"你覺得我是不是真的是一個xx、yy的人？"這種問題，要是別人回答後又要反駁和反擊的話，不如就不要問好了嘛…
這幾天頭腦很懶(今天也感覺腦部只有60%的功能)，連玩腦遊戲的表現也差了一截。這兩天排編電影節的時間表也花光了我的腦能量，是幸也是不幸，今年的電影比較有趣，所以要考慮的撞時間、地點問題也較煩。
財政預算案，我有每一頁都看過，電視問答大會也看了，電台也聽了，不過除了一些故有的範圍加錢，以及一些以前用過的一次性寬免和給予(就是不喜歡派錢這個字，容易打亂討論的方向)，不能說沒有承擔(如醫療和教育的加碼是比較高幅度的)，但上年所說︰"發現不能單依賴金融業，要發展新產業來開發新的經濟增長點"這個點子，這年隻字未提怎樣支持新產業。當然…除了成立什麼基金之外，要是施政報告沒有提出政策方針，很難找位給錢…
另外一點比較為意的，就是輿論的反應，似乎一面倒傾向了"為什麼不退稅"、"退稅是不是會增加通漲"的範圍，dominated了大部份的注意力。基本上，有能力交稅的人都有某個水平的收入，雖然有大量被稱為中產的人也會因為租不了公屋又買不起單位的成為夾心階層，生活不是富裕，但比起要憂三餐的大量貧窮人士，他們的要求(退稅)也是有點道理，但在應吸收到的注意力比例上，感覺不是很好。
至於通漲的疑慮…個人愚見︰其實會不會加劇通漲不是重點，而是所花的錢的效應能否在target groups上有大到蓋過通漲影響幅度的效果，這兩天便有些文章在討論通漲的成因其實有多種。如果硬要說派錢這個字，又會令人以為是沒有選擇性地人人有份，然後掉進其他枝節的沉悶、絕望輪迴的論戰。所以我想說是選擇性給予、或者真的立一些措施是幫助低下階層會更好(當然那似乎也要施政報告先給一些空間)。如果單單地說通漲就是不好，那似乎會令人覺得連為了未來競爭力而做的基建項目的錢也不要花了的意思。
還有就是強積金戶口注入六千元，我難致可否，但有人提到以二百幾億換來那多年後的uncertain amount拿去做更有意義的即時措施可能更好、硬塞錢給保險公司和銀行賺的事，也是很值得思考。
腦袋混亂。
2011年2月22日 星期二
電影Wall Street
今天一口氣看了電影Wall Street第一和第二集。
比較傾向於刻劃個人層面的貪婪和良心的掙扎，而較少提及政府方面的問題(行內人管行內人)。不過以主角們的故事和週邊角色，也映照出當時的人民是怎樣想的。
在第二集是2008年金融海嘯前後的背景，不過也是講主角的週邊事情多一點(畢竟不是記錄片)，但第一集的大鱷出獄後發表新書發佈演說的那一段話，實在很棒很到肉。
由這裏copy過來的︰http://political-economy.com/wall-street-money-never-sleeps/
------------
You are all pretty much screwed. You do not know it yet, but you are the ninja generation. No income, no job, no assets. You’ve got a lot to look forward to.
Someone reminded me the other day that I once said, greed is good. Well it appears greed is not only good, it is legal. We are all drinking the same cool-aid.
But it is greed that makes my bartender buy three houses, he cannot afford with no money down. And it is greed that makes your parents refinance their 200,000 dollar mortgage for 250,000 dollars. Now they take that extra 50,000 dollars and go to the shopping mall so they can buy a new plasma TV, cell phones, computers and an SUV. And hey, why not a second home while we are at it.
Gee Wiz, we all know the prices of houses in America always go up. Right?
It is greed that makes the government of this country cut the interest rates to 1% after 9/11 so we can all go shopping again.
They got all these fancy names for trillions of dollars for credit, CMO, CDO, SIV, ABS. You know I honestly think there are only 75 people in the world that knows what they are.
But I will tell you what they are, they are WMDs. Weapons of mass destruction.
When I was away, it seemed that greed, got greedier. With a little bit of envy mixed in.
Hedge fund managers came home with 50 to 100 million bucks a year.
So Mr. Banker, he looks around and says.
My life looks pretty boring.
So he starts leveraging his interest up to 40%, 50% to 100%. With your money not his.
Yours. Because he could.
You are supposed to be borrowing not them.
And the beauty of the deal is no one is responsible.
Because everyone is drinking the same cool-aid.
Last year ladies and gentlemen, 40% of all corporate profits came from the financial services industry.
Not production, not anything remotely to do with the needs of the American public.
The truth is we are all part of it now.
Banks, consumers they are moving the money around in circles.
We take a buck, we shoot it full of steroids and we call it leverage. I call it steroid banking.
Now I have been considered a pretty smart guy when it comes to finance.
Maybe I was in prison too long, but sometimes it is the only place to stay sane, looking out from the bars and say, hey is everyone out there nuts?
It is clear as a bell to those who pay attention. The mother of all evil is speculation. Leverage debt. The bottom line is, it is borrowing to the hilt.
And I hate to tell you this, but it is a bankrupt business model.
It will not work. It is septicemic, malignant and its global. Like cancer, it is a disease. And we got to fight back. How we going to do that?
How we are going to leverage that disease, back in our favor?
I will tell you, three words…BUY MY BOOK!
比較傾向於刻劃個人層面的貪婪和良心的掙扎，而較少提及政府方面的問題(行內人管行內人)。不過以主角們的故事和週邊角色，也映照出當時的人民是怎樣想的。
在第二集是2008年金融海嘯前後的背景，不過也是講主角的週邊事情多一點(畢竟不是記錄片)，但第一集的大鱷出獄後發表新書發佈演說的那一段話，實在很棒很到肉。
由這裏copy過來的︰http://political-economy.com/wall-street-money-never-sleeps/
------------
You are all pretty much screwed. You do not know it yet, but you are the ninja generation. No income, no job, no assets. You’ve got a lot to look forward to.
Someone reminded me the other day that I once said, greed is good. Well it appears greed is not only good, it is legal. We are all drinking the same cool-aid.
But it is greed that makes my bartender buy three houses, he cannot afford with no money down. And it is greed that makes your parents refinance their 200,000 dollar mortgage for 250,000 dollars. Now they take that extra 50,000 dollars and go to the shopping mall so they can buy a new plasma TV, cell phones, computers and an SUV. And hey, why not a second home while we are at it.
Gee Wiz, we all know the prices of houses in America always go up. Right?
It is greed that makes the government of this country cut the interest rates to 1% after 9/11 so we can all go shopping again.
They got all these fancy names for trillions of dollars for credit, CMO, CDO, SIV, ABS. You know I honestly think there are only 75 people in the world that knows what they are.
But I will tell you what they are, they are WMDs. Weapons of mass destruction.
When I was away, it seemed that greed, got greedier. With a little bit of envy mixed in.
Hedge fund managers came home with 50 to 100 million bucks a year.
So Mr. Banker, he looks around and says.
My life looks pretty boring.
So he starts leveraging his interest up to 40%, 50% to 100%. With your money not his.
Yours. Because he could.
You are supposed to be borrowing not them.
And the beauty of the deal is no one is responsible.
Because everyone is drinking the same cool-aid.
Last year ladies and gentlemen, 40% of all corporate profits came from the financial services industry.
Not production, not anything remotely to do with the needs of the American public.
The truth is we are all part of it now.
Banks, consumers they are moving the money around in circles.
We take a buck, we shoot it full of steroids and we call it leverage. I call it steroid banking.
Now I have been considered a pretty smart guy when it comes to finance.
Maybe I was in prison too long, but sometimes it is the only place to stay sane, looking out from the bars and say, hey is everyone out there nuts?
It is clear as a bell to those who pay attention. The mother of all evil is speculation. Leverage debt. The bottom line is, it is borrowing to the hilt.
And I hate to tell you this, but it is a bankrupt business model.
It will not work. It is septicemic, malignant and its global. Like cancer, it is a disease. And we got to fight back. How we going to do that?
How we are going to leverage that disease, back in our favor?
I will tell you, three words…BUY MY BOOK!
