The largest and the biggest tenbagger

Source:

Chart: The Largest Companies by Market Cap Over 15 Years

过去15年,全球赚钱机器排行榜:谷歌没进前十!

Google (Alphabet) IPO: 12 Years Later

10年117倍,美国功能饮料Monster的启示

 

 

largest-companies-by-market-cap-chart

ENERGY DOWNTURN, TECH UPTURN

Crude Oil Pricesスクリーンショット 2016-08-31 22.08.44.png

SCALE IS IN STYLE

– To reach more people, Walmart had to build more stores, expand complex supply chains, and hire new employees, which takes a lot of capital and manpower, and the stakes are high for each new expansion.

+ Amazon on the other hand, can bring in more revenues with less of the work or risk involved. Scale allows tech companies to get bigger without getting bogged down by many of the problems that companies with millions of employees can run into.

(I guess here it means that in a technological world, a big firm will not produce large management cost, supervision cost or even institutional inertia like traditional business giants do)

The world’s best tech companies are also able to gain competitive advantages that are extremely difficult to supplant.

(I might agree that engineers and computer science competency are their core competitive advantages, if not the cheap money and high valuation)

Google-vs-SP-500Best-Stocks-since-GOOG-IPOWorst-Stocks-since-GOOG-IPO-1

スクリーンショット 2016-08-31 23.17.54.png

成立于2002年的Monster Beverage是美国第二大功能性饮料生产商,仅次于红牛。

创始人南非商人Rodney Sacks。一个在欧洲做过多年律师的南非人。他移民美国后,一直在寻找好的投资机会。1992年和朋友用1460万美元收购了Monster Beverage的前身汉森公司。当时的汉森以生产天然苏打水和果味饮料,只有12名员工,年销售额有1700万美元。在欧洲的经历,让Sacks发现功能性饮料已经迅速崛起了,而此时的美国依然以可口可乐这种碳酸饮料为主。但人们开始关注一些新的饮料类型。

美国功能性饮料的风口

2009到2014年,全美功能性饮料销量增长了50%。
Monster Beverage定位18到30岁的年轻人,这些人是最大的美国功能性饮料消费者。

和红牛错位竞争

  1. 品牌定位更强悍
  2. 安呢基是红牛的五倍
  3. 性价比也更高
  4. 宣传渠道上,更加精准。
    红牛以大规模电视广告为后台,而Monster Beverage从极限运动入手,切中更细分的目标用户。

(Red Bull commanding approximately 42% of market share and Monster at around a 39% market share.)

护城河

从定价权看,Monster Beverage的净利率能够做到15%。可口可乐和百事可乐的净利率才12%左右,康师傅方便面净利率只有3%。定价权背后是产品的品牌,消费者认可度,护城河。

(数字有问题,coca cola的净利率并不低,最近几年MNST的收入成长也并不算快,虽然盈利性有提高。关键应该还是行业的增长,以及竞争不大。前有可乐后有功能性饮料,为何饮料的护城河如此之强,值得再研究下)

スクリーンショット 2016-08-31 23.42.48スクリーンショット 2016-08-31 23.37.20

 

How to Create a Millennials Positioning Strategy for a New Energy Drink Brand

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Japan’s Government are Losing Revenues

Source:

Kuroda Money-Go-Round Undercuts Japan Negative-Rate Windfall

BOJ’s Eventual Stimulus Exit Could Eat Up Reserve in Five Months

BOJ Bond Valuation Losses Are Said to Be $8 Billion in 2015

 

2016/09/09 update

1x-123

 

Japan’s government is not profiting from negative yields!

1.The BOJ buys debt from the market
-> pushes prices up and yields down
-> gives extra money to the MOF.

1x-116

2. The Finance Ministry pays interest income to the BOJ for the bonds it now holds
-> although rates is low (10-year notes currently at 0.1 percent)
-> the amount is huge (BOJ owns almost 327 trillion yen in sovereign debt)
-> interest income in 2015: 1.29 trillion yen

1x-117

3. The BOJ then uses some of its income to pay for the valuation losses on owned bonds
<- Because BOJ buys debt for more than the face value, and has to write it down [1]
-> BOJ wrote down the value of JGB holding by 874 billion yen in 2015, 40% of the interest income

1x-118

-> Obviously, if the amortization losses from the BOJ’s bond buying operations become too large, income could go less, even negative

-> The BOJ will buy 120 trillion yen worth of bonds this year(80 QE+40 Redemptions) [4]
-> if it buys 100 yen bonds at 103 yen, that would mean a total loss of 3.6 trillion yen
-> if we assume the average period is 10 year, that would mean 0.36 trillion loss increased per year !  [2]
-> BOJ’s last year coupon income is about 1.3 trillion, it will take only 4 years to make it negative under recent price level.
-> And don’t forget with prices high and coupons low, more and more of the debt on its books will have a negligible income and a high price that needs to be written down.

1x-119スクリーンショット 2016-08-28 19.30.22

-> Therefore BOJ could go bankruptcy if bond purchasing continues! [3] 
# but of course BOJ can prolong the duration of its holdings

4. BOJ then returns much of its leftover profits to the MOF as dividend.

1x-121

5. BOJ has to cut dividend so that it could back up its reserve 

-> In 2015, the BOJ cut 450 billion yen from its dividend to the government so it could increase its reserve to cover potential losses on bond holdings. [6]
-> According to Bloomberg, Japanese Government benefited 110 billion yen extra money from NIRP [5]
-> The government’s revenue actually decreased under massive stimulus!!

