技术进步对投资思维和操作的改变
新一代已投入使用的人工智能技术使得“投资”思维和决策过程都发生了重要的改变。立法机构正在思考、并已经进行了对投资行业的监管方式和界定合法违法的标准。
传统不对称信息“优势(Edge)”所带来的实际利益越来越少。“投资”股市和投资房地产的投资者都应该思考：
1，Day Trading的对手是谁，个人有多大Edge?
2，股市“投资者”除了"Follow Smart Money"之外，还应思考什么Bigger Picture?
3，投资房地产的，"Location, Location, Location"等等的信息"优势(Edge)"意义还大么?
4，Realtor的Professional Advice除了在与法律程序有关的标准动作外，对房产的“是否有价值”建议还是那么有效么?
5，各财团和银行有全部地区房地产的最真实Raw Data，大多都未公布。这些机构有了成熟的人工智能模型分析工具，房地产市场还是Local么?Local与Local之间的比较会有什么Advantage/Disadvante?财团和银行应该会有什么相应动作?个人和小房产投资公司应该如何判断这种动作并作相应策略调整?
6，财团大规模地进入租房市场，给个体和小房地产投资公司有什么启示?Margin更低了?市场更小了?Cash Flow更重要了?升值潜力更小了?一旦财团出租公司有了基本的规模，将用什么方式整合各Local市场，对个体和小房地产投资公司有什么影响?
7，为什么房价“便宜”了，贷款却难得到了?
etc...
SAC Charges Another Win for Machines Over Human Traders
新一代已投入使用的人工智能技术使得“投资”思维和决策过程都发生了重要的改变。立法机构正在思考、并已经进行了对投资行业的监管方式和界定合法违法的标准。
传统不对称信息“优势(Edge)”所带来的实际利益越来越少。“投资”股市和投资房地产的投资者都应该思考：
1，Day Trading的对手是谁，个人有多大Edge?
2，股市“投资者”除了"Follow Smart Money"之外，还应思考什么Bigger Picture?
3，投资房地产的，"Location, Location, Location"等等的信息"优势(Edge)"意义还大么?
4，Realtor的Professional Advice除了在与法律程序有关的标准动作外，对房产的“是否有价值”建议还是那么有效么?
5，各财团和银行有全部地区房地产的最真实Raw Data，大多都未公布。这些机构有了成熟的人工智能模型分析工具，房地产市场还是Local么?Local与Local之间的比较会有什么Advantage/Disadvante?财团和银行应该会有什么相应动作?个人和小房产投资公司应该如何判断这种动作并作相应策略调整?
6，财团大规模地进入租房市场，给个体和小房地产投资公司有什么启示?Margin更低了?市场更小了?Cash Flow更重要了?升值潜力更小了?一旦财团出租公司有了基本的规模，将用什么方式整合各Local市场，对个体和小房地产投资公司有什么影响?
7，为什么房价“便宜”了，贷款却难得到了?
etc...
SAC Charges Another Win for Machines Over Human Traders
In pursuing and winning an indictment of SAC Capital Advisors LP, federal prosecutors are intent on shuttering one of the largest and most successful stock-trading hedge-fund firms in Wall Street history.
If they succeed, it will also mark the effective demise of a whole mode of hedge fund investing, that which hunts for returns based on fleeting, human-driven information advantages – legal or, allegedly, not – from analysts, industry executives and brokerage-house trading contacts.
Since the tech-stock bubble burst in 2000, changes in regulation, stock-trading mechanics and the march of technology have increasingly squeezed the old ways of gaining and maintaining a trading “edge” that prevailed through the 1980s and '90s, when Steven Cohen was building his remarkable track record of market-trouncing annual returns.
Nick Colas, chief market strategist of institutional-brokerage firm ConvergEx Group, worked at SAC from 1999 to late 2001, following a long stint as a Wall Street auto-industry analyst. While he is quick to say he never witnessed anything illegal, he offered some thoughts on the ways markets have changed since those years, restricting funds’ ability to exploit information advantages gleaned from human observation and connections.
Regulation FD, imposed 13 years ago, prohibited companies from selectively disclosing material business information only to favored analysts or investors. This made it far tougher for sell-side analysts to pick up crucial intelligence about company results or strategic decisions and pass them along to their favored commission-paying customers. SAC has long been known as the biggest commission-generator in the business, giving its trader the “first call” more often when new information emerged.
In the past decade, regulatory changes meant to foster more competition for stock orders have upended the old model of central exchanges dominated by biped mammals called floor specialists and market makers. Today trading is essentially all electronic, operating through largely automated algorithmic execution programs – machines talking to machines at the speed of light. The tape has become harder to “read” with the human eye, and the brokers servicing hedge funds have a murkier view on where the flows are headed.
Regulators have drawn a sharper line that more aggressively defines gray areas of information collection as insider trading. One hedge-fund response to Reg FD was to rely more on “expert networks,” collections of professionals in various industries who might, for a fee, shed light on key trends in a sector (market-share numbers, supply-chain intelligence, drug-trial results and the like). While such networks still exist, the Securities and Exchange Commission a couple of years ago signaled closer scrutiny of them with insider-trading charges against several individuals.
Even the language of the indictment set a higher standard for how funds handle information than one that was thought to prevail in years past. SAC, it said, “fostered a culture that focused on not discussing inside information too openly, rather than not seeking or trading on such information in the first place.”
Some critics of the SEC’s intense focus on levying insider-trading charges contend it amounts to criminalizing activities that used to pass as standard procedure. Maybe so. But bigger structural changes perhaps had more to do with the way SAC’s tactics, and those of similar firms, came to be squeezed.
Today the search for an information edge is alive and well, only it tends to focus on letting computers “listen” to and filter social media chatter, crunching massive data sets using third-party sources or writing code to sniff out tiny, fleeting anomalies in sub-second stock price patterns.
Colas casts this as an evolution in the way “hedge funds interact with capital markets.” This means less reliance on “my guy on the Street,” more quantitative modeling of asset-price interactions. While not always popular to point out, Colas says "information is much more fairly distributed now." While many retail investors obsess over the way high-frequency trading systems try to take advantage of public stock-order data, they mainly collect barely discernible fractions of pennies over milliseconds – and even this business has had its profits sapped by competition.
Hedge funds, broadly speaking, have moved away from the traditional structure of a small group of intense trader-investors going all-in on a handful of high-conviction positions, and have become more similar to traditional institutions as the industry has grown and matured.
Big institutions focus on asset allocation, modulating their exposures based on valuation and market conditions, simply trying to stay on the right side of uncertain market probabilities. They try to simply produce a decent batting average over multiple years.
As Colas says, “Hedge funds didn’t used to be about batting average. They were about ‘Yes-or-No?’ and ‘Do you have an edge?’ and you were expected to be right every time.”