Rant 2: 木頭人 (貼於2月19日)
第二輯<<窮富翁大作戰>>開播了…
第一個case是田北辰，我還記得當我還在土瓜灣上班的時候，在區議會選舉其間收到他的政綱，鮮明地反對最低工資。然後上年就發生了跟張廿蚊針鋒相對的事件，在回應傳媒是立場是有明顯改變。
雖然我覺得兩日實在太少，有某些說話感覺有點假，不過這件事本身就頗有意思的。
還真希望張廿蚊可以也來參加看看，示範做二十元時薪的工作可以怎樣生活。
PART 1: http://www.youtube.com/watch?v=novZbeT8sUw
PART 2: http://www.youtube.com/watch?v=CQQpo5Vt9jU
以下的rant毫無組織可言︰
雖 然連自由市場的內容是什麼都不去考究就說"咁自由市場呀嘛"，搬出名詞就當成答案的怪人已經少了，但還有一些論調和態度有時會irritate我。(當然 現在比較會懂己調節和卸掉這些精緒) 就是那些一聽到比較詳細的問題描述、或是應該要達到什麼的話，涉及問題和理想狀況，就馬上表達無奈然後嗤之以鼻的態度，有些還要搬出"世界就是這樣"，只 接受世界由從前變成現在的樣子的事實，卻覺得現在世界的樣子永遠都會不變；然後就是強調個人責任、指責依賴政府的風氣的論調，恍惚所有問題都只有一個原 因，與之爭辯，對方就會跳進"不是靠自己就是靠政府"、"你說政府有責任就等如個人責任都可推卸"的二元思維，並將政府支出單純地當成施捨，而不是保持競 爭力的手段，他們亦忽略了現實上，其他國家的政府在推動經濟發展其實有一定角色和動作，香港過去幾十年的經濟發展模式本來就有不正常和不獨立的地方，而有 些現代社會支柱還是緊抱著以前那套致富理念(儲錢、買、放的無生產力促進行為)，然後又跳出以前的人比較能幹的結論，不過當提到這些時，又會有些人跳到了 政府干預經濟的恐怖感，以一些失敗的政府計劃當成政府干預經濟注定失敗的邏輯跟你說上一陣子了。
清醒的人是多了，不過發生這麼多事後，還是有些木頭人的存在令我有點irritate到。
在The Upside of Irrationality有個實驗，就是讓兩個不相識的人，在見不到對方的情況下，由其中一方得到20美元(這裡說成是A吧)，A可以決定分多少錢給對 方(B)，例如19:1、15:5，如果B接受，他們便可以拿錢離開，但如果B不接受就會拉倒，大家都拿不到錢。
在這個基礎上在不同的條件作改變來測試不同的東西，其中一個是︰讓其中一組的B看一套關於一個熱誠的工程師被老闆刻薄和無理解僱的故事的電影，另一組的B會看肥皂劇FRIENDS。實驗結果是，看FRIENDS的人會比較容易接受刻薄比例的金錢。
也 許關係不是很緊密，我聯想到的，是當一個地方的人們慣於overdosed with 某類娛樂如富豪、明星的八卦和炒作新聞，報喜不報憂、只以不愁生活只愁升遷和愛情的人們做主角的主流電視劇等等…是不是令有些人不向更強者抽刃，卻向更弱 者抽刃的心態，在心理學上有一定的影響力存在……
(註︰我是喜歡看FRIENDS的，只想強調媒體在發送什麼節目、製造什麼環境認知的因素)
第一個case是田北辰，我還記得當我還在土瓜灣上班的時候，在區議會選舉其間收到他的政綱，鮮明地反對最低工資。然後上年就發生了跟張廿蚊針鋒相對的事件，在回應傳媒是立場是有明顯改變。
雖然我覺得兩日實在太少，有某些說話感覺有點假，不過這件事本身就頗有意思的。
還真希望張廿蚊可以也來參加看看，示範做二十元時薪的工作可以怎樣生活。
PART 1: http://www.youtube.com/watch?v=novZbeT8sUw
PART 2: http://www.youtube.com/watch?v=CQQpo5Vt9jU
以下的rant毫無組織可言︰
雖 然連自由市場的內容是什麼都不去考究就說"咁自由市場呀嘛"，搬出名詞就當成答案的怪人已經少了，但還有一些論調和態度有時會irritate我。(當然 現在比較會懂己調節和卸掉這些精緒) 就是那些一聽到比較詳細的問題描述、或是應該要達到什麼的話，涉及問題和理想狀況，就馬上表達無奈然後嗤之以鼻的態度，有些還要搬出"世界就是這樣"，只 接受世界由從前變成現在的樣子的事實，卻覺得現在世界的樣子永遠都會不變；然後就是強調個人責任、指責依賴政府的風氣的論調，恍惚所有問題都只有一個原 因，與之爭辯，對方就會跳進"不是靠自己就是靠政府"、"你說政府有責任就等如個人責任都可推卸"的二元思維，並將政府支出單純地當成施捨，而不是保持競 爭力的手段，他們亦忽略了現實上，其他國家的政府在推動經濟發展其實有一定角色和動作，香港過去幾十年的經濟發展模式本來就有不正常和不獨立的地方，而有 些現代社會支柱還是緊抱著以前那套致富理念(儲錢、買、放的無生產力促進行為)，然後又跳出以前的人比較能幹的結論，不過當提到這些時，又會有些人跳到了 政府干預經濟的恐怖感，以一些失敗的政府計劃當成政府干預經濟注定失敗的邏輯跟你說上一陣子了。
清醒的人是多了，不過發生這麼多事後，還是有些木頭人的存在令我有點irritate到。
在The Upside of Irrationality有個實驗，就是讓兩個不相識的人，在見不到對方的情況下，由其中一方得到20美元(這裡說成是A吧)，A可以決定分多少錢給對 方(B)，例如19:1、15:5，如果B接受，他們便可以拿錢離開，但如果B不接受就會拉倒，大家都拿不到錢。
在這個基礎上在不同的條件作改變來測試不同的東西，其中一個是︰讓其中一組的B看一套關於一個熱誠的工程師被老闆刻薄和無理解僱的故事的電影，另一組的B會看肥皂劇FRIENDS。實驗結果是，看FRIENDS的人會比較容易接受刻薄比例的金錢。
也 許關係不是很緊密，我聯想到的，是當一個地方的人們慣於overdosed with 某類娛樂如富豪、明星的八卦和炒作新聞，報喜不報憂、只以不愁生活只愁升遷和愛情的人們做主角的主流電視劇等等…是不是令有些人不向更強者抽刃，卻向更弱 者抽刃的心態，在心理學上有一定的影響力存在……
(註︰我是喜歡看FRIENDS的，只想強調媒體在發送什麼節目、製造什麼環境認知的因素)
Rant 1: Where the rage comes from? (貼於2月19日)
魯迅︰勇者憤怒，抽刃向更強者；怯者憤怒，卻抽刃向更弱者。
最近那個失業碩士生的新聞，在fb被迫要看到一堆批評當事人的短留言，更有趣的，也有一些跟他年紀不是相差很多的人，以長輩教導後輩的語氣留言，但在自己的wall留言，對方又怎會看到呢…
很容易想像的，又一個例子被無限放大，跑出"現在的年輕怎樣不濟"、"不要什麼都靠人"之類的結論。關於這些statement的可疑之處，下篇再說。
然後，我又想到了在機場見識到的港人的rage，我覺得這些不只是一連串個別事件，而是人們的精神受到壓抑，急於找東西發洩的潛意識。於是，我想起了上面這句魯迅的句子，至於"更強者"是誰，請自行想像，因為說了出來又要寫長篇大論…
本來動氣的迫力恢復了不少，想要大rant 一番，不過今天剛好有篇專欄說的跟我想的都差不多，所以就貼下來。
----------------------
碩士被嘲記 (陳強)
am730 (2011年2月18日)
我 有個很大的問題——每當大家一面倒在罵一個人或嘲諷一個人，我發現我總是站在另一個位置。我發毒誓說，我從不為了效果而站在我不相信的地方，因此我更懷疑 自己是不是有甚麼缺憾了。最近的一個例子，就是本周內所有年輕一輩都有談論過的「碩士生面試失敗二百次」的新聞，我一看畢那個報道，全民嘲諷，我竟然沒有 想過罵他而是替他難過……
首先他究竟做錯了甚麼？他又沒犯法，也沒傷害人，他找不到工作，面試失敗二百次又何以能夠成為一項罪名呢？可是，當碩士 生上了頭版，每個人看完他的故事都好像要罵他幾句抽抽水。我卻完全沒有半絲意圖想去罵他。假若我們學那麼老氣橫秋的長輩說「罵他其實是教精他呀」，我那麼 忙，為何要花時間去「教精他」而不去「教精」一下我更在意的堂弟呢？因此呀，今天我要寫他，不是因為我需要寫他，而是因為我覺得我們根本不需要寫他。
不 需要寫他，也不需要這樣寫他。報紙如果要借人家的故事來表達立場，假設她要說的是「香港教育制度真不濟」吧，其實說「有一名碩士生」就好了，何需把別人的 樣子和全名都放出來呢？在我看來，那碩士生是有點笨的，笨不在他沒有見工技巧，而是給人利用得那麼徹底，為何還是沒發覺出來呢？可是，這也不應該是罪名。 我說過了，笨怎麼會是錯呢？你應該幫助他才對啊！純粹利用他的才是真正犯錯的人！
接著我還發現大家的邏輯都是對他不公平的。碩士生找不到工作，又 不是因為他是碩士生！那怎麼能在中間加上那麼大的關聯？倒過來想，假設香港有個零分會考生，一見工即成，大家會認為他見工成功，是「因為」他會考零分嗎？ 兩件事沒有「因果」關係吧！一個人沒自信，見工技巧欠佳，是個別的問題呀，學校也真的沒有考！零分至三十分的會考生也會有性格缺憾，碩士生也可以有性格缺 憾。
最後我更覺得大家不要輕藐他甚至攻擊他，因為這裡是自由社會呀！他見工不濟，就沒有人請！這就是他的「懲罰」呀！哪還需要給他更多的折磨嗎？他發了達又跟你有關係麼？
最近那個失業碩士生的新聞，在fb被迫要看到一堆批評當事人的短留言，更有趣的，也有一些跟他年紀不是相差很多的人，以長輩教導後輩的語氣留言，但在自己的wall留言，對方又怎會看到呢…
很容易想像的，又一個例子被無限放大，跑出"現在的年輕怎樣不濟"、"不要什麼都靠人"之類的結論。關於這些statement的可疑之處，下篇再說。
然後，我又想到了在機場見識到的港人的rage，我覺得這些不只是一連串個別事件，而是人們的精神受到壓抑，急於找東西發洩的潛意識。於是，我想起了上面這句魯迅的句子，至於"更強者"是誰，請自行想像，因為說了出來又要寫長篇大論…
本來動氣的迫力恢復了不少，想要大rant 一番，不過今天剛好有篇專欄說的跟我想的都差不多，所以就貼下來。
----------------------
碩士被嘲記 (陳強)
