分析师s Among the Alpha Gods

To properly settle into my new role as亚博赞助欧冠’s resident dissident, I took stock of theIIdominion and quickly felt like an anthropologist who had come upon a civilization that fetishizes a minor deity in a polytheistic religion. I am, of course, referring toII’s ranking of equity analysts, exceptionally manifested in the annual All-America Research Team.

This idolatry of analysts is a new phenomenon to me, which got me thinking: What is the analyst’s role in the pantheon of the Alpha Gods?

While buy- and sell-side analysts might have different commercial motives, their raison d’être is the same: Through a typically fundamental-driven research process, uncover valuable insights about an industry or specific companies and distribute these insights to a select group of users — namely, the portfolio managers and traders who make capital allocation decisions.

分析师的价值应与他或她的效果相称：研究是否有助于用户提高投资决策？它提供了边缘吗？

获得信息边缘非常困难，特别是因为分析师和投资者一般，往往使用相同的定量信息（价格，经济和财务数据）和类似的方法 - 例如，现金流量或股息折扣模型或发言通过管理 - 从这些信息中提取见解。

似乎是最好的分析师，如最好的投资者，是那些超越Groupthink的人，并使用所有可用的信息和调查技巧来获得优势。（如果我选择的话，这可能是标记线IIanalyst of the year: “Rising above groupthink, despite my CFA.”)

This abandonment of the status quo should naturally lead analysts to embrace big data — both the large-scale, complex nontraditional data sets whose size, variety, velocity, and complexity are beyond the capabilities of typical database software (think social, geospatial, security, health, news) and the computational methods such as machine and deep learning that identify patterns and discover relationships within these data.

The use of satellite imagery to count cars in a Walmart parking lot or tweets to predict stock price are not examples of this transcendental embracement. Instead, consider that analysts universally value private calls with company management, believing that these behind-the-scenes conversations could reveal unique information. Yet because of Regulation FD’s restrictions on selective disclosure, management is typically cautious in their discourse with analysts: The late Dwayne Andreas famously told analysts that “getting information from me is like frisking a seal.”

分析师s must extract insights from these constrained conversations by deciphering nonlinguistic cues for emotive content. For example, according to a recent paper on the industry, one analyst claimed to be able to “read [management’s] body language — even on the phone — and get a feel for how optimistic they are or how realistic something might be.”

Instead of relying on such well-intended but antiquated tactics to uncover a speaker’s emotional valence, I would expect analysts to engage in an arms race to acquire and use the best machine learning algorithms to automatically detect and measure, in real time, a variety of human emotions in a speaker’s voice with a relatively high degree of accuracy. And if analysts believe private communications with management are high-value opportunities, then they should be conducting these conversations via webcam, which would allow the analyst to quantify the speaker’s emotional expressions through real-time video analysis of facial expressions. (Also, I would expect the best management teams to employ such biometric analytics when they speak with analysts so they can measure and respond to the analyst’s emotive state.)

世界已经改变,captured by Paul Tudor Jones’s dictum, “No man is better than a machine. And no machine is better than a man with a machine.” But the précis of each 2016 All-America analyst reveals no evidence that they have embraced this truth.

实际上，这种人机共生是一种季节状态，代表了分析师在投资万神殿中的阿洛夫。我同意两个西格玛的大卫塞尔格尔那个“最终，那么没有人类投资经理将能够击败电脑的时间。”

算法很快将完全取代分析师，这意味着这些算法本身将符合条件，原则上是为了选举IIAll-America Research Team (although country of origin will be an alien concept).

Of course, this will requireIIto dispossess itself of its long-held fetish and devise a new selection methodology. After all, who would vote for an algorithm? •