September 2013
Intermediate to advanced
264 pages
5h
Chinese
Content preview from Bad Data 技術手冊
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121
第九章
當資料與現實不相符時
Spencer Burns
想要打敗股票市場是非常困難的，這應該是個常識。但在事情的表面上，它看起來單純
是個分析問題，而不是一個資料問題。建立模型是很困難的；建立一個時間序列應該是
簡單的。在每天（或每分鐘、每毫秒），每支不同股票都會列出買進的價格，並且你可
以在其後將它賣出，藉由比較兩者的價錢來計算出收益。
每個在事前敘述的假設通常都是對，但它們常常會失敗而造成模型的毀損。股票資料序
列看起來是有結構性且乾淨的，但該特性也隱藏了該股票在今天的整體走勢。它是個將
混亂的真實掩蓋起來的「好」資料。
股票資料並不是從市場的獨立觀察而來的；該資料是市場的一個不可或缺的部分。會存
在一個緊密的回饋迴圈，讓關於市場狀態的資料影響市場本身（例如，提高價錢反而
會讓人們將價格拉的更高）。並且，在市場中發生的事情也能夠改變市場的本質。例如
說，當跌價的公司離開了市場，或是漲價的公司購買了其他公司，或是當市場崩潰而造
成交易規則的改變時。
在實際上的意義是，今天的股票市場與昨天的股票市場並不是完全相同的，每天都是一
個新的實驗。資料必須要持續性的微調，來讓昨天可以跟今天一起比較。
放大這個問題，多數的交易策略平均來說都只能勉強獲利（或是無法獲利），藉由大量
的小交易來換得有意義的結果。所以就算是微小的系統性錯誤都能夠累積成讓分析嚴重
錯誤的結果。可用的資料也許能夠緊密的表現真實狀況，但微小的誤差是會有影響的。
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