Risk Aversion and Job Creation

The current consensus view is that the economy will remain weak until consumers start spending again. In the near-term, consumers will increase spending as they become more confident about their job situation and in the long-term, they will need to repair their balance sheets before returning to the level of spending we were accustomed to. This is what many people refer to as “New Normal” since consumers are unlikely to be capable of returning to the old level of spending without another debt bubble. Many people believe that jobs will be created as economic activity increases but this circular argument is being refuted as the largest public companies accumulate cash and continue to grow profits. This is where politics complicates the narrative as analysts attribute the lack of job creation to uncertainty about taxes, healthcare or some other partisan issue. The bottom line is that we still live in a capitalist system regardless of the marginal tax rate or the method of healthcare care administration and business is only rewarded for successfully taking on risk.

Business risk is largely borne by small business since large company employees are rewarded for maintaining the status quo in order to protect their existing competitive advantage. (When you make buggy whips, it doesn’t matter if you’re a fox or a hedgehog and every business eventually enters the buggy whip stage.) As more of our economic activity is dominated by large companies, there is a decreasing impact from real entrepreneurs that are willing to take risk and we should not be surprised when the risk aversion of the entire economy increases. While tax cuts certainly increase profits, there is no direct connection to wealth creation as opposed to wealth transfer. Jobs will be created when businesses are willing to show some leadership and take on risk. Everything else is just politics.

Behavioral Finance

After one of my finance professors declared that there was insufficient literature on any financial theories that contradict the CAPM, I realized that the academic establishment did not simply ignore behavioral finance; it ignored any theory that did not resemble a mathematical proof. While I do enjoy a particularly clever proof now and then, mathematics does not offer sufficient accuracy for market prediction and I doubt that it ever will. The problem exists in both the tools that we use to tackle the fundamental problems of finance as well as the philosophy that determines the assumptions of those models.

Much of what underlies the belief in efficient markets is based on the Expected Utility Hypothesis formulated by Daniel Bernoulli in 1738. The theory states that the value of an asset is equal to the probability weighted expected values. Using a lottery example, one would expect to pay $1 for a ticket where the payout is $1 million and there are 1 million tickets sold. Expected utility works fine when the probabilities are known but fails to explain why investors are averse to ambiguity.

The Ellsberg paradox describes violations to the expected utility hypothesis due to uncertain probabilities. The existence of ambiguous probabilities separates the market from the casino exposing investors to Knightian uncertainty and results in real world decision making that does not conform to expected utility. The missing ingredients in the expected utility hypothesis are the miscalculations of risk about risk and the psychological costs or benefits associated with extreme events.

In 2002, Daniel Kahneman received the Nobel Prize in Economics for his work with Amos Tversky on behavioral finance. Kahneman and Tversky introduced Prospect Theory in 1979 which was later developed into Cumulative Prospect Theory through the use of rank-dependent weightings in 1992. The big new idea presented in CPT is that people base decisions on a reference point rather than absolute utility and people exhibit risk aversion and risk seeking behavior as the magnitude of the outcome increases. This leads to a utility function that has both concave and convex portions while also exhibiting overall risk aversion since more utility is gained from minimizing losses than maximizing gains. In the concave portion, people exhibit risk aversion and engage in behavior such as buying insurance against a low probability event with a large negative effect. In the convex portion, people exhibit risk seeking behavior such as buying a lottery ticket that has a low probability large payout. The key point of CPT is that the same individual will engage in both behaviors because there is additional utility gained or lost when a large effect from a low probability event occurs. This is why gamblers prefer bets on an unlikely winners than expected winners. For a more quantitative explanation, read up on stochastic dominance. Here’s Daniel Kahneman addressing the Georgetown University class of 2009 via Fora.

One problem I do have with behavioral finance is that there seem to be many theories that explain everything while predicting nothing. This is fine for philosophers in search of Truth but not very good for those of us managing money. Perhaps someone a little more or a lot less intelligent than I might conclude that human behavior is inherently unpredictable, but that is likely what draws most of us to study finance; the promise of predictability is just over the next mountain.

Quant N00bs

Boys go through a dinosaur phase, girls go through a horse phase, and it seems that many engineers and computer programmers go through a quant phase. Having studied some nonlinear dynamics, they believe that with so many numbers, the market is just another system that can be tamed with a few well written equations. Despite my somewhat condescending tone, I do not wish to dissuade any more whiz kids from daytrading. I made good money beating these guys on the buy side in the equity markets and I would have made far more on commissions if I were a broker. The one thing I would ask is that you not keep comparing the market to a casino.

