Predicting the success of start-ups with machine learning

Good startups often originate from a simple idea and a few people identifying a solution to address a particular need and an existing market gap. On the other hand, there are angel investors, venture capitalists and corporate venture capitalists who provide the capital, and sometimes the necessary assistance, to make this dream a reality. If you are an investor, there are a few things you will always want to know: "Where are the good startups?", "How to learn?" and "Is it worth the investment?". Discovery capabilities are often built through years of networking and branding efforts. Making the right investment comes from intuitively trying to figure out why one venture succeeds and others fail. Investing has become incredibly competitive in recent years and looking for a the needle in a haystack that will make you rich has never been more difficult. The results show that machine learning can support venture investors in their decision-making processes to find opportunities and better assess the risk of potential investments. Good startups usually consist of a simple idea and a few people who come up with a solution to fill a specific need and an existing market gap. On the other hand, there are angel investors, venture capitalists and corporate venture capitalists who provide the capital and sometimes the necessary assistance to make the solution a reality. If you are an investor, there are a few things you will always want to know: "Where are the good startups?", "How to learn?" and "Is it worth the investment?". Investors often build discovery capabilities through years of networking and branding efforts. Learning to make the right investment comes from intuitively trying to figure out why one venture succeeds and others fail. In the Financial Services Industry, venture capital investments are considered a high-risk, high-yielding asset class. Investing has become incredibly competitive in recent years, and "looking for a the needle in a haystack" that will make you rich has never been more difficult. In venture investments, there are usually 6 steps as sector acceptance. These are; deal sourcing, deal selection, valuation, deal structure, post investment value added, exit. Venture capital analysts need to interview as many startups as possible and evaluate the startups that fit their investment thesis, and most startups interviewed often do not fit the investment thesis. The basic assumption any venture capital or fund makes is that most of their investment will be a loss and all return will be driven by no more than 10% of the investment made. The risk-return profile is therefore very unstable, forcing investors to seek opportunities that can only turn into "unicorns" (a company valued at more than $1 billion) and reject investments that are good but cannot make. However, if there were a better way to predict a company's success, an investor, instead of looking for a unicorn, could easily invest in a portfolio of companies all yielding 2x the return, and end up with a higher return than most funds today. In this context, the aim of this study is to investigate the effect of machine learning algorithm in predicting the future success of venture capital investments in order to better evaluate the risk of potential investments based on the startups in the field of game, financial technologies and artificial intelligence, which have the largest share in the entrepreneurial ecosystem in Turkey.