am730 (2011年2月18日)
我 有個很大的問題——每當大家一面倒在罵一個人或嘲諷一個人，我發現我總是站在另一個位置。我發毒誓說，我從不為了效果而站在我不相信的地方，因此我更懷疑 自己是不是有甚麼缺憾了。最近的一個例子，就是本周內所有年輕一輩都有談論過的「碩士生面試失敗二百次」的新聞，我一看畢那個報道，全民嘲諷，我竟然沒有 想過罵他而是替他難過……
首先他究竟做錯了甚麼？他又沒犯法，也沒傷害人，他找不到工作，面試失敗二百次又何以能夠成為一項罪名呢？可是，當碩士 生上了頭版，每個人看完他的故事都好像要罵他幾句抽抽水。我卻完全沒有半絲意圖想去罵他。假若我們學那麼老氣橫秋的長輩說「罵他其實是教精他呀」，我那麼 忙，為何要花時間去「教精他」而不去「教精」一下我更在意的堂弟呢？因此呀，今天我要寫他，不是因為我需要寫他，而是因為我覺得我們根本不需要寫他。
不 需要寫他，也不需要這樣寫他。報紙如果要借人家的故事來表達立場，假設她要說的是「香港教育制度真不濟」吧，其實說「有一名碩士生」就好了，何需把別人的 樣子和全名都放出來呢？在我看來，那碩士生是有點笨的，笨不在他沒有見工技巧，而是給人利用得那麼徹底，為何還是沒發覺出來呢？可是，這也不應該是罪名。 我說過了，笨怎麼會是錯呢？你應該幫助他才對啊！純粹利用他的才是真正犯錯的人！
接著我還發現大家的邏輯都是對他不公平的。碩士生找不到工作，又 不是因為他是碩士生！那怎麼能在中間加上那麼大的關聯？倒過來想，假設香港有個零分會考生，一見工即成，大家會認為他見工成功，是「因為」他會考零分嗎？ 兩件事沒有「因果」關係吧！一個人沒自信，見工技巧欠佳，是個別的問題呀，學校也真的沒有考！零分至三十分的會考生也會有性格缺憾，碩士生也可以有性格缺 憾。
最後我更覺得大家不要輕藐他甚至攻擊他，因為這裡是自由社會呀！他見工不濟，就沒有人請！這就是他的「懲罰」呀！哪還需要給他更多的折磨嗎？他發了達又跟你有關係麼？
被道具主宰自己 (貼於2月17日)
在http://danariely.com 瀏覽時見到一篇留言，提到人們常常未弄清後果就行動的特點，看到這篇文章。雖然有點長，不過真的可以引起不少思考，我比較在意的是出事後的問責性的可塑性是否會被模糊化。
本來，電腦化只是個中性的字。不過從文中見到的事件，這些Algorithms還會進行對沖和短炒。金融市場本來功能是提供企業家融資的平台，得到資本後 進行生產或提供服務。對沖和短炒對促進生產力沒有幫助…反而會製造波動。這種過度效率，還會加速把這些沒有實際作用的提升價值集中到少數人手中。
電腦不過是人造的，所以可以一直調較…可是在文中，似乎當事者(使用的人)自己也不太清楚它的行為模式，在寵大而複雜多變的實際市場上的反應是怎樣。從提到的上年那些事件中，看來是每次出事都來補救…
也許這一方面在將來會因不斷出事而不斷改善(汗)，但這又顯出了人們容易把看起來很amazing的東西，其後果未完全弄清楚、未完善化就大幅度及廣泛應用的危險行為。我覺得這現象有"被道具主宰自己的感覺"，好像是一種不自覺的瘋狂…
聯想到了Metal Gear Solid裏的PATRIOTS系統…
-------------------
原址︰http://www.wired.com/magazine/2010/12/ff_ai_flashtrading/all/1
Algorithms take control of Wall Street (by Felix Salmon and Jon Stokes)
Last spring, Dow Jones launched a new service called Lexicon, which sends real-time financial news to professional investors. This in itself is not surprising. The company behind The Wall Street Journal and Dow Jones Newswires made its name by publishing the kind of news that moves the stock market. But many of the professional investors subscribing to Lexicon aren’t human—they’re algorithms, the lines of code that govern an increasing amount of global trading activity—and they don’t read news the way humans do. They don’t need their information delivered in the form of a story or even in sentences. They just want data—the hard, actionable information that those words represent.
Lexicon packages the news in a way that its robo-clients can understand. It scans every Dow Jones story in real time, looking for textual clues that might indicate how investors should feel about a stock. It then sends that information in machine-readable form to its algorithmic subscribers, which can parse it further, using the resulting data to inform their own investing decisions. Lexicon has helped automate the process of reading the news, drawing insight from it, and using that information to buy or sell a stock. The machines aren’t there just to crunch numbers anymore; they’re now making the decisions.
That increasingly describes the entire financial system. Over the past decade, algorithmic trading has overtaken the industry. From the single desk of a startup hedge fund to the gilded halls of Goldman Sachs, computer code is now responsible for most of the activity on Wall Street. (By some estimates, computer-aided high-frequency trading now accounts for about 70 percent of total trade volume.) Increasingly, the market’s ups and downs are determined not by traders competing to see who has the best information or sharpest business mind but by algorithms feverishly scanning for faint signals of potential profit.
Algorithms have become so ingrained in our financial system that the markets could not operate without them. At the most basic level, computers help prospective buyers and sellers of stocks find one another—without the bother of screaming middlemen or their commissions. High-frequency traders, sometimes called flash traders, buy and sell thousands of shares every second, executing deals so quickly, and on such a massive scale, that they can win or lose a fortune if the price of a stock fluctuates by even a few cents. Other algorithms are slower but more sophisticated, analyzing earning statements, stock performance, and newsfeeds to find attractive investments that others may have missed. The result is a system that is more efficient, faster, and smarter than any human.
