Algorithmic VC Investment & Due Diligence: A Compatibility Analysis

The Evolving Landscape of Algorithmic Investing

Within the realm of algorithmic investing, quantitative metrics are employed by investors to assess the viability of potential investments. However, the removal of subjective judgment can inadvertently introduce new challenges in conducting thorough due diligence on founders poised to receive substantial funding.

Ironically, efforts to mitigate bias can sometimes generate novel, less apparent vulnerabilities in the investment process.

The Rise of Data-Driven Investment

Algorithmic investing, in principle, aims to counteract investor preconceptions and minimize the influence of emotional factors. Companies like Clearco and SignalFire have dedicated years to developing data-centric investment methodologies, a trend recently adopted by AngelList and Hum Capital. While not a completely new concept, the emphasis on data-driven decisions appears to be gaining momentum alongside the increasing availability of capital.

The utilization of key performance indicators is becoming increasingly prevalent, even at the earliest stages of a startup’s development.

AngelList’s recently established early-stage venture fund bases all investment decisions on a single, crucial metric: a startup’s demonstrated capacity to attract and retain talent.

Abraham Othman, head of the investment committee and data science at AngelList Venture, explained that their data-driven approach often leads to successful deal closures, as they are perceived as less adversarial by potential portfolio companies. “Our methodology is straightforward – we analyze the data and determine if an investment is warranted,” he stated.

Streamlined Due Diligence

This approach minimizes the need for extensive traditional due diligence procedures.

AngelList Talent attracts approximately 2 million job applicants each quarter. Around 35,000 companies seek talent through the platform quarterly, though only half qualify as investable early-stage ventures.

Othman observed a significant trend early on: a substantial number of applicants were drawn to startups located in India. Consequently, his venture operation has expanded its investment focus to include India, alongside the United States, highlighting the influence of hiring demand on investment strategy.

Potential Pitfalls of Metric-Focused Investing

However, relying heavily on specific signals for investment carries inherent risks. Certain metrics can be inherently biased; for instance, while an investor might consciously disregard a founder’s personality, that founder’s personal brand could be a primary driver of applicant interest. Furthermore, a surge in applications could be attributable to competitive compensation, geographic location, or positive media coverage – factors that may indicate success, but could also simply reflect effective marketing efforts.

Michele Romanow, co-founder of Clearco, while a proponent of algorithmic investing, believes that hiring demand alone is insufficient as an investment signal.

“We maintain that no single indicator provides a comprehensive assessment of a business’s success or future growth. We strive to analyze as much data as possible, and while hiring is one aspect, it is, in isolation, overly subjective and susceptible to manipulation,” Romanow explained. Her company, formerly Clearbanc, offers businesses non-dilutive capital based on a carefully selected set of metrics, initially focused on marketing expenses and now encompassing inventory and payroll.

“We periodically hear discussions about the industry shifting towards this approach, but it remains challenging,” she added. “It demands significant technical expertise and a corresponding product infrastructure. While appealing in theory, many revert to traditional methods, relying on human judgment based on intangible qualities.”

The Value of Nuance and Triangulation

Mat Sherman, founder of Seedscout, a database of early-stage startup data, suggests that the most valuable metrics are often somewhat ambiguous.

“By the time a startup is actively expanding its hiring efforts, access to resources is typically already established,” he noted. “For a fund, this makes logical sense. However, the true advantage lies in predicting a company’s future hiring needs *before* they begin to actively recruit.” Sherman declined to detail his specific investment vetting process, characterizing it as proprietary, but did state, “The metric is more a convergence of various data points that culminates in a feeling…and that feeling *is* the metric.”

Balancing Data and Intuition

At its core, algorithmic investing isn’t about rejecting emotions; it’s about acknowledging their limitations. It may be overly optimistic to assume that a startup’s fate can be determined solely by a handful of numbers, particularly during favorable market conditions.

However, when utilized effectively, algorithms can inform our decisions in ways that our emotions cannot. Recognizing our inherent biases, it may be prudent to place greater emphasis on data analysis to arrive at more objective and well-informed conclusions.

• Key Metric: Hiring ability is a primary indicator for AngelList's investment decisions.
• Data-Driven Approach: Firms like Clearco and SignalFire prioritize data in their investment processes.
• Potential Bias: Metrics can be influenced by factors beyond a company's fundamental strength.