Forecasting Startup Return using Artificial Intelligence Methods and Econometric Models and Portfolio Optimization Using VaR and C-VaR

One of the main concepts in every economy are startups because they have some characteristics and qualifications such as innovation, job creation, boosting economic productivity and etc. that differentiate them from other companies. So, it is important to better identify them and make familiar with their revenue generations and valuations. In this paper, we have tried to study the main role of startups in economy, their characteristics, main goals and etc. The main goal of article is prediction of startup's return using artificial intelligence methods such as genetic algorithm (GA) and artificial neural network (ANN). There are multiple startup valuation models such as Berkus model, DCF model, venture capital method and etc. Since, there is not any information about startups such as sale, market size, profit and etc. and most of the models works with database, so, we have tried to analyze startups that are in stock markets and passed IPO stage. Some global indices such as S&P500, DJAI, and economic indicators such as 10 years Treasury yield, Wilshire 5000 Total Market Full Cap Index along with some other special indicators in startups like team, idea, timing and etc. are used as input variables. GA is used as feature selection and finding the most important variables. ANN is used as an optimization model and prediction of startup's returns. We used econometric models such as regression analysis. We have estimated Value at risk (VaR) and Conditional Value at risk (C-VAR) for considered portfolios including three startups (public company) such as Dropbox, Inc. (DBX), Scout24 SE (G24.DE) and TIE.AS and optimal portfolio formation. The results show that AI based methods are more powerful in prediction of startup's return. On the other hand, VaR and C-VaR models are very beneficial approach in minimizing risk and maximizing return. We found that artificial intelligence based models having high predictability and qualifications such as speed up calculations, improve by training, no assumption, ease of use and etc. But econometric models have some qualifications and assumptions such as normality, linearity, stationarity and etc. which are the limitation.