.
b. Practice data analysis of financial indicators.
c. Evaluate the information properties of exploratory and explanatory visualizations.
d. Recommend possible improvements for more effective visualizations.

3. Topic 3: Practice data visualization. Discuss data selection to "drive" data reading in a certain direction - data selection bias (example: Airbnb IPO vs others).

Objectives:
a. Get familiar with a data visualization tool.
b. Practice data analytical skills using specific examples (e.g., Airbnb IPO).
c. Evaluate and select data to present a particular point of view (strong sell as compared to strong buy argument).
d. Discuss possible data visualizations to effectively communicate a particular perspective.

4. Topic 4: Examine financial information and perform financial analysis. Select the main financial inputs to elaborate a strong buy or sell recommendation.

Objectives:
a. Examine the main financial inputs used in financial analysis,
b. Identify patterns in the financial data.
c. Discuss the main aspects of financial analysis using a real-world business example.
d. Select financial inputs for data visualization in support of opposing recommendations (strong buy and strong sell recommendation)

5. Topic 5: Introduce Google Colab and examine easy-to-use Python resources for financial analysis and visualization.

Objectives:
a. Examine examples in Python related to: data upload, import of stock market data using for example Yahoo Finance API, sentiment analysis, along with respective data visualizations.
b. Learn to modify or customize ready-made Python codes.

Description	%
Class discussion	30
Group project	70

Assessment criteria

Class participation (30%)
Final project (70%)
The objective of the project is to examine financial indicators of different companies and selectively present visualizations of financial information in favor of a "strong buy? (for the groups with a buyer role) and a "strong sell? (for the groups with a seller role) recommendation. Both profiles, buyer and seller, are long-term investors (i.e., temporary price fluctuations are relevant to the extent to which they expose the investor to a high level of uncertainty regarding long-term stock profitability prospects). The purpose of the project is to: a) demonstrate that the same sources of financial information can be used to defend completely opposing recommendations (i.e., "data picking? in financial analysis), b) practice the visualization of financial indicators typically used in fundamental analysis, and c) prepare effective visualizations in favor of a pre-defined advisory position. The course will finish with final presentations when the results of the group project will be presented.

Type:	OPT	Curs:	3,4	Period:	S semester
ECTS Credits:	2 ECTS

Data analytics and visualization in financial statement analysis (2225.YR.014155.1)

General information

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Curs:

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Prerequisites

COURSE CONTRIBUTION TO PROGRAM

Course Learning Objectives

CONTENT

1. Topic 1: Introduction to data visualization in financial statement analysis. Review and discussion of viz examples with financial data. Differentiation of effective and ineffective visualizations with financial data

2. Topic 2: Examine representative financial metrics discussed in corporate filings and other corporate documents.

3. Topic 3: Practice data visualization. Discuss data selection to "drive" data reading in a certain direction - data selection bias (example: Airbnb IPO vs others).

4. Topic 4: Examine financial information and perform financial analysis. Select the main financial inputs to elaborate a strong buy or sell recommendation.

5. Topic 5: Introduce Google Colab and examine easy-to-use Python resources for financial analysis and visualization.

ASSESSMENT

ASSESSMENT BREAKDOWN

Assessment criteria

Timetable and sections

Timetable Year 3

Timetable Year 4