监督和风险企业控制权安排的关系分析

在风险企业中,风险资本家的主要功能是监督。通过他的监督,降低了创业家选择好行为的机会成本,减少了为了满足他的激励兼容约束而必须给予他的收益,放松了项目得以实施对创业家自有资本的要求,提高了项目融资的可能性。为了保证风险资本家监督的有效性,必须赋予他对风险企业的控制权。与成熟企业不同,风险资本家的控制权大小与他投入的资金多少并没有直接联系。他往往具有超比例的控制权,甚至控制董事会的权力。     

试论高技术企业融资契约的选择与安排

风险资本家与企业创业家的关系在本质上是一种委托代理关系,由于高技术企业的不确定特性,风险资本家必须面对更加复杂的管理问题、技术问题,以及市场问题,因而风险投资公司和高技术企业必须构建合理的融资契约来协调双方的责权利关系。     

可转换债券对创业板市场民企相机治理的作用

创业板市场上民营企业主要依赖内部治理机制即相机治理机制,而相机治理机制主要通过可转换债券发挥作用,因此可转换债券对于创业板市场上民营企业治理结构具有重要作用.主要体现在两方面:创业资本家通过对可转换债券投资收益率与期望收益率的对比,将剩余索取权和剩余控制权进行配置;创业资本家可通过可转换债券条款上的限制,拥有更多的控制权.     

风险投资交易设计研究

风险投资是一种高风险与高收益并存的投资，风险资本家作为投资理性人总是希望把风险控制在最低限度内，获取最大化收益，然而，风险资本家与创业家之间的信息总是不对称的，从而出现两之间的风险和收益的分摊问题，本分析了合理的投资交易设计对解决这一问题的重要性，而且重点地分析了合理投资交易设计的基本途径及制度环境。     

风险企业控制权安排的信息经济学解释

信息不对称是风险企业的显著特征,也是形成风险企业独特控制权安排的主要原因。利用信息经济学的方法对风险企业控制权安排的三个特点进行分析,弄清风险企业控制权安排的不同方面都是为了解决因信息不对称而引起的某个方面的问题。     

解读风险投资

风险投资作为一种投资体系已经广泛引起理论界和产业界的重视。风险投资的成功之处在于它在风险资本家与投资者或资金供应者之间建立了一套规范的融资机制,在风险企业建立了一套严格的投资管理机制。通过融资机制与投资管理机制的有效运行将投资者、风险资本家和企业经理的目标统一起来,克服了传统融资机制的信息不对称,实现了资金、人力资源、技术和信息这四大资源的有机整合。     

试论风险资本与高技术创业企业的有效融合

风险资本产生于资本、市场、企业等各自发展且彼此联系的历史进程中,体现出在这样的一系列联系中发育成长的实质。在高技术企业发展的整个历程中,风险资本家无论在控制权安排,还是融资结构选择方面都起着举足轻重的作用。因此,风险资本作为联系融资合约双方的纽带,无疑成为分析高技术企业融资机制的切入点。     

信息不对称条件下的风险投融资安排

本文通过建立模型对债券、普通股以及优先股三种不同融资工具差异的分析，来探讨不同融资工具在风险投资过程中对风险资本家与风险企业家行为产生的影响，得出结论认为普通股与优先股的结合能够有效地解决信息不对称情况下的投机行为。     

高技术风险企业董事会的功能及风险资本家的作用

一、监控功能 企业的契约理论将企业看成是由一系列"显性"和"隐性"的契约组成.由于信息不对称和未来的不确定性,使得这些契约不可能对未来所有的情形(contingence)进行详细的描述,即所谓合同的不完全性.这样就会导致剩余控制权与剩余索取权的分配,尤其是控制权的分配是区别公司治理结构的重要特征.Williamson(1996)将董事会看成是代表剩余索取者(股东)行使控制权的工具和手段.董事会的监控功能在于使管理层与股东的目标最大程度地趋于一致,采取的手段主要是激励与约束手段,如聘用和解雇管理层,制定管理层的薪酬计划,审查公司财务业绩,重大决策的批准与执行监督等.尽管在处于创业阶段的高技术风险企业中,企业的所有者与管理层之间的角色重叠(创业企业家既是所有者又往往是CEO),利益冲突比上市公司要小,但是这种利益冲突仍然存在,因为企业家并没有拥有100%的股权,而且高技术企业技术创新复杂性决定了风险企业比传统企业的信息不对称性更加严重.因此,风险企业的董事会的监控功能仍然是必需的,风险资本家在董事会中的监控作用也更加积极和直接.     