It is also harder to understand, predict, and regulate. Algorithms, like most human traders, tend to follow a fairly simple set of rules. But they also respond instantly to ever-shifting market conditions, taking into account thousands or millions of data points every second. And each trade produces new data points, creating a kind of conversation in which machines respond in rapid-fire succession to one another’s actions. At its best, this system represents an efficient and intelligent capital allocation machine, a market ruled by precision and mathematics rather than emotion and fallible judgment.
But at its worst, it is an inscrutable and uncontrollable feedback loop. Individually, these algorithms may be easy to control but when they interact they can create unexpected behaviors—a conversation that can overwhelm the system it was built to navigate. On May 6, 2010, the Dow Jones Industrial Average inexplicably experienced a series of drops that came to be known as the flash crash, at one point shedding some 573 points in five minutes. Less than five months later, Progress Energy, a North Carolina utility, watched helplessly as its share price fell 90 percent. Also in late September, Apple shares dropped nearly 4 percent in just 30 seconds, before recovering a few minutes later.
These sudden drops are now routine, and it’s often impossible to determine what caused them. But most observers pin the blame on the legions of powerful, superfast trading algorithms—simple instructions that interact to create a market that is incomprehensible to the human mind and impossible to predict.
For better or worse, the computers are now in control.
Ironically enough, the notion of using algorithms as trading tools was born as a way of empowering traders. Before the age of electronic trading, large institutional investors used their size and connections to wrangle better terms from the human middlemen that executed buy and sell orders. “We were not getting the same access to capital,” says Harold Bradley, former head of American Century Ventures, a division of a midsize Kansas City investment firm. “So I had to change the rules.”
Bradley was among the first traders to explore the power of algorithms in the late ’90s, creating approaches to investing that favored brains over access. It took him nearly three years to build his stock-scoring program. First he created a neural network, painstakingly training it to emulate his thinking—to recognize the combination of factors that his instincts and experience told him were indicative of a significant move in a stock’s price.
But Bradley didn’t just want to build a machine that would think the same way he did. He wanted his algorithmically derived system to look at stocks in a fundamentally different—and smarter—way than humans ever could. So in 2000, Bradley assembled a team of engineers to determine which characteristics were most predictive of a stock’s performance. They identified a number of variables—traditional measurements like earnings growth as well as more technical factors. Altogether, Bradley came up with seven key factors, including the judgment of his neural network, that he thought might be useful in predicting a portfolio’s performance.
He then tried to determine the proper weighting of each characteristic, using a publicly available program from UC Berkeley called the differential evolution optimizer. Bradley started with random weightings—perhaps earnings growth would be given twice the weight of revenue growth, for example. Then the program looked at the best-performing stocks at a given point in time. It then picked 10 of those stocks at random and looked at historical data to see how well the weights predicted their actual performance. Next the computer would go back and do the same thing all over again—with a slightly different starting date or a different starting group of stocks. For each weighting, the test would be run thousands of times to get a thorough sense of how those stocks performed. Then the weighting would be changed and the whole process would run all over again. Eventually, Bradley’s team collected performance data for thousands of weightings.
Once this process was complete, Bradley collected the 10 best-performing weightings and ran them once again through the differential evolution optimizer. The optimizer then mated those weightings—combining them to create 100 or so offspring weightings. Those weightings were tested, and the 10 best were mated again to produce another 100 third-generation offspring. (The program also introduced occasional mutations and randomness, on the off chance that one of them might produce an accidental genius.) After dozens of generations, Bradley’s team discovered ideal weightings. (In 2007, Bradley left to manage the Kauffman Foundation’s $1.8 billion investment fund and says he can no longer discuss his program’s performance.)
Bradley’s effort was just the beginning. Before long, investors and portfolio managers began to tap the world’s premier math, science, and engineering schools for talent. These academics brought to trading desks sophisticated knowledge of AI methods from computer science and statistics.
And they started applying those methods to every aspect of the financial industry. Some built algorithms to perform the familiar function of discovering, buying, and selling individual stocks (a practice known as proprietary, or “prop,” trading). Others devised algorithms to help brokers execute large trades—massive buy or sell orders that take a while to go through and that become vulnerable to price manipulation if other traders sniff them out before they’re completed. These algorithms break up and optimize those orders to conceal them from the rest of the market. (This, confusingly enough, is known as algorithmic trading.) Still others are used to crack those codes, to discover the massive orders that other quants are trying to conceal. (This is called predatory trading.)
The result is a universe of competing lines of code, each of them trying to outsmart and one-up the other. “We often discuss it in terms of The Hunt for Red October, like submarine warfare,” says Dan Mathisson, head of Advanced Execution Services at Credit Suisse. “There are predatory traders out there that are constantly probing in the dark, trying to detect the presence of a big submarine coming through. And the job of the algorithmic trader is to make that submarine as stealth as possible.”
Meanwhile, these algorithms tend to see the market from a machine’s point of view, which can be very different from a human’s. Rather than focus on the behavior of individual stocks, for instance, many prop-trading algorithms look at the market as a vast weather system, with trends and movements that can be predicted and capitalized upon. These patterns may not be visible to humans, but computers, with their ability to analyze massive amounts of data at lightning speed, can sense them.
The partners at Voleon Capital Management, a three-year-old firm in Berkeley, California, take this approach. Voleon engages in statistical arbitrage, which involves sifting through enormous pools of data for patterns that can predict subtle movements across a whole class of related stocks.
Situated on the third floor of a run-down office building, Voleon could be any other Bay Area web startup. Geeks pad around the office in jeans and T-shirts, moving amid half-open boxes and scribbled whiteboards. Cofounder Jon McAuliffe is a stats wonk from Berkeley and Harvard University whose rè9sumè9 includes a stint at Amazon.com working on the company’s recommendation engine. The other cofounder, CEO Michael Kharitonov, is a computer scientist from Berkeley and Stanford who formerly ran a networking startup.
To hear them describe it, their trading strategy bears more resemblance to those data-analysis projects than to classical investing. Indeed, McAuliffe and Kharitonov say that they don’t even know what their bots are looking for or how they reach their conclusions. “What we say is ‘Here’s a bunch of data. Extract the signal from the noise,’” Kharitonov says. “We don’t know what that signal is going to be like.”
“The kind of trading strategies our system uses are not the kind of strategies that humans use,” Kharitonov continues. “We’re not competing with humans, because when you’re trading thousands of stocks simultaneously, trying to capture very, very small changes, the human brain is just not good at that. We’re playing on a different field, trying to exploit effects that are too complex for the human brain. They require you to look at hundreds of thousands of things simultaneously and to be trading a little bit of each stock. Humans just can’t do that.”
In late September, the Commodity Futures Trading Commission and the Securities and Exchange Commission released a 104-page report on the May 6 flash crash. The culprit, the report determined, was a “large fundamental trader” that had used an algorithm to hedge its stock market position. The trade was executed in just 20 minutes—an extremely aggressive time frame, which triggered a market plunge as other algorithms reacted, first to the sale and then to one another’s behavior. The chaos produced seemingly nonsensical trades—shares of Accenture were sold for a penny, for instance, while shares of Apple were purchased for $100,000 each. (Both trades were subsequently canceled.) The activity briefly paralyzed the entire financial system.
The report offered some belated clarity about an event that for months had resisted easy interpretation. Legislators and regulators, spooked by behavior they couldn’t explain, much less predict or prevent, began taking a harder look at computer trading. In the wake of the flash crash, Mary Schapiro, chair of the Securities and Exchange Commission, publicly mused that humans may need to wrest some control back from the machines. “Automated trading systems will follow their coded logic regardless of outcome,” she told a congressional subcommittee, “while human involvement likely would have prevented these orders from executing at absurd prices.” Delaware senator Ted Kaufman sounded an even louder alarm in September, taking to the Senate floor to declare, “Whenever there is a lot of money surging into a risky area, where change in the market is dramatic, where there is no transparency and therefore no effective regulation, we have a prescription for disaster.”
In the months after the flash crash, the SEC announced a variety of measures to prevent anything like it from occurring again. In June, it imposed circuit breakers, rules that automatically halt trading if a stock’s price fluctuates by more than 10 percent in five minutes. (In September, the SEC’s Schapiro announced that the agency might tweak the circuit breakers to prevent unnecessary freezes.) The agency is considering requiring trading algorithms to include a governor, which limits the size and speed at which trades can be executed. And it has also proposed the creation of a so-called consolidated audit trail, a single database that would collect information on every trade and execution, and which would—in the words of an SEC press release—”help regulators keep pace with new technology and trading patterns in the markets.” Others have suggested implementing a transaction tax, which would impose a particular burden on massive, lightning-fast trades.
