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The purpose of this paper is to empirically demonstrate that drivers of venture capital (VC) investments are different across three broadly defined sectors: high-technology manufacturing, medium-technology manufacturing and services, and low-technology services. Moreover, such differences also exist across industries within each of these sectors.

The basic hypothesis is that “not only different stages of VC investments have different drivers, but VC investments in different sectors of the economy are also driven by different drivers.” The paper tests this hypothesis using a Poterba (1989) type supply and demand framework in the multivariate time-series regression analysis.

This paper empirically demonstrates that drivers of VC investments are different across three broadly defined sectors: high-technology manufacturing, medium-technology manufacturing and services, and low-technology services. Moreover, such differences also exist by stages of investment and across industries within each of these sectors. In particular, the paper finds that the importance of the number of VC-led initial public offering (IPO) transactions as the main driver of VC investment decreases with the level of technology involved in the sector. IPO transactions are particularly important in software, networking and equipment, and business products and services industries. In contrast to earlier literature, however, the paper do not find a more pronounced effect of IPOs for seed and late stages of VC investments. Similarly, the positive impact Sarbanes-Oxley Act of 2002 – which mainly impacts public companies – also intensifies with a decrease in the level of technology involved in the sector, and the paper do not find a negative impact. The Act is important particularly for VC investments in medium- and low-tech sectors and in early or expansion stages.

In analyzing the determinants of VC in a supply and demand framework as in Poterba (1989), the paper differentiates between different sectors (17 industries) and stages of VC (four stages: seed, early, expansion, late). Such level of differentiation is novel and allows more refined and better targeted public policy measures.

The purpose of this paper is to investigate how the innovative firm’s proprietary information has an impact on its debt financing preference. This study also examines the impact of industry-level competition on the debt financing orders and investigates how two exogenous shocks impacted on innovative firms’ financing policies.

This paper uses the three types of debt data, including bonds, private debt placements and bank loans and patent application data, in the USA from 1987–2008. The number of patents applications and industry-level competition are used as proxies for a firm’s innovation and industry-level sensitivity. In addition, to minimize endogenous concern, this study uses the propensity score matching analysis and difference-in-differences.

The patents are the primary determinants for innovative firms to choose the debt types. The paper shows that innovative firms have the debt preference order – public debt, private placement and bank loans. However, as competition increases, innovative firms devise the order reverse. Finally, the paper provides evidence that the American Inventor’s Protection Act (AIPA) and the tech bubble crash made investors depend more on firms with more patents.

This paper is the first to study the impact of the AIPA on innovative firms’ financial policies using the propensity score matching analysis. The findings imply that both patents and industry-level competition are important factors to understand the capital structures for innovative firms.

Groupon, an online coupon company, was one of many companies that considered an initial public offering (IPO) during what might be a second technology/internet/social media IPO boom in 2011. Some companies chose to postpone their IPOs, while others took advantage of the media attention focussed on technology companies, and in particular, social media firms. Should investors hop on the tech IPO bandwagon, or hold off to better evaluate the long-term prospects of tech companies, and in particular social media companies? Would the valuation of Groupon justify an investment in IPO shares?

The case was researched from secondary sources, using Groupon's IPO filing information, news articles about the IPO and industry research sources, such as IBIS World.

This case is appropriate for an advanced undergraduate or MBA corporate finance or investment elective. Most introductory finance classes do not have the time to cover later chapters in a finance textbook, where information about IPOs is generally found. It could also be used at the end of a core finance course, where the instructor wanted to introduce this topic through a case study of a hard-to-value internet-based company to illustrate the difficulties in setting IPO prices. The case could also be used in an equity analysis class, an entrepreneurial finance class or an investment class, to spur discussion about valuing an internet company and choosing appropriate investments for pension fund investing. This case could also be used in a strategy class, focussing on the five forces question, and eliminating the valuation question.

There is a great deal of literature about IPOs and their long-term performance. An excellent source is Jay R. Ritter's research, http://bear.warrington.ufl.edu/ritter, which has a longer time period and more data than could be contained in this case. IPO puzzles include persistent undervaluing of IPOs; in other words, the offer price is lower than, and sometimes substantially lower than, the first day close price. A second issue is the generally poorer long-run performance of companies after their IPO when compared to similar firms that did not do an IPO.

This study provides valuable insights to managers aiming to increase the effectiveness of their diversification and growth portfolios. The purpose of this paper is to examine the value of utilizing a neural networks (NNs) approach using mergers and acquisition (M&A) data confined in the US technology domain.

Using data from Bloomberg for the period 2000–2016, the results confirm that an NN approach provides more explanation between financial variables in the model than a traditional regression model where the NN approach of this study is then compared with linear classifier, logistic regression. The empirical results show that NN is a promising method of evaluating M&A takeover targets in terms of their predictive accuracy and adaptability.

The findings emphasize the value alternative methodologies provide in high-technology industries in order to achieve the screening and explorative performance objectives, given the technological complexity, market uncertainty and the divergent skill sets required for breakthrough innovations in these sectors.

