[st0ckthief]

Witziger und interessanter Artikel, der zwar nichts direkt mit Börse zu tun hat, aber sicher Fragen beantwortet, die sich jeder schon mal gestellt hat.

Robbing banks: Crime does pay – but not very much, Barry Reilly et al.

Den Artikel gibts als PDF (3 Seiten) im Moment umsonst direkt bei Wiley. So wie ich den Verlag kenne, bleibt das nicht lange so...

It is here that we can answer our original question of why, statistically speaking, rob- bing banks is a bad idea. The return on an average bank robbery is, frankly, rubbish. It is not unimaginable wealth. It is a very modest £12706.60 per person per raid. Indeed, it is so low that it is not worth the banks’ while to spend as little as £4500 per cashier position at every branch on rising screens to deter them.

A single bank raid, even a successful one, is not going to keep our would-be robber in a life of luxury. It is not going to keep him long in a life of any kind. Given that the average UK wage for those in full-time employment is around £26 000, it will give him a modest life- style for no more than 6 months. If he decides to make a career of it, and robs two banks a year to make a sub-average income, his chances of eventually getting caught will increase: at 0.8 probability per raid, after three raids or a year and a half his odds of remaining at large are 0.8 × 0.8 × 0.8 = 0.512; after four raids he is more likely than not to be inside. As a profit- able occupation, bank robbery leaves a lot to be desired.

It is worth noting that the criminals them- selves seem to have learnt this. Robbing banks is no longer what you could call the crime of choice. Bank robberies and attempted bank robberies have been decreasing, in both the USA and the UK; in the UK, robberies from security vans are on the increase. Security vans offer more attractive pickings. Our framework provides a way of thinking about this, partly by allowing us to look at the expected value of committing a robbery, but also because it effec- tively introduces a competing product into the robbers' "product space" and asks them to think about which will generate more proceeds.

via Die wunderbare Welt der Wirtschaft, da gibts eine nette Zusammenfassung auf Deutsch; bei Ars technica eine auf Englisch.

[st0ckthief]

Nochmal NNT:

A Method of Detection of Fragility: How to Detect Who Will Go Bust — Nassim Taleb

So here is something to use. And the technique, a simple heuristic, called fragility (and antifragility) detection heuristic works as follows. Let’s say you want to check if a town is overoptimized. Say you measure that when traffic increases by 10,000 cars, travel time to grows by ten minutes. But then if traffic increases by 10,000 more cars, travel time now extends by an extra thirty minutes. Such asymmetry shows that traffic is fragile and you have too many cars, and need to reduce traffic until the acceleration becomes mild (acceleration, I repeat, is acute concavity, or negative convexity effect).

Likewise government deficits are particularly concave to changes in economic conditions. Every additional deviation in, say, the unemployment rate—particularly when the government has debt —makes the deficits incrementally worse.

Ich will dieses Buch! Leider erst ab November lieferbar. Vielleicht gibts dann auch schon Hedge Fund Market Wizards von Schwager in einer günstigeren Paperback-Version.

[st0ckthief]

Wall Street's Earnings Forecasts, Marposa Capital Management

Wie gut sind die Earnings-Vorhersagen von Analysten wirklich? (Nicht gut, steht schon in Post #17.)

Der Artikel von Mariposa Capital ist schon zwei Jahre alt, aber soviel wird sich da nicht geändert haben (Hervorhebungen sind von mir):

The appeal of using future earning is obvious. Which sounds more interesting to you: past earnings, or estimated future earnings incorporating all available information including past earnings? Surely, research analysts employed and trained by Wall Street firms are able to do a decent job in forecasting earnings. Well, let’s look at the evidence.



The graph above compares earnings forecasts over time versus realized earnings (green lines are forecasts, blue dots are realized values). Almost all forecasts decrease as their estimated time period gets closer, meaning that forecasts are almost always too optimistic. Over the past 25 years, the earnings growth rates used by research analysts have consistently been much higher than actual growth rates.

As an individual investor, what does this mean for your investment portfolio? Stick to a long-term asset allocation strategy, and limit any bets to strategies that have actually passed the test of evidence. Stop watching CNBC, and get on with your life.

[Egoist]

Zitat von st0ckthief

(Nicht gut, steht schon in Post #17.)

Aber alles geht davon aus, dass sich nichts Entscheidendens ändert. Dann treffen die Optimisten spätestens 2 Jahre später ins Schwarze.

