Misreporting: Simple definition

Misstatements that affect firms’ accounting statements and were done seemingly intentionally by management or other employees at the firm.

Traditional accounting fraud

1. A company is underperforming
2. Management cooks up some scheme to increase earnings
   • Wells Fargo (2011-2018?)
     • Fake/duplicate customers and transactions
3. Create accounting statements using the fake information

Reversing it

1. A company is overperforming
2. Management cooks up a scheme to “save up” excess performance for a rainy day
   • Dell (2002-2007)
     • Cookie jar reserve, from secret payments by Intel
       • Up to 76% of quarterly income
3. Recognize revenue/earnings when needed in the future to hit earnings targets

Other accounting fraud types

• Apple (2001)
  • Options backdating
• China North East Petroleum Holdings Limited
  • Related party transactions (transferring funds to family members)
• Keppel O&M (2001-2014)
  • Bribery ($55M USD in bribes to Brazilian officials for contracts)

How do misstatements come to light?

1. The company/management admits to it publicly
2. A government entity forces the company to disclose
   • In more egregious cases, government agencies may disclose the fraud publicly as well
3. Investors sue the firm, forcing disclosure

This is what we can leverage to detect fraud!

Where are these disclosed?

In the US:

1. SEC AAERs: Accounting and Auditing Enforcement Releases
   • Generally highlight larger or more important cases
   • Written by the SEC, not the company
   • To get a sense what these are, you can read the Summary section (starting on page 2) of this AAER against Sanofi
2. 10-K/A filings (/A means amendment)
   • Note: not all 10-K/A filings are caused by fraud!
     • Benign corrections or adjustments can also be filed as a 10-K/A
     • Audit Analytics’ write-up on this for 2017
3. By the US government through a 13(b) action
4. In a note inside a 10-K filing
   • These are sometimes referred to as “little r” restatements
5. In a press release, which is later filed with the US SEC as an 8-K
   • 8-Ks are filed for many other reasons too though

Main question

How can we detect if a firm is involved in a major instance of missreporting?

• This is a pure forensic analytics question
• “Major instance of misreporting” will be implemented using AAERs

Approaches to detection

• 1990s: Financials and financial ratios
  • Misreporting firms’ financials should be different than expected
• Late 2000s/early 2010s: Characteristics of firm’s disclosures
  • How long, how positive, word choice, …
• Late 2010s: More holistic text-based machine learning measures of disclosures
  • Modeling exactly what the company talks about in their annual report

All of these are discussed in Brown, Crowley and Elliott (2018) – I will refer to the paper as BCE for short

Changing methods

Why did we shift away from accounting ratios?

Dealing with infrequent events

• Fraud is infrequent
• A few ways to handle this:
  1. Very careful model selection (keep it sufficiently simple)
  2. Sophisticated degenerate variable identification criterion + simulation to implement complex models that are just barely simple enough
     • The main method in BCE
  3. Automated methodologies for pairing down models (LASSO, XGBoost)
     • Also implemented in BCE

The BCE model

• Retain the variables from the previous models regressions
• Add in a machine-learning based measure quantifying how much documents talked about different topics common across all filings
  • Learned on filings from the 5 years prior
    • Optimal to have 31 topics per 5 years

Topic

What the topics look like

Theory behind the BCE model

• From communications and psychology:
  • When people are trying to deceive others, what they say is carefully picked
    • Topics chosen are intentional
• Putting this in a business context:
  • If you are manipulating inventory, you don’t talk about it

How to do this: LDA

• LDA: Latent Dirichlet Allocation
  • Widely-used in linguistics and information retrieval
    • Available in C, C++, Python, Mathematica, Java, R, Hadoop, Spark, …
  • Used by Google and Bing to optimize internet searches
  • Used by Twitter and NYT for recommendations
• LDA reads documents all on its own! You just have to tell it how many topics to find

An example of LDA

From David Blei’s website

How well does it work?

Topics driving our model

Case studies

To learn more

• Detail of how, exactly, to build this model will be presented later this month
  • Data Science Singapore (DSSG)
  • March 27, 7:00pm
  • Ngee Ann Kongsi Auditorium
  • Register on meetup.com
• Technical details publicly available at SSRN
• Some other details on rmc.link