6. Things might be going to worse

The BOJ has approximately 2.7 trillion yen in provisions for potential bond losses after setting aside 450 billion yen in 2015, given its financial statement 

If the BOJ tapers stimulus, it will face potential losses on

  1. bond holdings
  2. higher interest payments on lenders’ reserves

Policy maker Takahide Kiuchi estimated the central bank could face losses of 7 trillion yen per year during a taper of its stimulus.1x-122

“When people realize the limits to the BOJ’s finances, it could possibly create a massive shock”
“The bank has about 7 trillion yen in capital, but that would be eaten up quickly.”

7. Conclusions

A. If BOJ continues its recent project, both the government and BOJ will lose money and go bankruptcy

B. If BOJ suddenly exits from its unprecedented easing policy, existing reserves will be insufficient and it will go bankruptcy

C. The BOJ have to exit, or do helicopter money. But it will definitely avoid selling its bond holdings, and “instead will probably try to maintain its balance sheet by raising the deposit rate”

 

[1]

So that the book value eventually equals the principal. The basic point is that as BOJ committed to hold these bonds until maturity, it doesn’t value the bonds at market price but takes the markdown gradually so that at maturity the book value equals the principal.

More specifically, for the most recent 10-year note, the MOF initially auctioned it for 101.96 yen and the BOJ probably paid more than that. It will now have to take a 2 yen or more loss on each of the bonds in that series it owns, so that when it matures in 2026, the price on its balance sheet will be back at 100 yen. The benchmark bond price was 101.779 yen, with a yield of minus 0.08 percent

[2]

In its purchase operations on June 10, the BOJ bought 416 billion yen worth of the No. 342 10-year bond, at an average price of about 102.65 yen.

The BOJ will earn 416 million yen income annually from the 0.1 percent coupon on these bonds, and will have to write down 1.1 billion yen each year to account for the 2.65 yen by which the purchase price exceeded the principal.

And don’t forget BOJ’s purchases often occur at a slight premium to the current market price.

 

[3]

“The BOJ couldn’t go bankrupt in the way a private bank could”

“One could make an economic case that the balance sheet of the central bank should be of marginal relevance at best to the determination of monetary policy,” Bernanke said in the speech, made years before he enacted unprecedented stimulus as Fed chair. “There are many essentially cost-less ways to fix” it, including assistance from the Ministry of Finance, he said.

[4]

1x-120

[5]

Japan’s Ministry of Finance made about 110 billion yen ($1.1 billion) more in the year to April than it would have if yields had been zero

[6]

The central bank is holding on to as much as half of the profits from the interest received on its bond holdings, after an accounting rule change in November.

Why Fundamental Analysis Works?

Source:

The Fundamental Reason Buffett Beats the Market

The Fundamental Attribution Error, or Why Predicting Behavior is So Hard

The Market Outsmarts Everyone

 

(夜半的胡言乱语系列)(切莫当真)

Noah Smith 在讨论最近Farma和Thaler的有效市场之争的时候介绍了一篇14年的新论文。两个学者用了14个常用的BS项和14个IS项去和市值跑回归然后用residual来确定高估低估然后建立portfolio。结果是4-9%的超额收益。事实上这都不能被称为Fundamental analysis,因为与其说是基本面判断,这只是证明了市场价格都是错误定价的,并且mispricing arising from convergence to fair value。

首先Farma的有效市场肯定是错的。这种错误和经济学人的理性假设是类似的。这种理论都是提供一种精准的概念来贴近模糊的正确,为了用数学做稳定的模型。市场的短期波动是没有稳定度的。因为他受到了太多并且太复杂的因素的影响。(牛顿相对量子力学的错误却仍有意义这点和这完全不同。)

上次我在谈经济学的时候谈到了稳定这个问题,但是没有深究。为什么恒星行星的运动可以被预见但是金融市场不行。为什么人的行为不稳定,如何定义和界定稳定度。我有一个想法是收到能产生作用的力的环境程度。人的行为可以被太多因素影响,基因,场所,童年,家庭,今天的天气,早上的意外,太多太多。这是一个互相交汇的无数力产生交互作用的场所,所以异常不稳定。动物也不稳定,但是因素肯定比人少。植物,气候,星球,稳定度受环境情况而定。

但是人的行为的稳定度并不是一致的,其内中也有相对稳定的情况。一个过去表现优秀的学生取得好成绩的概率一定是大于吊车尾的学生。一个具有优良品质,足够的动力,和良好的心态的人有很大的可能成功,尽管我们并不知道是什么方式,什么过程,要过多久。所以我们在即便很不稳定中的系统里也能找到一定稳定度,并带来一种概率上确定。

所以这是我个人认为的为什么Fundamental Analysis能成功的原因。其实并不只是基本面分析或者哪一类的投资方式。关键是掌握了具有稳定度的东西。精通财务报表的背后是对经营者和公司模式的认知。这部分可以具有相当的稳定度。而反观技术投资这样的模式是绝对没有任何长期利益的,因为他本身建立在了收到无穷多的影响的脆弱的无数的投资者行为上。

而AQR这样的Momentum的成功是建立在一种行为模式的稳定度上。简单来说就是求导后的降维后的稳定度。

至于Noah Smith 最后说 “Of course, the EMH may get the last laugh, in a way. As often happens, money managers will read this paper and write code to do more sophisticated versions of what the professors did. They will trade on the mispricing, and it will mostly vanish, allowing proponents of efficient markets theory to declare victory.” 我是不这么认为的。从人的行为的稳定上来看的话。

基本面分析的成功来自两件事,市场先无视价值,然后市场又正视价值了(或者是摆向了另一种情感极端)。[1] 这并不会有太多改变。市场参与者无视价值是因为不稳定,收到外界影响,内在影响,固有观念。哪怕明知在统计学上毫无道理,但是我们还是会下意识用外表着装来判断一个理财师的专业度。短期的市场决策收到太多毫无稳定性的力的影响。另辟蹊径的对冲基金的alternative策略可能会逐渐失去有效性。但是无视商业稳定性的价格偏移会一直存在。

 

[1]

Fundamental analysis succeeds if two things are true. First, the market has to have overlooked important things about a company’s value — things that can be observed by carefully scrutinizing publicly available information. Second, the market has to eventually realize the company’s true value.

