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算法可以帮风险投资家做出更好的投资决策吗?

算法可以帮风险投资家做出更好的投资决策吗?

Kirk Kardashian 2015年08月10日
长期以来,风投资本家一直用主观方法进行投资。像电影《点球成金》里那样的方法能为他们提供帮助吗?

    在风险投资界,无论向谁问起业务模式问题,他们给你的答案可能都是关键在于“命中率”。在风投领域,“命中”是指初创公司发展壮大,并带来风投基金初始投资的许多倍的回报。包括投资者、创业者和求职者在内,命中对所有人来说都美妙无比。然而,问题在于这种情况概率不高。曾对苹果、Genentech和谷歌进行过早期投资的风投创奇人物威廉·哈姆布雷特认为,命中的可能性为十分之一,“有那么几个会成功,但许多都是赔本买卖。”

    但是,如果风投资本的命中率能达到50%,甚至三分之二呢?2014年,风投公司拿出了480亿美元资金。如果能达到那样的命中率,它们就不会投资那些在残酷竞争之下本没有什么生存机会的初创企业,从而避免巨额损失。难点在于要赶在市场否决这些创业者的点子之前,早早地发现这些可能掉队的公司;更重要的一点则可能是要先于别人发现那些有望大获成功的项目。长期以来,风投资本一直靠主观方法和直觉来评估初创企业。不过,随着越来越多的风投公司把数据科学用于决策并且保持决策的一致性,这种情况正在发生改变。

    哈姆布雷特于1968年创立了自己的投资银行。说到按计算结果进行投资,大家可能不会首先想到他,但近来他确实在采用这种方法。哈姆布雷特是风投公司WR Hambrecht Ventures的合伙人,后者隶属于专门从事IPO的投资银行WR Hambrecht and Company。他和常务董事托马斯·瑟斯顿紧密合作,创立了一种投资策略,把预测模型和克雷顿·克里斯滕森的颠覆性创新理论合二为一。瑟斯顿曾在英特尔担任业务开发经理,还是Growth Science的创始人,这是一家由三名成员组成的公司。瑟斯顿说,这是一家营利性智囊机构,“它的科研工作及其开发的工具都围绕着一个问题,那就是怎样才能更好地预测初创公司、新产品或者收购计划等创新活动能否存活下来,还是会以失败告终?”Growth Science采用专有数据库、数据采集方法以及算法在数据统计领域进行创新,以此计算业务模式和新技术获得成功的可能性。哈姆布雷特和瑟斯顿的合作方式非常有针对性——WR Hambrecht Ventures投资的所有公司都通过了Growth Science的预先检验。瑟斯顿解释说:“在这个过程中,所有环节都没有人的主观性参与,我们通过各种算法,最终得出肯定或否定的结果。”

    这些结果取决于很多因素,但瑟斯顿不愿意进行详细说明。不过,他把这些因素分为两类,一类在初创公司内部,另一类来自外部。瑟斯顿说:“我们发现,来自初创公司本身的预测因素只有20%左右(比如团队),另外80%则是初创公司以外的东西”,比如市场、消费者、竞争者、技术趋势和时机等。他们还把这种测算方法设计为动态模式,而非静态。瑟斯顿指出:“我们更关心事物可能出现的变化,而不是目前的情况如何。”

    那么,哈姆布雷特的风投基金表现如何呢?下定论为时尚早。通常,风投基金需要10-15年才能把资金返还给投资者,而哈姆布雷特和Growth Science的合作时间只有八年(他的投资对象包括价值15亿美元的移动短信服务商Tango)。不过,已经出现了有利迹象。哈姆布雷特介绍说:“基于后续发行估算,我们的投资组合资产已经是原来的五倍,,而且是在还没有哪家公司上市或以高价转让的情况下。所以我们认为这些基金的回报率将非常高。我们觉得有几家公司将会上市,可能是在明年。”

    Growth Science的另一个特点是它和几家“成员”公司合作,后者包括英特尔、3M和Cray Computer等。这些公司付费使用Growth Science的预测工具,具体做法是登录到Growth Science的网站上,就某项创新或新业务回答一系列问题,然后由Growth Science提交报告,告诉它们这项创新或新业务有多大的几率获得成功。

    罗恩·霍夫纳是3M战略业务开发集团的高级经理,负责3M和Growth Science的联络事务。加入3M前,霍夫纳在联合健康集团的创新实验室工作,研究旨在消除市场不确定因素的技术。得知Growth Science可以对业务模式进行模拟后,霍夫纳顺理成章地将其用于风险管理。3M首先在不同环境下对Growth Science的预测工具进行了测试,比如建立新业务、实施并购或进行创新,随后才把这项技术用于指导实际决策。3M对后者的准确性感到满意,已将其用于自己的医疗保健业务,而且频繁地用它进行创新管理。对霍夫纳来说,Growth Science的模拟回答了三个关于新产品或创新的关键问题:市场规模大吗?采用的技术合适吗?使用的方法和3M的业务模式一致吗?

