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When James Tobin won the Nobel memorial prize in 1981, a journalist asked him to summarise his research in simple language. The great macroeconomist attempted to respond to this challenge, and one wire service dutifully reported that Professor Tobin had won the prize "for his work on the principle of not putting all your eggs in one basket".
当詹姆斯•托宾(James Tobin)获得1981年诺贝尔(Nobel)经济学奖时，一名记者请他用简单通俗的语言概括自己的研究内容。这位伟大的宏观经济学家尝试回应了这一挑战，对此一家通讯社进行了忠实的报道，称"托宾教授凭借对‘不要将所有鸡蛋放在一个篮子里'理论的研究获得了诺贝尔经济学奖"。
A newspaper cartoon then appeared announcing the award of a Nobel prize for "an apple a day keeps the doctor away".
随后一幅卡通画出现在了报纸上，画中宣称"每日一苹果，医生远离我"理论被授予诺贝尔奖。
But Tobin perhaps anticipated the awkward history of the Nobel memorial prize and financial economics. Robert Merton and Myron Scholes won in 1997 for their work on option pricing – less than a year before the dramatic bailout of Long-Term Capital Management, a hedge fund in which Merton and Scholes were closely involved.
但托宾或许已经预见了诺贝尔奖在金融经济学领域将要出现的一系列尴尬。1997年，罗伯特•默顿(Robert Merton)和迈伦•斯克尔斯(Myron Scholes)凭借在期权定价领域的研究成果获得诺贝尔经济学奖，此后不到一年默顿和斯克尔斯担任合伙人的对冲基金长期资本管理公司(Long-Term Capital Management)就受到了戏剧性的救助。
Harry Markowitz, who shared the prize in 1990, was really the founder of the whole "don't put all your eggs in one basket" school of portfolio allocation. Markowitz showed how investors could pick an optimal portfolio of assets, minimising risk for any given expected return, or maximising expected return for any given risk. (The basic idea is simple enough to be worthy of Tobin: if you hold shares in a sunblock manufacturer and an umbrella company, your finances will be fine in all weathers.)
1990年诺贝尔经济学奖的获奖人之一哈里•马科维茨(Harry Markowitz)是"不要将所有鸡蛋放在一个篮子里"的资产配置学派的真正创始人。马科维茨给出了投资者如何建立最佳资产投资组合的方法，能在给定预期收益的情况下实现风险最小化或在给定风险的情况下实现预期收益最大化。(马科维茨基本理论的简明程度与托宾有关鸡蛋和篮子的表述不相上下：如果你同时持有一家防晒霜生产商和一家雨伞制造商的股票，那么在任何天气情况下你的整体投资回报都会较为稳定。)
In 1952, Markowitz had had the perfect opportunity to put his theory to good use. He joined the Rand corporation and had to decide how to invest his pension. Did he compute the efficient risk mitigating frontier? He did not. He split his contributions 50/50 between stocks and bonds. So there.
1952年，马科维茨曾有一次绝佳的机会将自己的理论付诸实践。当时他加入了智库兰德公司(Rand Corporation)，因此必须决定如何投资自己的养老金。不过他并没有费神计算能够减弱风险的有效边界，而是将自己定期缴纳的养老金一半投资于股票，一半投资于债券。
Here's a question, though: are these practical tips from Markowitz and Tobin as useful as their sophisticated academic theories? Could it be that simply dividing your money equally between a bunch of different assets – known as the "1/N" strategy – a perfectly good approach to investment?
由此产生的一个问题是：这些来自马科维茨和托宾的实用小窍门是否和他们复杂的学术理论一样有效？将手头的钱平均投资于多种不同资产——所谓的"1/N"策略——是否有可能是一个足够完美的投资方案？
It might seem implausible: after all, the "1/N" strategy is arbitrary and ignores useful information about historical risks, returns and correlations across asset classes. We know, thanks to the research of the behavioural economists Shlomo Benartzi and Richard Thaler, that many investors do exactly what Markowitz did. Surely this is an error, or at least clear evidence of our cognitive limitations?
该策略看起来也许缺乏说服力："1/N"策略带有随意性，忽略了单类资产的风险、收益历史数据以及不同资产间的历史相关系数等有用信息。受益于行为经济学家什洛莫•贝纳茨(Shlomo Benartzi)和理查德•泰勒(Richard Thaler)的研究成果，现在我们知道很多投资者都会和马科维茨一样行事。那么"1/N"策略是否是个明显的错误，或者至少清楚证明了我们的认知能力存在局限性？
Perhaps. But here's the intriguing thing about the financial theory that Markowitz developed: it's extremely difficult to apply in practice. If you know for certain the distribution of returns for all the assets in which you are investing, you can compute an efficient frontier. But you don't. You can only guess.
这种可能性是存在的。但马科维茨提出的金融理论的耐人寻味之处在于：它极难应用于实践。如果你确定无疑地知道将要投资的所有资产的收益率分布，你就能通过计算得出有效边界。但现实中你并不知道收益率的真实分布，因此在计算中只能使用估计值。
One problem is that historical correlations are poor guides to future ones. Imagine the shares of two oil companies, for instance: as the oil price rises and falls, so would the shares, which would seem highly correlated. If one company then ran into some kind of trouble – another Deepwater Horizon, for instance – then the shares might well become negatively correlated as the unaffected company picked up market share from the affected one.
由此引申出的一个问题是，我们无法基于不同资产的历史相关系数有效预测它们在未来的相关性。考虑两家石油公司的股价走势：随着石油价格的涨跌，这两家公司的股价也将随之波动，进而表现出较强的相关性。如果其中一家公司随后遇到了某种麻烦——例如又一次"深水地平线"(Deepwater Horizon)事故——那么这家公司的股价走势很可能将变得与未受影响的公司负相关，因为后者或侵蚀事故公司的市场份额。
A second problem is that even with lots of historical data, it is hard to estimate the likelihood of rare events. (By definition, there will be few or no historical examples.)
还有一个问题是，即使我们掌握了大量历史数据，也依然难以预估小概率事件发生的可能性(根据定义，小概率事件的历史案例数量极少甚至从未发生过)。
Portfolio theorists have produced a variety of sophisticated methods to try to update Markowitz's ideas for an uncertain world. But in research published in 2009 in the Journal of Financial Studies, Victor DeMiguel, Lorenzo Garlappi and Raman Uppal showed that the naive 1/N approach outperforms far more complex calculations until a vast amount of historical data are available with which to calibrate them. How much data? For a 50-asset portfolio, about 500 years. Perhaps "don't put all your eggs in one basket" is financial wisdom enough.
研究投资组合理论的专家们发明了多种复杂方法，试图改良马科维茨的理论，使之适应充满不确定性的真实世界。但维克多•迪米基尔(Victor DeMiguel)、洛伦佐•盖勒普(Lorenzo Garlappi)和拉曼•尤博尔(Raman Uppal)2009发表于《财务金融学刊》(Journal of Financial Studies)的一篇论文指出，简单的"1/N"投资策略收益率高于更为复杂的投资策略，除非能有海量历史数据为复杂策略校准。那么需要多少历史数据呢？对于有50种资产的投资组合来说，大约需要500年的历史数据。从这个角度来看，也许"不要将所有的鸡蛋放在一个篮子里"已经足以成为金融领域的至理名言了。