But these are not ways of controlling the algorithms—they are ways of slowing them down or stopping them for a few minutes. That’s a tacit admission that the system has outgrown the humans that created it. Today a single stock can receive 10,000 bids per second; that deluge of data overwhelms any attempt to create a simple cause-and-effect narrative. “Our financial markets have become a largely automated adaptive dynamical system, with feedback,” says Michael Kearns, a computer science professor at the University of Pennsylvania who has built algorithms for various Wall Street firms. “There’s no science I’m aware of that’s up to the task of understanding its potential implications.”
For individual investors, trading with algorithms has been a boon: Today, they can buy and sell stocks much faster, cheaper, and easier than ever before. But from a systemic perspective, the stock market risks spinning out of control. Even if each individual algorithm makes perfect sense, collectively they obey an emergent logic—artificial intelligence, but not artificial human intelligence. It is, simply, alien, operating at the natural scale of silicon, not neurons and synapses. We may be able to slow it down, but we can never contain, control, or comprehend it. It’s the machines’ market now; we just trade in it.
本來，電腦化只是個中性的字。不過從文中見到的事件，這些Algorithms還會進行對沖和短炒。金融市場本來功能是提供企業家融資的平台，得到資本後 進行生產或提供服務。對沖和短炒對促進生產力沒有幫助…反而會製造波動。這種過度效率，還會加速把這些沒有實際作用的提升價值集中到少數人手中。
電腦不過是人造的，所以可以一直調較…可是在文中，似乎當事者(使用的人)自己也不太清楚它的行為模式，在寵大而複雜多變的實際市場上的反應是怎樣。從提到的上年那些事件中，看來是每次出事都來補救…
也許這一方面在將來會因不斷出事而不斷改善(汗)，但這又顯出了人們容易把看起來很amazing的東西，其後果未完全弄清楚、未完善化就大幅度及廣泛應用的危險行為。我覺得這現象有"被道具主宰自己的感覺"，好像是一種不自覺的瘋狂…
聯想到了Metal Gear Solid裏的PATRIOTS系統…
-------------------
原址︰http://www.wired.com/magazine/2010/12/ff_ai_flashtrading/all/1
Algorithms take control of Wall Street (by Felix Salmon and Jon Stokes)
Last spring, Dow Jones launched a new service called Lexicon, which sends real-time financial news to professional investors. This in itself is not surprising. The company behind The Wall Street Journal and Dow Jones Newswires made its name by publishing the kind of news that moves the stock market. But many of the professional investors subscribing to Lexicon aren’t human—they’re algorithms, the lines of code that govern an increasing amount of global trading activity—and they don’t read news the way humans do. They don’t need their information delivered in the form of a story or even in sentences. They just want data—the hard, actionable information that those words represent.
Lexicon packages the news in a way that its robo-clients can understand. It scans every Dow Jones story in real time, looking for textual clues that might indicate how investors should feel about a stock. It then sends that information in machine-readable form to its algorithmic subscribers, which can parse it further, using the resulting data to inform their own investing decisions. Lexicon has helped automate the process of reading the news, drawing insight from it, and using that information to buy or sell a stock. The machines aren’t there just to crunch numbers anymore; they’re now making the decisions.
That increasingly describes the entire financial system. Over the past decade, algorithmic trading has overtaken the industry. From the single desk of a startup hedge fund to the gilded halls of Goldman Sachs, computer code is now responsible for most of the activity on Wall Street. (By some estimates, computer-aided high-frequency trading now accounts for about 70 percent of total trade volume.) Increasingly, the market’s ups and downs are determined not by traders competing to see who has the best information or sharpest business mind but by algorithms feverishly scanning for faint signals of potential profit.
Algorithms have become so ingrained in our financial system that the markets could not operate without them. At the most basic level, computers help prospective buyers and sellers of stocks find one another—without the bother of screaming middlemen or their commissions. High-frequency traders, sometimes called flash traders, buy and sell thousands of shares every second, executing deals so quickly, and on such a massive scale, that they can win or lose a fortune if the price of a stock fluctuates by even a few cents. Other algorithms are slower but more sophisticated, analyzing earning statements, stock performance, and newsfeeds to find attractive investments that others may have missed. The result is a system that is more efficient, faster, and smarter than any human.
It is also harder to understand, predict, and regulate. Algorithms, like most human traders, tend to follow a fairly simple set of rules. But they also respond instantly to ever-shifting market conditions, taking into account thousands or millions of data points every second. And each trade produces new data points, creating a kind of conversation in which machines respond in rapid-fire succession to one another’s actions. At its best, this system represents an efficient and intelligent capital allocation machine, a market ruled by precision and mathematics rather than emotion and fallible judgment.
But at its worst, it is an inscrutable and uncontrollable feedback loop. Individually, these algorithms may be easy to control but when they interact they can create unexpected behaviors—a conversation that can overwhelm the system it was built to navigate. On May 6, 2010, the Dow Jones Industrial Average inexplicably experienced a series of drops that came to be known as the flash crash, at one point shedding some 573 points in five minutes. Less than five months later, Progress Energy, a North Carolina utility, watched helplessly as its share price fell 90 percent. Also in late September, Apple shares dropped nearly 4 percent in just 30 seconds, before recovering a few minutes later.
These sudden drops are now routine, and it’s often impossible to determine what caused them. But most observers pin the blame on the legions of powerful, superfast trading algorithms—simple instructions that interact to create a market that is incomprehensible to the human mind and impossible to predict.
For better or worse, the computers are now in control.
Ironically enough, the notion of using algorithms as trading tools was born as a way of empowering traders. Before the age of electronic trading, large institutional investors used their size and connections to wrangle better terms from the human middlemen that executed buy and sell orders. “We were not getting the same access to capital,” says Harold Bradley, former head of American Century Ventures, a division of a midsize Kansas City investment firm. “So I had to change the rules.”
Bradley was among the first traders to explore the power of algorithms in the late ’90s, creating approaches to investing that favored brains over access. It took him nearly three years to build his stock-scoring program. First he created a neural network, painstakingly training it to emulate his thinking—to recognize the combination of factors that his instincts and experience told him were indicative of a significant move in a stock’s price.
But Bradley didn’t just want to build a machine that would think the same way he did. He wanted his algorithmically derived system to look at stocks in a fundamentally different—and smarter—way than humans ever could. So in 2000, Bradley assembled a team of engineers to determine which characteristics were most predictive of a stock’s performance. They identified a number of variables—traditional measurements like earnings growth as well as more technical factors. Altogether, Bradley came up with seven key factors, including the judgment of his neural network, that he thought might be useful in predicting a portfolio’s performance.
He then tried to determine the proper weighting of each characteristic, using a publicly available program from UC Berkeley called the differential evolution optimizer. Bradley started with random weightings—perhaps earnings growth would be given twice the weight of revenue growth, for example. Then the program looked at the best-performing stocks at a given point in time. It then picked 10 of those stocks at random and looked at historical data to see how well the weights predicted their actual performance. Next the computer would go back and do the same thing all over again—with a slightly different starting date or a different starting group of stocks. For each weighting, the test would be run thousands of times to get a thorough sense of how those stocks performed. Then the weighting would be changed and the whole process would run all over again. Eventually, Bradley’s team collected performance data for thousands of weightings.
Once this process was complete, Bradley collected the 10 best-performing weightings and ran them once again through the differential evolution optimizer. The optimizer then mated those weightings—combining them to create 100 or so offspring weightings. Those weightings were tested, and the 10 best were mated again to produce another 100 third-generation offspring. (The program also introduced occasional mutations and randomness, on the off chance that one of them might produce an accidental genius.) After dozens of generations, Bradley’s team discovered ideal weightings. (In 2007, Bradley left to manage the Kauffman Foundation’s $1.8 billion investment fund and says he can no longer discuss his program’s performance.)
Bradley’s effort was just the beginning. Before long, investors and portfolio managers began to tap the world’s premier math, science, and engineering schools for talent. These academics brought to trading desks sophisticated knowledge of AI methods from computer science and statistics.