NN methods do not provide for a fuller analysis of significance for each of the autonomous variables in the model as traditional regression methods do. The generalization breadth of this study is limited within a specific sector (technology) in a specific country (USA) covering a specific period (2000–2016).

Investors value firms before investing in them to identify their true stock price; yet, technology firms pose a great valuation challenge to investors and analysts alike as the latest information technology stock price bubbles, Silicon Valley and as the recent stratospheric rise of financial technology companies have also demonstrated.

Numerous studies have shown that M&As are more often than not destroy value rather than create it. More than 50 percent of all M&As lead to a decline in relative total shareholder return after one year. Hence, effective target identification must be built on the foundation of a credible strategy that identifies the most promising market segments for growth, assesses whether organic or acquisitive growth is the best way forward and defines the commercial and financial hurdles for potential deals.

Technology firm value is directly dependent on growth, consequently most of the value will originate from future customers or products not from current assets that makes it challenging for investors to measure a firm’s beta (risk) where the value of a technology is only known after its commercialization to the market. A differentiated methodological approach used is the use of NNs, machine learning and data mining to predict bankruptcy or takeover targets.

In view of discussion of two crises, Asian Financial crisis, 1997 and global meltdown, 2008 spreading over more than two decades, the objective of this article is to present econometrically whether productivity growth across countries can be a remedial measure toward tackling global recession pervaded during recent two or three decades worldwide and also to shed light on the aspect of whether productivity can truly act as a driver of growth of selected six economies like Korea Republic, Japan, India, China, USA, UK, and world economy as a whole. The panel data for the six selected countries for the period 1990–2018 were constructed keeping eyes on the 1997 Asian financial crisis and then the 2008–09 global economic crisis and a random effects model was applied after Hausman test. The empirical findings disclosed that the impacts on the growth of economies (represented by growth of GDP) from the growth rates of the manufacturing sector, labor productivity of manufacturing sector, and labor quantity are positive and statistically significant; while the effects of growth of the capital deepening and labor composition on economic growth of those sampled countries are statistically significant but negative. Some key factors that are likely to affect future productivity performance are centered on some issues like facilitating global learning spillovers; allowing productive firms to thrive; and making the most of human capital that should be taken care of.

Towers Watson (TW) has always conducted its own research into alternative approaches to market cap investing. A senior investment consultant with TW, impressed by a recent presentation by the CIO of Research Affiliates (RA) about an innovative investing concept called the “Fundamental Index methodology,” thinks it might be an important innovation in applying nonmarket cap approaches. But he has some concerns about the approach and whether or not it would be appropriate for TW's clients who depend on the firm to keep them on the cutting edge of institutional investing.

This study aims to examine whether two periods of extreme market conditions – the governance crisis and Sarbanes-Oxley Act regulatory shock of 2002 and the 2007–2008 global financial crisis – incrementally impacted the self-fulfilling prophecy effect, by examining the propensity of US firms receiving going concern modification (GCM) opinions to go bankrupt relative to their non-GCM distress risk-matched counterparts during these two crisis periods.

To assess the potential influence of the governance/regulatory shock of 2002 and the global financial crisis moderate or mitigate the self-fulfilling prophecy effect, the authors use multivariate logit analysis, regressing t + 1 bankruptcy status on time t GCM and other bankruptcy determinants, interacting crisis period dummies with the GCM variable.

GCM firms were more likely to declare bankruptcy than their distressed non-GCM counterparts, confirming prior research documenting the existence of a self-fulfilling prophecy effect. The authors also find that the self-fulfilling prophecy effect was exacerbated by the governance crisis/Sarbanes-Oxley Act regulatory shock, but not the global financial crisis, a financial/banking sector shock.

This study contributes to the financial crisis and auditing literatures by examining whether exogenous shocks exacerbate the self-fulfilling prophecy effect. The present analysis and findings have implications for future academic research related to systemic shocks and for auditors in documenting the inducement effect arising from the issuance of GCMs during crisis periods.

The purpose of this paper is to create a quantitative measure that captures the effects of investor sentiment in an objective way.

The author introduced risk estimation bias (REB) to examine the effects of forecasting error of future market volatility on fund alpha. The author also used GARCH to model the volatility of the REB.

The author documented a statistically significant relation between REB and realized market volatility. The author also found that the REB plays a significant role in explaining fund alpha.

REB is the first quantitative measure to examine the effects of investor sentiment on risk estimation and fund performance. The GRACH properties of REB provide important information on how investor sentiment fluctuates over time.

The chapter (re)assesses the impact of the 2008 financial crisis on global foreign direct investment (FDI). Based on updated data and renewed analyses, the author explores the crisis’ overall effects on global FDI flows, the different consequences on developed and developing countries, and the subsequent rise of emerging economies as both recipients and sources of FDI. Implications for policy and international business theory are delineated. By so doing, the author extends the theoretical and empirical studies on FDI determinants to the global level and provides lessons that are particularly useful against the backdrop of the COVID-19 pandemic.