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Alt 22.04.2012, 21:03   #21
Gimme a reason...
 
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Zitat:
Zitat von LongTrader Beitrag anzeigen
Anleger in Aktien-und Rentenfonds performen langfristig 6,5% p.a. schlechter als jeweilige Index:
Interessanter Link, vielen Dank!
 
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Alt 22.04.2012, 23:24   #22
Gimme a reason...
 
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Low-volatility Strategien waren die letzten Monate ziemlich häufig Thema in diversen Blogs. Hier ein schöner Artikel dazu: Is modern portfolio theory bunk?, Investment News

Zitat:
“Lower-risk investments won't participate in your runaway bull markets or junk rallies, but they also don't lose as much in a down market, so it is essentially a case of winning by not losing,” said Michael McCune, a portfolio manager at Robeco Investment Management, which manages more than $195 billion, including $2.8 billion in low-volatility strategies.
Der Artikel erwähnt u.a. das mittlerweile ziemlich bekannte Paper von Baker MP et al. Benchmarks as Limits to Arbitrage: Understanding the Low Volatility Anomaly.

Wirklich neu ist der Verweis auf eine aktuelle Studie von Robert Haugen (Link), in der er über 20 internationale Märkte untersucht. Das ganze ist nicht in typischer Form publiziert, aber hier ist das Ergebnis (Hervorhebungen sind von mir):

Zitat:
The low risk deciles continue to have low risk. On average, the lowest risk decile has a 12% lower standard deviation than the highest risk decile portfolio. This was as expected because the risk of individual stocks does not change much relative to other stocks - lower risk stocks remain lower risk and higher risk stocks generally remain high risk due to their fundamentals.

The most interesting result is that the low risk decile outperforms the high risk decile in every country. On average, the lowest risk decile wins by more than 17% per year over the high risk decile. Although the consistency varies some across countries, the low risk decile wins in 75% of the years on average. This is called the Hit Rate" and is calculated by counting the number of years the low risk portfolio wins and dividing by 22 (the number of years in the test period).

The evidence is extremely compelling: high-risk stocks consistently underperform low-risk stocks, both across time and across countries.
 
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Alt 24.04.2012, 22:42   #23
Gimme a reason...
 
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Jump on the Post–Earnings Announcement Drift

Das Abstract sieht ganz interessant aus:

Zitat:
The authors examined the potential profitability of a strategy that exploits the post–earnings announcement drifts contingent on jump dynamics identified in stock prices around earnings announcements. With long positions in positive-jump stocks and short positions in negative-jump stocks, their hedge portfolio achieved an annualized abnormal return of 15.3% and an annualized Sharpe ratio of 1.52 over the last four decades. Neither conventional risk factors nor common company characteristics explain the abnormal return.
Hat jemand Zugriff auf das Journal?
 
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Alt 10.05.2012, 19:33   #24
Gimme a reason...
 
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Tailspotting: How Disclosure, Stock Prices and Volatility Change When CEOs Fly to Their Vacation Homes, Yermack D

via Freaconomics

Habs nicht gelesen, aber das Abstract klingt interessant (Hervorhebungen sind von mir):

Zitat:
This paper shows close connections between CEOs’ vacation schedules and corporate news disclosures. I identify vacations by merging corporate jet flight histories with real estate records of CEOs’ property owned near leisure destinations. Companies disclose favorable news just before CEOs leave for vacation and delay subsequent announcements until CEOs return, releasing news at an unusually high rate on the CEO’s first day back. When CEOs are away, companies announce less news than usual and stock prices exhibit sharply lower volatility. Volatility increases immediately when CEOs return to work. CEOs spend fewer days out of the office when their ownership is high and when the weather at their vacation homes is cold or rainy.
 
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Alt 10.05.2012, 19:39   #25
Gimme a reason...
 
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A Seasonal Quandary, McClellan Financial

Ist vielleicht schon ein bißchen spät dafür, aber der Artikel beschäftigt sich mit "Sell in May".

Zitat:
The truth is more complicated, and it boils down to this: It's different in an election year.
Und hier ist der Unterschied zum einem "normalen" Jahr:

Zitat:
The real story during election years, especially when a Democrat is in the White House, is that May is a month for correcting, while June is a month for screaming higher.
Zitat:
A big strong June and July is wholly contrary to the old saw about "Sell In May...". Most of the time that rule does work, at least in part, but in election years a whole different rule goes into effect. If the correlation persists this year as well as it has been doing up until now, we can look forward to a big rally in June before the market finally enters a plateau in July, when the media's attention is tuned to the campaign promises being slung by the presidential candidates.