And they started applying those methods to every aspect of the financial industry. Some built algorithms to perform the familiar function of discovering, buying, and selling individual stocks (a practice known as proprietary, or “prop,” trading). Others devised algorithms to help brokers execute large trades—massive buy or sell orders that take a while to go through and that become vulnerable to price manipulation if other traders sniff them out before they’re completed. These algorithms break up and optimize those orders to conceal them from the rest of the market. (This, confusingly enough, is known as algorithmic trading.) Still others are used to crack those codes, to discover the massive orders that other quants are trying to conceal. (This is called predatory trading.)
The result is a universe of competing lines of code, each of them trying to outsmart and one-up the other. “We often discuss it in terms of The Hunt for Red October, like submarine warfare,” says Dan Mathisson, head of Advanced Execution Services at Credit Suisse. “There are predatory traders out there that are constantly probing in the dark, trying to detect the presence of a big submarine coming through. And the job of the algorithmic trader is to make that submarine as stealth as possible.”
Meanwhile, these algorithms tend to see the market from a machine’s point of view, which can be very different from a human’s. Rather than focus on the behavior of individual stocks, for instance, many prop-trading algorithms look at the market as a vast weather system, with trends and movements that can be predicted and capitalized upon. These patterns may not be visible to humans, but computers, with their ability to analyze massive amounts of data at lightning speed, can sense them.
The partners at Voleon Capital Management, a three-year-old firm in Berkeley, California, take this approach. Voleon engages in statistical arbitrage, which involves sifting through enormous pools of data for patterns that can predict subtle movements across a whole class of related stocks.
Situated on the third floor of a run-down office building, Voleon could be any other Bay Area web startup. Geeks pad around the office in jeans and T-shirts, moving amid half-open boxes and scribbled whiteboards. Cofounder Jon McAuliffe is a stats wonk from Berkeley and Harvard University whose rè9sumè9 includes a stint at Amazon.com working on the company’s recommendation engine. The other cofounder, CEO Michael Kharitonov, is a computer scientist from Berkeley and Stanford who formerly ran a networking startup.
To hear them describe it, their trading strategy bears more resemblance to those data-analysis projects than to classical investing. Indeed, McAuliffe and Kharitonov say that they don’t even know what their bots are looking for or how they reach their conclusions. “What we say is ‘Here’s a bunch of data. Extract the signal from the noise,’” Kharitonov says. “We don’t know what that signal is going to be like.”
“The kind of trading strategies our system uses are not the kind of strategies that humans use,” Kharitonov continues. “We’re not competing with humans, because when you’re trading thousands of stocks simultaneously, trying to capture very, very small changes, the human brain is just not good at that. We’re playing on a different field, trying to exploit effects that are too complex for the human brain. They require you to look at hundreds of thousands of things simultaneously and to be trading a little bit of each stock. Humans just can’t do that.”
In late September, the Commodity Futures Trading Commission and the Securities and Exchange Commission released a 104-page report on the May 6 flash crash. The culprit, the report determined, was a “large fundamental trader” that had used an algorithm to hedge its stock market position. The trade was executed in just 20 minutes—an extremely aggressive time frame, which triggered a market plunge as other algorithms reacted, first to the sale and then to one another’s behavior. The chaos produced seemingly nonsensical trades—shares of Accenture were sold for a penny, for instance, while shares of Apple were purchased for $100,000 each. (Both trades were subsequently canceled.) The activity briefly paralyzed the entire financial system.
The report offered some belated clarity about an event that for months had resisted easy interpretation. Legislators and regulators, spooked by behavior they couldn’t explain, much less predict or prevent, began taking a harder look at computer trading. In the wake of the flash crash, Mary Schapiro, chair of the Securities and Exchange Commission, publicly mused that humans may need to wrest some control back from the machines. “Automated trading systems will follow their coded logic regardless of outcome,” she told a congressional subcommittee, “while human involvement likely would have prevented these orders from executing at absurd prices.” Delaware senator Ted Kaufman sounded an even louder alarm in September, taking to the Senate floor to declare, “Whenever there is a lot of money surging into a risky area, where change in the market is dramatic, where there is no transparency and therefore no effective regulation, we have a prescription for disaster.”