浅谈融资筹划中风险与收益关系的处理

融资做为企业筹措资金、进行企业发展的重要手段,拥有多种渠道,但是在这些融资渠道中,受制于企业及融资机构的情况差异,存在财务风险与经营风险的双重影响,造成融资风险指数也不一样,企业面临破产等风险。企业如何去进行正确的决策,建立稳定的融资筹划,从而规避风险,获得高收益是当前企业界着力解决的问题,本文通过对融资筹划中各类风险的分析,提出相应的处理机制,以期对当前企业融资作出一些贡献。     

Angel Financing and the Performance of High‐Tech Start‐Ups

In this paper, we investigate what drives the performance of high‐tech start‐ups receiving angel financing, while taking a closer look at the capabilities (i.e., experience) and investment behavior of business angels (BAs). We exploit a new data set (extracted from Crunchbase), which consists of 1,933 high‐tech start‐ups that received at least one financing round from a BA. The results indicate that the experience of BAs in early stage investments is positively associated with additional receipt of follow‐on rounds of financing and sequential capital injections from venture capitalists (VCs). Later‐stage experience is positively associated with the start‐up's success (i.e., probability to be listed or acquired), but reduces the need for new VCs to invest in the start‐up. Furthermore, we find consistent evidence that start‐ups that combine BA and VC financing experience higher levels of funding amounts, additional VC financing, and an improved likelihood of success. Finally, we find that the co‐localization of BA investors and start‐ups in the same area facilitates the attraction of VC financing.     

Broadening versus reinforcing investor portfolios: Social structure and the search for venture capital investors

This paper highlights the venture capital investor (VC) portfolios of startups, and explores how the portfolios evolve. We emphasize the important trade-off between broadening and reinforcing VC portfolios (i.e., expanding to new VCs versus relying on existing VCs). This is because, to startups, new and existing VCs generate very different opportunities and constraints. Focusing on the social structure of existing VCs, we argue that startups are more likely to opt for new VCs when the internal networks of existing VCs are denser, when the external networks of existing VCs are smaller, and when the status of existing VCs is lower. Additionally, we not only focus on whether new VCs are on board, but also pay attention to which new VCs are introduced, by analyzing the ex-ante embeddedness between existing and newly-introduced VCs. We stress that when new VCs are highly embedded with existing VCs, their involvement makes only a limited contribution to broadening a startup's portfolio and network. We test the hypotheses using a sample of VC financing rounds in the U.S. and find broad support.     

The nature of information and overconfidence on venture capitalists' decision making

Venture capitalists (VCs) are considered experts in identifying high potential new ventures—gazelles. Thus, the VC decision process has received tremendous attention within the entrepreneurship literature. Yet, most studies on VC decision-making focus on which decision criteria are central to selecting gazelles. Although informative, the majority of these studies has neglected cognitive differences in how VCs make decisions. This is surprising considering the influence cognitive differences are likely to have on the exploitation of an opportunity as well as its influence on likely success. The current study investigates whether VCs are overconfident, as well as the factors surrounding the decision that lead to overconfidence.Overconfidence describes the tendency to overestimate the likely occurrence of a set of events. Overconfident people make probability judgments that are more extreme than they should, given the evidence and their knowledge. In the case of the new venture investment decision, overconfident VCs may overestimate the likelihood that a funded company will succeed.The results of the current study indicate that VCs are indeed overconfident (96% of the 51 participating VCs exhibited significant overconfidence) and that overconfidence negatively affects VC decision accuracy (the correlation between overconfidence and accuracy was 0.70). The level of overconfidence depended upon the amount of information, the type of information, and whether the VC strongly believes the venture will succeed or fail.As more information becomes available, people tend to believe they will make better decisions; they are making a “more informed decision.” More information ideally should enable the VCs to assess any potential pitfalls. However, additional information makes the decision more complex. Information factors may contradict and relate to other information in unexpected ways. Even if more information is available, people usually don't analyze all of it (even though they believe they do). Thus, more information creates greater confidence, but it also leads to lower decision accuracy.The type of information that is available also impacts overconfidence and decision accuracy. VCs are intuitive decision makers. When people are familiar with a decision and the structure of the information surrounding that decision, they resort to automatic information processing. On the other hand, if information surrounding the decision is structured in an unfamiliar way, people need to decipher what each piece of information means and how that impacts their overall accuracy. In the case of expert VCs, that means they must deviate from their intuitive style. It seems that forcing them outside their “comfort zone” has a negative effect on their confidence and has an even greater effect (negative) on their accuracy.There is evidence of an “availability bias” in VC decision-making; VCs rely on how well the current decision matches past successful or failed investments. VCs are overconfident in their prediction of venture success when they predict a very high level of success. VCs are also overconfident in their prediction of venture failure when they predict a very low likelihood of success. This high level of overconfidence in success predictions (or failure predictions) may encourage the VC to limit information search and fund a lower potential investment (or prematurely reject a stronger potential investment).Although overconfidence in itself does not necessarily lead to a wrong decision, the bias is likely to inhibit learning and improving the decision process. Overconfident VCs may not fully consider all relevant information, nor search for additional information to improve their decision. Moreover, the natural tendency for people to recall past successes rather than failures may mean that VCs will make the same mistakes again. VCs can take simple steps to reduce the effect of overconfidence, including counterfactual thinking (i.e., imaging scenarios where current assumptions might not hold), formally recording how past decisions were made at the time of the decision (versus trying to recall how that decision was made from memory), and using actuarial decision aids that decompose decisions into core components. Reducing overconfidence may lead to stronger decisions. It is hoped that this study illustrates the power of cognitive theories for understanding VC decision-making.     

