Enhanced FSA with Data Analytics

Description

This class directly supports the Final Project by introducing advanced financial statement analysis (FSA) techniques that leverage data analytics tools. Students will learn how to apply analytical procedures using Alteryx and FactSet Screener to screen stocks and support their long and short positions. The session will cover methods for screening stocks based on a quick residual income valuation, enabling students to identify potentially under and over valued stocks within their industry of choice. The class will also cover an introduction to enhancing forecasting with data analytics, and scenario testing with automation. By integrating these analytics techniques into their FSA toolkit, students will be better equipped to support their conduct FSA and valuation assessments for their final project.

Motivation

This class helps teams finalize their stock selections for the final project. Stocks will be screened using a shortened residual income valuation model to identify potentially under and over valued stocks within their industry of choice.

Review

This class follows the Preliminary Proposals where each team met with the instructor to discuss their plans for the final project. The material covered in this class follows directly from these meetings providing an opportunity for each team to finalize their stock selection. Specifically this class provides students with advanced techniques for enhancing financial statement analysis (FSA) using data analytics tools. By learning how to screen stocks and perform quick residual income valuations, students can apply these skills to their final project, where they will need to analyze and value companies within their chosen industry. The class builds on the data analytics exercises covered in the EDGAR explorer classes, allowing students to better support their final project deliverables using financial data.

Preparation

• There is no preparation for this class other than that discussed in the proposal meetings (if at all).
• Students should be comfortable using Factset to screen stocks to their industry, add specific variables, and be prepared to export the data for a secondary screen in Alteryx.

Class plan

• This class will explain stock screening using a valuation approach based on a short-hand version of the residual income model using book values and earnings as the inputs.
• The explanations are based on academic research that take a quantitative screening approach which mimics how hedge-funds select portfolios.
• These approaches are based on average results for portfolios of stocks (typically the top and bottom deciles average stock return based on the screening criteria is significantly different from zero). On average stocks with a high Value-to-Price Ratio have higher returns (outperform) stocks with low value-to-price ratios
• After extracting the data from FactSet, students will work in the labs to complete the stock screening exercise using Alteryx by calculating a simplified version of the residual income model and dividing it by price to obtain a value-to-price ratio.
• The goal of the exercise is to help each team finalize their stock selections for the final project, identifying stocks that have a higher chance of being over (low value-to-price) or undervalued (high value-to-price).

Tools

• Alteryx
• FactSet Screener

Concepts

• Stock Screening
• Residual Income Valuation