So I suppose that the revised motto should really be, "Sell In May And Go Away, Except If It Is An Election Year, And Seasonality Is Running A Week Behind, In Which Case Expect A May 14 Bottom, And A Really Strong Month Of June".
 
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Alt 10.05.2012, 19:46   #26
Gimme a reason...
 
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James Montier, der Guru der Behavioural Finance war auf der CFA Conference und einer hat seinen Vortrag in Stichpunkten mitgeschrieben: klick

Absolut lesenwert, auch wenn es nur einzelne Stichpunkte sind
 
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Alt 13.05.2012, 14:03   #27
Gimme a reason...
 
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How Wall Street Killed Financial Reform, Rolling Stone

Schöner und langer Artikel für einen Sonntag nachmittag:

Zitat:
It's bad enough that the banks strangled the Dodd-Frank law. Even worse is the way they did it - with a big assist from Congress and the White House.
Zitat:
The fate of Dodd-Frank over the past two years is an object lesson in the government's inability to institute even the simplest and most obvious reforms, especially if those reforms happen to clash with powerful financial interests. From the moment it was signed into law, lobbyists and lawyers have fought regulators over every line in the rulemaking process. Congressmen and presidents may be able to get a law passed once in a while – but they can no longer make sure it stays passed. You win the modern financial-regulation game by filing the most motions, attending the most hearings, giving the most money to the most politicians and, above all, by keeping at it, day after day, year after fiscal year, until stealing is legal again. "It's like a scorched-earth policy," says Michael Greenberger, a former regulator who was heavily involved with the drafting of Dodd-Frank. "It requires constant combat. And it never, ever ends."

That the banks have just about succeeded in strangling Dodd-Frank is probably not news to most Americans – it's how they succeeded that's the scary part. The banks followed a five-point strategy that offers a dependable blueprint for defeating any regulation – and for guaranteeing that when it comes to the economy, might will always equal right.
 
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Alt 28.05.2012, 12:49   #28
Gimme a reason...
 
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Gerade noch im RSS-Reader aufgetaucht, bevor ich raus in die Sonne wollte...

True Out-of-Sample Test of "Best" Technical Trading Rules, CXO Advisory Group

Zitat:
How do the technical trading rules that work best in a past study perform for new data? In the March 2012 version of their paper entitled “Predictability of the Simple Technical Trading Rules: An Out-of-Sample Test”, Jiali Fang, Ben Jacobsen and Yafeng Qin re-test performances of the 26 best technical trading rules from a 20-year old study with new data.
Und hier das Ergebnis:
Zitat:
  • Overall, there is little or no evidence from 1987- 2011 DJIA data supporting belief in the continued effectiveness of the 26 best technical trading rules from 1897-1986. In fact, returns for most sell signals in the new data are positive.
  • Removing the post-2008 financial crisis subperiod does not affect this conclusion.
  • Nor does evidence from 1987-2011 S&P 500 Composite Index data or 1885-1895 DJIA data support belief in effectiveness of these 26 trading rules.
  • The 1897-1986 profitability of these rules does not gradually attenuate after 1986 but rather disappears abruptly in the new data, indicating that sample bias rather than increasing market efficiency is the likely explanation of the past effectiveness.
Das Originalpaper gibts bei SSRN: http://papers.ssrn.com/sol3/papers.c...act_id=2066182

Abstract:
Zitat:
In a true out of sample test we find no evidence that several well-known technical trading strategies predict stock markets over the period of 1987 to 2011. Our test is free of the sample selection bias, data mining, hindsight bias, or any of the other usual biases that may affect results in our field. We use the exact same technical trading rules that Brock, Lakonishok and LeBaron (1992) showed to work best in their historical sample. Further analysis shows that this poor out-of-sample performance most likely is not due to the market becoming more efficient - instantaneously or gradually over time - but probably a result of bias.
Das gucke ich mir definitiv in den nächsten Tagen nochmal genauer an!
 
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Alt 31.05.2012, 22:59   #29
Gimme a reason...
 