International entrepreneurship, venture capital networks, and reinvestment decisions

Prior research has established that venture capitalists (VCs) may face significant obstacles in financing ventures from emerging or transition economies. Such hurdles are usually attributed to the weaknesses of host countries’ institutional systems, especially regulatory. These institutional pitfalls may thwart VCs’ ability to exit a portfolio company leading to lower returns than expected. Developing this approach, we argue that exit strategies may also be difficult to execute when VCs expand into advanced economies although for different reasons. Thus, we show that both necessity entrepreneurship prevalent in emerging economies and opportunity entrepreneurship prevalent in advanced economies are positively associated with the number of investment rounds received by portfolio companies. In contrast, we establish that VC firm capital and network density are negatively associated with the number of rounds provided to portfolio companies across distinct institutional environments. This suggests that VCs may improve their performance by choosing an appropriate strategy to navigate unfamiliar institutional environments to minimize their liability of foreignness. Finally, we find that the interaction of VC capital and network density is positively related to the number of VCs’ investment rounds. Apparently, resource-rich VC firms may not fully realize the informational benefits of their dense “knowledge networks” due to insufficient collaboration with partners. At the same time, such VCs may no longer enjoy access to free information flows from prospective allies. Hence, network density and superior resources combined may lead to a greater number of investment rounds.     

Patents and Early‐Stage Financing: Matching versus Signaling

This paper investigates the role of patents on early‐stage financing. We consider two questions: are patents signals of quality and why do patents relate to venture capital (VC) financing but not angel financing? Analyzing data from 468 Canadian early‐stage ventures, we show that patents are not signals of quality. Instead, the data support a match‐on‐financing need hypothesis: ventures match with VCs, who have financial capacity to support their patent protection strategies.     

Cross-border venture capital investments in Asia: Selection and exit performance

We investigate the investment behavior and exit performance of VCs that have pursued expansion outside their home locations, specifically, in Asia. Our findings indicate that, in the Asian VC markets, foreign VCs have relative advantages over local VCs in terms of size and experience while they are at a disadvantage in information collection and monitoring due to both geographic and cultural distances. When investing alone, foreign VCs are more likely to invest in more information-transparent ventures. Partnership with local VCs helps alleviate information asymmetry and monitoring problem and has positive implication for the exit performance of local entrepreneurial firms. Specifically, we find that after controlling for the endogeneity of selection, firms with both foreign and local VC partnership are about 5% more likely to successfully exit.     

VC political connections and IPO earnings management

We examine the effect of VC political connections on accrual and real earnings management (EM) of IPOs controlled by private entrepreneurs in China. We find IPOs backed by government-controlled VCs exhibit severe IPO-year EM, which is driven by those VCs exiting their investments immediately after the VC lock-up expiration. In contrast, IPOs with politically connected private VCs have lower IPO-year EM and are not associated with VC exits. Therefore, IPOs with government-controlled VCs who immediately exit their investments are most likely to engage in ‘window-dressing’ of financial performance, and to have poorer long-run stock performance. The results remain unchanged after a battery of robustness tests.     

A lack of insight: do venture capitalists really understand their own decision process?

