Monthly Archives: November 2011
Lady to guy with open carry: “why do you have a gun? Are you expecting trouble?”
Guy: “No, maam, if I was expecting trouble I would have brought my shotgun.”
“I carry a gun because a cop is too heavy”
“Give a man a gun and he can rob a bank. Give a man a bank and he can rob the world.”
“Well, in the first place an armed society is a polite society. Manners are good when one may have to back up his acts with his life. For me, politeness is a sine qua non of civilization.” –Robert A. Heinlein, supporting character, Claude Mordan to protagonist,
Hamilton Felix, in “Beyond This Horizon,” copyright 1942, Street Publications
“A fear of weapons is a sign of retarded sexual and emotional maturity” – Sigmund Freud
He has some nice tables showing real-world data.
Power-law distributions occur in many situations of scientific interest and have significant consequences for our understanding of natural and man-made phenomena. Unfortunately, the detection and characterization of power laws is complicated by the large fluctuations that occur in the tail of the distribution—the part of the distribution representing large but rare events—and by the difficulty of identifying the range over which power-law behavior holds. Commonly used methods for analyzing power-law data, such as least-squares fitting, can produce substantially inaccurate estimates of parameters for power-law distributions, and even in cases where such methods return accurate answers they are still unsatisfactory because they give no indication of whether the data obey a power law at all. Here we present a principled statistical framework for discerning and quantifying power-law behavior in empirical data. Our approach combines maximum-likelihood fitting methods with goodness-of-fit tests based on the Kolmogorov–Smirnov (KS) statistic and likelihood ratios. We evaluate the effectiveness of the approach with tests on synthetic data and give critical comparisons to previous approaches. We also apply the proposed methods to twenty-four real-world data sets from a range of different disciplines, each of which has been conjectured to follow a power-law distribution. In some cases we find these conjectures to be consistent with the data, while in others the power law is ruled out.
From a follow-up read from “I’ll have what she’s having”, a paper by Bently called “Evolving social influence in large populations”.
He presents a number of applications of preferential attachment in sociology. He then presents a model which is essentiallly a Dirichlet process/Chinese restaurant scenario, with the twist that:
a fixed number of people enter at each step (not just one)
Table choice is based on people who have sat at that table in the last m rounds; thus the process has limited memory (and may no longer be exchangeable!!).
He makes analogy to the neurtral drift aspect of evolution, and comments that evolutionary models are inherently dynamic.
N initial population
n enter the restaurant at each time-step
mu agent chooses a new table
1-mu agent chooses an existing table, with probability proportional to # at the table
So, can I tie this idea into the quasispecies as memory concept?
He begins with the grim picture. 2 events
- Oil at $147/barrel. This was June, 2011. He calls it the earthquake, the financial collapse several months later was only an aftershock. Our economy is based on oil. Transport, food, energy, clothes, pharmacuticals. And according to the conservative industry estimates, peak oil was in 2006. When oil hits ~150, the economy shuts down. He predicts a continuing 4 year cycle: economy stops, oil price drops, economy re-starts, oil rises until at 150 it shuts everything down again.
- Copenhagen. No agreement on climate change. Yet the median estimate is +3 degrees in the next 50 years, which will radically change weather patterns (each 1 degree change allows the atmosphere to hold 7% more water)
Society moves by finding new sources of energy and communication, which then define the social-political realm. First industrial revolution. Water and steam, combined with cheap printing and pervasive public education to create a literate workforce which could function in it. And gave us the top-down, centralized control nation state. Because a nation/state is about as much as can be controlled in a top-down manner. Second i.r. Electricity based communication, oil based energy. Cars and suburbs. These are all elite energy sources. Elite in that they are concentrated and take huge capital investment to make useable. Also need to be militariliy protected. The vision and the realityI will describe it as vision. He claims it is real, today, and Germany is leading the way. First communication: the internet has revolutionized it, just as the printing press did. The key element is decentralization. Anyone can put up a blog. Next comes decentralized engery. Because energy is everywhere. A bit of solar, a bit of wind, a bit of garbage/sewage, a bit of geothermal… He based it on 5 pillars of the third industrial revolution.
- Renewable, small-scale energy generation (solar, wind, hydro, geothermal, tidal, biomass).
- Buildings as power plants. Build the power generation into the archetecture.
- Hydrogen storage tech. Hydrogen fuel cells can transform energy much more efficiently than anything else known (add electricty to water, and you get hydrogen + ox. Burn the hydrogen + ox, and you get almost the full amount back as heat).
- Smart power grid. Use off the shelf internet tech (routers etc) to make the power grid a distributed resource
- Electric transport (cars/trucks/trains/shipping).
These pillars are now a EU parliament directive.
The cultural shift. Socialism/capitalism is dead. Now the divide is top-down, opaque vs open source, transparent, distributed.
Just want to remember it, by no means advising it. With the world slipping into recession, a transportation stock seems like a very risky bet. But this one has some merit, and I may want to look into it more.
Box Ships (NYSE: TEU ) .
The company was spun off from Paragon Shipping (NYSE: PRGN ) back in April via an IPO. After a few vessel acquisitions, Box Ships now operates a fleet of seven containerships that transport goods across the globe. Containerships carry some 90% of the world’s dry cargo, and Box’s fleet is among the youngest, with an average age of just 39 months.