I know you hear the media often referring to “gambling” with other people’s money but having played some on your own, you should now know the difference between Knightian risk and Knightian uncertainty. If you manage to stay solvent long enough, you will likely come to the conclusion that markets exhibit less Knightian risk than Knightian uncertainty despite what you may have read about VaR, Bachelier, GARP, or A Random Walk Down Wall Street. (And no, Taleb was not the first one to think of this though he was the first to make the watered down version accessible to the general public and make far more money than he is worth in speaking fees.)

Does this mean that the distinction between Knightian risk and uncertainty should not exist? Ask any entrepreneur or manager with real operational responsibility and they will tell you how much time they spend trying to develop an organizational structure that handles the usual problems while keeping the organization flexible enough to deal with unknown problems. By assuming that all problems cannot be anticipated, the organization is not prepared to handle any problem whether expected or unexpected and is equivalent to sticking your head in the sand. The same is true in investing. By trading stocks when you don’t understand how the company makes money, you are exposing yourself to the same total risk as the skilled investor as you enter the position but increasing your risk as you stay invested since you have less information than the skilled investor. The access to information comes not from reports,  insider trading, or the latest rumor; it comes from not being able to interpret the information that you already have access to. The longer you stay invested, the more the interpretation of information matters.

Given that last statement you may be wondering if you don’t know anything about a company, why shouldn’t you stick to technical analysis and trade frequently. The reason is simple. Long time horizons are made up of many short time horizons. Like VaR, the fact that there is a 99% chance of not losing greater than x amount of your portfolio in the next 10 days does not mean that the probability of losing more than x amount in the next 10 days is 1% because assumptions on methodology will be proven wrong when you need them most. When the market moves against you, there is not always a forward looking indicator and when there is, it may not have been reliable in the past. Even understanding the fact that this time is not different does not necessarily mean that historic data will help you because history rhymes but doesn’t repeat itself.

Now, before you show me the massive amounts of money you have made, please learn how to compute compound time-weighted returns and include all operating costs such as commissions, leverage, and information access. If you need help, look up GIPS. Understand that telling me you made 800% on your favorite trade does not constitute any meaningful performance information.


Financial markets depend on people’s decisions and are therefore irrational. This is not an indictment of investors’ cognitive abilities but a simple understanding that people are not machines and do not make decisions like them. The inconvenient consequence of this fact is that no set of mechanical rules can ever either predict the future with precision or fully explain the past and present. Fortunately, we can still provide value to our clients without needing to work in absolutes. Not only can we live in a world of constant imperfect knowledge, but we can be successful without being 100% correct. I do not mean to imply that we should discard all quantitative approaches to finance, rather we need to understand all the possible risks associated with a model and be conservative in how we value the information it produces.

Security Analysis


I created this post to introduce students to securities analysis. The information provided on this page is not intended to be comprehensive but is intended to provide students with enough information to start learning on their own. There are many perspectives on investment analysis but this page will be limited to fundamental analysis which is best suited to long time horizons. This approach can also be used to evaluate projects in corporate finance.

An investment recommendation needs a minimum of three components: a specific security, the price of that security, and a specific time. The first two are self explanatory but the third is a proxy for a particular information set. This means that buying a particular security at a particular price may or may not be a good idea depending on what we know about the state of the world at any given time. (I will discuss this more under the Process section.)

There are two basic approaches to valuation: relative value and intrinsic value. Relative value utilizes market multiples as a common basis of comparison between two firms. The most common market multiples are: price to earnings, price to book, price to sales, and price to cash flow. Certain market multiples may be useful in some industries and not others. Remember to account for differences in fiscal year end when comparing multiples. Four of the most commonly used time frames for the market multiple denominators are: last fiscal year, trailing twelve months (TTM), next twelve months (NTM), and next fiscal year. Each of these time frames has positives and negatives that vary from industry to industry. Intrinsic valuation is most commonly done using discounted cash flows methodologies such as the dividend discount model, free cash flow to the firm, or free cash flow to equity.


Securities analysis is as much art as science. In addition to making reasonable assumptions about financial data, the analyst must consider many qualitative aspects of the company and the market.

Identify the information set that will determine asset prices in the future.
What factors matter to the firm’s business model? Which of these factors will matter to the firm years from now?
Is this different from the information set that is currently determining prices?
Are other investors basing their valuations off of short term factors such as next quarter’s results, an investment theme, technical analysis, etc…
Is the relevant information set accurate?
Are the assumptions being used to value the firm reasonable? If they differ from your assumptions, is it because you are missing information or is there a disagreement on the interpretation of available information?

Research Questions

These are some of the questions you should ask yourself as you learn about a company. Again, this list is not intended to be comprehensive; only thought provoking.

