Forecasting success in equity crowdfunding

One of the main challenges an entrepreneur faces when launching an equity crowdfunding campaign is deciding how much money to raise and which percentage equity to sell, thereby setting the company’s valuation. This has important implications for the success of the equity crowdfunding campaign, as setting an unrealistically high goal may discourage investors from participating. Indeed, existing empirical evidence from both reward-based and equity-based crowdfunding suggests that larger targets are negatively associated with success (Mollick 2014; Vulkan et al. 2016). Moreover, the percentage of equity offered (the complement of which is the percentage of equity retained) has important consequences for retaining control after subsequent funding rounds. Offering too much equity to new investors would dilute the current investors’ claim on future wealth.

In the framework of signaling theory, retaining equity has traditionally been considered as a strong sign of quality. In their seminal paper, Leland and Pyle (1977) argue that the actions of entrepreneurs “speak” for the quality of their venture and that their willingness to invest in their own project sends a positive signal to investors. For instance, by retaining more equity, entrepreneurs can effectively signal their confidence in the future potential of their business. On the contrary, entrepreneurs who are less dedicated to their company are likely to sell a higher amount of equity, that way shifting a higher proportion of potential future losses onto investors. Such behavior is likely to be taken into account by equity crowdfunding investors thereby impacting the success probability of equity crowdfunding campaigns. Previous empirical findings are largely in support of this argument. Both Ahlers et al. (2015) and Vismara (2016) show that the amount of equity offered by the entrepreneurs is negatively associated with equity crowdfunding success.

In contrast to traditional financing sources for early-stage ventures (business angels and venture capital), equity crowdfunding is specifically aimed at a large group of small “armchair” investors, who typically lack experience in evaluating investment opportunities. Under conditions of high risk and uncertainty where venture quality is not directly observable, individuals are likely to rely on the actions of others when judging about the quality of young companies and making their decision to invest (Stuart et al. 1999). In the context of equity crowdfunding, Vismara (2018) has shown that investors are largely concerned with who has previously invested in the company and that information cascades among individual investors are crucial for the success of equity crowdfunding campaigns. Furthermore, companies previously successful at raising external financing send a strong signal of legitimacy and financial viability, which is likely to increase the investors’ confidence in the future performance and value of the company, thus, impacting equity crowdfunding success.

Business accelerators are becoming an increasingly important framework for facilitating young venture success. Accelerator programs are cohort-based and among others offer start-ups seed investment, entrepreneurship schooling, mentoring, and a network. These services can be especially beneficial for young innovative businesses (Hallen et al. 2018) and are associated with an increased new venture performance (Gonzalez-Uribe and Leatherbee 2017), as well as with more seed and venture-capital deals in the region where the accelerator program operates (Fehder and Hochberg 2018). Given the highly selective nature of accelerators and the restricted access to their services, having attended an accelerator may be perceived by investors as a strong signal of venture quality, thus increasing the probability of success of equity crowdfunding campaigns.

By launching a crowdfunding campaign, entrepreneurs publicly disclose their business ideas and strategy, thereby risking misappropriation of that information by others. This introduces the need for protection of the firm’s intellectual capital via patents or trademarks. The protection of intellectual property can be viewed as a credible signal that conveys information to investors about the firm’s innovation capabilities, the ability to stake technological claims, as well as managerial skills (Long 2002). Extant literature has already recognized the signaling power of patents for raising funds in an IPO and venture capital context (Heeley et al. 2007; Hsu and Ziedonis 2013). However, their signaling value diminishes with firm maturity and the decrease of information asymmetries (Hoenen et al. 2014), which highlights their important effect on start-up financing.

There is greater uncertainty associated with younger ventures, as they have limited or no track record of past performance. Younger firms are in general more risky and suffer from greater information asymmetry problems, so one would expect that they are less likely to succeed. To mitigate this concern, investors on equity crowdfunding platforms may turn to information about the founders and the management team (such as the number of directors, their age, and the presence of non-founding directors) as a proxy for the quality of the human capital of the firm. Anecdotal evidence suggests that many investors believe that young people are especially likely to produce more successful new ventures. Human capital attributes have typically been regarded as a critical determinant for the success of entrepreneurial ventures (Florin et al. 2003) and as the most important selection criterion for business angels and venture capital investors (Zacharakis and Meyer 2000). Existing literature has additionally shown that higher human capital is related to superior outcomes in various aspects of the start-up businesses (see, e.g., Colombo and Grilli 2010; Cooper et al. 1994), including the access to additional resources (Baum and Silverman 2004) and equity crowdfunding success (Piva and Rossi-Lamastra 2018).

