# Wiki Wiki Web

## Motivation

Passive indexes provide investment benchmarks that are simple to replicate (the largest position being in the companies with the largest float) and limit rebalancing needs (as a market capitalization rule implies never needing to trade). However, such a passive approach implies that the market weights are best, which is a strong efficient market assumption.

We saw such market capitalization rules being egregiously wrong in the past:

• the Japanese stock market in the late 80s had more value than the US market, it is now 10x smaller than the US market. The valuation did not make sense based on fundamentals, and with hindsight, we can ascribe the inefficiency to the availability of leverage.
• TSLA, a stock that never made money and barely produces cars, is priced as if there was any possibility of maintaining a moat in the automobile industry. The jury is still out on this one, but it seems clear that shorting TESLA is much more dangerous than just omitting it from a portfolio.

We will review some fundamental pricing considerations aiming to put a reasonable price on growth stock, and will apply this to the stock market. You can read more about value investing in this section.

## Fundamental Pricing Methodology

If the discount rate required by stock investors is $y$, a company that can produce a steady flow of dividend $D$ coming from earning per shares $E$ with payout ratio $f$ is valued as:

$P = D ((1+y)^{-1} + (1+y)^{-2} + ... )= D /y = f E / y$

The payout ratio $f$ is such an important component that it deserves its own discussion there , let's say that an optimist will set it at 70% and a pessimist at 30%, a perfectionist (like Buffet or Graham) will compute their own earnings measure. The main objection to the above formula is that earnings per share change. They are subject to revenue growth, margin changes, and corporate share issuance policy. To determine the likely evolution of the earnings per share, we point out that for a stable business, revenue $R$ grows with some volatility and a margin $m$ which depends on the business type can be extracted from the revenue. The number of shares $S$ is typically constant, but may be increased if the managers are extracting value from the company by granting themselves stocks or stock options. The earning per share is thus give by:

$E_t = f R_t m_t / S_t$

We then can compute the mean and standard deviation of :

• annual revenue growth rate $g=\ln(R_{i+1}/R_i))$
• net margin rate $m_i$
• shares dilution rate $d=\ln(S_{i+1}/S_i))$

Following Lindy Law, we'll posit that the company will have the same growth, margin and dilution for the next 5 years as in the previous 5 years, after that, the earnings are assumed to mean revert for the next 5 year to a stable state. This means assuming growth for 7.5 year and suddenly no growth after that is a possible hypothesis.

We use log returns because it allows compounding values by averaging them, the current earning yield is denoted $e = \ln(1+E/P)$, the future fair price is given by $P_T = R_0 \exp((e+g-d)T) / y m f$. Discounting this price to today using a discount rate $y$, the present fair price to sales ratio is given by:

$P_0/R_0 = \exp((e+g-d-y)T) /y m f$

Therefore, the current price to revenue ratio $P/R$ (aka price to sales) can be fundamentally judged against an assumption about earnings, growth, dilution and discount yield and about margin.

Using a shorter time $T$ growth period will lead to our favoring value stocks, whereas a longer time will favor growth stocks, provided dilution is low.

We can also compute a stressed value by shifting $e,g,d,y,m$ by a certain number $a$ of standard deviations, this does away with much of the handwaving present in the fundamental litterature, that earnings should demonstrate some stability.

## Some real world application in May 2022

For this first study, we look at 1800 stocks from US, Japan, Europe and Australia, use last 5 years of account based on interactive brokers financial data. We used $f=1$ and $y=6$%.