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Noise and Signal — Nassim Taleb, Farnam Street

Nassim Nicholas Taleb ist einer dieser Autoren, dessen Bücher man liest, um danach mit anderen Augen durch die Welt zu gehen. Seine Argumente sind klar und die Konzepte im Grunde einfach, so daß man sich immer fragt, warum man nicht selber drauf gekommen ist.

Zitat:
The more frequently you look at data, the more noise you are disproportionally likely to get (rather than the valuable part called the signal); hence the higher the noise to signal ratio. And there is a confusion, that is not psychological at all, but inherent in the data itself. Say you look at information on a yearly basis, for stock prices or the fertilizer sales of your father-in-law’s factory, or inflation numbers in Vladivostock. Assume further that for what you are observing, at the yearly frequency the ratio of signal to noise is about one to one (say half noise, half signal) —it means that about half of changes are real improvements or degradations, the other half comes from randomness. This ratio is what you get from yearly observations. But if you look at the very same data on a daily basis, the composition would change to 95% noise, 5% signal. And if you observe data on an hourly basis, as people immersed in the news and markets price variations do, the split becomes 99.5% noise to .5% signal. That is two hundred times more noise than signal —which is why anyone who listens to news (except when very, very significant events take place) is one step below sucker.
Zitat:
There was even more noise coming from the media and its glorification of the anecdote. Thanks to it, we are living more and more in virtual reality, separated from the real world, a little bit more every day, while realizing it less and less. Consider that every day, 6,200 persons die in the United States, many of preventable causes. But the media only reports the most anecdotal and sensational cases (hurricanes, freak incidents, small plane crashes) giving us a more and more distorted map of real risks. In an ancestral environment, the anecdote, the “interesting” is information; no longer today. Likewise, by presenting us with explanations and theories the media induces an illusion of understanding the world.

And the understanding of events (and risks) on the part of members of the press is so retrospective that they would put the security checks after the plane ride, or what the ancients call post bellum auxilium, send troops after the battle. Owing to domain dependence, we forget the need to check our map of the world against reality. So we are living in a more and more fragile world, while thinking it is more and more understandable.
 
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Alt 10.06.2012, 12:10   #30
Gimme a reason...
 
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Tails of the Unexpected, Andrew G Haldane

Eine Rede von Haldane zum Thema Fat Tails; 36 Seiten, aber leicht und gut zu lesen.

Es dreht sich alles um die Normalverteilung:

Zitat:
But as Nassim Taleb reminded us, it is possible to be Fooled by Randomness (Taleb (2001)). For Taleb, the origin of this mistake was the ubiquity in economics and finance of a particular way of describing the distribution of possible real world outcomes. For non-nerds, this distribution is often called the bell-curve. For nerds, it is the normal distribution. For nerds who like to show-off, the distribution is Gaussian.
Kleiner Schwenk in die Spieltheorie:

Zitat:
[...] paper/scissors/stone is no game of chance. Played repeatedly, its outcomes are far from normal. That is why many hundreds of complex algorithms have been developed by nerds (who like to show off) over the past twenty years. They aim to capture regularities in strategic decision-making, just like the twins. It is why, since 2002, there has been an annual international world championship organised by the World Rock-Paper-Scissors Society.

The interactions which generate non-normalities in children’s games repeat themselves in real world systems – natural, social, economic, financial. Where there is interaction, there is non-normality. But risks in real-world systems are no game. They can wreak havoc, from earthquakes and power outages, to depressions and financial crises. Failing to recognise those tail events – being fooled by randomness – risks catastrophic policy error.
Dann gehts mit einer kurzen historischen Übersicht über die Normalverteilung weiter. Und schließlich landen wir bei Markowitz und Black-Scholes:

Zitat:
Harry Markowitz was a member of the Cowles Commission. In 1952, he wrote a paper which laid the foundations for modern portfolio theory (Markowitz (1952)). In line with his Cowles contemporaries, Markowitz assumed financial returns could be characterised by mean and variance alone – conveniently consistent with normality. That assumption was crucial, for from it followed Markowitz’s mean-variance optimal portfolio rule.

