20190125【充实计划】第962期

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昨日阅读2小时。 总阅读时间93小时

Book of Value - The Fine Art of Investing Wisely 2016(AnuragSharma)

https://bbs.pinggu.org/forum.php?mod=viewthread&tid=6303889&from^^uid=109341(Page 247-252)

阅读到的有价值的内容段落摘录

Diversification and Modern Portfolio theory

Around 1950, before Markowitz, intuition about the benefits ofdiversification was well established. That this was common knowledge is evident,for instance, in the well-known 1924 book Common Stocks as LongTerm Investments by Edgar Lawrence Smith. But noobjective measures of risk existed at the time, especially for the joint riskof a bundle of investments. So, Markowitz took up this problem as a topic forhis doctoral dissertation in economics and, using concepts from the statisticstexts of the time, formalized the concept of portfolio risk. In particular, hedefined the risk of a stock as the standard deviation of its returns and thenformulated portfolio, or joint, risk using conditional probabilities, orcovariances of returns of all stocks in the portfolio. Of special note inMarkowitz’s equation was the correlation between the stocks in the portfolio.3If the returns correlate perfectly, then portfolio riskcomputes relatively simply and is for the most part

uninteresting, as investors wouldinstinctively know to try to avoid having two such stocks in their portfolio..That is simple enough. But what excited Markowitz was that most stocks did notmove in perfect unison. Subject to the same systemic factors, such as theeconomy and political invironment, correlation between stocks was usuallypartial but almost never zero. It was such imperfect correlations that made thepractical implications noteworthy.

In order to reduce portfolio risk sodefined, investors now had to solve the problem of finding stocks that hadminimal or even negative correlations. But Markowitz went a step further: hedefined efficient portfolios as those groupings in which the proportion of eachstock is such that the portfolio risk is minimal for a given expected return ofthe portfolio. As the number of stocks in the portfolio increased, the numberof terms in the Markowitz equation multiplied fast; calculating portfolio varianceand risk became computationally intense. But, with increasing computationalpower, such math became less and less of a problem. In essence, Markowitz’sgenius was in showing how to lower investment risk, mathematically andspecifically, by grouping unrelated stocks into a portfolio. An implication ofthis formulation was that investors ought to be thinking of risk not in termsof single stocks but in terms of portfolios of stocks. Particularly fascinatingwas the corollary that the risk (or valuation) of a single stock by itselfshould mean very little in large, well-diversified portfolios. The number ofcovariance terms increases as the size of the portfolio increases, and theyovershadow the effect of the weighted variance of any one stock. When you add asingle stock to an existing portfolio of 30 stocks, for example, the new stock adds its own weightedvariance term but also 30 covarianceterms—the covariance of the new stock with each of the already existing stocks.These 30 additionalcovariance terms influence the change in portfolio risk much more than the riskof one new stock by itself.

Twoimplications are usually derived from the math of computing portfolio variance:(1) the risk of a single stock does notmatter that much; and (2) eachbuy or sell decision must be made in light of whatever stocks you already havein the portfolio. As we will see later, the second implication is certainlyvery helpful, but the first implication that the risk of any one stock does notmatter is patently misleading. At the time, though, this was breakthroughthinking, and Markowitz earned a Nobel Prize for conceiving portfolio risk andespecially for delivering the math to make it possible. This way of thinkingabout investing revolutionized finance and is now deeply entrenched in

investmenteducation. One of the early extensions to Markowitz’s work was by WilliamSharpe, with his idea that the risk of a single stock ought to be evaluated inrelation to the overall market risk. Think of a fully diversified investor whoowns a basket filled with all the stocks in the market. To that market basketyou want to add another stock; the issue is the degree to which that additionalstock will increase or decrease the risk of the market portfolio. This concept,beta (β), is simply a mathematical formulation that normalizescovariance between the stock and the market basket (index)—usually approximatedby the S&P 500 Index.This formulation

madeit possible for each stock to have a number, β, that captured its covariance with the overall market. Othermodifications followed, but the core ideas that took hold were that we canapproximate the risk of a stock with a simple statistic (standard deviation)and that we can compute portfolio risk using returns data for each stock in theportfolio.

