Using Data Mining Approaches to Predict and Answer the Needs of Venture Capital

Subject Areas : Financial Knowledge of Securities Analysis

Farshid Ghasedi Ghazvini ¹ , Farshad Faezi Razi ² , Farzaneh Heydarpour ³

1 - دانشگاه آزاد اسلامی، واحد علوم و تحقیقات، گروه مدیریت صنعتی، تهران، ایران
2 - گروه مدیریت صنعتی، واحد سمنان، دانشگاه آزاد اسلامی، سمنان، ایران
3 - دانشیارگروه حسابداری ، دانشکده اقتصاد و حسابداری ، واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران

Received: 2016-05-18 Accepted : 2016-08-10 Published : 2017-09-23

Keywords: Start-up, Venture Capital, Investment Risk Assessment, Neural Network, Multiple Criteria Decision Mak,

Abstract :

Nowadays the economy of developed countries work based on small and medium enterprises and knowledge-base industries. Researchers experimental findings indicate financing of small and medium enterprises and start-ups with innovative activities have heterogeneity and special characteristics to start a business. This heterogeneity and special characteristics of start-ups confront essential problems in financing for them.Hence, for solving this problem, usage and continuity of venture capital process is necessary in order to encouragement and financing of innovative activities. Beside, in this process, venture capital firms in confrontation by pillar of financial markets need to conformity with the market regulations and policies. On the other hand venture capital firms in face of entrepreneurs and innovators confront challenges by how and state of ventures selection based on recognition and assessment of their risks for success or fail prediction of investments. The purpose of this research is response to this investorschallenges that lead investors to make superior evaluation and decision making in their start-ups investments through identification of the effective criteria on venture capital investments and their risk assessment for making trade-offs between them through multi criteria decision making method by usage of data mining and artificial intelligence

References:

*      اسلامی بید گلی, غ., و احمدی اول, م. (1389) بررسی عوامل اثرگذار بر ارزیابی طرح‌های کارآفرینانه در شرکت‌های سرمایه‌گذاری خطرپذیر (با استفاده از روش فرایند تحلیل شبکه‌ای). نشریه توسعه کارآفرینی, 3(8), صص99-120.

*      اسلامی بید گلی, غ., و بیگدلو, م. (1384). بررسی معیارهای موثر در ارزیابی کسب وکارهای مخاطره آمیز کار آفرینانه. فصلنامه دانش مدیریت (منتشر نمی شود), 18(1) ,صص 29-3.

*      بیگدلو, م., و مصطفوی, ا. (1383). مدل چندمعیاره ارزیابی ریسک برای شرکت های سرمایه گذاری مخاطره پذیر. اولین کنفرانس ملی سرمایه گذاری مخاطره پذیر.

*      جلیلی, م., و احمدی, م. (1385). شناسایی انواع ریسکهای موجود در صنعت سرمایه گذاری مخاطره پذیر با مطالعه موردی صنعت انرژی در جهان. دومین کنفرانس ملی سرمایه گذاری مخاطره پذیر.

*      دستگیر, م., و شفیعی‌سردشت, م. (1390). فناوری داده کاوی؛ رویکردی نوین در حوزه مالی. دانش حسابرسی, 5(11),صص 6-27. 

*      ریاحی, ح. (1385). مدیریت ریسک در صنعت سرمایه گذاری خطرپذیر. دومین کنفرانس ملی سرمایه گذاری مخاطره پذیر.

*       شفیعا, م., شوال پور, س., حسینی, م., وحسینی, ر. س. (1393). ارائه الگویی برای سنجش ریسک تجاری سازی فناوری های برتر در ایران با تأکید بر نقش سرمایه گذاران خطرپذیر چهارمین کنفرانس بین المللی و هشتمین کنفرانس ملی مدیریت فناوری.

*      عرفانیان, ا., و شیرزادی, س. (1385). ساختار مطلوب برای انجام سرمایه گذاری های خطرپذیر توسط شرکت های سرمایه گذاری. فصلنامه رشد فناوری, شماره11

*      کدخدایی, ح. (1383). کاربرد ابزارهای مالی در صنعت سرمایه گذاری خطرپذیر. اولین کنفرانس ملی سرمایه گذاری مخاطره پذیر.

*      محمدی, س. (1385). انتخاب اندازه پرتفوی بهینه سرمایه گذاری در شرکت های سرمایه گذاری خطرپذیر. دومین کنفرانس ملی سرمایه گذاری مخاطره پذیر.

*      Admati, A. R., & Pfleiderer, P. (1994). Robust financial contracting and the role of venture capitalists. The Journal of Finance, 49(2), 371-402.

*      Afful-Dadzie, E., Oplatková, Z. K., & Nabareseh, S. (2015). Selecting Start-Up Businesses in a Public Venture Capital Financing using Fuzzy PROMETHEE. Procedia Computer Science, 60, 63-72.

*      Alemany, L., & Villanueva, J. (2014). Early-Stage Investors’ Criteria and New Venture Financial Performance: Are They Related? Available at SSRN 2456197.

*      Alperovych, Y., Hubner, G., & Lobet, F. (2013). Venture capital-backing and public investor: Belgian evidence. Retrieved from

*      Amit, R., Glosten, L., & Muller, E. (1990). Entrepreneurial ability, venture investments, and risk sharing. Management science, 36(10), 1233-1246.

