After all, you only find out who is swimming naked when the tide goes out.
There a basically two main reasons for a total loss of capital
- companies that are cooking the books; financial statements manipulation and fraud
In most cases these risk are frequently found together.
Management's desire to put a positive spin on financial results has been around as long as corporations and investors themselves. Dishonest companies have long used these tricks to prey on unsuspecting investors, and it is unlikely that they will ever cease to do so.
It is a understatement to say that investing in companies subject to financial statement manipulation or fraud are surely not the best investments you can do....(and certainly not in bear markets).
So how can you detect these companies and remove them from your lists? There is a way to find financial shenanigans
The Beneish M -score
Messod Beneish, an accounting professor at Indiana University's Kelley School of Business, outlined a quantitative approach to detecting financial statement manipulation in his 1999 paper "The Detection of Earnings Manipulation". He based his model on forensic accounting principles, calling it the probability of manipulation or PROBM model.
He collated a sample of known earnings manipulators. Then he identified their distinguishing characteristics and used those characteristics to create a model for detecting manipulation.
The PROBM model includes variables that are designed to capture either the effects of manipulation or preconditions that may incentivise management to engage in such activity. The resulting PROBM model predicts future financial statement manipulators. In out-of-sample tests, the PROBM model identifies approximately half of the companies involved in earnings manipulation prior to public discovery.
The model correctly identified, ahead of time, 12 of the 17 highest-profile fraud cases in the period 1998 to 2002. The PROBM model (or M-score) also consistently predicted stock returns over 1993 to 2007. Between 1993 and 2007, a 15-year period after the model's data, stocks that were flagged as potential earnings manipulators by the model returned 9.7 percent lower returns each year than stocks that were not flagged.
Interestingly, students from Cornell University using the PROBM (or M-score) ;correctly identified Enron as an earnings manipulator, while experienced financial analysts failed to do so.
For more info about how the PROBM or M -score is constructed, click here.
Other red flags, warning of likely underperformance to come , interesting to check to avoid distressed companies :
While none of these factors are unusually strong in and of themselves, each points out an aspect of a company's financial and/or business condition that is indicative of corporate health or disease.
Cash flow (operation) on capex
The operating cash flow to capex strategy screens for companies that have more than ample cash to fund capital spending needs. This factor makes a good building block according to Richard Tortoriello and works well accros sectors
Capex on ppe
The capex to property,plant & equiment (ppe) strategy attempts to provide a measure of capital intensity. Companies with higher ratios may be becoming more capital intensive and underperform in general. Those with a lower ratio outperform.
Free Cash Flow on Debt ratio
The free cash flow (fcf) to debt strategy is a debt payback ratio that looks at cash flow adequacy relative to debt levels. It combines well with profitability,valuations and price-momentum factors.
This ratio works much better than the often used ratios such as long-term debt to equity, total debt to assets, and total debt to invested capital. (see our back test study ) .The market has rewarded companies with high debt-to-capital ratios, at least over our 12-month test period.
Free cash flow (FCF) to debt is a debt payback ratio. This ratio indicates how long it might take a company to pay back its outstanding debt, given current FCF levels. The ratio also serves as a barometer reading of the financial health of the company. Companies do not go bust because they have debt, they go bankrupt because they can not pay back there debt.
The Z-score formula for predicting bankruptcy was published in 1968 by Edward I. Altman, who was, at the time, an Assistant Professor of Finance at New York University. The formula may be used to predict the probability that a firm will go into bankruptcy within two years. Z-scores are used to predict corporate defaults and an easy-to-calculate control measure for the financial distress status of companies in academic studies. The Z-score uses multiple corporate income and balance sheet values to measure the financial health of a company.