經(jīng)濟(jì)學(xué)家，作家

????1960年當(dāng)我進(jìn)入芝加哥大學(xué)（the University of Chicago）時(shí)，我接觸到了一些進(jìn)行新興金融研究的教授。那時(shí)金融還算不上是一門學(xué)科，正在誕生之中，而我恰巧就到了它正在誕生的地方。因此，我有點(diǎn)著迷，當(dāng)時(shí)學(xué)校里每個(gè)人都感興趣。

????第一年，我學(xué)了門中級(jí)統(tǒng)計(jì)，授課教授是哈瑞·羅伯茨。當(dāng)時(shí)我21歲。他和我很像——他喜歡一切體育運(yùn)動(dòng)，是位跑步健將。我在本科時(shí)做過(guò)很多統(tǒng)計(jì)工作，早就在和數(shù)據(jù)打交道。因此當(dāng)我開(kāi)始學(xué)時(shí)，就比其他人領(lǐng)先了很多。但我從哈瑞那里學(xué)到的是理念。他教給我的是對(duì)統(tǒng)計(jì)的態(tài)度，直到現(xiàn)在我也還是堅(jiān)持這樣的態(tài)度。

????正統(tǒng)的統(tǒng)計(jì)學(xué)是給出一個(gè)假設(shè)，然后檢驗(yàn)。哈瑞總是說(shuō)，你的標(biāo)準(zhǔn)不應(yīng)該是接受、還是否決這個(gè)假設(shè)，而是能從數(shù)據(jù)中了解到什么。最好是能用這些數(shù)據(jù)強(qiáng)化你對(duì)世界的描述。這一直是我研究的指導(dǎo)明燈。應(yīng)該利用市場(chǎng)數(shù)據(jù)更好地了解市場(chǎng)，而不是說(shuō)這個(gè)或那個(gè)假設(shè)是真是假。沒(méi)有一個(gè)模型是嚴(yán)格意義上正確的。真正的標(biāo)準(zhǔn)應(yīng)該是：當(dāng)我完成時(shí)，是比剛開(kāi)始時(shí)更了解市場(chǎng)了嗎？我當(dāng)老師的49年來(lái)，我也一直把哈瑞的建議教給學(xué)生們。

Eugene Fama

Economist, author

????When I came to the University of Chicago in 1960, I was exposed to professors who were involved in the nascent subject of finance, which didn't exist as a discipline at the time. It was all being born, and it just happened that I had come to the place where that birth was happening. So I kind of got into it because everybody there was interested in it.

????In my first year I took an intermediate statistics class with a professor named Harry Roberts. I was 21 at the time. He was very much like me -- he was into all kinds of sports, and he was a runner. I had done a lot of statistics work as an undergraduate and had already worked with data, so I was pretty advanced when I started. But what I learned from Harry was a philosophy. He gave me an attitude toward statistics that has stuck with me ever since.

????With formal statistics, you say something -- a hypothesis -- and then you test it. Harry always said that your criterion should be not whether or not you can reject or accept the hypothesis, but what you can learn from the data. The best thing you can do is use the data to enhance your description of the world. That has been the guiding light of my research. You should use market data to understand markets better, not to say this or that hypothesis is literally true or false. No model is ever strictly true. The real criterion should be: Do I know more about markets when I'm finished than I did when I started? Harry's lesson is one that I've passed on to my students over the 49 years that I've been a teacher.