In the months after the flash crash, the SEC announced a variety of measures to prevent anything like it from occurring again. In June, it imposed circuit breakers, rules that automatically halt trading if a stock’s price fluctuates by more than 10 percent in five minutes. (In September, the SEC’s Schapiro announced that the agency might tweak the circuit breakers to prevent unnecessary freezes.) The agency is considering requiring trading algorithms to include a governor, which limits the size and speed at which trades can be executed. And it has also proposed the creation of a so-called consolidated audit trail, a single database that would collect information on every trade and execution, and which would—in the words of an SEC press release—”help regulators keep pace with new technology and trading patterns in the markets.” Others have suggested implementing a transaction tax, which would impose a particular burden on massive, lightning-fast trades.
But these are not ways of controlling the algorithms—they are ways of slowing them down or stopping them for a few minutes. That’s a tacit admission that the system has outgrown the humans that created it. Today a single stock can receive 10,000 bids per second; that deluge of data overwhelms any attempt to create a simple cause-and-effect narrative. “Our financial markets have become a largely automated adaptive dynamical system, with feedback,” says Michael Kearns, a computer science professor at the University of Pennsylvania who has built algorithms for various Wall Street firms. “There’s no science I’m aware of that’s up to the task of understanding its potential implications.”
For individual investors, trading with algorithms has been a boon: Today, they can buy and sell stocks much faster, cheaper, and easier than ever before. But from a systemic perspective, the stock market risks spinning out of control. Even if each individual algorithm makes perfect sense, collectively they obey an emergent logic—artificial intelligence, but not artificial human intelligence. It is, simply, alien, operating at the natural scale of silicon, not neurons and synapses. We may be able to slow it down, but we can never contain, control, or comprehend it. It’s the machines’ market now; we just trade in it.
大工地上︰中國農民工之歌 (貼於2月15日)
在渡假的時候，在兩天內看完了此書。雖然有些段落大意重覆過幾回，不過還是在新聞上很有閱讀價值。
本港的傳媒(我不知道內地 和其他國家是否如此)在提及內地經濟的時候，焦點總是放在房地產、內企CEO的觀點(近期在書店的Best Seller，有本書叫"巨龍的腳印"，原來是內企CEO的訪談集，企業CEO被捧上巨龍的位置了)、"自由神"的購買力等等，其實有為數幾千萬的農民建 築工的情況被忽略了。
本書講述了發展商如何透過刻意多層外包，迴避勞工法的責任，透過拖欠工資，作為變相無利息貸款，以一倍的資本蓋幾倍價值的房產。突出某些政府默許的情況所形成的社會結構性問題，已經不是說"發展是硬道理"的時候了。
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原址︰http://chuiyung.blogspirit.com/archive/2011/01/21/%E5%A4%A7%E5%B7%A5%E5%9C%B0%E4%B8%8A.html
大工地上 (張翠容介紹文)
「如果有一天我老無所依，請把我留在，在那時光裏；如果有一天我悄然離去，請把我埋在這春天裏。」
一首由內地歌手汪峰演繹的歌曲《春天裏》，一次由兩名農民工翻唱，反而令到這首歌充滿血肉，寫照出農民工的無奈，道盡了農民工心底裏的一番話。
專門研究中國農民工的香港理工大學社工學系教授潘毅，以及她的團隊，最近終於出版了一本書《大工地上中國農民工之歌》。
去 年有機會跟着潘毅的團隊，走訪北京偏西北一個大工地，親身觀察他們在工地上與農民工的工作，驚訝於有如斯一位香港學者和她的內地研究人員，可以這 樣深情地走進農民工的生活裏，與農民工一起走過艱辛的春夏秋冬，體味他們的悲歡離合、喜怒哀樂，卻又不失研究學者的一雙冷靜眼睛。
究竟冷與熱應該怎樣拿捏呢？比記者報道更深層次的，就是學者的理論功夫，潘毅等人寫來軟硬兼施，讓讀者投入其滿載情感的文字故事裏之餘，仍能保持批判與省思。
如果我們關心中國的命運，便不得不也關心農民工的命運，因為中國的繁榮是用他們的一雙手打拼出來的。
他們那雙無形的手，其實是這般實在地出現在我們的眼前，為甚麼我們可以視而不見？
我們只關注中國在現代化過程中，新的中產階層如何形成；可是，我們又有沒有看到，農民工作為中國新工人階級的出現，他們所扮演的角色又是甚麼？
今年是辛亥革命一百周年，中國從封建走向共和，而民主又是怎麼一回事？中產階段高喊：人權萬歲，但中國最底階層的聲音在哪裏？
我們聽到了嗎？就在《大工地上》。
去年一個富士康的跳樓潮，我們才驚覺三十年的開放改革有哭聲。可是，討論熱了一陣子，又歸於沉寂。
中國大工地上的沙塵依然滾滾，隆隆聲音繼續震耳欲聾。《大工地上》一書出得及時，它把我們的視綫拉回農民工身上，叫人不要忘記；更何況作為香港人的作者潘毅，她所代表的就是香港人在中國內地一片大工地上的一道鵲橋，好讓農民工模糊的面貌在香港得以被看見。
香港在分享中國改革三十年成果的同時，不應漠視背後默默播種耕耘的農民工。
記憶所及，內地有好幾位作家寫過中國工人的作品，例如戴晴、賈章柯、陳貴隸與吳春桃，但香港人詳細書寫中國工人現況的書籍，仍是鳳毛麟角，因此，《大工地上》是值得我們去親近的。
在 中國內地，農民工的身份是非常特殊的。我們甚至不太了解「農民工」是怎麼一回事，只知道每當走過中國各大城市的火車站，總會看到一群又一 群密麻麻農民樣子的人，或疲累地蹲在地上，或漫無目的站在車站附近，頭髮油滑、皮膚黝黑、衣服破舊，一雙鞋子也差點兒踏破了，口中操着土話。我們下意識地 與他們保持距離，又或心中帶點恐懼、厭惡，繞道而行。
《大工地上》還了他們一個公道，首先讓他們的身份以正視聽。為甚麼他們總擺脫不了農民這個身份？中國的戶籍制度令他們永遠被釘在農村這個印記上，他們建設城市卻無法享受城市，種種一切權力，他們成為城市重重疊疊的孤影。
「南柯一夢三十年，放下左右逢源。都說你真美好，風有風的風骨，雨有雨的輕泣。輕輕的你倒下，夜夜守護家鄉的星空，不帶走一分工錢。」
借《大工地上》，讓農民工走到香港人的面前，他們的聲音如雨水打在我們的心房。
本港的傳媒(我不知道內地 和其他國家是否如此)在提及內地經濟的時候，焦點總是放在房地產、內企CEO的觀點(近期在書店的Best Seller，有本書叫"巨龍的腳印"，原來是內企CEO的訪談集，企業CEO被捧上巨龍的位置了)、"自由神"的購買力等等，其實有為數幾千萬的農民建 築工的情況被忽略了。
本書講述了發展商如何透過刻意多層外包，迴避勞工法的責任，透過拖欠工資，作為變相無利息貸款，以一倍的資本蓋幾倍價值的房產。突出某些政府默許的情況所形成的社會結構性問題，已經不是說"發展是硬道理"的時候了。
----------------------
原址︰http://chuiyung.blogspirit.com/archive/2011/01/21/%E5%A4%A7%E5%B7%A5%E5%9C%B0%E4%B8%8A.html
大工地上 (張翠容介紹文)
「如果有一天我老無所依，請把我留在，在那時光裏；如果有一天我悄然離去，請把我埋在這春天裏。」
一首由內地歌手汪峰演繹的歌曲《春天裏》，一次由兩名農民工翻唱，反而令到這首歌充滿血肉，寫照出農民工的無奈，道盡了農民工心底裏的一番話。
專門研究中國農民工的香港理工大學社工學系教授潘毅，以及她的團隊，最近終於出版了一本書《大工地上中國農民工之歌》。