Economists have become Data Scientist

Source:

How Economics Went From Theory to Data

Data Geeks Are Taking Over Economics

Most of What You Learned in Econ 101 Is Wrong

 

Theory’s dominance peaked in 19831x-114

From 1960s to 1980s, the majority of the articles published in the field’s most influential journals [1], were works of theory.

 

Reasons for the shift:

  1. personal computers became commonplace
    crunching data became much easier, which impulsed the biggest shift toward empirical work which benefits from a huge stock of untested theories
  2. the subsequent rise of the Internet and digitization
    a huge new array of data to crunch
  3. the grand models — particularly the macroeconomic ones — didn’t explain the world very well
    Economic theory may have become so abstruse even for editors

 

Session title from nowadays: “Data Gold! Exploiting the Rich Research Potential of Lifetime Administrative Earnings Data Linked to the Census Bureau’s Household SIPP Survey.” / Or study linked data from the European Patent Office, the Finnish statistical agency and the Finnish military to research “whether people with high IQs invented more things and made more money than others”

 

The data can’t tell us everything

  • Economics in the U.S. had an earlier empirical heyday in the 1920s and 1930, but flummoxed by the Great Depression.

 

Econ 101 theories are found wrong now,

as the core of economics theory is based on individual optimization.

For example:

A.

In the last two decades, empirical economists have looked at a large number of minimum wage hikes, and concluded that in most cases, the immediate effect on employment is very small.In reality, employment probably depends on a lot more than just today’s wage level.[2]

B.

Recent empirical studies have shown that rather than negative effect, occasionally, welfare programs even make people work more. [3]

  1. For professional theorists, empirical failures simply mean more work to do.
    Many labor economists are now working on complex theories that model the process of employees looking for work and employers looking for people to hire.
  2. But for Econ 101 classes, leaving economic majors thinking that the theories they learned are mostly correct isn’t good.

 

 

(As I once discussed, complicated model along with mathematical skill won’t make economics more convincible as they are not working on solid objects from which one can grab some basic laws)

 

A new way of empirical economics

Instead of a complicated model about optimization and utility functions to compare model to data (structural estimation),

  • a so-called natural experiment [4] just look for a case where some kind of random change in the economy
    E.g. you could study a random influx of refugees to answer the question of how immigration affects local labor markets. You don’t need a complicated theory of how workers and companies behave — all you need is a simple linear model of how X affects Y.

1x-115

(It increased, but is still minority. Contrary to Noah Smith’s expectation, I doubt the future of this method as economists need sophisticated math to appeal their value. And  this method is actually close to the way of economic historian, whom have been long contempt by the mainstreams.)

 

[1]

American Economic Review, Journal of Political Economy and Quarterly Review of Economics

[2]

Theory tells us that minimum wage policies should have a harmful impact on employment.Basic supply and demand analysis says that in a free market, wages adjust so that everyone who wants a job has a job. If you set a price floor, a bunch of low-wage workers would be put out of a job as their productivity is lower than that price floor.

The problem is that employment also depends on predictions of future wages, on long-standing employment relationships and on a host of other things too complicated to fit into the tidy little world of Econ 101.

[3]

Theory assumes welfare gives people an incentive not to work. If you subsidize leisure, simple theory says you will get more of it.

[4]

This approach are promoted by economists Joshua Angrist and Jörn-Steffen Pischke. Also called quasi-experimental methods — the “credibility revolution.” And their book about the subject is titled “Mostly Harmless Econometrics.”

Consumption Revolution in China

Source:

今日资本徐新:4大变化下的创投新趋势

 

We once discussed the new consumption pattern in the U.S due to demograpic change:  Investment Themes in US market

  1. lower income of the Millennial generation and less shopping of baby boomer
  2. E-commerce-Biased consumption and Social Media as advertising, not brand-conscious
  3. Experiences than material goods, especially on health and education

And we once introduced China as Internet leader: China = Internet Leader on Many Metrics

  1. nowadays most GDP growth comes from Service Industries
  2. 668M Internet Users, 200 minutes Daily Mobile Time Spent
  3. share of online advertising & E-commerce companies, Smartphone-Based Payment  engagement compare favorably to USA

Moreover, we once talked about the impact of IT revolution on business pattern: 20 Years Downside Trend In Real Interest Rate

  1. lowering price of both hardware and software makes investment more effective
  2. internet economy(cloud, big data, IoT, share economy) breakdown the boundary of traditional industry
  3. internet largely lowers transaction cost, save complicated process, connects manufacturers and consumers more directly
  4. consumption needs are met and thus evolved in an increasing speed.

Today’s article gives a closer and anecdotic look on the recent Consumption Revolution in China. And I will continue add contents to this topic when other similar and valuable articles are found.

