Coresignal logo
Use cases
Solutions
BlogDo not sell my personal info
Back to blog

Venture Capital and the Benefits of Machine Learning Methods

Coresignal logo

Coresignal

February 24, 2021

Venture capital (VC) investments are often associated with psychology and a thorough decision-making process that can be affected by human error, as a recent study suggests. Traditionally, investors use intuition and deduction to analyze and pick startups, which is why more and more investment managers have recently started to seek external help in the form of alternative data. This article explores the benefits of machine learning in venture capital and how it can help investors correctly identify reward opportunities.

The emergence of data-driven investments

While traditional investment models have been prevalent and successful, their caveats can turn disastrous. Theories have different shortcomings, such as false assumptions, that do not characterize real-world markets. As a result, the reality is more complex and has anomalies that are not considered in theoretical models.

Seeking more sophistication, investors have turned their attention to data-driven investments. Also referred to as alternative data, this information comes from a wide range of non-traditional sources, often web-based. According to Greenwich Associates 2018 Alternative Data Customer Journey Study, 48% of investment managers access web-scraped data, while 45% choose other sentiment sources, followed by credit card and POS systems, search trends, and many others.

Alternative data is listed as one of the five venture capital trends of this year. It is an important source of information because it is more varied than traditional data sources; for example, alternative data can be used to identify numerous insights into any company regardless of its growth stage.

For instance, the American retail company JCPenney outperformed nearly all expectations in the second quarter of 2015, followed by an impressive rise in its share prices. However, a big data company known as RSMetrics obtained satellite imagery of its parking lots, showing that its stores’ traffic increased. Investors who bought this type of external (alternative) data could see in advance that the company’s performance was growing more than the predicted values and could capitalize on this information.

How is machine learning used in investment decisions?

Machine learning is a branch of artificial intelligence (AI). It refers to algorithms or models which are created to increase their accuracy over time by learning from data without the need for any further programming.

Investors now have access to more and more data thanks to technological advancements. Machine learning methods are used in order to sort or analyze impressive amounts of data while becoming more efficient and accurate over time.

VCs use this alternative data to assess the future success of startups. In other words, machine learning can help by collecting and analyzing alternative data following models that can predict future rewards.

Machine learning in high-frequency trading

Machine learning is also inevitably a part of high-frequency trading (HFT). HFT refers to algorithmic trading in which an impressive amount of orders are conducted in a short time, providing liquidity on the market. Given this simplistic definition of HFT, artificial intelligence seems to be the most efficient way to process all the information. These models show increases or decreases in the market pricing and then make the correct bids. Most HFT companies nowadays integrate machine learning into their processes.

Machine learning and signal generation

Another aspect developed in the past years using ML-based algorithms is signal generation. The traditional seasonal signals use historical price patterns to make predictions. On the other hand, when powered by artificial intelligence, investors can generate better signals that include a wide range of additional data, such as volatility, sentiment, price action, structural changes, valuation metrics, and many more.

Given the abundance of such information, ML-based models can compute new trading signals. In addition to this, some models can make real-time predictions, and it continuously learns new data and adapts to the real-world market environment.

Why is machine learning important in venture capital?

Smart algorithms have become more and more important nowadays. From tech startups such as Kensho and Alphasense to Silicon Valley established companies, more and more investment firms implement data-related developments in their processes.

Unmatched efficiency

Hone is a Beijing-based company that partnered with AngelList, a firm with great access to different data types. They created a machine-learning algorithm using more than 30,000 deals from the past ten years from different sources, including PitchBook, Crunchbase, and MatterMark.

This allowed them to analyze more than 400 characteristics, including the founders’ background, historical conversion rates, money raised, and more. This resulted in a top 20 list of companies with the most potential for future success. Such data-related developments in investments can help venture capital firms further curate long lists of startups and minimize or completely avoid losses.

In short, machine learning can help to identify rewarding opportunities with unmatched efficiency. Although VC companies subscribe to different databases that list numerous startups’ data, it is obvious that the human brain cannot process such impressive amounts of information.

Big data and human limitations

Cukier and Mayer-Schönberger published a book on big data entitled “Big Data: A Revolution That Will Transform How We Live, Work, and Think.” They discuss that humans cannot consider and compare three characteristics of three different products simultaneously. Despite this, a venture capital manager should consider factors regarding both past and future startups and the market, resulting in the need to make an investment decision that could be worth millions of dollars.

This is where machine learning can improve the current practices and outcomes of VC. Since this technology can quickly identify the optimal matches using this rich data, venture capitalists can focus on suitable investments while eliminating low or no returns startups.

Machine learning and competitive advantage

Some voices go as far as to suggest that implementing machine learning in venture capital can result in a significant competitive edge. This is because technology enhances the decision-making process that could differentiate between a bad investment and $15 million returns. This is consistent with research showing that machine learning methods help investors find better opportunities and reduce the uncertainty associated with VC.

To achieve long-term success, a venture capital firm must have a strong competitive edge. Researchers firmly state that strategic advantages are tightly related to information due to the current knowledge-based economy. In other words, better access and processing of information can lead to superior decisions and choices. As a result, machine learning can make all operations quicker and more efficient while reducing human bias.

Quotation mark

People's behavior can be predicted to a degree when you have many past examples, but there is a limit — what data would have predicted the success of Google in 1998 or Facebook in 2003? – Gregory Piatetsky-Shapiro

Five benefits and applications of machine learning in venture capital

1. Finding companies looking for funding

As briefly discussed above, one of the most obvious uses of machine learning is to screen startups. This type of technology allows venture capitalists to filter a myriad of companies in different industries, verify whether they are seeking funding, if they already received investments, and many more. The list can then be curated using financial or other company-specific factors.

2. Identifying opportunities

Machine learning can identify new companies that experience early signs for growth. These can be analyzed using indicators such as hiring plans and the expansion of their teams. Finding early-stage companies is crucial for VC; machine learning helps to identify firms or products that are relatively new and receive impressive amounts of attention. This can be combined with social data to conduct sentiment analysis.

Another alternative data application is to identify and track down elite professionals, graduates, and professionals and their career paths. This information can indicate when a possible high-growth company is in its early stages. For example, machine learning can generate signals that inform VCs that several Ivy League graduates have just founded a company.

3. Timing and investment

One of the success factors in any type of investment is timing. Machine learning can help VCs identify companies’ growth stages so they can invest at the right time. For instance, technology can determine which teams are growing the fastest, the speed of growth, hiring strategy, business actions, and many more indicators.

4. Growth tracking

One of the relatively new investment strategies used in VC is growth tracking. Investors analyze the company’s online presence to determine its popularity; this is conducted by monitoring online reviews, social media sites, and even new job ads. Machine learning identifies companies that do well online, indicating future growth.

5. Satisfaction tracking

Finally, machine learning can monitor employee satisfaction. This can be a powerful indicator for VCs because employee dissatisfaction may indicate severe company or management issues. On the other hand, general satisfaction suggests that the company does not have any apparent problems.

Summary

All in all, machine learning is quickly gaining popularity in the investment world. Venture capital firms use artificial intelligence to adapt to the new knowledge-based society and reduce information asymmetry. Machine learning methods can ultimately help VCs separate companies with low or no chances for success from those with impressive potential to generate returns.

Stay ahead of the game with the freshest data

coresignal logo white

Use cases

Contact us

LinkedInTwitter

Coresignal © 2021 All Rights Reserved