Prof. Damarodan, known as the “Dean of Valuation”, apparently does not have much respect for pricing, perhaps righteously so. By pricing he means how a stock is priced in comparison to other equites, as opposed to intrinsic valuation whereby one discounts the Free Cash Flow of a business over its lifetime to arrive at its intrinsic value. As many of his students will eventually practice finance in which not a small part of their job surrounds pricing, he would have his students rather do “an honest pricing” than make a lousy show for it. As a result, he devises a framework for pricing stock properly and for finding out which elements the market are pricing heavily.
In pricing, the most common ratio is PE ratios. This post tests a P/E pricing model expounded by Prof. Damodaran on over 500 companies listed in Germany. The data is scraped from Yahoo Finance.
First things first…
In the dividend discount model, a stock’s price is affected by dividend per share, cost of equity, and EPS growth rate:
Dividing both two sides of the equation by EPS, while DPS is allowed to grow at the rate g, we arrive at a formula for P/E ratio:
According to Damodaran, payout ratio, risk (cost of equity), and expected growth rate are the main drivers of P/E ratios. This blog post tests out this theory on equities in the German market.
Also, as opposed to intrinsic valuation, P/E ratios are a pricing game in which analyst has to find out what factors are the market taking into account as it determines the price of each equity. As a result, I identify other elements on which the market is putting an emphasis such as ROA and sector/industry information and use such elements to build a model to predict PE ratios.
Data analysis and feature selection
PE ratios across industry suggest that the coronavirus crisis has left both winners and losers in its wake. From the graph below, the biggest winner is understandably department stores as they are essential businesses and are largely unaffected by the pandemic.
Other big winners come mostly from tech sectors (Communication Equipment, Telecom Services to name a few) as people are equipping themselves for home-office.
As demand for ventilators surges and the market is speculating which company will come out with the first vaccine for Covid-19 , a good chunk of capital is devoted to the healthcare sector. As a result, drug and medical device manufacturers have been priced favorably.
Losers include Airlines (not meaningful PE), airports and air-services(average PE of 8.64), and auto manufacturers (average PE of 6.35). While airlines unsurprisingly suffer as a consequence of travel bans, listed auto and autopart manufacturers in Germany have been systematically declining in recent years. The automakers are struggling to find new venues for growth because demand decelerates, EVs are preferred overs ICEs, and emerging ride-sharing businesss that cut into the the market share of these OEMs. This economic crisis aggravates the aforementioned problems that these companies are facing as sales flop and consequently investments in EVs are cut.
One can observe that PE ratios hinge upon the industry in which the company operates. In fact, running a regression model on just dummy variables representing various industries yields a decent R-squared of 0.87. Industries that have significant t-test results in this regression models at 95% confidence level are medical device manufacturers, airports, department stores, etc.
On the other hand, a regression for each financial metric against PE ratios suggests that several key financial indices such as ROA, ROE, beta, expected EPS growth rate influence PE ratios. Below are the pairplots of these factors.
Among the financial indices, ROA and ROE both measure efficiency: how much return investors get from investing one dollar of asset/equity. That’s why ROA and ROE are highly correlated (correlation coefficient of 0.6). However, ROA is much stronger at predicting P/E ratios than ROE, potentially because as earnings are heavily distorted by the financial crisis (higher financing cost in distressed situation, low revenue) and central bank actions (QE Infinity), investors are turning to return on operating assets (ROA) as a proxy for companies’ health. As a result, ROE is omitted from the regression model to avoid overfitting, leaving ROA to be the sole measure of return in the model.
The model produces a decent R-squared result. What’s more important, the t-values confirms the statistically significant relationship between factors such as ROA, beta (representing risk), payout ratio, and PE ratios. Surprisingly, the EPS growth’s t-value is not significant, perhaps because investors are putting much heavier emphasis on survivability than on growth prospect to decide where to allocate their capital. Also, companies are most heavily punished for being risky (beta’s coefficient of -6.2 log scale larger in absolute value than any other financial indices in the model), suggesting that investors are looking for safe bets rather than for high-growth yet risky companies.
On the qualitative side, companies in healthcare and scientific instrument industry are on the receiving end of an enormous amount of capital: taking in an extra 160 Euros or more in market cap for 1 Euro of earnings they make. On the contrary, investors are moving away from gambling (coefficient of -9.5), as consumers brace for the economic crisis and would rather spend their hard-earned extra-cash on something more useful than gambling, though the relationship is not that clear-cut (low t-value).
In conclusion, investors are diverting their capital away from luxury companies and focusing on essential and healthcare businesses. In this time of crisis, growth no longer has a strong impact on P/E, as suggested by the formula above. More importantly, high efficiency (represented by ROA), less risk, and cash-generative business models are primary criteria investors are seeking for in this time of crisis.
Top 5 stocks that appear the most cheapest are (in percentage to its current PE):
Top 5 stocks that appear the most expensive are (in percentage to its current PE):
The model is created to screen for stocks that look cheap. A deeper fundamental analysis and financial modelling is required in order to make an investment recommendation.
The way forward
One might notice that industries at the short end of the stick such as airlines and apparel retails do not show up in the model. One reason for absence may well be that they have a negative earnings this quarter and as a result do not have a meaning P/E ratio. Another reason might be lack of data. Thus, other measures such as EV to EBITDA, EV to FCFF, Price to FCFE (Free Cash Flow to Equity) should be used flexibly in addition to PE in order to accurately take stock of a company’s equity.
Getting data is very difficult these days, as a Bloomberg Terminal is not readily available at the comfort of your own house while schools are closed for an unforeseeable future. In case you are interested in analyzing the dataset and making a conclusion to yourself, please have a look at my data here:
I used Python to scrape, clean, transform, visualize, and analyze the data. If you need to scrape data from other markets and do the same analysis that I did on them, here is the link to my code:
You can't perform that action at this time. You signed in with another tab or window. You signed out in another tab or…
Any clap and comment for the post is highly appreciated!
Catch you at the next post !:)