 

消费商业的变化

A. 消费者和消费者习惯

  • 80、90后成为消费的主力:
  1. 平均上网3-5小时天,网购年次数很高
  2. 网购可以是碎片时间的冲动消费
  3. 朋友圈推荐,推荐引擎推荐
    (从PC端到移动端的过程中,搜索已经变成了刷屏 – 碎片化时间中被动地接受信息)
  • 传统广告战略不再有效:评价和粉丝(热点) 成为关键

(从经济学来讲,机会成本是决策时才发生的成本。发现机会成本的成本是决定机会成本在决策中作用的关键。互联网降低了发现机会成本的成本,增加了决策的有效性,这同时就会降低了品牌这种长期固化观念的影响度。而互联网在降低搜寻成本的同时本身也增加了决策发生的频率,这会使得需求弹性较高的商品受益。)

  • 一些例子:
  1. 万科在调查后发现90后不做饭,他们决定缩小厨房面积,将空间集合做成社区食堂。
  2. 美团外卖成长很快,已经达到每天四百万单,这对没有外卖传统行业的打击很大
    (本身餐饮行业的利润在交完税之后就是7-8%,如果老店同比下降10%就比较危险了 [1])
  • 分享经济的逐渐盛行:效率极大提升、边际成本极大降低的时候,分享经济就会发生

(这涉及产权的交易成本问题等一些隐藏的成本问题还值得研究,暂时不多讨论)

  • 消费升级:
  1. e.g. “三只松鼠,完全是从淘宝发展起来的,2012年刚成立,今年营业额预计40亿,因为它抓住了消费升级和电子商务的浪口。”
  2. e.g. 农夫山泉的成功就是凭借精准的品牌定位,直接瞄准中高端人群 [2]
  • 原来便宜是入口,以后可能是人是IP
  1. 滴滴和美团依靠补贴烧钱便宜教育市场大大缩短了淘宝京东那样长的发展时间
  2. 信息量在不断地提升,时间价值在被动提升,IP力量显现

 

B. 渠道分化

  1. 百货商店的老店同比下降严重
    以前的卖场都是“生鲜+干货+房租”模式:生鲜拉眼球、干货做毛利,多余的空间租给麦当劳肯德基赚取房租收入。但现在人流下滑,租金下降,利润较高的干货类被互联网挤占。
  2. 购物中心从2014年开始供给过剩,业务分化
    传统购物中心中只有餐饮区域还能拉人流,导致购物中心餐饮占比从20%上升到35-40%;目前有一种三线城市密集开店专卖店的模式仍有商机
  3. 互联网电商目前总量3.88万亿,每年成长高于30%,并且全面覆盖,从服装+3C到食品+个护+家居+母婴,再到最后的生鲜
    电商整体占比只有12%,服装已经达到20-30%,3C也是20%多。“根据研究,在新生产物取代旧事物的过程中,20%渗透率是拐点”
    传统行业原本的毛利大都五十来个点,利润十多个点,如果20%的生意没有了,利润基本就没有了,第一反应是不敢开店了,第二是削减成本、服务质量下降,就进入了戴维斯双杀的恶性循环。
  • 零售的实质就在于产品极大丰富、价格实惠以及体验良好
    “京东在毛利10%的时候已经赚钱了,苏宁毛利16%的时候还是亏损”
  • 中国电商能够超过美国的原因在于:线下连锁不够强大及人口密度更高
  1. 中国从一开始网购就便宜20-30%,线下连锁规模小,被颠覆很容易,美国的线下连锁店都已经有几十年历史了,他们本身的价格就非常具有竞争优势
  2. 送货的密度大,成本也比较便宜,所以体验很好

(但中国互联网增速也已经放缓,互联网上市公司今年一季度的业绩增速明显放缓,年增长从原来的60-70%立马降到了30-40%,甚至20%多,因为移动互联网的红利和人口红利都吃完了)

 

从拼市场增量成长速度到护城河竞争力 [3]

好市多(Costco)在面临亚马逊竞争下,老店同比照样保持5-7%的年增长:

  1. 食品占比60%,高频刚需且亚马逊仍有不足
  2. 性价比高(独家定制的大包装),自有品牌占比高
  3. 会员制,年费$99,全城最便宜的加油站等绑定

打败沃尔玛的折扣店阿尔迪:

  1. 精选(一个商品一个选择)和自有品牌(占比90%多),价格比沃尔玛还便宜20%
  2. 每一个店500-800平米,不到1,000个SKU,毛利15%-17%,净利2%-3%,存货周转只有2周,效率比电商还高。

再来看亚马逊:

  1. 打尽所有品类
  2. 发展 Prime 会员提高购买频次 (据说向Costco学习的)
  3. 宣布做自有品牌,持续创新中:AWS、Kindle、无人机、Echo

总结一下就是找到挖深一个定位 position 战略。其次掌握供应链很关键。

 

移动互联网红利已经不再,超级平台会很值钱

  1. 1亿-2亿用户,而且每个用户每年买八次以上
  2. 三四线城市用户主动下载APP不超过20个,获客成本差别很大
  3. 用户占领无限可能,大数据、云计算、互联网金融,以及将来的AI和VR
  4. 互联网消费垄断很明显:
    搜索引擎、电商平台、在线酒店预订,大头的市占率都能达到60-70%,核心业务的 EBITDA margin 能达到50%以上

(这点其实和消费者行为的改变有冲突,可能更多层面是互联网规模效应后导致进入成本太大。但是我们看到现在市场的垂直化和尾部化其实是同时出现的,需要进一步考量)

 

[1]

“大家都说外卖烧钱不赚钱,但用这个方法可以教育市场,用户感到方便、养成习惯,即使提价他也会留下来。”

[2]

“曾经的饮用水行业成本最高的是瓶子而不是水,所以康师傅把瓶子做的特别薄,降低成本,又靠5万大军打开了三四线城市和城乡结合部的销售渠道,靠低廉的价格,康师傅的瓶装水卖的非常好。随着时间的推移,消费者开始对过薄的瓶子产生不满,一开瓶,水就洒在手上了。这时另外一家公司农夫山泉,凭借精准的品牌定位,直接瞄准中高端人群,提出“农夫山泉有点甜”,用广告强化“我们不生产水,我们是大自然的搬运工”等精心挑选的概念,顺利抓住消费升级并且坚持了下来。”

