Why Are Allocators in the Manager Selection Stone Age?

Selecting asset managers is an essential part of every allocator’s job.

这是一个不可否认的不完美的过程，特别是在罗素投资自1969年以来，罗素投资聘请了其第一经理分析师以来，这是经理返回和更高的风险的资产课程。改善过程 - 提高选择的准确性和提高过程的效率 - 是完全基于信托义务。

讨论 - 辩论 -亚博赞助欧冠columnist Angelo Calvello spoke with Christopher Schelling, director of private equity at the Texas Municipal Retirement System.

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Calvello:Chris, how does the manager selection process start for you?

Schelling:It typically starts with reverse inquiries from managers seeking to get on our radar. We generally get about 500 unsolicited pitch decks a year — in private equity alone.

Calvello:How do you cull the herd?

Schelling:We usually start with a cursory review of the manager materials applying the traditional Five Ps framework: people, philosophy, process, portfolio, and performance. This might lead to a quick intro phone call — quick because we don’t have the time to commit to anything longer. We are looking to see if the manager meets some minimum standards (e.g., team tenure, resources, alignment) and presents an interesting proof of concept.

Calvello:Let me offer an alternative scenario; tell me if it could improve either your accuracy or efficiency. Imagine your introductory call with a prospective manager went like this:

专有的德克萨斯市政退休系统人工智能BOT安排了一个视频通话的时间（可以用任何网络摄像机进行，并通过询问经理和他的团队进行许可。亚慱体育app

However, unlike the customer services bot we regularly encounter, this bot sounds so natural that the manager will likely not even know it is a bot (although it would be prudent to disclose this to the manager). Importantly, this bot has been trained for the explicit purpose of extracting the information you, the allocator, deem critical to properly assess whether the manager meets your minimum standards and your proof-of-concept criteria. The bot’s interrogation process does not follow a predetermined, rigid, linear Q&A format — but because it is guided by a deep learning program, it is capable of understanding the manager’s answers and comments within context and adroitly pivoting to dig deeper into these comments as they occur.

Simultaneously, and in real time, another AI-based technology is automatically capturing and analyzing the team’s emotional valence by reading their linguistic (e.g., word choice) and paralinguistic expressions (including accent, pitch, volume, speech rate, and modulation) and over facial micro-expressions, including their vascular responses.

接受后，呼叫的逐字成绩单是机器人制作的，突出了经理可能已经表现出的任何优势或弱点。视频和成绩单都可以亚慱体育app通过特定情感来搜索（例如，经理何时表现出蔑视？厌恶？）。

How does this sound?

Schelling:这肯定听起来像情感识别technology would be a big leap forward — it would provide me with quantifiable data on qualitative attributes. We could use the output from this introductory meeting to prioritize any potential next steps. It would also allow us to take more initial introductions, as our time would be spent more efficiently reviewing analysis as opposed to sitting in meetings.

Calvello:Let’s assume the manager passes this autonomous introductory interview. What would you do next?

Schelling:The next step in the review process is what we refer to as desk work. This is the traditional collection and analysis of specific requested materials (historical return data, ownership structure, team biographies, ethics policies, sample investment memorandums, quarterly letters, SEC ADVs, etc.) and the manager’s standardized due diligence questionnaire and private placement memorandum. This usually requires a review of 1,000 or more pages of printed materials. It is a very laborious and time-consuming process, and, in general, TMRS, like all allocators, lacks the time and human resources to fully vet all the materials in detail.

Calvello:让我来帮你一些单调乏味的desk work by using natural language processing technology that would review the DDQ and other printed materials and turn this text into structured data that could be flagged and analyzed for insights.

幽默我并假设经理也通过这些传统屏幕;接下来你通常会做什么？

Schelling:我认为大多数分配者会同意我的意见，经理选择的定性方面是最关键的考虑因素，但通常是最难调查的。

虽然历史回报通常是采摘管理人员的必要但不足，但研究支持定性经理信息对未来表现的影响。这是我将深入挖掘许多分配者称之为无形资产的地方：团队是否承诺？他们与投资者对齐吗？他们是诚实，勤劳的，责任吗？

At TMRS we try to supplement our qualitative opinions about these issues with personality inventories and aptitude tests, which allow us to at least put metrics around some of these otherwise subjective items. And while we realize the process isn’t perfect, it’s the best we’ve got.

Calvello:我们上面使用的情感检测技术肯定有助于辨别一些无形资产。我们可以使用其他基于AI的技术来补充这一点和您当前的个性测试。

For example, you could request information derived from the manager’s internal application ofubiquitous computing那specifically, data passively collected from employees’ sociometric ID badges. These badges are embedded with microphones and sensors and automatically quantify individual and collective signals at the millisecond level. These signals, integrated with information from the employees’ emails and calendars, provide a comprehensive picture of how employees spend their time at work, a picture that scientists say is highly predictive of group performance.

We also could review data from the company’s real-time, automatic compliance monitoring software of landline and cell phones. And let’s complement this granular data with a report from the company’s happiness meter — AI-based technology that uses employee cell phones to measure the collective happiness of the organization.

Let’s top this off with technology that screens social media to determine if a manager’s public posts and profiles reveal a possible risk to the company that could adversely impact your investment — like racist or misogynistic behavior.

Schelling:All of these technologies would be very helpful — but it almost doesn’t seem possible, at least today.