*      Baum, J. A., & Silverman, B. S. (2004). Picking winners or building them? Alliance, intellectual, and human capital as selection criteria in venture financing and performance of biotechnology startups. Journal of business venturing, 19(3), 411-436.

*      Berry, M. J., & Linoff, G. (1997). Data mining techniques: for marketing, sales, and customer support: John Wiley & Sons, Inc.

*      Bocken, N. (2015). Sustainable venture capital–catalyst for sustainable start-up success? Journal of Cleaner Production, 108, 647-658.

*      Chen, X.-P., Yao, X., & Kotha, S. (2009). Entrepreneur passion and preparedness in business plan presentations: a persuasion analysis of venture capitalists' funding decisions. Academy of Management Journal, 52(1), 199-214.

*      Cochrane, J. H. (2005). The risk and return of venture capital. Journal of financial economics, 75(1), 3-52.

*      Hall, J., & Hofer, C. W. (1993). Venture capitalists' decision criteria in new venture evaluation. Journal of business venturing, 8(1), 25-42.

*      Kantardzic, M. (2011). Data mining: concepts, models, methods, and algorithms: John Wiley & Sons.

*      King, R. G., & Levine, R. (1993). Finance and growth: Schumpeter might be right. The quarterly journal of economics, 717-737.

*      Kirkos, S., & Manolopoulos, Y. (2004). Data mining in finance and accounting: a review of current research trends. Paper presented at the Proceedings of the 1st international conference on enterprise systems and accounting (ICESAcc).

*      Klonowski, D. (2007). The venture capital investment process in emerging markets: Evidence from Central and Eastern Europe. International Journal of Emerging Markets, 2(4), 361-382.

*      Lerner, J. (2000). The government as venture capitalist: the long-run impact of the SBIR program. The Journal of Private Equity, 3(2), 55-78.

*      MacMillan, I. C., Siegel, R., & Narasimha, P. S. (1986). Criteria used by venture capitalists to evaluate new venture proposals. Journal of business venturing, 1(1), 119-128.

*      MacMillan, I. C., Zemann, L., & Subbanarasimha, P. (1987). Criteria distinguishing successful from unsuccessful ventures in the venture screening process. Journal of business venturing, 2(2), 123-137.

*      Manigart, S., Wright, M., Robbie, K., Desbrieres, P., & De Waele, K. (1997). Venture capitalists' appraisal of investment projects: An empirical European study. Entrepreneurship: Theory and Practice, 21(4), 29-44.

*      Martens, D., Vanhoutte, C., De Winne, S., Baesens, B., Sels, L., & Mues, C. (2011). Identifying financially successful start-up profiles with data mining. Expert Systems with Applications, 38(5), 5794-5800.

*      Mason, C., & Stark, M. (2004). What do investors look for in a business plan? A comparison of the investment criteria of bankers, venture capitalists and business angels. International Small Business Journal, 22(3), 227-248.

*      Muzyka, D., Birley, S., & Leleux, B. (1996). Trade-offs in the investment decisons of European venture capitalists. Journal of business venturing, 11(4), 273-287.

*      Öun, M., & Pettersson, I. (2012). Critical Factors for Successful Strategy Formation in Venture Capital Funded Companies: A study made for SEB Venture Capital.

*      Petty, J. S. (2009). THE DYNAMICS OF VENTURE CAPITAL DECISION MAKING. Paper presented at the Academy of Management Proceedings.

*      Ruhnka, J. C., & Young, J. E. (1991). Some hypotheses about risk in venture capital investing. Journal of business venturing, 6(2), 115-133.

*      Sahlman, W. A. (1990). The structure and governance of venture-capital organizations. Journal of financial economics, 27(2), 473-521.

*      Sharma, A. (2015). Venture Capitalists’ Investment Decision Criteria for New Ventures: A Review. Procedia-Social and Behavioral Sciences, 189, 465-470.

*      Tyebjee, T. T., & Bruno, A. V. (1984). A model of venture capitalist investment activity. Management science, 30(9), 1051-1066.

*      Yepez, C., & Student, M. E. (2004). A Behavioral Model of Venture Capital Bubbles. Dept. of Systems and Computer Engineering Carleton University, 2.

*      Zacharakis, A. L., & Meyer, G. D. (1998). A lack of insight: do venture capitalists really understand their own decision process? Journal of business venturing, 13(1), 57-76.

*      Zhang, G., Chen, Y., & Hu, D. (2006). Construction of Forewarning Risk Index Systems of Venture Capital Based on Artificial Neural Network Knowledge Enterprise: Intelligent Strategies in Product Design, Manufacturing, and Management (pp. 812-817): Springer.

*      Zhang, X. (2012). Venture capital investment selection decision-making base on fuzzy theory. Physics Procedia, 25, 1369-1375.

*      Zider, B. (1998). How venture capital works. Harvard business review, 76(6), 131-139.

*      Zinecker, M., & Bolf, D. (2015). Venture capitalists’ investment selection criteria in CEE countries and Russia. Business: Theory and Practice/Verslas: Teorija ir Praktika, 16(1), 94-103.