[3]

巴菲特分析的竞争优势

3种品牌优势

  1. 强大的品牌:可口可乐和吉列:品牌的巨大吸引力、产品的出众特质与销售渠道的强大实力
  2. 专利权:药品专利:全球最大的注射器及医用一次性产品的供应商BD公司,全球最大的处方药公司强生公司,葛兰素史克公司
  3. 政府许可权:管制产业:中美能源拥有多家电力公司,伯灵顿铁路公司,信用评级公司穆迪(投资者服务业务利润率高达50%)

3种成本优势

  1. 低购买成本 (大采购批量和精准时机把握):毛利率很低但销量很大、市场份额很高的商业连锁零售企业,沃尔玛,好事多,家得宝和美国劳氏;汽车保险公司GEICO(不代理直接销售)
  2. 客户高转换成本:金融行业:富国银行、美国运通、合众银行
  3. 低网络扩张成本:1)价值随客户增加而增加:信用卡、在线拍卖、证券交易所;2)扩张用户成本极低:UPS快递 (大规模配送网络极难复制,更易形成自然垄断和寡头垄断,往往是超宽经济护城河的源泉)

 

The U.S. Recovery Debate

Source:

Economic Survey of the United States 2016

The U.S. Economy Is in Great Shape (Compared with Its Peers)

The U.S. Recovery Is Not What It Seems

CEOs Turn More Bullish About Business Investment

 

Strongest recovery in the OECD

スクリーンショット 2016-08-22 3.55.32スクリーンショット 2016-08-22 4.01.12.pngスクリーンショット 2016-08-22 4.10.56.pngスクリーンショット 2016-08-22 4.00.16スクリーンショット 2016-08-22 3.59.49

スクリーンショット 2016-08-22 4.02.33

Actually the investment is no longer an advantage.

Facts holding back business investment:

  1. the ample availability of workers at modest wages, with firms choosing labor over capital
    (“Employment growth could slow as labor becomes more scarce,”  “At the margin, businesses might find it more efficient to increase capital expenditures”)
  2. the retrenching of the energy industry
    (“That trend might be over with crude oil prices returning to the $50 range.”)

 

Not the case: Taking account of population growth

A. 

The U.S. population is growing much faster than those of either Europe or Japan, so its economy should almost automatically grow faster as well.

スクリーンショット 2016-08-22 4.12.55

The U.S. is still ahead, but not by much. And within the euro area, Germany actually exceeded the U.S. by 5 percentage points.

B.

スクリーンショット 2016-08-22 3.48.49.png

The fraction of those aged 25 to 54 with a job was about 2.5 percentage points lower in 2015 than in 2007. In the U.K. and Japan, the prime-aged employment-to-population ratio already exceeds its 2007 level.

If the U.S. recovery actually hasn’t been so comparatively strong,

  • Federal Reserve’s unconventional monetary-policy measures – e.g large-scale asset purchases – have been not so much effective than other central banks.

 

2 More important Figures

スクリーンショット 2016-08-22 4.21.59.png

 

Recent Ideas

(夜半的胡言乱语)

前两天突然想了解一下一些数学的原始概念于是读了一篇很有趣的文章

数学里的 e 为什么叫做自然底数?是不是自然界里什么东西恰好是 e?

顺便借此反思了一些oxthdox和heterodox的经济学问题

数学或者物理学和经济学的起点和本质都是观察和发现 但是区别是目标的稳定度 这导致后者不得不人为假设稳定度 从某种方面来说这没有错 因为这大概是人类找到的进一步架构和组建的唯一一种方式 但是结果就是只能很好地运用在auction等变量维度非常少的稳定场景之中

所以现代经济学的一条出路变成了工程师产品经理也就是一个理所当然的事了。

All of a Sudden, Economists Are Getting Real Jobs

online auctions, online advertising, organ donations and the like提供了很好的进行各种优化求解求最优的平台。

但是除此之外他们的理论影响力甚微 [1]。 比起凯恩斯所说的经济学家的奴隶的言论 [2],事实肯能更多是来自于相反的方向。市场的力量被过分的相信了。得到被扭曲的资本价格而竭尽所能降低交易成本创造新商业形态的企业家一定程度上超出了世俗经济学家的范畴。(你看他们都帮经济学家找到了新的工作)

事实上在我曾经的研究中发现,所谓的纯经济制度其实更多的是一些社会制度初始设定后的自然衍化。连经济学本身的历史也是如此。比如新古典主义只是繁盛于二战之后的第二次全球化和自由主义的潮流的当代显学。

这里岔开一下,最近有一个很不成熟的思想是一些最基本的经济尺標可能值得再深思。比如通货膨胀Inflation shouldn’t be Worried。比如工业时代的GDP能否在digital era中适用。体验和虚拟内容开始越来越多的代替产品,而在被扭曲的资本价格之下,原本定价和衡量方式是否仍然适用值得考虑。

反过来回到正统非正统经济学的问题。

不进行架构肯定也是不行的。不架构就没有办法继续发展。

非正统比如说经济史提供了一些很有趣也有意义的观点和方向。但是单纯成为一个历史片段是需要避免的。即便成为了一种理论,形而上的形态也会导致无法使用被主流束之高阁。比如明斯基时刻。Minsky’s moment Economics Without Math Is Trendy, But It Doesn’t Add Up 。再比如科斯的交易成本。即便有williamson的数理公式扩展,仍旧变得最后无人问津。像汪丁丁说的,无法定义,就只能永远讨论。