Da vor allem diejenigen, die heute schon den Kredit dafür aufnehmen und verbraten gesellschaftsfähig sind... gibt es langfristig nur noch einen Ausweg.


...


Wann war die letzte Währungsreform? Und wie lange lässt die nächste im Schnitt auf sich warten?

[st0ckthief]

The 11 Ways That Consumers Are Hopeless at Math, The Atlantic

Sehr interessanter Artikel, der auf den zweiten Blick auch einiges mit Trading zu tun hat.

(3) We're terrified of extremes. We don't like feeling cheap, and we don't like feeling duped. Since we're not sure what things are worth, we shy away from prices that appear too high or too low. Stores can employ our bias for moderation against us. Here's a great story:

People were offered 2 kinds of beer: premium beer for $2.50 and bargain beer for $1.80. Around 80% chose the more expensive beer. Now a third beer was introduced, a super bargain beer for $1.60 in addition to the previous two. Now 80% bought the $1.80 beer and the rest $2.50 beer. Nobody bought the cheapest option.

Third time around, they removed the $1.60 beer and replaced with a super premium $3.40 beer. Most people chose the $2.50 beer, a small number $1.80 beer and around 10% opted for the most expensive $3.40 beer.

(4) We're in love with stories. In his book Priceless, William Poundstone explains what happened when Williams-Sonoma added a $429 breadmaker next to their $279 model: Sales of the cheaper model doubled even though practically nobody bought the $429 machine. Lesson: If you can't sell a product, try putting something nearly identical, but twice as expensive, next to it. It'll make the first product look like a gotta-have-it bargain.

(11) (...) An experiment by the economist Dan Ariely tells the story beautifully. Ariely pretended he was giving a poetry recital. He told one group of students that the tickets cost money and another group that they would be paid to attend. Then he revealed to both groups that the recital was free. The first group was anxious to attend, believing they were getting something of value for free. The second group mostly declined, believing they were being forced to volunteer for the same event without compensation.

[st0ckthief]

Webinar Telvent DTN / Nanex on High Frequency Trading, Big Mike's Trading Forum

Aufzeichnung eines zweistündigen Webinars über HFT (keine Ahnung, ob man sich dafür bei Big Mike registrieren muß). Ist ziemlich komplex und mit viel Q&A, daher leider etwas unstrukturiert und schwer zu verfolgen, aber eine sehr gute Einführung.

[st0ckthief]

Investors’ 10 Most Common Behavioral Biases, Robert P. Seawright

Eine sehr schöne Zusammenstellung der zehn psychologischen Fallen, in die man beim Trading oder im Leben allgemein tappt:

1. Confirmation Bias. We like to think that we carefully gather and evaluate facts and data before coming to a conclusion. But we don’t. Instead, we tend to suffer from confirmation bias and thus reach a conclusion first. Only thereafter do we gather facts and see those facts in such a way as to support our pre-conceived conclusions.

Passiert mir selber immer wieder und selbst in Kenntnis des Problems kann man ihm nur schwer ausweichen.

Sehr schön auch in diversen Threads hier zu beobachten

2. Optimism Bias. This is a well-established bias in which someone’s subjective confidence in their judgments is reliably greater than their objective accuracy. (...) We are only correct about 80% of the time when we are “99% sure.” (...) In a finding that pretty well sums things up, 85-90% of people think that the future will be more pleasant and less painful for them than for the average person.

3. Loss Aversion We are highly loss averse. Empirical estimates find that losses are felt between two and two-and-a-half as strongly as gains. Thus the disutility of losing $100 is at least twice the utility of gaining $100. Loss aversion favors inaction over action and the status quo over any alternatives. Therefore, when it comes time for us to act upon the facts and data we have gathered and the analysis we have undertaken about them, biases 2 and 3 – unjustified optimism and unreasonable risk aversion – conflict. As a consequence, we tend to make bold forecasts but timid choices.

Das hat definitiv Wiedererkennungswert...

4. Self-Servig Bias. (...) Self-serving bias pushes us to see the world such that the good stuff that happens is my doing (“we had a great week of practice, worked hard and executed on Sunday”) while the bad stuff is always someone else’s fault (“It just wasn’t our night” or “we simply couldn’t catch a break” or “we would have won if the refereeing hadn’t been so awful”).

5. The Planning Fallacy. (...) Most of us overrate our own capacities and exaggerate our abilities to shape the future. The planning fallacy is our tendency to underestimate the time, costs, and risks of future actions and at the same time overestimate the benefits thereof.

