Makine öğrenmesi ile girişimlerin (startupların) başarılarının tahmin edilmesi
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Dosyalar
Tarih
2023
Yazarlar
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Yayıncı
Bursa Teknik Üniversitesi, Lisansüstü Eğitim Enstitüsü
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
İyi girişimler genellikle basit bir fikirden ve belirli bir ihtiyaç ve mevcut bir pazar boşluğunu doldurmak için bir çözüm üreten birkaç kişiden oluşur. Diğer tarafta, üretilen çözümü gerçeğe dönüştürmek için sermaye ve bazen gerekli yardımı sağlayan melek yatırımcılar, risk sermayedarları ve kurumsal risk sermayeleri yani yatırımcılar vardır. Eğer bir yatırımcıysanız, her zaman bilmek isteyeceğiniz birkaç şey vardır: "İyi şirketler nerede?", "Nasıl öğrenilir?" ve "Yatırım yapmaya değer mi?". Yatırımcıların keşif yetenekleri genellikle yıllarca süren ağ (network) oluşturma ve markalaşma çabalarıyla oluşur. Doğru yatırımı yapmayı öğrenmek ise bir girişimin neden başarılı olurken diğerlerinin başarısız olduğunu sezgisel olarak çıkarmaya çalışmaktan ortaya çıkar. Finansal hizmetler endüstrisinde girişim sermayesi yatırımları, yüksek riskli, yüksek getirili varlık sınıfı olarak kabul edilmektedir. Son yıllarda yatırım yapmak inanılmaz derecede rekabetçi hale gelmiş ve samanlıkta kişiyi zengin edecek iğneyi bulmak hiç bu kadar zor olmamıştır. Girişim yatırımlarında genellikle sektör kabulü olarak 6 adım bulunmaktadır. Bunlar; anlaşma kaynağı bulma (deal sourcing), anlaşma seçimi (deal selection), değerleme (valuation), anlaşma yapısı (deal structure), yatırım sonrası katma değer (post investment value added), çıkış (exits)'tır. Risk sermayesi analistleri mümkün olduğu kadar çok fazla girişim ile görüşüp yatırım tezlerine uygun olan girişimleri değerlendirmeleri gerekmektedir ve görüşülen çoğu girişim çoğu zaman yatırım tezine uygun olmamaktadır. Herhangi bir girişim sermayesinin ya da fonun yaptığı temel varsayım, yatırımlarının çoğunun bir kayıp olacağı ve tüm geri dönüşün yapılan yatırımların en fazla %10'u tarafından yönlendirileceğidir. Risk-getiri profili bu nedenle çok istikrarsızdır, bu da yatırımcıları yalnızca "unicorn"a (değerlemesi 1 milyar doları geçen şirket) dönüşebilecek fırsatların peşine düşmeye ve iyi olmasına rağmen yapamayacakları yatırımları reddetmeye zorlar. Bununla birlikte, bir şirketin başarısını daha iyi tahmin etmenin bir yolu olsaydı, bir yatırımcı, unicorn aramak yerine, tümü 2 kat getiri sağlayan bir şirketler portföyüne kolayca yatırım yapabilir ve sonunda günümüzde çoğu fonun elde ettiğinden daha yüksek bir getiri elde edebilir. Bu bağlamda bu tezin amacı; Türkiye'deki girişim ekosisteminde en büyük payı alan (Global Startup Ecosystem Report 2022, 2023) oyun, finansal teknolojiler ve yapay zekâ alanındaki girişimlerden yola çıkarak potansiyel yatırımların riskini daha iyi değerlendirmek için girişim sermayesi yatırımlarında karar verme süreçlerinde makine öğrenmesi algoritmalarının; oyun, finansal teknolojiler ve yapay zeka alanındaki girişimlerin gelecekteki başarılarının tahmin edilmesindeki etkiyi araştırmaktır.
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.
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.
Açıklama
Anahtar Kelimeler
Science and Technology, Endüstri ve Endüstri Mühendisliği, Industrial and Industrial Engineering, Bilgisayar Mühendisliği Bilimleri, Bilgisayar ve Kontrol, Computer Engineering and Computer Science and Control, Bilim ve Teknoloji