所以我们无法在经济学里找到正真的universal law [3]了吗。现在所发现的,要么真实而模糊,要么清晰而虚假,要么只存在于短暂的过去的历史之中。

问题太大,换个小点的。

我做这个blog站最初也是因为研究上的困惑。平时接触的知识太碎片,几乎留不下什么痕迹。

munger说要跨领域,要建立起能相互联系起来的知识。

我自己最近的体会是逻辑可以简化,可以验证,这个过程可以更加高效。但是这本身价值还不够。

研究是观察,发现,架构。每一步都有很多功夫可以下。观察上比如说Python做数据挖掘 怎样用 Python 做一些有趣的数据挖掘? 就能做出一些很有价值的研究。发现上主要是归纳法,单纯的相关性的归纳可以交给机器学习来提升效率,但更难是爱因斯坦所说的直觉性经验性归纳。我之前也有见过一些羚羊挂角般的叹为观止的研究,这些可能一需要足够多的经验二需要思维确实地去发散。最后架构有可能是更加难的地方,因为刚才说过的现实世界的不稳定性,无法像数学一般建立精妙的推论。所以比起严谨的推论发现可能最后会积累变成一种比较玄的直觉。

这样来回看,我至今为止做的阅读笔记只是做了一个资料和观点的堆放站。事实上最近在一些场合我更多只是使用了一些现象来表达观点而非更内在的东西。发现的不够深刻,更勿论进一步的架构了。现象过目即忘,再多也是無駄。

比如分析公司,行业,比起一个个孤例子,应该关注用什么框架去分析,框架的优劣,优化。要提升研究的维度,在这上面思考和进步。然后每次的孤例要尽量进行联结和发现。最后在考虑进一步跳出框架的可能。

路漫漫,唯自勉。

 

[1]

就比如我最近在某个micro seminar看到做教育的spence signal的,但是显然我们无法基于此去写信要求校长说明年把本科改成1年研究生改成5年。

[2]

“The ideas of economists and political philosophers, both when they are right and when they are wrong, are more powerful than is commonly understood. Indeed the world is ruled by little else. Practical men, who believe themselves to be quite exempt from any intellectual influence, are usually the slaves of some defunct economist.”

[3]

“The supreme task of the physicist is to arrive at those universal elementary laws from which the cosmos can be built up by pure deduction. There is no logical path to these laws; only intuition, resting on sympathetic understanding of experience, can reach them. “Albert Einstein

China’s Housing Boom

Source:

房价还会涨吗?房子还能买吗?

房地产市场的拐点已经到来

过半都是房贷 招行财报这组数字告诉了我们什么?

十次危机九次地产!中国房地产周期研究(上)

 

 

1456732134bth9syrmrh

1x-113

 

1. 什么决定房价?

商品房供求:1.居住需求 和 2.投机需求,分别对应商品属性和金融属性。

投机需求主要跟货币投放和低利率有关,它反应了商品房的金融属性。

  1. 居住需求: 商品属性的基本面:城镇化、居民收入和人口年龄结构 

    过去几十年中国房价持续上涨存在一定基本面支撑:经济高速增长、快速城镇化、居民收入持续增长、20-50岁购房人群不断增加和家庭小型化但是,2000年以后尤其2014-2016年房价涨幅远远超过了城镇化和居民收入增长等基本面数据所能够解释的范畴。房价上涨的另一部分要靠货币超发来解释。

  2. 投机需求: 金融属性的驱动力:货币超发和低利率 

    低利率和货币超发推动房价上涨,所有房价大周期见顶以及房市泡沫崩盘都跟货币紧缩和加息有关,比如2007年的美国、1991年的日本。[1]

11222

3344

2008年以来,中国经历了三轮房价上涨周期,2009、2012、2014-2016年,都跟降息和货币供应增速加快有关。这三轮房价上涨周期,政策都试图通过放松货币金融环境刺激房地产以稳增长。比如最近一次:

  • 不断降息提高了居民支付能力。自2014年930新政和1121降息以来,房价启动新一轮上涨。2015年330新政和下半年两次双降,房价启动暴涨模式。
  • 货币超发导致房价涨幅远超GDP和居民收入。2015年M2-GDP达到6.9个百分点,货币超发程度在过去十多年仅次于2009年,也大大超过了年均2.8个百分点的历史平均水平。
  • 近期M1增速创新高而M2增速创新低,也是一个居民加杠杆企业减杠杆改善现金流、企业存款活期化的表现。
    M1上升未有效传导到实体经济和M2,货币流通速度下降,落入流动性陷阱,脱实向虚,压低无风险利率,推高债市、价值股和土地价格。

所以由于中国城镇化速度、居民收入增速和货币超发程度(M2-GDP)超过美国、日本等主要经济体,造就了中国房价涨幅冠全球。[1]

 

2. 其他规律:促进经济,周期性规律,信贷周期

A. 2009年后我国房地产周期与经济周期基本同步。

123

2008年以前房地产投资增速和GDP增速并无明显的同步关系,而2008年至今两者的相关系数高达到0.76。近年来的经济政策主要通过推动房地产市场增长来驱动GDP增长。

从2006年至今,伴随着经济政策的变化我国房地产市场经历了三轮完整的周期,目前正处于第四轮周期的上涨阶段。

B. 从这几轮市场波动来看,我国房地产市场有几个明显的周期性规律:

1, 每轮周期大约持续2-3年,其中从谷底到峰值的上涨期持续均不超过15个月;

2, 近年来房地产上行周期一轮弱于一轮,总体趋势向下;

3, 销售面积拐点领先房价拐点变化3-6个月;

4, 房价拐点与新开工面积基本同步或领先1-2个月。销售面积是房价和房地产投资的现行指标。

341

与前三轮周期不同,2015年启动的销售和房价上涨并没有很快带来新开工面积的上涨。直到2015年底,新开工面积同比仍为负增长,房地产开发投资完成额也处于历史低位。原因:

