But if I take too much risk, can I get superior returns? Time to get a few lotto tickets.
Yabut you gotta define too much risk.
At least with a lotto or any other game of chance you have defined your risk. It's either/or.
But if I take too much risk, can I get superior returns? Time to get a few lotto tickets.
Many investors have been making up for low yields by taking on more risk, whether it's by dipping into lower-quality bonds or holding more dividend-paying stocks. Exchange-traded fund sponsors have been busy launching products touting even higher payouts or more-exotic asset classes, including obscure oddities such as master limited partnerships, business-development companies, and bank loans. By seeking higher yields and greater risk, investors are likely setting themselves up for lower risk-adjusted returns and possibly worse absolute returns than if they had stood pat. Blame the strange relationship between risk and return.
High Volatility, Low Returns
Researchers have discovered a kink in the traditional risk-reward relationship. Professors Andrew Ang, Robert Hodrick, Yuhang Xing, and Xiaoyan Zhang discovered that the most volatile stocks have underperformed the least volatile stocks globally, both on absolute and risk-adjusted bases. The effect can't be explained by known risk factors such as size, value, momentum, and liquidity. Similarly, in the past four decades, the most distressed (and therefore most volatile) bonds have either lost money or underperformed investment-grade bonds on an absolute basis.
Who are these investors gleefully torching their money? Some blame investors who crave lottery-like securities, which, over the long run, lose money but have a small chance of paying out big. Others focus on wildly overoptimistic investors who push up the price of stocks that are hard to value--the winner's curse writ large. Investors should be careful when dipping their toes into the riskiest segment of any asset class, as they usually offer low risk-adjusted returns. An elegant model proposed by two researchers may tell us why.
If investment returns really do follow a normal distribution pattern [as portfolio models assume], then the 1987 crash never should have happened—literally not once in over 1 billion years. The problem is that the stock market is full of “once every billion years” days, even though we have only about 80 to 100 years’ worth of reliable data. So, what does that actually mean? Suffice it to say, the returns of the stock market are not normally distributed. This means that mainstream investment tools are flawed.
George$ wrote:If investment returns really do follow a normal distribution pattern [as portfolio models assume], ...
Recent studies of stock returns tend to use scale mixture or finite mixture of normal distributions.... Cauchy distribution has fatter tails than the finite mixture of normal, which in turn has fatter tails than the standard normal.
How many people have moved beyond the variance when thinking about volatility? (How many people think about volatility at all?)
ghariton wrote:.... John Tukey was recommending that analysts avoid assuming a Gaussian distribution fifty years ago. .....
George$ wrote:I never heard of Tukey until now. I gather his book "Exploratory Data Analysis" is a classic.
ghariton wrote: ...I would be pleased to lend you my copy by mail, but this is one book I do want returned eventually. Please PM me if you are interested.
• The Risk Index jumped more than 2.5 points to 110.5 in Q2, closing in on the historical
high reached in Q3 2010.
• Concern about an economic slowdown, unresolved sovereign debt issues in the US and
abroad, and questions about banking fundamentals all contributed to the uptick in risk
• The lack of sustained job growth, impact of an expanding trade deficit, stubbornly high
consumer debt levels and anemic growth in GDP were identified as US macro-economic
indicators to watch closely.
• Looking ahead to Q3, risk managers appear most concerned about Eurozone instability,
the practical implications of a US sovereign debt crisis, ineffective US monetary policy
initiatives, and the current and long-term implications of US dollar weakness. On the
bright side, the perceived impact of geopolitical tensions eased significantly.
So what does all this talk of betas and CAPM mean for you as an investor? In short, that you should eschew both very risky investments and very safe investments because people tend to overpay for both. Lotto-ticket type stocks are loved too much (think of the sexy allure of technology start-ups and junior mining companies), while guarantees are too highly valued (think of the hidden high fees in guaranteed income type products). As a result, taking the middle way with a moderate amount of risk appears to hit the sweet spot in many financial markets. Indeed, moderation appears to boost both returns and well-being more generally.
Scientists from a range of fields have been poring over financial data, finding some curious patterns in the process.
These patterns suggest that standard economic models based on the notion of equilibrium — markets will fluctuate but then settle down like the surface of a still pond — may not capture the whole story. Freak events may be a normal part of long-term economic behavior. If that’s true, then the mathematical methods guiding Wall Street’s estimation of risk are seriously flawed, offering a dangerous false sense of security...
The Gaussian bell’s roots in finance go back to work by French mathematician Louis Bachelier, who modeled changes in share prices in the early 1900s. Bachelier recognized that some of his model’s assumptions were flawed, including the premise that the probability of extreme events is vanishingly small (he reportedly called such events “contaminators”). Yet these assumptions were preserved in later models, including the Black-Scholes formula, which underlies much of Wall Street’s estimation of risk.
