A city’s resilience increases as its economy diversifies. Among metropolitan areas that faced nationwide economic slowdowns within the last five years, the 25 % most diversified normally slowed down 11 percent less than their respective countries, while those in to 25 percent slow up 4 percent more. In resilient towns, services’ talk about of their GDP grew at double the speed of nonresident cities on average, plus they had a higher share of services overall.
During the same period, the talk about of industries (such as production, mining, and utilities) in their GDP shrunk by 8 percent-more than 50 percent faster than in nonresident metropolitan areas. A city’s resilience has much to do with the training of its working people. The number of people with college-level or more advanced education was 8.6 percent higher in resilient than in nonresident cities.
On average, international citizens composed 11.6 percent of resilient metropolitan areas’ populations, greater than a full quarter higher than those of nonresident metropolitan areas. We count on immigration. One of the most resilient cities will be the most linked ones. Over the past five years, the annual number of airport passengers grew by 44 percent in resilient cities, double the speed of nonresident cities. The number of public-transit passengers grew by 4.7 percent in resilient cities over the past five years, compared with 0 just.4 percent in nonresident cities. Moscow’s investments in infrastructure have proved essential to its long-term power and resilience, and the town is using “smart” technologies for more efficient metropolitan development.
As major companies move to Moscow, the city is using tax revenue to build intelligent traffic control systems, and enhance the roads and metro, according to Aleksei Savrasov of WTC Moscow. The local government has invested in a good City Lab with 100 manufacturers and training programs, to more through efficientlyintegrate city services, smart buildings, public utilities, and public security. Last year Moscow created a good technology district where it’ll test, assess, and deploy new technologies to make the city more efficient and better able to adapt to change.
- 3 years
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- When depreciation estimates are revised, all years of the asset’s life are affected
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In reality there is considerable evidence that model of human behavior is totally and utterly incorrect. The field of behavioral economics looks for to explain the numerous instances of evidently irrational behavior that people see in financial markets. “Naive diversification” is worth explaining, as it also straight pertains to the idea of handcrafting. Thaler and Benartzi find that whenever people do diversify, they actually so in a naive fashion. They offer proof that in 401(k) plans, many people seem to use strategies as simple as allocating 1/n of their savings to each one of the n available investment options, whatever those options are.
Humans will not automatically choose to carry or rebalance portfolios in a theoretically ideal fashion, and instead will buy portfolios that aren’t sufficiently varied and are focused in ‘tale’ stocks, which they will over-trade. Therefore we need to use formal portfolio optimizer to protect ourselves from our very own cognitive flaws.
A systematic method for portfolio optimisation is a required condition for serious research into portfolio weighting. Unlike subjective methods it could be automated and properly backtested. The method can be run at set intervals over historical data, with each iteration looking backwards so that it is not polluted with future information.
This isn’t possible with subjective methods. Even if we could persuade a human being to repeatedly optimize profile weights, it might be difficult to erase the data of future events from their thoughts. The temptation to lessen the weight to stocks in past due 1999 and 2007 would be to hard to resist.
Small distinctions in these estimates can lead to highly unpredictable, extreme, weights. In practice it’s very difficult to forecast the mandatory parameters, or to know what they must have been in the past. I’ve discussed this extensively in the past (most recently with this talk). We can not know the perfect collection weights with any certainty. More importantly unsatisfactory weights, such as 0% and 100% in a two-asset problem, are common. These weights are intuitively unattractive to humans, and they are more likely to perform badly in out of sample testing also. Even under highly unrealistic laboratory conditions mean variance optimization does not deliver robust portfolio weights that’ll be acceptable to humans.