  1. 全国整体市场的商品房库存仍然在累积,企业对房地产业的前景判断有分歧
  2. 开发商本身的投资能力不足。(高杠杆,低收益率 [2])

C. 房地产周期的本质是信贷周期

收入(预期)是中长期影响购房需求的关键因素,但短期内的支付能力在很大程度上受货币政策(如降息)的影响。

  • 理论上房屋价值应等于未来房租收入的净现值,作为折现率的贷款利率下行意味着预期房价上涨,有利于提振购房需求。
    住房贷款利率与新增个人购房贷款额呈明显的负相关,显示居民住房贷款需求对利率的变化非常敏感。

5261

利率/信贷周期基本与新增购房贷款的增长呈反向变动,住房贷款利率的拐点先于购房贷款增长拐点。住房贷款利率通过新增购房贷款等信贷增长传导至销售端,信贷周期与商品房销售面积增速也呈现明显的负相关。

而销售增长的变化会进一步传导至商品房价格和新开工面积的变化,带动一轮房地产周期的波动。

由于房地产投资在GDP增长中起着重要作用,同时房地产价格和租金也会传到至企业的成本端从而影响整体物价,因此房地产周期与利率信贷周期之间形成了相互反馈的作用

  • 当货币政策放松时,购房需求得到提振,新增贷款和销售面积上涨,几个月内房价和房地产投资上涨,直接带动GDP并传导至整体物价。

7181

根据经验,CPI拐点形成后的6个月内货币政策开始转向,升息(或降息)周期持续数月至一年。
当CPI上行一段时间后,货币政策收紧,信贷增量收缩,房地产销售量和价格下跌,房地产投资下滑,对经济增长造成压力,物价增速放缓(甚至通缩),然后引发另一轮货币政策宽松。

近期M1增速创新高而M2增速创新低,主因是房地产销售火爆居民加杠杆企业现金流改善、企业存款活期化,M1上升未有效传导到实体经济和M2,货币流通速度下降,落入流动性陷阱,脱实向虚,压低无风险利率,推高债市、价值股和土地价格。

处在现在时点来看,本质是信贷周期的这一轮上涨需要信贷分析
(未来继续通过放货币托底经济,利率新低,货币继续超发的可能)

  1. 虽然短期内还看不到CPI上行和货币政策转向的迹象,但利率的下行空间也已经有限。考虑到部分城市房价暴涨和居民购房贷款的激增,针对房地产市场的宏观审慎政策已经明显转向。
  2. 从中期来看,对经济下行和未来收入增长放缓的预期也不支持居民部门继续大幅加杠杆,这意味着购房贷款增长的需求很可能已经见顶。
  3. 平均而言,一轮房价上涨和需求释放周期18个月左右,2014年底启动的这一轮房价上涨周期在接近尾声。

“无论从政策层面还是需求层面来看,房地产市场的拐点已经到来,促进近两个季度经济稳定的因素即将消失。” 

  • 而房地产投资增速放缓甚至负增长将对四季度和明年的经济带来下行压力。

+

  1. 回归中性的货币政策可能再度衰退式宽松:上半年经济L型企稳的逻辑已经破坏、通胀抬头预期弱化和房价暴涨制约待新一轮房地产新政的效果

 

长期来看

  1. 进一步改革转型之下,当前城镇化率56.1%还有十多个百分点的空间,将新增城镇人口2亿人左右,居民收入也有望增速换挡。
  2. 但是,根据国务院发展研究中心的测算,当前城镇户均1套住房,趋于饱和。但区域分化明显,三四线高库存,一二线城市由于人口流入、产业高端、公共资源富集等还存在供求缺口。

 

考虑以上因素在短期,预计未来房价从快速上涨期步入缓慢上涨期。但区域将明显分化,从中期角度,大都市圈中公共资源富集的核心区和未来受益于产业人口转移的环郊区最有吸引力。

 

3. 银行资产负债表的反应

 

招商银行披露了2016年中期业绩报告,上半年超过一半的新增贷款是房贷。

qq-20160819143114

这一组数据与刚刚公布的7月信贷数据遥相呼应。7月人民币贷款增加4636亿元,其中住户部门贷款增加4575亿元

  • 在目前宏观环境下,整个行业的趋势以房贷作为“保利润降风险”的突破口,呈竞争态势。[3]

1. 因为利润

房贷相对于企业贷款而言利润颇丰。招行半年报显示,在零售贷款余额(其中近半是房贷)与公司贷款规模大致相当的情况下,招行零售贷款利息收入超过公司贷款101亿元。

qq-20160819143953

2. 因为风险

银行贷款利率可划分为无风险利率和风险溢价。不管在何种经济情况下,我国商业银行历史贷款平均利率大致在6-8%之间波动(2015年后期刚刚跌破6%) ,也就是说银行贷款利率呈刚性特征。在经济情况不好的情况下,企业的风险溢价升高,倒逼银行只能做低风险业务,如房贷。

  • “惜贷行为的加剧,也反映了银行自身经营压力加大,比如不良处置压力较大。历史上看,银行不良包袱过重时,都会影响正常的信贷投放。私人部门的信贷疲弱,使银行放贷更加依赖政府部门和房地产销售:7月信贷几乎全是个人按揭,另有对公长贷(多为政府部门)。” [4]

“房地产本质是货币现象,是社会财富在不同资产之间重新配置的最终体现,更多的可能不是增量概念,是存量重新配置概念。

“商业银行是最大资产配置主体,未来判断的趋势是,它必然长期持续增加对于按揭贷款的配置。这是它在利率市场化和金融自由化背景下的必然选择,它必然不愿意承担其他资产配置带来的不必要的风险。”

然而问题

  1. “银行信贷回避了风险较高的私人部门,有可能暂时使其账面不良企稳,但长期看,这对经济增长造成负面影响,根本上还是影响银行资产质量的。”
  2. 对于部分还未抽回的私人部门贷款,则有可能恶化其风险
  3. 房地产价格目前已处高位,居民购房杠杆被迫继续提升。若价格再走高,影响销售,则会导致按揭信贷需求也走弱,到时候每月信贷投放就更成问题了。且资产价格风险也更集中。”

 

Continue reading “China’s Housing Boom”

Commodity Outlook

Source:

Unearthing Opportunities in Metals and Mining

 

Commodities Cycles

1. In the early 2000s, the commodities super cycle began as surging Chinese demand pulled forward the equivalent

2. From 2009 to 2013,the super cycle was amplified by inexpensive credit allowed mining companies to increase capital expenditure to build new mines and meet the demand eruption.