But when it comes to financial data, a growing body of research suggests that outliers can be more like babies than bathwater. Such events may still be very rare; Stanley says that the probability that stocks would crash as they did on Black Monday in 1987 was “as close as you can come to never.” Yet Black Monday still happened. And while much of finance does behave within the bounds of a normal distribution, ignoring the rare, large events doesn’t capture reality...
Instead of dismissing such tails because they don’t fit the models, researchers might need to rework the models because they don’t fit the data, Stanley and others argue. “The model should really be driven by the data,” he says. “For a physicist, there are no outliers. If I saw a glass of water float up in the air, we’d have to re-examine the law of gravity.”...
Or statistical mechanics and entropy. A homework problem might be calculating the probability of a pencil jumping 1 cm in the air because all the atoms happen to be moving in the same direction. This calculation involves some very large factorials, and gives a number that is larger than astronomical - say, 1 in 10100 or something like that.If I saw a glass of water float up in the air, we’d have to re-examine the law of gravity.”
andFor real risk, return-rate standard deviation and any additional return-rate uncertainty are only essential raw materials to be applied along with others in further analysis.
Calling return-rate standard deviation “risk” has the perverse effect that as the typical investor moves along the frontier to portfolios labeled as least in “risk,” she increases her real risk.
That mislabeling is also wonderful for the actively-managed-fund financial industry. It helps misfocus investors’ attention on their short-term fears for the individual year, where they cannot see the terrible long-term cost of high active-fund fees.
Please! With all your expertise and influence, you can help fight this terrible pair of labels of deception.
Larry, I believe you're using Frank Knight's* definitions of "risk" and "uncertainty." A recent thread commented on the issue of the public misunderstanding words like "expected" in financial writing, and I think there's a potential similar issue here. I would say that as you use them they are almost technical terms. Looking them up at http://www.m-w.com it is interesting to note the dictionary meanings of "risk" include only the negative aspects. The dictionary does not capture "risk" in the sense of standard deviation, of fluctuation either way, of volatility. And the definitions of risk use alarming words suggesting a serious matter: injury, hazard, peril:
Synonyms for "risk" are listed as "hazard, imminence, menace, peril, pitfall, danger, threat, trouble."
Synonyms for "uncertain" are listed as "capricious, changeable, changeful, flickery, fluctuating, fluid, inconsistent, inconstant, mercurial, mutable, skittish, temperamental, fickle, unpredictable, unsettled, unstable, unsteady, variable, volatile."
In fact, it almost seems to me as if the common definitions for "risk" and "uncertainty" are almost swapped around from the meanings given to them by Knight. The dictionary's "risk" means rare, catastrophic. The dictionary's definition of "uncertain" means vague, variable, wishy-washy. At any rate, the important distinction you and Knight wished to capture is certainly not captured by the ordinary meaning of the two words Knight chose to use.
*Frank Knight, 1885-1972, was a University of Chicago economist who wrote an influential book, Risk, Uncertainty, and Profit, available online here.
ghariton wrote: This approach also would explain why people are willing to buy lottery tickets, accepting a negative expected return as the "price" for a very skewed return.
deaddog wrote:You can have a model that works 95% of the time but if you don’t have a plan to deal with the other 5% you haven’t accepted the risk. You can measure risk to the Nth degree but if you can still lose all your money by doing nothing what good is it?
ghariton wrote:I've been reading Multi-moment Asset Allocation and Pricing Models, a book of essays edited by Emmanuel Jurczenko and Bertrand Maillet.
As we know, the mean-variance description of asset returns, and the CAPM model that flows logically from that description, are inadequate. What I didn't know is that there is a sizable literature that tries to explain asset prices in terms of four factors instead of just two (no, not Fama and French, which I've always thought of as ad hoc). Instead of looking at just expected return and variance (or similar measures of volatility) as the only two factors predicting price, this approach looks at expected return, variance, skewness and kurtosis.
ghariton wrote:Among other things, it would make sense (for me at least) of some of the results on risk that Norm R and others have been finding -- that for common stock, experienced returns can actually fall with increasing beta, at least within moderate ranges. If Norm's sample of high-beta shares is, as a side effect, also picking up shares with positive skewness, then we would expect investors to bid up the prices for these (and so accept lower expected returns). This approach also would explain why people are willing to buy lottery tickets, accepting a negative expected return as the "price" for a very skewed return.
You may think you have dealt with 100.000% of the risk but that's an illusion. Samples of what I'm pretty sure you have no protection against:
- you forgetting to put in place stop orders (e.g., sickness, old age forgetfulness etc) and/or your broker's system breaking down
- your holdings gapping down through your stops
- stock markets being suspended indefinitely
- communists taking over and nationalizing all private property
- a madmen pulling the trigger on atomic bombs
- an asteroid hitting the Earth
Jungle wrote:Would it be correct to say a mix of 20% cdn gov bonds and 80% equity should beat (or match) the returns of 100% equity over a long term ?
For example, the chart here http://www.finiki.org/wiki/Portfolio...d_Construction shows the mixture of 80% equity/20% bonds beat 100% equity! (1972-2008)
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