算法可以帮风险投资家做出更好的投资决策吗？

    伊恩·西格罗就是怀疑论者之一，他是风投公司Greycroft Partners创始人兼合伙人，在纽约和洛杉矶设有办事处。当然，西格罗不怀疑技术本身，Greycroft Partners的主要投资对象就是互联网和移动公司，他怀疑的是这种思路，也就是用数据来预测10年后的情况。西格罗说：“没有人具有那样的预见性，你既无法预测创业者会怎样掌控自己的公司，也无法捕捉到优秀的风投战略合伙人带来的价值。无论谁提供资金或者谁参与其中，想凭数字来解决这个问题的科学在我看来都有颇多疏漏之处。”

    和瑟斯顿不同，西格罗认为定量分析无法取代风投，就算很久以后也是如此。不过，他确实觉得数据有可能帮助风投资本家进行决策。举例来说，Greycroft Partners在过去的两个夏天都聘请了一位数据科学本科生。她的研究课题之一就是利用该公司软件的机器学习功能对公司进行尽职调查，并对效能和创新进行算法分析。她的另一项工作是对Greycroft Partners的一些投资对象进行数据分析，以判断市场变化是临时现象还是会较为持久。西格罗通过实例介绍说，最近有一家公司上门自荐，称自己的App连续几周都高居App排行榜首位。他说：“问题在于这家公司是下一个Snapchat，还是昙花一现？我们考察了这个App的持久性和使用数据，并从单次使用时长和使用频率角度考察了用户行为……我们的结论是，它不太可能成为下一个Snapchat，原因是在它的生命周期中并没有展现出类似于Snapchat的特质。”

    毫无疑问，数据分析已经渗透到了风投资本中——Google Ventures通过某种算法来协助进行投资决策，一家名叫Correlation Ventures的硅谷公司就建立在基于算法的投资策略之上。但详细研究并由人做出判断的老式做法仍将存在很长一段时间。问问Lux Research的人就会明白这一点，这是一家设在波士顿的新兴科技咨询机构。10年来，该公司那些理工出身的分析师一直在为新建立的科技公司进行广泛的商业环境评估，他们采访这些公司的员工，并把成功或失败的公司慢慢纳入自己的数据库。它通过九项关键因素来评估这些公司，并在自己的网站上用一篇名为《衡量和量化成功创新》的报告公开介绍了这些要素。该公司还通过《Lux观点》报告来介绍所评估公司的情况，它的评级分为几等，从“非常乐观”到“非常谨慎”。

    最近，Lux Research回顾了过去五年的评估对象，发现在获得“乐观”评级的公司中，有一半发展顺利。Lux Research对“顺利”的定义是首发上市、获得收购或者独立经营并实现盈利。考虑到新公司通常的生存率，Lux Research的工作看来大大提高了评估初创公司的可靠性。对首席研究官克里斯·哈茨霍恩来说，该公司的高准确率得益于两个因素，那就是业务能力和方法。Lux Research的分析师都是拥有博士学位的科研人员，很熟悉自己专业领域中最先进的技术。用哈茨霍恩的话说就是：“如果一家初创公司试图打破热力学定律，他们也明白是怎么回事。”说到方法，10年来他们一直用这九项要素来进行评估，因而可以按同样的标准来比较评估对象。

    人们经常说到创新在经济增长中的重要性。商业咨询机构Economic Innovation Group最近在民意摇摆不定的州进行了一次调查，75%的受访者都认为美国需要更多的创业者和投资者，以便解决长期存在的经济问题。哈茨霍恩认为，这就是说采取行动的时间到了，“创新经济在信息方面存在问题。左右创新经济的信息质量不高。一个国家怎样才能更有效地成为创新经济体呢？它得更好地为能实现增长并推动就业的初创公司提供资金。如果用错了地方，资金就会产生不利影响。而它本该发挥重要作用。”

    大数据也许真的能为人们提供帮助，但它更有可能成为拼图中的一块，而不是解决方案。比如说，学术研究表明，过去曾取得成功的连续创业者更有可能在新公司干出成绩。这就意味着回顾过去的情况对预测未来有一定的指导意义。不过，哈佛商学院投资银行学Jacob H. Schiff讲席教授乔希·勒纳认为：“创业的本质总是在不断变化。在相关文献中，大多数预测创业能否成功的回归方程的拟合优度（即R平方）都非常低，这表明在这个领域，《点球成金》中的那种方法有局限性。和预测哪些棒球运动员将表现出色相比，预测哪些初创公司能获得成功要难得多。这就好比棒球规则每年都会以无法预知的方式发生改变一样。”（财富中文网）