At around the same time, Kenneth Arrow and Gerard Debreu (1954) were developing the first genuinely general equilibrium economic model. In this Arrow-Debreu world, future states of the world were assumed to have knowable probabilities. Agents’ behaviour was also assumed to be known. The Arrow-Debreu model thereby allowed an explicit price to be put on risk, while ignoring uncertainty. Risky (Arrow) securities could now be priced with statistical precision. These contingent securities became the basic unit of today’s asset pricing models.

In the period since, the models of Markowitz and Arrow/Debreu, with embedded assumptions of normality, have dominated asset-pricing in economics and finance. In economics, the Arrow/Debreu equilibrium model is the intellectual antecedent of today’s real business cycle models, the dominant macro-economic framework for the past 20 years (for example, Kiyotaki (2011)). Typically, these models have Gaussian-distributed impulses powering a Quetelet-inspired representative agent.

In finance, the dominant pricing models are built on Markowitz mean-variance foundations and the Arrow-Debreu principle of quantifiable risk. They, too, are typically underpinned by normality. For example, the feted Black and Scholes (1973) options-pricing formula, itself borrowed from statistical physics, is firmly rooted in normality. So too are off-the-shelf models of credit risk, such as Vasicek (2002). Whether by accident or design, finance theorists and practitioners had by the end of the 20th century evolved into fully paid-up members of the Gaussian sect.
Haldane beschreibt dann die Entwicklung von Exponentialfunktionen (power laws) und ihre Eigenschaft, daß die Fat Tails besser abbilden.

Zitat:
This behaviour in the tail of the distribution makes power laws distinctive. Fat tails are a regularity. [...] Under the normal distribution means and variances are all that matter. For power laws with sufficiently fat tails, the mean and variance may not even exist.

In consequence, Laplace’s central limit theorem may not apply to power law-distributed variables. There can be no “regression to the mean” if the mean is ill-defined and the variance unbounded. Indeed, means and variances may then tell us rather little about the statistical future. As a window on the world, they are broken. With fat tails, the future is subject to large unpredictable lurches - what statisticians call kurtosis.
Dann gibts Charts mit Beispielen aus der realen Welt.

Zitat:
Charts 4-6 look at three social systems: the frequency with which different words appear in the novel Moby Dick; the frequency of citations of pieces of scientific research; and the population size of US cities.8 While seemingly disparate, each is in its own way shaped by a common human factor – social interaction.

Graphically, it is clear that all three of these social systems have a large upper tail. Indeed, they are all typically found to be power law-distributed. For city size, this distribution goes by the name Zipf’s Law (as first noted by Auerbach (1913)). It has a striking pattern: the largest city is twice the size of the second-largest, three times the size of the third and so on. A comparably self-similar pattern is also found in the distribution of names, wealth, words, wars and book sales, among many other things (Gabaix (2009)).
Und jetzt wirds extrem interessant, Haldane liefert nämlich Zahlen und Beispiele aus der Wirtschaft:

Zitat:
To bring to life the implications of these fat tails, consider an insurance contract designed to guard against catastrophes in the tail of the distribution of outcomes. In particular, assume that this insurance contract only pays out if outcomes are more than four standard deviations above their mean value in any one year (or below for the economic series). Under normality, payouts would be expected very rarely.

Now consider the actuarially-fair value of the insurance premium for this contract. This can be calculated under two different assumptions: first, assuming normality of the distribution of outcomes, and second using the observed, fat-tailed distribution of outcomes. The ratio of the difference between the resulting insurance premia is shown in Table 2.

These differences are enormous. For economic and financial series, they are typically multiples of 100 or more. This suggests the assumption of normality would result in massive under-pricing of catastrophe insurance risk, by two orders of magnitude. This mis-pricing is as or more acute when insuring against economic catastrophes (such as output crashes) as natural catastrophes (such as earthquakes).

Put differently, consider the implied probabilities of a three-sigma fall in GDP or equity prices. Assuming normality, catastrophe risk on this scale would be expected to occur approximately once every 800 years for GDP and once every 64 years for equities. In reality, for GDP it appears to occur roughly once every century, for equities once every 8 years.
Im nächsten Teil versucht er, den Ursprung von Fat Tails zu erklären. Danach gehts noch darum, was man aus Extremereignissen lernen kann und welche Maßnahmen ergriffen werden müßten. Ist auch extrem interessant, aber weitere Quotes spare ich mir, weil es sonst zu lang wird.
 
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