Who are the firm’s main competitors?
How do market multiples compare with the firm’s competitors?
What are the performance limits to the firm’s business model? What are peak margins and why?
How flexible is the business model? Do high fixed costs prevent it from quickly reacting to changes in demand?
What portion of the firm’s valuation is composed of physical assets and what portion is composed of intangible assets?
Is the market’s valuation of intangible assets reasonable?
What types of investors are invested in the firm and what types of investors are trading the security?

Risks: operational execution, financial execution, macroeconomic, legal, regulatory, headline risk
Management: quality, reliability, accounting standards, strategy, public relations, ethics

Getting Information

Financial websites have a good presentation of the superficial information about a company. A small sample would include Bloomberg, Google, MarketWatch, Reuters, Seeking Alpha, and Yahoo. Transcripts of earnings calls may be obtained from the company investor relations website or from other investor websites. There may be some well written blog posts that are useful for background but for a bottom up fundamental analysis of a company, you need to use primary sources.

The Securities and Exchange Commission (SEC) maintains a database of company filings called the EDGAR database. The most relevant form is the annual 10-K statement which includes the three financial statements (income statement, balance sheet, and statement of cash flows) as well as all the background information needed to understand the business. The 10-Q statement is the shorter version released on a quarterly basis. International firms file 6-K forms with similar information. On the top of the search results for a particular company’s filings is a link to the RSS feed. Copy this link and paste it into an RSS reader. Email applications like Outlook and Mac Mail also have RSS reader capabilities. When a company releases new information, the filing will appear similar to an email in your RSS reader. Press releases can be obtained from business news aggregator sites like Business Wire.

For economic information, the Federal Reserve maintains the FRED database. This is a good source for interest rates, GDP, inflation data, international trade, and other macroeconomic variables. The one year constant maturity treasury bill series DGS1 is a good series to use as the risk free rate.

Final Notes

I suggest using a forecast period of five years (current year plus four) for discounted cash flow models. The terminal value should use very conservative assumptions such as a growth rate in line with long term GDP growth rather than historic firm growth. In addition to learning Excel, I recommend a getting a financial calculator such as the BA II Plus from Texas Instruments or the HP-12C. Check the library for additional books, blogs, magazines, and movie recommendations.

Bob Farrell’s Ten Market Rules to Remember

1. Markets tend to return to the mean over time
2. Excesses in one direction will lead to an opposite excess in the other direction
3. There are no new eras, excesses are never permanent
4. Exponentially rapidly rising or falling markets usually go further than you think, but they do not correct by going sideways
5. The public buys the most at the top, the least at the bottom
6. Fear and greed are stronger than long-term resolve
7. Markets are strongest when they are broad, and weakest when they narrow to a handful of blue-chip names
8. Bear markets have three stages: sharp down, reflexive rebound and a draw-out fundamental downtrend
9. When all experts and forecasts agree, something else is going to happen
10. Bull markets are more fun than bear markets

Career Strategy for the Sell Side

I read a research report this morning from JP Morgan analyst Ehud Gelblum regarding the Qualcomm settlement with Nokia. The report was dated 25 July 2008 but used the stock price from 18 July 2008 to justify the Buy rating. If I knew that the stock was going to pop 15% then I would have bought more of it last week too. Usually, I would not be surprised given my complete lack of respect for sell side analysts but I met Dr. Gelblum at the JP Morgan Tech conference earlier this year and left with the impression of a sharp guy who actually understands technology rather than the usual cream puffs that JP Morgan calls financial analysts. I’m sure there is some jealousy involved in this post. As a buy side analyst I am evaluated on actual results and it would certainly be easier to get paid twice as much to tell people to buy stocks last week. If you want to be a historian, stop calling yourself an analyst. It’s embarrassing to the rest of us.

Reflections on the 2008 CES

This was my first visit to the Consumer Electronics Show in Las Vegas, NV. I spent everyday wandering through the endless displays of gadgetry goodness but was hard pressed to find any truly innovative products. I found the presentations to be the most useful for understanding how the industry thinks and what they can realistically deliver. Paul Otellini’s presentation was a good show but couldn’t deliver details on how or when the translation and GPS capabilities could be incorporated into real products because the whole thing was a simulation run on servers located backstage. Jerry Yang’s keynote was exciting but I doubt that the full platform will be released on time as presented. After a depressing presentation by several private equity investors, I realized that no one is interested in building a consumer centric company which is why Apple will probably continue to succeed in the consumer electronics space despite their rushed product life cycles and lack of respect for early adopters. It seems that consumer electronics firms succeed more on marketing and management capability than technological innovation.