What decision criteria do venture capitalists (VCs) use to make their investment decisions? This question has received much attention within entrepreneurship literature (i.e.,Wells 1974; Poindexter 1976; Tyebjee and Bruno 1984; MacMillan, Seigel, and Subba Narasimha 1985; MacMillan, Zeman, and Subba Narasimha 1987; Robinson 1987; Timmons et al. 1987; Sandberg, Schweiger, and Hofer 1988; Hall and Hofer 1993; Zacharakis and Meyer 1995) for a number of reasons. First, VC-backed ventures achieve a higher survival rate than non-VC-backed businesses (Kunkel and Hofer 1990; Sandberg 1986; Timmons 1994). Second, a better understanding of the decision process may lead to even better survival rates. Finally, entrepreneurs seeking venture funding benefit if they understand what factors are most important to the VC.Although past research has greatly contributed to our understanding of the decision, it may be biased and somewhat misleading. The majority of past studies rely on post hoc methodologies (e.g., interviews and surveys) to capture the decision process. Post hoc methods assume that VCs can accurately relate their own decision processes, but studies from cognitive psychology suggest that people, in particular experts, are poor at introspecting. Introspection is subject to rationalization and post hoc recall biases. Using social judgment theory and the associated lens model as a framework, the current study investigates how well VCs introspect about their own decision process and, by extension, whether the past research efforts are biased.The current research uses policy capturing, a real-time method common in cognitive psychology, to capture the VC's “actual theories in use” versus their “espoused theories” (Hitt and Tyler 1991). Policy capturing requires that VCs make a series of real-time decisions based on various information factors. Regression analysis of each VCs' decision captures how important each of the information factors is to her/his actual decision process. After the VCs make their decisions, they provided a weighting of how they believe they used the information factors. Comparing the captured decision policies to stated decision policies provides a measure of VC insight.The findings suggest that VCs are not good at introspecting about their own decision process. Even within the confines of a controlled experiment, which greatly reduces the amount of information considered, VCs lacked strong understanding of how they made decisions. Most decision-makers would like to have all relevant information available for their decision. However, as more information becomes available, insight diminishes. Finally, this study finds that VCs are very consistent in their decision process, even though they do not necessarily understand how they make their decisions.VCs face a plethora of information when making an investment decision (i.e., business plan, outside consultants, due diligence, etc.). It may be difficult for VCs to truly understand their intuitive decision process because of all the noise caused by this information overload. This lack of systematic understanding impedes learning. VCs cannot make accurate adjustments to their evaluation process if they do not truly understand it. Therefore, VCs may suffer from a systematic bias that impedes the performance of their investment portfolio. The methodology used in this experiment can be modified and used as a training tool for active VCs. In addition, the consistent nature of VC decision-making (even if they do not have a strong understanding of that process) is favorable to the development of decision aides. Decision aides can minimize the danger of salient information (e.g., the lead entrepreneur is a winner) clouding the VC's judgment.Past research also needs to be interpreted in a new light. Although VCs undoubtedly use some of the information cited in past studies, the relative importance of that information needs to be reevaluated. VCs may not, for instance, rely most on the background of the entrepreneurial team. In addition, it is likely that the past studies provide more information factors than VCs actually use. People have a tendency to overstate the information they believe they relied upon and to use far less information (typically three to seven factors) to make a decision than they actually think they use. The methodology used in this experiment has the potential to identify the more relevant information factors cited in previous work.Even though VCs are experts in the new venture funding realm, their decision process has room for improvement. Almost 40% of all backed ventures fail to provide a return to the VC. Considering the billions invested each year, a modest improvement in the failure rate can have a substantial impact on venture portfolio returns. That improvement starts by better understanding the decision process. This study is a step in that direction.     

The potential of actuarial decision models: Can they improve the venture capital investment decision?