It focuses on medium-size ships (from sub-Panamax to post-Panamax), since there’s a lack of supply in that space coming online in future years. The company’s strategy is to sign its ships to medium-length charters in order to capture rising containership rates. Currently, the fleet is chartered out for an average of 30 months, so there’s some cash flow stability in the business. And for 2012, the company already has 93% of revenue days secured.
Charter rates have been rough over the last six months or so, with rates being sliced in half and well below their 10-year average. But what matters for Box is what happens when it comes time to charter two vessels — two that produce the lowest revenue — whose contracts expire in August 2012. Management expects rates to pick up in early 2012, as supply comes into more balance with demand.
In fact, conditions look good in the containership market longer term, with demand expected to outstrip supply until at least 2015. That should be good news for shipping rates.
Don’t get this market confused with dry bulk shippers, where oversupply has crippled the industry. There, shippers such Dry Ships (Nasdaq: DRYS ) , Diana Shipping (NYSE: DSX ) , and Frontline (NYSE: FRO ) have been hurting for several years, and once-generous dividends have dried up faster than a California raisin in Death Valley. Navios Maritime (NYSE: NM ) is one of the few that has managed to maintain a dividend somewhere close to its payout of a few years ago. In contrast, the containership market looks much stronger.
In its most recent quarter, the company earned $0.32 per share, a number that should be fairly stable given its chartered ships. So its dividend of $0.30 per share is high for a normal company, but it’s not outrageous for a shipper. Because growth capital expenditure is so high for shippers, it’s necessary to raise capital via debt or equity offerings anyway, so it can make sense to pay out all profits to shareholders. The company has promised a $0.30 dividend for the fourth quarter, too.
With the downturn in charter rates, the company is looking for opportunities to acquire other vessels at attractive prices. On the conference call, CEO Michael Bodouroglou promised that Box would look for only accretive acquisitions that could boost the dividend. With the company’s moderate leverage (for a shipper) of 52% of net debt/total capitalization, it should be able to issue more debt and not dilute equity holders.
With a contracted fleet of ships, we should have some confidence in the company maintaining its high payout.
An expected yield of 6%-8% would be in line with some of its larger peers such as Costamare and Seaspan, meaning the stock might gain somewhere between 46% and 95% from its current price.
As with any investment, there are risks to Box. Paragon still owns about 21% of the company from the spinoff IPO, and the CEO owns 18% of Paragon and 11% of Box as well. Bodouroglou is also the CEO of Paragon. Also of concern is that Bodouroglou owns the management company to which Box Ships pays a management fee based on daily charter rates. Yes, it’s a cozy relationship that bears some watching to see if management is self-dealing.
Another threat to the company is the fragile world economy. Container shipping has been on a decades-long upswing, but as we saw in 2008-2009, a global economic decline could really hurt the industry. And any type of supply buildout like we’ve seen among dry bulk shippers would wreak havoc on charter rates.
From the American Presidency Project
To the Congress:
As further and urgently necessary step in the program to promote economic recovery, I ask the Congress for legislation to protect small home owners from foreclosure and to relieve them of a portion of the burden of excessive interest and principal payments incurred during the period of higher values and higher earning power.
Implicit in the legislation which I am suggesting to you is a declaration of national policy. This policy is that the broad interests of the Nation require that special safeguards should be thrown around home ownership as a guarantee of social and economic stability, and that to protect home owners from inequitable enforced liquidation in a time of general distress is a proper concern of the Government.
The legislation I propose follows the general lines of the farm mortgage refinancing bill. The terms are such as to impose the least possible charge upon the National Treasury consistent with the objects sought. It provides machinery through which existing mortgage debts on small homes may be adjusted to a sound basis of values without injustice to investors, at substantially lower interest rates and with provision for postponing both interest and principal payments in cases of extreme need. The resources to be made available through a bond issue to be guaranteed as to interest only by the Treasury, will, it is thought, be sufficient to meet the needs of those to whom other methods of financing are not available. At the same time the plan of settlement will provide a standard which should put an end to present uncertain and chaotic conditions that create fear and despair among both home owners and investors.
Legislation of this character is a subject that demands our most earnest, thoughtful and prompt consideration.
This guy predicts that the market will blow in 2012. But he doesn’t know if this means huge gains or huge loss.
So he suggests the following:
You’re looking to capture a move from the average all the way out to an extreme
Set up a daily chart overlayed with two sets of Bollinger Bands – one with with a setting of 200, 3 (a 200-day simple moving average with bands 3-standard deviations above and below it) and the other with a setting of 200, 2.3.
On a day when price crosses and closes above the 200-day moving average, buy an ETF which tracks your chosen index higher.
Realize a portion (eg. 10-15%) of your gains each time price pierces the upper Bollinger Band at 2.3 standard deviations. Otherwise,
Sell your entire position if price moves back below the low of the day you bought. Occasionally raise the stop to just below a resistance point (a level to which prices fell, found support, then reversed back up and closed higher than the prior high. Position your stop order just underneath the low of that move.)