    译者：Charlie

    校对：詹妮

    Count Ian Sigalow among the skeptics. Sigalow is a co-founder and partner at Greycroft Partners, a venture capital firm with offices in New York and Los Angeles. Sigalow, of course, isn’t skeptical of technology—his firm invests mainly in Internet and mobile companies—but of the idea of using data to judge what’s going to happen in 10 years. “Nobody is that prescient, where you can figure out how an entrepreneur is going to pivot his or her business,” he says. “Nor can you capture the value of having a good strategic partner in your VC. Any science that tries to reduce this to a number—regardless of who funds it or who’s involved—I think is actually missing a lot.”

    Unlike Thurston, Sigalow doesn’t see quants taking over VC, even in the distant future, but he does see the potential of using data to help venture capitalists make decisions. Greycroft, for example, has employed a graduate student in data science for the past two summers. One project she’s working on is performing due diligence on companies using machine learning as part of their software, analyzing algorithms for efficacy and novelty. Greycroft is also having her analyze data from some portfolio companies to asses whether changes in the market are temporary or more lasting. More specifically, Sigalow explained that a company pitched his firm recently, touting that its app was at the top of app charts for a few weeks. “The question was: Is this company the next Snapchat or the next dud?” he says. “We looked at the persistence, the usage data, the behavior in terms of session length and frequency…and came up with the conclusion that this was unlikely to be the next Snapchat because it did not exhibit the characteristics that Snapchat did at that time in its lifecycle.”

    Data analytics are undoubtedly creeping into venture capital—Google Ventures uses an algorithm to help with investment decisions, and a Silicon Valley firm called Correlation Ventures is built upon an algorithmic investing strategy. But the old-fashioned process of detailed research and human judgment still has a lot going for it. Just ask the people at Lux Research, an emerging-technology consulting firm in Boston. For the past 10 years, Lux’s science-trained analysts have been scouring the business landscape for new technology firms, interviewing employees of those firms, and slowly compiling their own database of companies that succeeded or failed. Lux rates each company it profiles according to nine key factors, which are available to the public on its website in a report called “Measuring and Quantifying Success in Innovation.” The result of that rating is a company profile with a “Lux Take,” which ranges from “strong positive” to “strong caution.”

    The company recently looked back at five years worth of profiles and found that 50% of the companies that earned a “positive” rating went on to be successful, an outcome which Lux defines as an IPO, acquisition, or transition to standalone profitability. Given the usual odds of new business survival, the Lux system seems to inject a significant amount certainty into the process of evaluating startups. For Chris Hartshorn, Lux’s chief research officer, the company’s high rate of accuracy is attributable to two things: capability and methodology. Lux’s analysts are scientists with Ph.Ds who are familiar with the most advanced technology in their area of expertise. In other words, “they understand if a startup is trying to break the laws of thermodynamics,” Hartshorn says. And the Lux methodology, which rates companies on nine factors, has been in place for 10 years, allowing the firm to compare the companies it profiles in a consistent way.

    People talk a lot about the importance of innovation to economic growth. In a recent survey of voters in swing states by the Economic Innovation Group, 75% of those surveyed agreed that America needs more entrepreneurs and investors in order to improve long-standing economic problems. Hartshorn considers this a call to action. “The innovation economy has an information problem,” he says. “The information that drives it isn’t good. How can countries become innovation economies in a more efficient way? Let’s get better at funding the startup companies that will grow and drive employment. For every dollar that goes in the wrong place, that’s a shitty dollar. And it should matter.”

    Big data may indeed be able to help, but it’s more likely to be a piece of the puzzle, not the solution. For instance, academic studies have shown that serial entrepreneurs successful in the past are more likely to do well in new ventures. That implies there is some explanatory power in looking backwards for guidance on what’s ahead. “But the nature of entrepreneurship is always changing,” says Josh Lerner, the Jacob H. Schiff Professor of Investment Banking at Harvard Business School. “Most regressions predicting entrepreneurial success in the literature have very low goodness of fit (R-squared), which suggests the limits of a ‘Moneyball’ approach here. Predicting which startup is going to be successful is much harder than [predicting] which baseball player is. It is as if the baseball rules are being changed every year in unpredictable ways.”