    对于最后一个问题,Cray Computer首席执行官彼得·恩加罗举了一个具体的例子,说明了这样的模拟工具如何改变了该公司推出新产品的方式。Cray Computer生产超级计算机以及数据存储和分析平台,它刚刚进入某个市场时推出了一种性能更强的产品,还降低了购置总成本。考虑到Cray Computer属于新生势力,Growth Science对产品定位的建议是购置成本最低,而不是在高性能上做文章。恩加罗说:“这真的很有意思,特别是对Cray Computer这样的公司来说,因为我们的立足之本就是尽可能为用户提高性能。这个产品也是如此,但在那个市场,我们采用了另外一种定位。到目前为止,效果确实很好。”

    霍夫纳指出,这种方法说到底就是选择式决策,在这个过程中,可以通过有效信息推导出不同的行为,以及这些行为可能产生的结果。这样做最终会带来战略上的灵活性。霍夫纳说:“我确实觉得它属于更高的层次。在未来的管理活动中,人们会把它视为第一个带来数据驱动型管理的工具。”

    瑟斯顿把Growth Science的风投和企业业务视为同一枚硬币的正反两面。无论在哪个领域,管理者都想在投入资金前预测一下未来的情况。通过分析美国小企业管理局的数据以及他本人掌握的初创公司数据,瑟斯顿发现20-30%的新公司能生存10年。他说,和新开业的泰式餐厅、干洗店或者从大公司中剥离出来的业务相比,获得风投支持的初创企业好不到哪儿去,“我们[创业经济]在对业务进行预测方面做的更好一点儿吗?就我们的数据而言,答案是不,从统计角度来说数据不算亮眼。”不过,Growth Science表示它正在改变这种局面。瑟斯顿指出,在预测10年后公司能否存活的问题上,Growth Science的正确率是67%,而剩下的三分之一则预测错了。

    那么问题来了,如果Growth Science大幅提高了投资新技术和新公司的成功率,那为什么整个风投界并没有趋之若鹜呢?瑟斯顿回答说:“许多风投资本家都持怀疑态度,就像电影《点球成金》里的那些球探对运动员统计数据的态度一样。”

    Ask anyone in venture capital about their business model and they will probably tell you it’s all about the “hits.” In the VC world, a hit is a startup that makes it big, returning many multiples of a venture fund’s initial investment. Hits are great for everyone—investors, entrepreneurs, job seekers—but the problem is they don’t happen very often. William Hambrecht, a legendary venture capitalist who made early investments in Apple, Genentech, and Google, says the odds of a big hit are about one in 10. “A few others will work out, and you’re going to lose in a lot,” he says.

    But what if venture capital could boost its odds to 50-50, or even two out of three? With $48 billion in VC investment in 2014, such an improvement would prevent huge amounts of money from being lost on startups that never had much of a chance of surviving the harsh competitive environment. The challenge is to identify those likely laggards well before the market rejects their idea and, perhaps more importantly, to see the big hits before anyone else. Venture capital has long relied on subjective, intuitive methods of assessing startups, but that’s changing as more firms are bringing data science and consistency into their decision-making.

    Hambrecht, who started an investment bank in 1968, isn’t the first venture capitalist you’d expect to be investing according to the results of an algorithm, but that’s exactly what he’s doing these days. As a partner at WR Hambrecht Ventures, the VC arm of the IPO specialist WR Hambrecht and Company, Hambrecht works closely with managing director Thomas Thurston on an investment strategy that combines predictive modeling and Clayton Christensen’s disruption theory. Thurston, a former business development manager at Intel, is also the founder of Growth Science, a three person company he calls a for-profit think tank that’s “trying to do the science, build the tools, and do the research all around this one question: How can we better predict when innovations will survive or fail, both for startups and when corporations launch new products or do acquisitions?” he says. The company uses proprietary databases and data harvesting, along with algorithms, to bring innovations into the world of statistics, delivering probabilities on the success of business models and new technology. Hambrecht and Thurston join forces in a very specific way: Each company that WR Hambrecht Ventures invests in has gone through the Growth Science prediction engine and passed. “There’s no human subjectivity involved anywhere along the line,” Thurston explained. “All the algorithms converge on a discrete yes or no.”

