Lubos Pastor is a popular finance professor at the University of Chicago Booth School of Business.

In this interview, Professor Pastor challenges the conventional wisdom, popularized by Jeremy Siegel and others, that stock market risk decreases at longer time horizons. He also has some interesting comments on human capital, diversification, and market efficiency.

In my opinion, Pastor’s argument about risk and horizon is a bit subtle, and it is difficult to convey in this type of an interview. I’ve included a few links for those who want to explore this idea more thoroughly.

Regular readers know that I believe in building a diversified investment portfolio using index funds. However, I know that there are some investors who think they can do better with a more concentrated strategy.

Many individual investors I have spoken to prefer to make concentrated bets on individual stocks, and some even have a large portion of their net worth invested in the stock of their employer!

Needless to say, I think investing a sizable percentage of your total wealth in your employer’s stock is a terrible investment strategy. Not only does it result in a poorly diversified portfolio, but in a worst case scenario you could find yourself out of a job with a worthless portfolio….remember Enron and Worldcom?

Whenever I make the case for greater diversification, the response I get it always the same: “Yes, but it’s a great company!”. In other words, these investors acknowledge that diversification may be a good idea for other people, but they don’t believe that it applies in the case of their company.

Testing a Simple Investment Strategy

In this post, I look at the risk and return for a simple strategy that makes concentrated investments in “great companies”. Each calendar year the strategy invests 100% of total wealth in the most admired company in America. I’ve chosen the most admired company for each year based on a survey conducted by Fortune Magazine.

Fortune has published the list of America’s most admired companies every year since 1983. In the table below, I’ve listed the most admired company for each year and the total return (including dividends) that the company’s stock earned in the year that it was voted to be #1. I’ve also listed the S&P500 returns for each year.

Thinking about Valuation and Mean-Reversion in the Context of a Simple Discounted Cash Flow Model

The Gordon growth model is a simple discounted cash flow (DCF) model which can be used to value a stock, mutual fund, or even the entire stock market. The model is named after Myron Gordon who first published the model in 1959.

The Gordon model assumes that a financial security pays a periodic dividend (D) which grows at a constant rate (g). These growing dividend payments are assumed to continue forever. The future dividend payments are discounted at the required rate of return (r) to find the price (P) for the stock or fund.

Under these simple assumptions, the price of the security is given by this equation:

In this equation, I’ve used the “0” subscript on the price (P) and the “1” subscript on the dividend (D) to indicate that the price is calculated at time zero and the dividend is the expected dividend at the end of period one. However, the equation is commonly written with these subscripts omitted.

Obviously, the assumptions built into this model are overly simplistic for many real-world valuation problems. Many companies pay no dividends, and, for those that do, we may expect changing payout ratios or growth rates as the business matures.

Despite these limitations, I believe spending some time experimenting with the Gordon model can help develop intuition about the relationship between valuation and return.Continue reading »

In this screencast, I demonstrate how to use R to download historical price data from Yahoo! Finance. I also demonstrate how to convert the price data series into a return data series, and I show how to write the output to a file which can be read into a spreadsheet program such as Excel or Google Docs Spreadsheet.

I use the return data that this script generates to run CAPM and Fama-French regressions and to compute performance metrics such as Sharpe Ratios. However, I note in the presentation that I have found that Yahoo! Finance frequently has errors in the historical price series, so users should use caution when using the return data generated from these historical prices to make financial decisions.

The code used in this screencast is pasted below, and the slides used in the screencase are available here.

# Goal: download adjusted price data for selected security, convert to returns, and write to output file
library(tseries)
# Load security data from Yahoo! Finance
prices1 <- get.hist.quote("SPY", quote="Adj", start="2005-12-25", retclass="zoo")
prices1 <- na.locf(prices1) # Copy last traded price when NA
# To make week end prices:
nextfri.Date <- function(x) 7 * ceiling(as.numeric(x - 1)/7) + as.Date(1)
weekly.prices <- aggregate(prices, nextfri.Date,tail,1)
# To convert month end prices:
monthly.prices <- aggregate(prices1, as.yearmon, tail, 1)
# Convert weekly prices into weekly returns
r.weekly <- diff(log(weekly.prices)) # convert prices to log returns
r1.weekly <- exp(r.weekly)-1 # back to simple returns
# Convert monthly prices into monthly returns
r.monthly <- diff(log(monthly.prices)) # convert prices to log returns
r1.monthly <- exp(r.monthly)-1 # back to simple returns
# Write output data to csv file
write.zoo(r1.weekly, file="weekly.csv",sep=",",col.names=c("Dates","Percent Return"))
write.zoo(r1.monthly, file="monthly.csv",sep=",",col.names=c("Dates","Percent Return"))

I enjoy reading the discussions and debates on investment forums such as Bogleheads.org, and one very frequently discussed and debated topic is the Safe Withdrawal Rate (SWR).

The SWR is defined as the quantity of money that can be withdrawn from a portfolio over time, including inflation adjustments, without leading to a complete depletion (i.e. failure) of the portfolio during the investor’s lifetime.

Last week, Professor Kenneth French addressed the subject of the sustainable withdrawal rate in a video post on the Fama/French Forum website, and I’ve posted the video here:

Most SWR discussions are based on historical data or Monte Carlo simulations, which I think can be very valuable. However, Professor French’s approach of starting with the simplest case (long-term TIPS) and building from there helps to highlight that we are taking on real risk when we stretch for higher withdrawal rates.

With fuel prices near the $4 per gallon level, there has been a lot of discussion in the media about fuel economy. Last week, on the National Public Radio program, Marketplace, I was listening to a story about the rising demand for fuel efficient cars, and I was struck by this quote:

Jonathan Banks follows the used car market at the National Automobile Dealers Association. He says prices on used Prius models are up 30 percent since the beginning of the year. That’s more than for other used cars, though those are also in high demand.

Thirty percent seemed to me to be a quite a large price increase, and I decided to take a closer look at how fuel costs and fuel economy are related.

MPG vs. GPM

Most of us are familar with MPG as a measure of fuel economy, but it actually not the most intuitive measure to use. The problem with MPG is that it is inversely related to fuel consumption, and this can lead to misperceptions about the cost savings from an increase in MPG.

This effect is best illustrated with a graph. The graph below shows the cost of driving 10,000 miles for various measures of MPG. I assume a gasoline price of $4 per gallon.