6. Choice Paralysis. Intuitively, the more choices we have the better. However, the sad truth is that too many choices can lead to decision paralysis due to information overload. For example, participation in 401(k) plans among employees decreases as the number of investable funds offered increases. We are readily paralyzed by too many choices.

7. Herding

8. We Prefer Stories to Analysis (...) Keeping one’s analysis and interpretation of the data reasonably objective – since analysis and interpretation are required for data to be actionable – is really, really hard even in the best of circumstances. A corollary to this problem and to confirmation bias is what Nassim Taleb calls the “narrative fallacy” — looking backward and creating a pattern to fit events and constructing a story that explains what happened along with what caused it to happen.

Ein absoluter Klassiker! Ich sage nur: Börsennachrichten im Fernsehen. "Der DAX ist gestiegen, weil die Anleger auf eine Lösung der Eurokrise gehofft haben (als ob sie das nicht schon seit Monaten tun )." "Der DAX ist gefallen, weil..."

9. Recency Bias. We are all prone to recency bias, meaning that we tend to extrapolate recent events into the future indefinitely. As reported by Bespoke, Bloomberg surveys market strategists on a weekly basis and asks for their recommended portfolio weightings of stocks, bonds and cash. The peak recommended stock weighting came just after the peak of the internet bubble in early 2001 while the lowest recommended weighting came just after the lows of the financial crisis. That’s recency bias.

10. The Bias Blind-Spot. (...) We would always be wise to factor in these biases when performing analysis and making decisions. Unfortunately, we all tend to share a “bias blind spot” — the inability to recognize that we suffer from the same cognitive distortions that plague other people.

Ich kann den Artikel nur sehr empfehlen, da er auch eine Menge Links mit Hintergrundinfos und Quellen enthält, die ich hier nicht alle einbauen konnte.

[st0ckthief]

Preventing Financial Meltdowns, Tim Harford

Sehr interessanter Vortrag, ca. 20 Minuten. Harford vergleicht Finanzkrisen mit Industrieunfällen, wie z.B. Three Mile Island und dem Brand auf der Ölplattform Piper Alpha. Die Message ist, daß beides von Komplexität und "tight coupling" bestimmt wird.

You Tube

You Tube

Das ganze ist stark von Charles Perrow beeinflußt,

falls den jemand kennt

falls den jemand kennt

.

[st0ckthief]

Stupid Data Miner Tricks: How Quants Fool Themselves And The Economic Indicator In Your Pants, David Leinweber

Much to my bemusement, I am perhaps best know as the “Butter in Bangladesh” guy. Almost 20 years ago, with my colleague Dave Krider at First Quadrant, tried to illustrate this point by searching a completely non-financial database for series that had the highest correlation with the S&P 500 over a ten year period. It turned out to be butter production in Bangladesh. We had a good laugh over it, added a few more diary products and third world livestock, and lo and behold, found a regression that “explained” 99% of the S&P 500 using this nonsense.

This, to my endless embarrassment, has become something of a “meme” in quant investing. Do a search on “Butter in Bangladesh” and it fills the entire first page of results and then some. Most people get that it’s a joke, but I still get the occasional call asking for the current butter figures. Answers: (a) I don’t know, and (b) it really doesn’t matter. If you think it does, put your money in a sock under your mattress.

Steve Snider, a quant at the institutional side of Fidelity for many years, sent me a 2007 paper called “Exact Prediction of S&P 500 Returns”. You can have your very own copy here. The authors, both named Kitov, jump through many statistical and econometric hoops to establish the the US population of 9 year olds is an EXACT PREDICTOR of future stock returns. Furthermore, when the model fell apart in years out of their sample, they conclude that something has gone awry in way the US Census counts 9 year olds. I sent them some email on coming up with a good variation on the joke to make same point, and ended up flamed. They really believe this stuff.

[Ramonet]

Zitat von st0ckthief

Stupid Data Miner Tricks: How Quants Fool Themselves And The Economic Indicator In Your Pants, David Leinweber

Hatten wir auch schon mal:

Zitat von Lancelot

Kleiner Hinweis: zwei trendende Zeitreihen nebeneinander zu legen und daraus zu schliessen, dass eine hätte mit dem anderen was zu tun ist methodischer Nonsens. So kann ich auch den DAX mit der Produktion von Schafwolle in Pakistan erklären. Genau das habe ich gemeint Besserweiss, wenn ich sage, dass einfach nur Zahlen und Graphen auszupacken nix bedeutet bzw zu Fehlschlüssen führen kann, wenn man nicht genau versteht was man tut.