阅读到的有价值信息的自我思考点评感想

For value investing differs from modernportfolio theory in its focus on subjectively but deeply understanding the wealth-creatingmechanisms of businesses, and the difficulties and uncertainties that managersface as they construct and operate such mechanisms. It is this focus on thebusiness that was the intent of Graham and Dodd and, I’d argue, has been thebasis for the long-term success of many professional investors, includingWarren Buffett. All

these investors clearly recognize that, asEdgar Lawrence Smith had intuitively practiced and Graham had repeatedlyargued, investing is a group operation; spreading the bets intelligently acrosscompanies in a portfolio is sound practice. Markowitz tried to formalize thisidea and got it mostly right in theoretical terms, but the field of finance hasnot quite followed through with a credible and robust way to proxy for thetheoretical chance that investors might lose their invested principal. I submitthat the arms-length mathematical approach simply cannot adequately capture thecomplex of factors that comprise true risk. We need to understand thewealth-creating machinery by getting up close with the businesses in which weinvest.

Diversification will lower the risk of wrong investment,but it will also lower the profit even the investment is correct. Value investingis more concentrate on small number of portfolio. Value investing will concentrateat fewer portfolios. Most people will pick around 5-10 stocks of differentindustries to invest.

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昨日阅读1小时 累计阅读5小时 阅读内容 变量
曾鸣和威廉姆斯把中国企业的创新称为“穷人的创新”。
他们总结出三种“穷人的创新”。
一是整合创新，即通过整合现有的技术，在设计上更贴近用户需求，先从一点实现突破，再用模块化的方式做大规模定制，把原本细分的市场连接起来，一网打尽。比如海尔进军美国市场的时候，先从别人都不做的酒柜入手，把一个原本是高端用户才问津的小小的细分市场拓展成了大众都可以尝试的大市场。
二是流程创新，即通过把廉价劳动力和流水线整合起来，用灵活、低成本的“半自动化”战胜全自动化。比如，同样是生产锂电池，日本企业的一条生产线雇用200名工人，花费1亿美元投资，比亚迪则雇用了2000名工人，只花费5000万元进行设备投资。
三是颠覆性创新，即所谓的“蛙跳优势”或“后发优势”。比如当2G（第二代移动通信技术）升级为3G（第三代移动通信技术）的时候，西门子、爱立信等跨国公司首先考虑的是如何更好地利用自己已有的产品，而华为在2G市场上本来就没有市场份额，所以才能轻装上阵，在全球第一个实现了软交换的3G项目。

昨日阅读2小时。 总阅读时间93小时
Book of Value - The Fine Art of Investing Wisely 2016(Anurag Sharma)
https://bbs.pinggu.org/forum.php?mod=viewthread&tid=6303889&from^^uid=109341(Page 247-252)
阅读到的有价值的内容段落摘录
Diversification, Modern Portfolio theory
Around 1950, before Markowitz, intuition about the benefits of diversification was well established. That this was common knowledge is evident, for instance, in the well-known 1924 book Common Stocks as Long Term Investments by Edgar Lawrence Smith. But no objective measures of risk existed at the time, especially for the joint risk of a bundle of investments. So, Markowitz took up this problem as a topic for his doctoral dissertation in economics and, using concepts from the statistics texts of the time, formalized the concept of portfolio risk. In particular, he defined the risk of a stock as the standard deviation of its returns and then formulated portfolio, or joint, risk using conditional probabilities, or covariances of returns of all stocks in the portfolio. Of special note in Markowitz’s equation was the correlation between the stocks in the portfolio.3 If the returns correlate perfectly, then portfolio risk computes relatively simply and is for the most part
uninteresting, as investors would instinctively know to try to avoid having two such stocks in their portfolio.. That is simple enough. But what excited Markowitz was that most stocks did not move in perfect unison. Subject to the same systemic factors, such as the economy and political invironment, correlation between stocks was usually partial but almost never zero. It was such imperfect correlations that made the practical implications noteworthy.

In order to reduce portfolio risk so defined, investors now had to solve the problem of finding stocks that had minimal or even negative correlations. But Markowitz went a step further: he defined efficient portfolios as those groupings in which the proportion of each stock is such that the portfolio risk is minimal for a given expected return of the portfolio. As the number of stocks in the portfolio increased, the number of terms in the Markowitz equation multiplied fast; calculating portfolio variance and risk became computationally intense. But, with increasing computational power, such math became less and less of a problem. In essence, Markowitz’s genius was in showing how to lower investment risk, mathematically and specifically, by grouping unrelated stocks into a portfolio. An implication of this formulation was that investors ought to be thinking of risk not in terms of single stocks but in terms of portfolios of stocks. Particularly fascinating was the corollary that the risk (or valuation) of a single stock by itself should mean very little in large, well-diversified portfolios. The number of covariance terms increases as the size of the portfolio increases, and they overshadow the effect of the weighted variance of any one stock. When you add a single stock to an existing portfolio of 30 stocks, for example, the new stock adds its own weighted variance term but also 30 covariance terms—the covariance of the new stock with each of the already existing stocks. These 30 additional covariance terms influence the change in portfolio risk much more than the risk of one new stock by itself.

