There are many models that attempt to explain the risk-reward relationship of stocks relative to the market portfolio. Unfortunately, beta alone has not done a very good job of explaining observed market risk and returns. In order for the EMH to hold, investors must be risk-averse and always make rational investment choices. This has spawned an area of study known as behavioral finance or behavioral economics.
There have been many studies on behavioral finance by psychologists such as Daniel Kahneman, Amos Tversky, and Richard Thaler to name just a few. Stock market bubbles are a notable outcome of irrational decision making. Bubbles occur when market participants drive stock prices above their value relative to some system of stock valuation. In the 20th century, the stock market experienced a number of bubbles, including the bubble that preceded the Great Depression and the dot-com Bubble of the late s.
Both were fueled by speculative activity involving new technologies. The s saw amazing innovations including radio, automobiles, and aviation. In the s, the Internet and e-commerce technologies emerged. Some analysts, affirming crowd-sourced wisdom, theorize that price movements, even bubbles, really do reflect rational expectations of fundamental returns.
Behavioral finance theory, on the other hand, attributes stock market bubbles to cognitive biases, including the tendency to overpay for excitement, herd instinct, regret avoidance and other common human tendencies. In our view, stock market bubbles provide clear evidence that investors do not always act rationally. Stocks are tagged as high beta when they exhibit high growth and high price momentum. These stocks are expected to rise more than the overall market in good times and fall more than the market in bad times.
They may involve specific speculative risks, for example, uncertainty about an event that may produce either a profit or loss. However, investors who own these stocks are all too willing to take on added risk in hopes of a commensurate payoff. Blackjack, Baseball and Beta — Why People Overpay for Excitement Looking at irrational gambling behaviors may shed some light on why investors prefer high beta stocks.
Think about someone who gambles online or loses significant sums playing blackjack at a casino. They very likely view gambling as a low-risk, high-yield proposition. Blackjack, the most popular table game, returns a bit more in winnings than roulette, but it too is subject to what is called the house edge. The thrill of hitting a casino jackpot is often just too alluring — despite its improbability.
Excitement and anticipation create a natural high, an adrenaline rush. Studies show that people get this same feeling when buying exciting high momentum growth stocks. Three experts in the field of Neuroeconomics, Wolfram Schultz, Read Montague, and Peter Dayan have written studies showing that financial gains trigger the release of dopamine, a brain chemical that stimulates a natural high. The studies have shown that the less likely or predictable a financial gain, the more dopamine is released and the longer it affects the brain.
This may explain why gamblers are drawn to low-probability bets with high potential payoffs. When such bets pay off, they produce an actual physiological change; a massive dopamine release floods the brain with a soft euphoria. Through the use of MRI technology, Schultz and Montague also found similarity between the brains of people who have successfully predicted financial gains and the brains of people addicted to morphine or cocaine. After a few successful predictions, financial speculators can literally become addicted to the dopamine release.
A person with high dopamine levels typically becomes over-confident and tends to take undue risk. Addiction to the dopamine kick may keep them playing until they lose all their money. The excitement triggers dopamine. On the other hand, successful investors like Warren Buffet often make money in boring industries like insurance, finance, railroads, food, and beverage. Turning to baseball, another popular U. While most Major League Baseball teams hired expensive home run hitters that fans loved to watch, Oakland Athletics general manager Billy Beane favored an analytical, evidence-based approach to assembling a competitive team.
Oakland hired undervalued players who more consistently got on base with a walk or a single. Even though sabermetrics the empirical analysis of baseball stats clearly indicated that on-base percentage was critical to offensive productivity, the baseball labor market seriously undervalued that factor.
Many believe the Moneyball anomaly corrected once it was exposed, which may be true since there are only 30 MLB teams. These include: The Lottery Effect: Like casino games, lotteries are high risk, low yield games. According to the Multi-State Lottery Association, which oversees the Powerball Lottery, the odds of matching all numbers is ,, to 1. Payouts from lotteries are even worse than casinos, as states take a cut of total receipts to support government expenditures.
The rest goes to expenses associated with running the lottery and state capital projects. Why then is the lottery so attractive to so many people? One reason is that people tend to optimistically assign more weight to the probability of winning than is justified objectively. Second, people like the idea of spending a very small amount with the possibility no matter how slight of winning big. Similarly, many investors are attracted to low-priced, high-risk stocks for the slight possibility of jackpot size returns.
Herd Instinct: Nobody wants to miss a great opportunity and when a stock is rising, everyone wants to be on the bandwagon. This is true even for professional investors, who find safety in numbers. In practical terms, a manager is less likely to be fired when other investors and fund managers are buying the same favored stocks.
Regret Avoidance: What were people thinking in when stock prices entered astronomical territory? Beware betting against entrepreneurship and innovation. Valuation Now the most important question: what would be a reasonable valuation anchor for Tesla? Without a doubt, different analysts will value Tesla differently, and price target will vary significantly.
But here is how I would approach the task. First, I want to acknowledge that Tesla is a high-growth company. And accordingly, I want to base my analysis not on current numbers, but on what I believe could be achieved in Furthermore I assume a net-profit margin of Note that I expect sales volume to almost 10x.
In addition, I assume that for every dollar that Tesla generates selling cars, the company will be able to sell 20 cents of software solutions and insurance for reference, Apple generates about 30 cents worth of services for every dollar of hardware sales. Author's Assumptions; Author's Calculations What if we vary the discount rate and number of car sales?
I have enclosed a sensitivity table that shows the result of different combinations. Accordingly, investors should be prepared to stomach volatility, even though Tesla's fundamental outlook remains unchanged. Personally, I do not believe that increasing competition in the race for electrification will influence the demand for Tesla -- like "other" smart phone makers do not influence the demand for iPhones. The increased competition could, however, exacerbate Tesla's supply challenges, as more competition chases for a limited supply of raw materials and key manufacturing components.
Conclusion I am late to the party. And, arguably, some readers might consider my article as a contra indicator — arguing when bears turn bullish, it is time to sell. Without a doubt, Tesla has proven to deserve this "win. This article was written by 2.
Currently working towards the CFA charter. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it other than from Seeking Alpha. I have no business relationship with any company whose stock is mentioned in this article. Additional disclosure: not financial advise.
BTC ADMISSION ELIGIBILITY
The hypothetical performance shown was derived from the retroactive application of a model developed with the benefit of hindsight. Hypothetical performance results are presented for illustrative purposes only. Diversification does not eliminate the risk of experiencing investment loss. Certain publications may have been written prior to the author being an employee of AQR. This material is intended for informational purposes only and should not be construed as legal or tax advice, nor is it intended to replace the advice of a qualified attorney or tax advisor.
AQR Capital Management is a global investment management firm, which may or may not apply similar investment techniques or methods of analysis as described herein. The views expressed here are those of the authors and not necessarily those of AQR.
In the next section, I will try and translate the strategy into the retail investing environment using real-world variables. Practical Considerations There are a number of practical considerations to implementing an abstract strategy. Since you are trying to capture systematic effects, you need a fair number of securities in the portfolios at the extrema of the SML so as not to have your strategy diluted by idiosyncratic risk.
An educated guess is that you would probably need at least about 30 in each component portfolio. You are also going to have to find these 60 securities by estimating betas, which unless you are setup with good data and processing capabilities, could prove challenging. For most retail investors a stock-based implementation would represent a both a computational challenge and large up-front cost. The obvious candidates are the paired Invesco high-beta and low-volatility ETFs.
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