去 年有機會跟着潘毅的團隊，走訪北京偏西北一個大工地，親身觀察他們在工地上與農民工的工作，驚訝於有如斯一位香港學者和她的內地研究人員，可以這 樣深情地走進農民工的生活裏，與農民工一起走過艱辛的春夏秋冬，體味他們的悲歡離合、喜怒哀樂，卻又不失研究學者的一雙冷靜眼睛。
究竟冷與熱應該怎樣拿捏呢？比記者報道更深層次的，就是學者的理論功夫，潘毅等人寫來軟硬兼施，讓讀者投入其滿載情感的文字故事裏之餘，仍能保持批判與省思。
如果我們關心中國的命運，便不得不也關心農民工的命運，因為中國的繁榮是用他們的一雙手打拼出來的。
他們那雙無形的手，其實是這般實在地出現在我們的眼前，為甚麼我們可以視而不見？
我們只關注中國在現代化過程中，新的中產階層如何形成；可是，我們又有沒有看到，農民工作為中國新工人階級的出現，他們所扮演的角色又是甚麼？
今年是辛亥革命一百周年，中國從封建走向共和，而民主又是怎麼一回事？中產階段高喊：人權萬歲，但中國最底階層的聲音在哪裏？
我們聽到了嗎？就在《大工地上》。
去年一個富士康的跳樓潮，我們才驚覺三十年的開放改革有哭聲。可是，討論熱了一陣子，又歸於沉寂。
中國大工地上的沙塵依然滾滾，隆隆聲音繼續震耳欲聾。《大工地上》一書出得及時，它把我們的視綫拉回農民工身上，叫人不要忘記；更何況作為香港人的作者潘毅，她所代表的就是香港人在中國內地一片大工地上的一道鵲橋，好讓農民工模糊的面貌在香港得以被看見。
香港在分享中國改革三十年成果的同時，不應漠視背後默默播種耕耘的農民工。
記憶所及，內地有好幾位作家寫過中國工人的作品，例如戴晴、賈章柯、陳貴隸與吳春桃，但香港人詳細書寫中國工人現況的書籍，仍是鳳毛麟角，因此，《大工地上》是值得我們去親近的。
在 中國內地，農民工的身份是非常特殊的。我們甚至不太了解「農民工」是怎麼一回事，只知道每當走過中國各大城市的火車站，總會看到一群又一 群密麻麻農民樣子的人，或疲累地蹲在地上，或漫無目的站在車站附近，頭髮油滑、皮膚黝黑、衣服破舊，一雙鞋子也差點兒踏破了，口中操着土話。我們下意識地 與他們保持距離，又或心中帶點恐懼、厭惡，繞道而行。
《大工地上》還了他們一個公道，首先讓他們的身份以正視聽。為甚麼他們總擺脫不了農民這個身份？中國的戶籍制度令他們永遠被釘在農村這個印記上，他們建設城市卻無法享受城市，種種一切權力，他們成為城市重重疊疊的孤影。
「南柯一夢三十年，放下左右逢源。都說你真美好，風有風的風骨，雨有雨的輕泣。輕輕的你倒下，夜夜守護家鄉的星空，不帶走一分工錢。」
借《大工地上》，讓農民工走到香港人的面前，他們的聲音如雨水打在我們的心房。
華欣 (貼於2月11日)
在這次渡假，充份享受了悠閒和慵懶(只差在還未嘗過在房間裏用room service送食物)，真的渡過了相比起前幾個月，真正沒憂慮的日子。可以在房間裏，聽著海浪聲睡午覺和閱讀，真是人生一大享受，只可惜在地下的海邊， 海風大得離譜，像打風的感覺，即使太陽很猛，也只是沒有覺得冷而已。
此行又見識到港人在外的焦慮、躁狂、過度反應、欠缺包容的金魚性格…先說這兩件事。
出 發那天(2月1日)，凌晨出門，六點幾開始排隊check in，因為臨近新年，泰國又是港人旅遊熱點，所以泰國的旅空公司那些櫃台附近，人多到難以理清排隊的人龍，進出時行李手推車也很難擠過人堆。那種時期，有 很多航班都是加班機，check in效率有點低，本來是可以預料的吧…不過排在我們後面的一對中年夫婦，那個女的超過半個小時不停地發出嘖舌聲，投訴說"很慢、怎麼人龍完全不動"，幾乎 不夠三分鐘就重覆一次，身體又大幅度左搖右擺張望，或者走到前面張望又回來，偶然會碰到我。到7:02 am的時候，她說"航班七點幾就要起飛了，現在已經七點幾了，分分鐘趕不及了"，我望後面，其實還有很多人在她後面…到了我們check in時，她的躁狂又進一步發作，連她的丈夫也受不了，說"我們已經在排隊了，妳還想怎樣"。我當時想到了一個很貼切的角色，星戰的C3PO…
在 回程時，下午四點幾到了曼谷機場，在櫃台附近看螢幕，班機由6:30pm延遲到7:50pm了。起初，排隊等check in的人也沒有怎麼鼓譟，不過到我們check in的時候，航空公司的系統當機了，大約等了二十分鐘。在這期間，有一個大叔走上前面，問我們是什麼事之後，變成C3PO，來回於自己排隊的位置和我們的 位置之間，偶然又說些抱怨的話(注意︰當時只是五點幾)，還有把手靠在櫃台之上，好像下個要check in的人就是他的樣子。
到 七點半左右，我們去了等候登機的地方，當時已經坐和站滿了人(大部份是港人)，我們一行人也靠著欄杆邊站著，到八點正時，在我旁邊坐著的一對男女在談話， 女的在大聲抱怨等了這麼久，公司至少也要賠乘客一枝水，男的又在附和，還有其他人也在抱怨，到八點十五分左右，終於可以登機了，那些港人大聲地"唉"了一 聲，比合唱團還要齊。其實在新春期間外遊，會有很多加班機，絕對是預料中事，由本來預定的起飛時間到實際可以登機的時間，才兩小時左右，又說要賠什麼又大 聲地發怨氣的行為，我覺得對外國人來說，是非常失態的。
說回那些悠閒的日子，在房間裏看電視時，除了有時看泰文節目，有時也看 Channel V，還有BBC WORLD NEWS(不過下午會自動跳頻切換成FRANCE 24)，大部份時間都是在說埃及示威的進展，(終於free from 何家的新聞)，親眼見著那個女報導員由我第一天開始渡假時精神奕奕，每過一天就臉色越來越變瘡白和眼袋增加…
經歷過一件有點有趣 但也有點令人毛骨悚然的事件。好像是第三天吧，我們在吃了自助早餐之後，我二姊和三姊要出去晒太陽，要我幫她們拿東西，我拿了之後，要進入餐廳門口時，見 到一個穿得很搶眼的婆婆，臉很白，粉紅色的唐裝，卻是……像紙紥人的那種粉紅色，真的很搶眼，她又望過我幾秒，真的感覺有點不自在。把東西給了老姊們後， 又說要再拿另一件物品，於是我又折返…當我按下升降機的鍵前，升降機是停留在地下，沒有正在要上落的樣子。按常理來說，在這樣的情況下，一般人會預料升降 機裏面是沒人的，可是…裏面竟然站著了那個婆婆，她的神態好像只是站著，而沒有"正在嘗試按鍵而升降機未有反應"的樣子。我就是心裏一寒，在思考要不要進 去，可是我又見到鏡子裏有她的倒映，如果她是"人"的話，我就太失禮了，於是就進去了，她對我說了一話，我聽不懂，我猜是泰語的問候語吧…總之我微笑了一 下，然後按鍵，她也沒有要按鍵的樣子…然後她跟我在同一層走出升降機。我之後沒有理她了。
照片在這裡，因為還是未正經地學會手動模式的技巧，還是有些陽光充足的場景被拍成陰天了。
http://www.flickr.com/photos/10613381@N05/sets/72157625871243139/with/5424801796/
那 幾天腸胃大放肆，吃了很多種東西(海鮮除外)，還有零食和啤酒(Chang, Leo和Tiger都不錯)。在超市時裏，發現只要是泰國本地製造的東西，就非常便宜，如一排MENTOS才三港元左右，一瓶果醬十港元也不到，若是入口 貨就要比香港貴了。食物方面，我們在那幾天吃的泰國菜，不是很醎就是很甜，味道很重，不過在夜市有個檔口賣的自製雪糕卻非常好吃。我姊和我都被食物打擊 過，覺得明明不會煮，又勉強去提供給人吃…那就是奶和水分開的乳酪，以及軟熟的Nachos…
此行又見識到港人在外的焦慮、躁狂、過度反應、欠缺包容的金魚性格…先說這兩件事。
出 發那天(2月1日)，凌晨出門，六點幾開始排隊check in，因為臨近新年，泰國又是港人旅遊熱點，所以泰國的旅空公司那些櫃台附近，人多到難以理清排隊的人龍，進出時行李手推車也很難擠過人堆。那種時期，有 很多航班都是加班機，check in效率有點低，本來是可以預料的吧…不過排在我們後面的一對中年夫婦，那個女的超過半個小時不停地發出嘖舌聲，投訴說"很慢、怎麼人龍完全不動"，幾乎 不夠三分鐘就重覆一次，身體又大幅度左搖右擺張望，或者走到前面張望又回來，偶然會碰到我。到7:02 am的時候，她說"航班七點幾就要起飛了，現在已經七點幾了，分分鐘趕不及了"，我望後面，其實還有很多人在她後面…到了我們check in時，她的躁狂又進一步發作，連她的丈夫也受不了，說"我們已經在排隊了，妳還想怎樣"。我當時想到了一個很貼切的角色，星戰的C3PO…
在 回程時，下午四點幾到了曼谷機場，在櫃台附近看螢幕，班機由6:30pm延遲到7:50pm了。起初，排隊等check in的人也沒有怎麼鼓譟，不過到我們check in的時候，航空公司的系統當機了，大約等了二十分鐘。在這期間，有一個大叔走上前面，問我們是什麼事之後，變成C3PO，來回於自己排隊的位置和我們的 位置之間，偶然又說些抱怨的話(注意︰當時只是五點幾)，還有把手靠在櫃台之上，好像下個要check in的人就是他的樣子。
到 七點半左右，我們去了等候登機的地方，當時已經坐和站滿了人(大部份是港人)，我們一行人也靠著欄杆邊站著，到八點正時，在我旁邊坐著的一對男女在談話， 女的在大聲抱怨等了這麼久，公司至少也要賠乘客一枝水，男的又在附和，還有其他人也在抱怨，到八點十五分左右，終於可以登機了，那些港人大聲地"唉"了一 聲，比合唱團還要齊。其實在新春期間外遊，會有很多加班機，絕對是預料中事，由本來預定的起飛時間到實際可以登機的時間，才兩小時左右，又說要賠什麼又大 聲地發怨氣的行為，我覺得對外國人來說，是非常失態的。
說回那些悠閒的日子，在房間裏看電視時，除了有時看泰文節目，有時也看 Channel V，還有BBC WORLD NEWS(不過下午會自動跳頻切換成FRANCE 24)，大部份時間都是在說埃及示威的進展，(終於free from 何家的新聞)，親眼見著那個女報導員由我第一天開始渡假時精神奕奕，每過一天就臉色越來越變瘡白和眼袋增加…
經歷過一件有點有趣 但也有點令人毛骨悚然的事件。好像是第三天吧，我們在吃了自助早餐之後，我二姊和三姊要出去晒太陽，要我幫她們拿東西，我拿了之後，要進入餐廳門口時，見 到一個穿得很搶眼的婆婆，臉很白，粉紅色的唐裝，卻是……像紙紥人的那種粉紅色，真的很搶眼，她又望過我幾秒，真的感覺有點不自在。把東西給了老姊們後， 又說要再拿另一件物品，於是我又折返…當我按下升降機的鍵前，升降機是停留在地下，沒有正在要上落的樣子。按常理來說，在這樣的情況下，一般人會預料升降 機裏面是沒人的，可是…裏面竟然站著了那個婆婆，她的神態好像只是站著，而沒有"正在嘗試按鍵而升降機未有反應"的樣子。我就是心裏一寒，在思考要不要進 去，可是我又見到鏡子裏有她的倒映，如果她是"人"的話，我就太失禮了，於是就進去了，她對我說了一話，我聽不懂，我猜是泰語的問候語吧…總之我微笑了一 下，然後按鍵，她也沒有要按鍵的樣子…然後她跟我在同一層走出升降機。我之後沒有理她了。
照片在這裡，因為還是未正經地學會手動模式的技巧，還是有些陽光充足的場景被拍成陰天了。
http://www.flickr.com/photos/10613381@N05/sets/72157625871243139/with/5424801796/
那 幾天腸胃大放肆，吃了很多種東西(海鮮除外)，還有零食和啤酒(Chang, Leo和Tiger都不錯)。在超市時裏，發現只要是泰國本地製造的東西，就非常便宜，如一排MENTOS才三港元左右，一瓶果醬十港元也不到，若是入口 貨就要比香港貴了。食物方面，我們在那幾天吃的泰國菜，不是很醎就是很甜，味道很重，不過在夜市有個檔口賣的自製雪糕卻非常好吃。我姊和我都被食物打擊 過，覺得明明不會煮，又勉強去提供給人吃…那就是奶和水分開的乳酪，以及軟熟的Nachos…
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