### Results by Sector

We observe the following:

• tech has high growth and low earning yield as predicted with low capital intensity business
• utilities and basic materials have high earning yield and low growth
• energy is highly cyclical
• financials have high earning yield
 sector count earning_yield growth margin dilution ratio sratio Basic Materials 125 6.46% 4.93% 7.30% -0.14% 164.14% 5.61% Consumer Cyclicals 270 5.09% 3.89% 5.55% -0.32% 109.78% 9.44% Consumer Non-Cyclicals 152 4.04% 2.15% 5.20% -0.15% 66.80% 18.57% Education 3 2.45% -0.08% 10.16% 0.04% 23.37% 0.02% Energy 59 5.02% 12.38% 1.39% 2.03% 6.67% -1.42% Financials 217 8.48% 3.38% 22.35% -0.36% 212.59% 32.24% Healthcare 175 3.33% 8.64% 9.87% 0.54% 67.74% 12.08% Industrials 306 4.25% 5.26% 6.66% -0.10% 96.05% 19.80% Real Estate 130 3.39% 5.40% 29.39% 2.99% 36.93% 1.28% Technology 301 2.83% 10.02% 7.73% 0.27% 67.04% 5.65% Utilities 68 4.26% 4.07% 10.31% 1.12% 70.47% 7.91%

### Results by Region

Looking at markets by region:

• eu and aus seem to have a growth problem, they are expensive
• us has highest growth, jp has great bargains
 region count earning_yield growth margin dilution ratio sratio aus 59 2.16% -12.14% 10.86% 0.57% 9.69% 0.08% eu 285 3.66% -6.69% 8.04% 0.00% 20.94% 0.21% jp 493 5.86% 2.60% 5.80% -0.08% 124.63% 34.38% us 966 4.03% 8.84% 9.14% 0.25% 86.50% 9.42%

### Results by Region and Sector

It is harder to make conclusion on region and sector, but the gist of it is that the trends seen above seem confirmed: eu and aus are expensive due to growth concerns, japan has best value.

The full table of results by region and sector can be found here

### Example Portfolio

The following portfolio was constructed by after filtering for each sector from lower than median dilution, better than median consensus ranking and price appreciation target and selecting the best 10 stressed fundamental valuation ratio amongst those. This is not investment advice of course, just for research purpose:

The portfolio selected following the criteria can be found here.

## Conclusions and Next Steps

The algorithm can find cheap companies relative to the market except in the energy and utility sector. One might decide to underweight utilities, which have government-regulated output costs and still get some exposure to energy extraction and mining given the inflationary outlook.

The following information is relevant to better understand the structure of a company's finances:

• whether margin is high
• whether R&D is high
• high debt low margin is a typical low moat pattern
• LT Debt history as indicia of serial aquirer
• current ratio = current asset/current liability needs to be high and not moving down
• turnover = sales / total asset is better if high

### Tax Effect on Dividends

After reading more books on value investing, it appears that I should refine earning estimates to be owner earnings rather than whatever management threw at the GAAP or IFRS.

For foreign investors in US and Australian stock with no tax treaty, the dividends are worth 70%. WHT information can be found on this pwc site. For Hong Kong residents:

• UK 0 WHT
• IT, FR, SP, NE, JP 10% WHT
• CAN: 15%
• GER: 25%
• AUS, US 30%

Arguably, there are two payout ratios: one that compute the fundamental value of the stock to one's tax situation through holding it, and another one for the average market participant, in which case he is computing a fair average market value.

### Transaction Costs

Transaction costs also need be amortized over the trade life, here are the costs for USD10,000 transactions with Interactive Brokers:

• UK: 50 dollar per trade (0.5%), levied on any transaction size in GBP
• France: 5 dollar + 32 dollar of tax (0.38%)
• Japan: 800 yen per million (0.08%)
• Swiss, Germany, NL: 5 dollar per trade (0.05%)
• US: 1 dollar per trade (0.01%)

We see that UK and France transaction costs charge a Tobin-like tax.

### Psychotic Allocation?

In the above study, we used an equity hurdle rate of 6%, the analytics penalized financials volatility with a factor $a=-1$ and assumed a payout ratio $f=0.6$ in buyback only. In fact, different assumptions can be used to obtain different allocation depending on the level of optimism underlying the assumptions.

For instance, a portfolio of high growth tech can be obtained by using a positive growth factor $a$ and a high value $f$ later. This would lead to a subportfolio that can own Google or Facebook.

A value portfolio can be obtained using a conservative portfolio $a<0$ with $f$ using actual real world after tax values.