    That yes or no depends on a lot of factors, and Thurston declined to be very specific about what they are. But he did separate them into two categories: those inside the startup, and those external to the startup. “We’ve found only around 20% of the predictive value to come from details specific to the startup itself (e.g., the team),” he says, “whereas 80% comes from things outside of the startup,” which he listed as the market, customers, competitors, technology trends, and timing. The model is also designed to be dynamic rather than static: “we care more about how things are likely to change, rather than how things are today,” he says.

    So how are Hambrecht’s funds doing? It’s too early to say for certain. VC funds typically take 10 to 15 years to return money to investors, and Hambrecht has been using the Growth Science method for eight. (Among the firm’s investments: Tango, a mobile messaging service worth $1.5 billion.) With that said, the signs are positive. According to Hambrecht, “the portfolios are up five times, just based on subsequent offerings, and nothing has gone public or sold out big yet, so we think these funds are going to have very high returns. We have several we think will go into the public market, probably within the next year.”

    Another facet of Growth Science is its work with “member” companies, such as Intel, 3M, Cray Computer, and a few others. These firms pay Growth Science for access to its prediction engine; they can log in to a website, answer a set of questions about an innovation or a new business, and Growth Science sends them a report on its likelihood of success.

    Ron Hoffner is a senior manager in the strategic business development group at 3M , and the company’s liaison with Growth Science. Prior to joining 3M, Hoffner worked in UnitedHealth’s innovation lab, researching techniques to decrease uncertainty in markets. When he learned of Growth Science’s business model simulation, it made perfect sense to him as a risk management tool. Before using it to guide real decisions, 3M did a few tests with the model in different environments, such as new ventures, mergers and acquisitions, and innovations. Satisfied with its accuracy, 3M has put the predictive model to use in its health care business group, where it’s used frequently to manage a portfolio of innovations. To Hoffner, the simulation answers three key questions about a new product or innovation: Is the market going to be big? Is this the right technology? Does the approach align with the company’s business model?

    On that last question, Peter Ungaro, the CEO of Cray Computer, provided a concrete example of how the simulation changed the company’s direction with a new product rollout. Cray, which makes supercomputers, data-storage, and analytics platforms, was a new entrant in a particular market, introducing a product with an increase in performance, but also a reduced total cost of ownership. Based on the company’s status as a new entrant, the model recommended positioning the product as the one with the lowest cost of ownership, instead of playing up its high performance. “That’s a really interesting thing, especially for a company like Cray, which is built on providing our customers with the most performance possible,” Ungaro says. “The product still does that, but we position it in the market in a different way. So far, the outcome has been really good.”

    This approach, Hoffner says, boils down to an options-based form of decision-making, where the right information can translate into various courses of action and their likely outcomes. The result, he says, is strategic flexibility. “I really think it is the next level,” Hoffner says. “If you look at the future of management, this is something people will look back on as the first tool to introduce data-driven management.”

    Thurston views Growth Science’s VC and enterprise work as two sides of the same coin. In each case, managers are trying to predict the future before investing money in it. According to Thurston’s analysis of data from the Small Business Administration, and his own data on startups, between 20% and 30% of new businesses survive to their 10th birthday. Startups with VC-backing, he says, aren’t doing much better than new Thai restaurants, dry cleaners, or spin-offs from large corporations. “Have we [the entrepreneurship economy] gotten any better at predicting any business?” Thurston asked. “Our data suggest no, not in any statistically significant amount.” Growth Science, however, claims it is turning that around. When it comes to predicting survivorship of companies after a 10-year period, Thurston says Growth Science has been right 67% of the time, and totally wrong on the remaining third.

    That raises the question: If Growth Science has vastly improved the odds of investing in new technologies and businesses, why isn’t the entire VC world knocking on its door? Thurston’s answer: “A lot of venture capitalists, like the scouts in Moneyball reacting to sabermetrics, are skeptical.”

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