Venture capitalists (VCs) are considered experts in identifying high-potential new ventures—gazelles. VC-backed ventures survive at a much higher rate than those ventures backed by other sources Kunkel and Hofer 1991, Sandberg 1986, Timmons 1994. Thus, the VC decision process has received tremendous attention within the entrepreneurship literature. Nonetheless, VC-backed firms still fail at a surprisingly high rate (20%). Moreover, another 20% of the VC's portfolio fails to provide any return to the VC. Therefore, there is room for improvement in the VC investment process.The three staged investment process often begins with venture screening. First, VCs screen the hundreds of proposals they receive to assess which deserve further consideration. Those ventures that survive the initial stage are then subjected to extensive due diligence. Finally, the VC and entrepreneur negotiate terms of the investment. Considering the amount of time that due diligence and negotiation of terms may take, it is imperative that VCs minimize their efforts during screening so that only those ventures with the most potential proceed to the next stage. Yet, at the same time, the screening process should also be careful not to eliminate gazelles prematurely. VCs are in a quandary. How can they efficiently screen venture proposals without unduly rejecting high potential investments? The answer may be to use actuarial decision aides to assist in the screening process.Actuarial decision aides are models that decompose a decision into component parts (or cues) and recombine those cues to predict the potential outcome. For example, an actuarial model about the VC decision might decompose a venture proposal into decisions about the entrepreneurial team, the product, the market, etc. The sub-component decisions are than recombined to reach an overall assessment of the venture's potential. Such models have been developed in a number of decision domains (e.g., bank lending, psychological evaluations, etc.) and been found to be very robust. Specifically, these models often outperform the very experts that they are meant to mimic.The current study had 53 practicing VCs participate in a policy capturing experiment. The participants examined 50 ventures and judged each venture's success potential; would the venture ultimately succeed or fail. Likewise, identical information about each venture was input into two different types of actuarial models. One actuarial model—a bootstrap model—used information factors that VCs had identified as being most important to making a good investment decision. The second actuarial model was derived by Roure and Keeley (1990). The Roure and Keeley model best distinguished between success and failure in a study of 36 high-technology ventures. The bootstrap model outperformed all but one participating VC (he achieved the same accuracy rate as the bootstrap model). The Roure and Keely model, although less successful than the bootstrap model, outperformed over half of the participating VCs.The implications of this study are that properly developed actuarial models may be successful screening decision aides. The success of the actuarial models may be attributed to their consistency across different proposals and time. The models always weight the information cues the same. VCs, as are all human decision makers, may often be biased by differing salient information cues that cause them to misinterpret or ignore other important cues. For example, a VC may overlook product weaknesses if (s)he is familiar with the entrepreneur putting forth a particular proposal. Although the current study developed a generalized actuarial model, each VC firm could create screening models that fit it's particular decision criteria. The models could then be used by junior associates or lower level employees to perform an initial screen of received venture proposals thereby freeing senior associates' time.     

Limited attention and the role of the venture capitalist

This research analyzes the venture capitalist's incentives to maximize the profits of the entrepreneurs of ventures and the limited partners of a venture fund. Venture capital is a professionally managed pool of capital invested in equity-linked private ventures. Entrepreneurs turn to venture capitalists for financing because high-technology startup firms have low or negative cash flows, which prevent them from borrowing or issuing equity. In addition, venture capitalists are actively involved in management of the venture to assure its success. This solves the problem of startup firms that do not have the cash flows to hire management consultants.Venture capital contracts have three main characteristics: (1) staging the commitment of capital and preserving the option to abandon, (2) using compensation systems directly linked to value creation, and (3) preserving ways to force management to distribute investment proceeds. These characteristics address three fundamental problems: (1) sorting the venture capital among the entrepreneurial ventures, (2) providing incentives to motivate venture capitalists to maximize the value of the funded ventures, and (3) providing incentives to motivate entrepreneurs to maximize the value of the ventures. Venture capitalists fund only about a dozen projects a year out of a thousand evaluated. Each project may receive several rounds of financing. Payoffs to VCs can be very high or be a complete loss.The typical venture capital (VC) firm is organized as a limited partnership, with the venture capitalists serving as general partners and the investors as limited partners. General partner VCs act as agents for the limited partners in investing their funds. VCs invest their human capital by placing their reputation on the line. The goal is to begin to convert the investment into cash or marketable securities, which are distributed to the partners. VC management companies receive a management fee equal to a percentage (usually 2.5%) of the capital of each fund. They also receive a percentage (15–30%) of the profits of each fund, called carried interest. Periodic reports are made by the VC firm to the limited partners. Usually these are only costs of managing the fund, and so revenues are negative. Most contracts specify the percentage of time that the VC will devote to managing the fund.The analysis of this research deals with the incentives of the VC who has limited attention to be allocated between improving current ventures and evaluating new ventures for possible funding. The analysis shows that the VC, as agent for both the entrepreneur and the general partners, does not have the incentives required to maximize their profits. The VC allocates attention among ventures and venture funds less frequently than required to maximize the entrepreneurs' and limited partners' profits. However, the VC does maximize the total profits of all ventures. Because the VC considers the opportunity cost of attention, the VC's allocation of attention is efficient. The implication of this result is that, although the entrepreneurs and limited partners could be made better off with a different allocation of the VC's time, this would be an inefficient use of the VC's time.