Calvello:这些技术可能似乎是未来的，但今天所有人都可以使用，并且在许多情况下，他们被用于非投资商业目的。

The bot used for the screening call is an appropriation of Google’s new双工那“a new technology for conducting natural conversations to carry out通过电话的“现实世界”任务。”

Emotion and sentiment speech recognition technology is offered by numerous firms and widely used in several verticals. For example,经济学家reportsthat Chinese insurer Ping An Insurance (Group) Co. allows prospective borrowers to apply for a loan by answering “questions about their income and plans for repayment by video, which monitors around 50 tiny facial expressions to determine whether they are telling the truth. The program, enabled by artificial intelligence (AI), helps pinpoint customers who require further scrutiny.”

Natural language processing is certainly being used by some investors. For example, one company,Prattle那says it uses it to automate “investment research by quantifying language” and to “produce analytics that predict the market impact of central bank and corporate communications.”

Humanyze.那a people analytics firm, offers employers thesociometric badgesdescribed above. The company claims that several Fortune 500 companies use the badges and underlying technology.

Numerous companies offer landline and cell phone compliance technology (for example,Behavox). According to彭博，一些银行已经使用该技术来通过扫描“鼠标的数据，标记了从常规进行进一步调查的规范来监控交易台活动。这可能是看似无害的，因为在电话里喊叫，在半夜访问工作计算机，或者比同事更多地访问洗手间。“

More generally, employee monitoring software is widely available. These tools, according toone vendor那allow companies to monitor “almost 100 percent of employee activity and communication,” including internet and app usage, email, phone use, and vehicle location.

这种监控软件也被广泛使用。根据A.surveyof 1,627 large and midsize firms by the American Management Association — whose clients and members employ more than a quarter of the U.S. workforce — “nearly 80 percent of major companies now monitor employees’ use of email, internet, or phone,” up from 35 percent in 1997.

Hitachi已开发和部署AI，即“利用智能手机嵌入的加速度计数据以衡量组织的集体幸福水平。”

And while Hitachi’s happiness gauge might be a bit Orwellian, technology that compiles and reports a person’s social media profile is amplyavailable。

So Chris, as my prototypical allocator, the big question is: Would you use these technologies?

Schelling:他们很酷，他们会显然有助于我们我们的尽职调查。

但我希望清楚的是，最终决定 - 是否雇用经理 - 仍然会与人类休息。但是，在我或可能是任何其他分配器之前，仍然必须处理一些问题，可以使用它们。

Let’s put aside the issue of cost and simply assume we could afford them.

A big issue would be institutional buy-in. Public pensions — and most fiduciaries — are averse to change, and without definitive proof of the benefit, it could be hard to convince the relevant constituents to support the process.

我也关注员工隐私问题。我理解我们将收到的信息（例如，来自社会计量徽章或监控软件审查）将是匿名的，但雇主的使用监控软件似乎是违反员工隐私。

Calvello:I was concerned about this, too, until I learned that this monitoring software does not appear to violate privacy laws.

For example, I readthis commentin theChicago Tribune通过隐私律师：“'一般在工作场所，没有隐私权。”“Tribune说，“管理层可以看出一名工人在工作或公司设备上创造的任何东西。这意味着电子邮件，社交媒体帖子，互联网搜索，文本或即时消息以及跟踪员工下落的GPS设备。“

Schelling:Still, there could be some concerns about Freedom of Information Act issues, although in most states, laws support the stance that such sensitive and confidential material acquired during the diligence process is non-disclosable.

Calvello:资产所有者的另一个热按钮问题是什么 - 他们的信托义务。你看到那里有任何疑虑吗？

Schelling:Certainly, all asset owners take their fiduciary duty seriously. Delegation of decision-making authority to staff is sometimes considered unacceptable because it is viewed as abdication of fiduciary duty, and relying on technology for decision-making, even if it is merely prioritization or ranking, could be viewed similarly.

However, because the decision to invest/not invest ultimately still rests with human beings, all this technology is doing is giving us new and different data and streamlining the entire process — so I’m not sure the potential violation-of-fiduciary-duty argument would hold water. Moreover, fiduciary case law has held that the process, not the outcome, is the measure of the standard of care, and I would argue that a process utilizing these tools is one that is more robust, with more data, and less subjective. It would arguably be a tighter fiduciary standard, not weaker. But I would defer to counsel for a legal opinion.

Calvello:I’d also argue that the technologies create an auditable diligence record to support that a prudent level of research led to the investment decision.

Schelling:The last potential difficulty is manager participation. Managers might be reluctant to participate in monitored and taped video calls or to share even anonymized HR data, assuming they even have this data.

Allocators need sufficient leverage to ensure compliance. I can see this being applied in asset classes where the investors have all the power, or by someone with more competitive strength with the hard-to-access managers. In general, investment consultants might be best positioned to both benefit from the improved accuracy and increased efficiency these technologies offer and to require managers to comply with their requests.

Calvello:I understand these objections — but they relate more to cultural or behavioral issues, not to the technologies themselves. As I said before, companies are using these technologies but, after speaking with a mix of allocators, I could not find one that was using the available technologies mentioned above or really any other AI-based technologies as part of their manager selection process.

Given the universal opinion that the process could be improved, I found this odd. Screening a manager’s online presence with Google Alerts does not qualify, and planning to develop an AI tool to scrape a manager’s PDF documents does not meet my bar. However, I did discover a multi-manager shop (Weiss Multi-Strategy Advisers) that is using machine learning to vet the performance of its internal managers.

Schelling:In an ideal world, I’d be exploring how we could use these tools as soon as practically possible.

In the real world, however, while the potential is clear, it seems unlikely we’ll see broad adoption — even from the consultants who would most benefit from them.

然而,我相信我们的受托责任包括udes the obligation to always try to do better for our beneficiaries, and if tools and technologies exist that can improve the likelihood of successful manager selection, I, and we collectively as allocators, need to be considering them.

Calvello:From your mouth to God’s ear . . . .