How Technology is Helping Venture Capitalists Make Investment Decisions
From stone tools to the invention of the wheel to steam engines to the internet, technology has come a long way, it has overtaken nearly all major aspects of our lives. Just take this article for example, you wouldn’t be reading it if it wasn’t for technology.
However, we’d be narrowing down our focus solely on the investment world and the impact technology has had over it over the years. The amount of tech applications employed by investors (venture capitalists) is innumerable so to speak.
But for starters, here are some main gizmos helping venture capitalists make investment decisions.
1. EBITDA Applications
EBTIDA or ‘Earning before Interest, Tax, Depreciation and Amortization’ is the scale which measures a company’s operational performance. In essence, it is the measurement of how a company performs on the operational front without factoring in the accounting decisions or the tax environment.
Investors use this scale to determine a company’s operational capacity to determine how well it functions in isolation. The consequences for an investor are huge if this variable is not considered before they finance the company.
A company’s tax environment and financial decisions may show it to be profitable. However, it may have a relatively poor operational performance, so venture capitalists usually consider EBITDA factor as well before they pour in their capital in the company.
2. Machine Learning
Seems odd? Well hopefully not for our geeky comrades out there reading this article, they’d probably already have guessed where we are going with this.
Machine learning is a subfield of artificial intelligence which employs human brain mimicking artificial neural networks to analyze data, identify patterns and predict future likelihoods.
Predict future likelihoods? We hope you’ve got the gist of it by now! Yes, venture capitalists have been intensively using machine learning applications to make their investment decisions. Consider stock markets. A good old school stock investor would be sitting in front of a desktop, observing market fluctuations to invest in a particular stock that he/she would hope, bring profits.
However, due to the inherent limitations of the human mind to analyze and calculate large factors in short amount of time, the old school strategy wasn’t as effective.
This has been compensated with machine learning applications, which monitor stocks 24/7, analyze and identify statistical patterns and make probabilistic predictions that more likely than not, generate profit for the investor. ML also drastically reduces the margin for human error to absolute zero. Just let that effectiveness sink in for a moment. This is how machine learning is rocking the world of stock markets.
3. Applications for price elasticity of supply and demand
Most of the world operates self-regulating free markets which function on a very elegant law, the so called law of supply and demand. Essentially, it implies that the price of a product or a service decreases when its demand decreases or supply increases more than the demand and reciprocally, the price increases when the demand increases or the supply decreases more than the demand.
This simple order of free-market is influenced by a huge number of factors and externalities which are easy to miss and hard to track. However, investors have now been using applications that predict a product’s or service’s future demands based on historical trends and geopolitical dynamics. Supply chain tracking applications also exist that allow investors to predict the temporal aspects of a commodity’s availability.
This allows them to invest in products/services that either decrease in supply or increase in demand in the immediate future, thus generating profit.