評論
打印
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".
當詹姆斯•托賓(James Tobin)獲得1981年諾貝爾(Nobel)經濟學獎時，一名記者請他用簡單通俗的語言概括自己的研究內容。這位偉大的宏觀經濟學家嘗試回應了這一挑戰，對此一家通訊社進行了忠實的報道，稱"托賓教授憑借對‘不要將所有雞蛋放在一個籃子里'理論的研究獲得了諾貝爾經濟學獎"。
A newspaper cartoon then appeared announcing the award of a Nobel prize for "an apple a day keeps the doctor away".
隨后一幅卡通畫出現在了報紙上，畫中宣稱"每日一蘋果，醫生遠離我"理論被授予諾貝爾獎。
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.
但托賓或許已經預見了諾貝爾獎在金融經濟學領域將要出現的一系列尷尬。1997年，羅伯特•默頓(Robert Merton)和邁倫•斯克爾斯(Myron Scholes)憑借在期權定價領域的研究成果獲得諾貝爾經濟學獎，此后不到一年默頓和斯克爾斯擔任合伙人的對沖基金長期資本管理公司(Long-Term Capital Management)就受到了戲劇性的救助。
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年諾貝爾經濟學獎的獲獎人之一哈里•馬科維茨(Harry Markowitz)是"不要將所有雞蛋放在一個籃子里"的資產配置學派的真正創始人。馬科維茨給出了投資者如何建立最佳資產投資組合的方法，能在給定預期收益的情況下實現風險最小化或在給定風險的情況下實現預期收益最大化。(馬科維茨基本理論的簡明程度與托賓有關雞蛋和籃子的表述不相上下：如果你同時持有一家防曬霜生產商和一家雨傘制造商的股票，那么在任何天氣情況下你的整體投資回報都會較為穩定。)
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，馬科維茨曾有一次絕佳的機會將自己的理論付諸實踐。當時他加入了智庫蘭德公司(Rand Corporation)，因此必須決定如何投資自己的養老金。不過他并沒有費神計算能夠減弱風險的有效邊界，而是將自己定期繳納的養老金一半投資于股票，一半投資于債券。
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?
由此產生的一個問題是：這些來自馬科維茨和托賓的實用小竅門是否和他們復雜的學術理論一樣有效？將手頭的錢平均投資于多種不同資產——所謂的"1/N"策略——是否有可能是一個足夠完美的投資方案？
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?
該策略看起來也許缺乏說服力："1/N"策略帶有隨意性，忽略了單類資產的風險、收益歷史數據以及不同資產間的歷史相關系數等有用信息。受益于行為經濟學家什洛莫•貝納茨(Shlomo Benartzi)和理查德•泰勒(Richard Thaler)的研究成果，現在我們知道很多投資者都會和馬科維茨一樣行事。那么"1/N"策略是否是個明顯的錯誤，或者至少清楚證明了我們的認知能力存在局限性？
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.
這種可能性是存在的。但馬科維茨提出的金融理論的耐人尋味之處在于：它極難應用于實踐。如果你確定無疑地知道將要投資的所有資產的收益率分布，你就能通過計算得出有效邊界。但現實中你并不知道收益率的真實分布，因此在計算中只能使用估計值。
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.
由此引申出的一個問題是，我們無法基于不同資產的歷史相關系數有效預測它們在未來的相關性。考慮兩家石油公司的股價走勢：隨著石油價格的漲跌，這兩家公司的股價也將隨之波動，進而表現出較強的相關性。如果其中一家公司隨后遇到了某種麻煩——例如又一次"深水地平線"(Deepwater Horizon)事故——那么這家公司的股價走勢很可能將變得與未受影響的公司負相關，因為后者或侵蝕事故公司的市場份額。
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.)
還有一個問題是，即使我們掌握了大量歷史數據，也依然難以預估小概率事件發生的可能性(根據定義，小概率事件的歷史案例數量極少甚至從未發生過)。
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.
研究投資組合理論的專家們發明了多種復雜方法，試圖改良馬科維茨的理論，使之適應充滿不確定性的真實世界。但維克多•迪米基爾(Victor DeMiguel)、洛倫佐•蓋勒普(Lorenzo Garlappi)和拉曼•尤博爾(Raman Uppal)2009發表于《財務金融學刊》(Journal of Financial Studies)的一篇論文指出，簡單的"1/N"投資策略收益率高于更為復雜的投資策略，除非能有海量歷史數據為復雜策略校準。那么需要多少歷史數據呢？對于有50種資產的投資組合來說，大約需要500年的歷史數據。從這個角度來看，也許"不要將所有的雞蛋放在一個籃子里"已經足以成為金融領域的至理名言了。