Two implications are usually derived from the math of computing portfolio variance: (1) the risk of a single stock does not matter that much; and (2) each buy or sell decision must be made in light of whatever stocks you already have in the portfolio. As we will see later, the second implication is certainly very helpful, but the first implication that the risk of any one stock does not matter is patently misleading. At the time, though, this was breakthrough thinking, and Markowitz earned a Nobel Prize for conceiving portfolio risk and especially for delivering the math to make it possible. This way of thinking about investing revolutionized finance and is now deeply entrenched in
investment education. One of the early extensions to Markowitz’s work was by William Sharpe, with his idea that the risk of a single stock ought to be evaluated in relation to the overall market risk. Think of a fully diversified investor who owns a basket filled with all the stocks in the market. To that market basket you want to add another stock; the issue is the degree to which that additional stock will increase or decrease the risk of the market portfolio. This concept, beta (β), is simply a mathematical formulation that normalizes covariance between the stock and the market basket (index)—usually approximated by the S&P 500 Index. This formulation
made it possible for each stock to have a number, β, that captured its covariance with the overall market. Other modifications followed, but the core ideas that took hold were that we can approximate the risk of a stock with a simple statistic (standard deviation) and that we can compute portfolio risk using returns data for each stock in the portfolio.

阅读到的有价值信息的自我思考点评感想
For value investing differs from modern portfolio theory in its focus on subjectively but deeply understanding the wealth-creating mechanisms of businesses, and the difficulties and uncertainties that managers face as they construct and operate such mechanisms. It is this focus on the business that was the intent of Graham and Dodd and, I’d argue, has been the basis for the long-term success of many professional investors, including Warren Buffett. All
these investors clearly recognize that, as Edgar Lawrence Smith had intuitively practiced and Graham had repeatedly argued, investing is a group operation; spreading the bets intelligently across companies in a portfolio is sound practice. Markowitz tried to formalize this idea and got it mostly right in theoretical terms, but the field of finance has not quite followed through with a credible and robust way to proxy for the theoretical chance that investors might lose their invested principal. I submit that the arms-length mathematical approach simply cannot adequately capture the complex of factors that comprise true risk. We need to understand the wealth-creating machinery by getting up close with the businesses in which we invest.
Diversification will lower the risk of wrong investment, but it will also lower the profit even the investment is correct. Value investing is more concentrate on small number of portfolio. Value investing will concentrate at fewer portfolios. Most people will pick around 5-10 stocks of different industries to invest.

昨日阅读1小时，累计阅读1小时。
昨天开始计划学习，希望我能坚持下去。

昨日阅读1小时，2019年阅读27小时。今天分享一下读《变量》的感受。
整体来说何帆老师的书娓娓道来，很喜欢读，说明文字功底高。我一口气读完了这本书，还是讲感触最深的3点。
1.人生很长，从长远的目标来看，每30年可以做为一个阶段。现在社会发展很快，也要思考阶段里的共性。
2.大趋势与小趋势的关系。众多小趋势影响了大趋势，我们要把握大趋势的方向，努力做好自己的小趋势。
3.任何时代，独立思考的能力很重要。因为环境我们不能完全做我们自己，但是我们不能忘初心，一定要往心之向往的地方走。

1.https://wallstreetcn.com/articles/3475959

2.加州公用事业巨头PG&E本因涉嫌引发加州两场大火面临300亿美元的赔偿，将在月底申请破产保护。但在加州官方宣布其中一场与其无关后，公司股价报复性大涨，或许能有“起死回生”的机会.

3.感想：企业的风险管理意识必须加强，像PG&E这样的百年老店一年2个月就能面临破产，这次算是运气好，其实需要反思的地方非常多，同时PG&E是否能够渡过难关仍然是未知数，因为它仍然面对220亿美元负债（超过总资产）

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