3. In the latter half of 2014, the new supply paradigm unexpectedly met with a slowdown in Chinese demand, catching many investors off guard

china-fixed-asset-inv-graph-06-625px

The biggest concern is the unknown timeline of China’s demand curtailment.
Credit driven stimulus to fund fixed asset investment is nearing an end, given

  1. total leverage in China represents roughly 250% of GDP
  2. incremental credit expansion is having a less meaningful impact on the economy [1]

4. In 2015, expectation of rising rates in the U.S. contributed to a stronger dollar during the period, adding to volatility and pressure in commodities.

  • Elevated dollar strength pushed down currencies in many emerging market countries where miners operate, and lower local costs incentivized increased production. [2]
  • When metal prices began to decline as a result, the capital expenditure began eroding balance sheets.

Industrial Metals¹ & the U.S. Dollar²: An Inverse Relationshipmetals-usd-correlation-graph-01-625px

Ratings Actions by Moody’s: U.S. Metals & Mining Sector : deteriorating fundamentalsup-down-grades-graph-04-625px

 

Iron ore

Miners of iron ore have a tendency to push production by adding additional capacity in order to reduce unit cost. This dynamic leads to oversupplied markets and downward price pressure.iron-ore-steel-pricing-graph-02-625px

The balance of the year for U.S. producers looks brighter in light of

  1. recent trade case announcements.
  2. high strength steel demand in the automotive and construction markets
    (although weakness remains in the heavy equipment, agricultural and energy markets)

Also opportunity in Australia and New Zealand

  1. As commodity-based economies with strong links to China, further easing by the local central banks to cultivate early sprouts of growth and inflation in the region.

ausie-exports-graph-05-625px

Continue reading “Commodity Outlook”

Inflation shouldn’t be Worried

Source:

The stuff we really need is getting more expensive. Other stuff is getting cheaper.

Why slightly higher inflation might benefit the U.S. economy

Overcoming Our Inordinate Fear of Inflation

 

Fixing America’s Roads Is a Great Opportunity

 

1. The conception of inflation itself may deserve a revision.

(Although eating and housing are still the most important parts for common life, so does smart phone in 21th)

price_changes

A.

  1. Most of the things falling in price are manufactured goods, due to technological improvements and productivity gains for decades.
  2. International trade is another reason. Many manufactured goods come from overseas, where labor costs are cheaper. So does global competition.

B.

  1. things like education and medical care can’t be produced in a factory, insulted from global competition
  2. Private and public insurance companies pay most medical costs, so there tends to be little incentive for individuals to shop around for cheaper medical care. So does student loan

 

2. How about a higher inflation target

fredgraphThe U.S. Federal Reserve Board hasn’t hit its stated inflation target of 2% in more than four years

a higher inflation rate of, say, 4 percent would

  • allow inflation-adjusted interest rates to go even further below zero to help boost economic growth during a possible future downturn.

But:

  1. price dispersion happens
    “when companies want to change their prices but for some reason can’t, inflation distorts prices from what they should be, which decreases economic efficiency.
  2. price hikes happen more frequently when inflation is higher, which gives more credence to “menu-cost” models of price setting

 (but studies find during the late 1970s and early 1980s, an era of high inflation, the absolute price changes don’t vary that much regardless of the inflation rate.)

 

3. We shouldn’t care inflation!

  1. Inflation time was also a time of slow growth, deep recessions and terrible asset returns.
  2. Harms like shoe-leather costs and menu costs don’t matter that much in a digital age.
    (economists shows inflation has almost no perceptible impact on productivity — and hence, on human well-being.)
  3. The costs of 10 percentage points inflation is only about as harmful as a 1 percent reduction in gross domestic product, investigated by professor Robert Lucas
  4. Real problems is that when prices rise fast, they also tend to bemore volatile — high inflation equals uncertain inflation [1]
  5. Although the historical correlation between inflation and inflation uncertainty is well-documented, that doesn’t mean the one causes the other.

a higher inflation target can get more people back into the ranks of the employed and shouldn’t be worried as long as it’s stable [2]

 

4. Government spending on Infrastructure

スクリーンショット 2016-08-19 1.18.02There is considerable unused labor remaining in the economy, especially prime-aged men who would benefit the most from an infrastructure push.

Government spending to roads and bridges benefits

  • If private-company interest rates rise, it means that capital is becoming more scarce for businesses because the government is crowding them out. If that looks like it’s starting to happen, we can always hit the brakes.

 

 

[1]

If inflation is predictable, lenders and borrowers can build it into their financing deals; nominal interest rates simply rise to take into account the shrinking value of money.Workers can ask for cost-of-living increases in their paychecks, effectively indexing wages to inflation. And businesses can build inflation into their investment plans.

[2]

The Fed’s so-called dual mandate, as laid out by Congress, is “to promote effectively the goals of maximum employment, stable prices and moderate long-term interest rates.”