Crowdfunding platforms play an important role on the equity crowdfunding market that goes beyond facilitating the information transfer between entrepreneurs and investors. Over time, platforms have introduced services to help entrepreneurs develop their businesses, successfully launch and effectively manage a crowdfunding pitch, and better engage with their community. In some cases, crowdfunding platforms even take an equity stake in the companies that launch campaigns on their websites. Crowdfunding sites also offer a range of post-campaign services (such as exit assistance and secondary market), which are found to increase the annual number of successful campaigns (Rossi and Vismara 2018). Most importantly, equity crowdfunding platforms undertake due diligence efforts, which mitigate information asymmetries between backers and entrepreneurs. These efforts not only positively impact funding success, but also help to increase the total amount of capital raised (Cumming and Zhang 2018).

At the same time, equity crowdfunding platforms also differ significantly in the range of services they offer and the amount of screening they perform. An important difference from a corporate governance perspective is how voting rights are distributed among investors. Some platforms assign voting rights to individual investors, while others work under a nominee structure. This has direct consequences for success in equity crowdfunding, as platforms delivering individual voting rights are associated with fewer successful offerings (Rossi et al. 2018).

We identify campaigns on both Crowdcube and Seedrs via the database of TAB (available on Thomson Reuters Eikon App Studio).¹⁰ This database contains information about both successful and failed campaigns on Crowdcube and Seedrs during the years 2012 to 2017. In addition, we have scraped data from the websites of Crowdcube and Seedrs during that same period and use these to complement the data we obtained from TAB. This resulted in a list of 2539 successful and failed campaigns. Table 1 provides an overview of our selection criteria.

We exclude 97 campaigns from non-UK firms, 42 campaigns that have issued (convertible) bonds instead of equity and 65 campaigns for other reasons. We lose 164 campaigns because of missing information about the campaign or company. Our final sample therefore consists of 2171 campaigns (by 1932 unique companies) launched on either Crowdcube or Seedrs between January 1, 2012 and December 31, 2017. 189 of thеse companies launched more than one campaign (total of 239 follow-on campaigns). Out of the 2171 campaigns 961 are successful at reaching their targets.

Table 2 provides an overview of the variables used in the analysis. The main variable of our interest is equity crowdfunding success. We define equity crowdfunding success as a binary outcome, which takes the value of one if the equity crowdfunding campaign achieved at least 100% of its target (values above 100% are possible in the case of overfunding), and zero otherwise. The choice of this measure is driven by the “all-or-nothing” character of Seedrs and Crowdcube. The independent variables in our study are based on different campaign, company and director characteristics. We retrieve information about the campaigns (such as the date the campaign has been launched, the target, the amount of equity offered, the amount raised, whether it’s a first time campaign, and whether it was launched on Seedrs or Crowdcube). For this purpose we rely on the TAB database, but also on the individual websites of the Crowdcube and Seedrs platforms.

We gather information about the companies’ previous financing from the Beauhurst database, which gives us detailed access to the transaction histories of high-growth companies in the UK. Based on that we define a dummy variable external financing that takes the value of 1 if the company raised money from grants, business angels, angel networks, family offices, corporate investors, venture capital and/or private equity prior to launching a campaign. The Beauhurst platform also contains data on 200 UK accelerators, which offer programs to start-up companies and provide services such as mentoring, business education, accessing networks of contacts, office spaces, etc. Based on that information we define a dummy variable accelerator attendance that equals 1 if the company has been part of an accelerator program prior to launching a campaign. Examples of accelerators that support businesses in our sample include among others: Accelerator Academy, Wayra, Entrepreneurial Spark, Ignite Accelerator, Grow to Go, Octopus Labs Accelerator, Seedcamp and the UK Accelerator Programme.

Information on other company and director characteristics we retrieve from different sections of the Orbis database. For example, to define our intellectual property rights dummy, we collect data on the number of patents and trademarks from the Intellectual property section of the database. On Orbis we also have access to legal (e.g., incorporation date) and director data (e.g., number of directors), including personal data (such as the birthdates of the directors). We use these to calculate the company age and average director age. Additionally, we define a dummy variable non-founding directors that equal 1 if non-founding directors (directors that were appointed after the incorporation of the firm) are sitting on the board of the company at the time of the crowdfunding campaign.

Tables 3, 4, 5, and 6 present the descriptive statistics of the whole sample, Crowdcube and Seedrs separately, the sample split between successful and unsuccessful campaigns, as well as for different periods in time. Table 3 shows that the average success rate in our sample is 44%.

For explaining equity crowdfunding success we first consider two important campaign characteristics – the target amount and the percentage of equity offered. The average target amount in our sample is around £250,000 and ranges between as little as £700 and £5 million. The average equity offered is around 14%, which suggest that on average 86% of the equity is retained by the initial owners of the company. Campaigns on Crowdcube tend to be significantly larger than those launched on Seedrs and give up a significantly larger proportion of their equity (Table 4).

We further look how different company characteristics (such as age, intellectual property rights, external financing and accelerator attendance) may affect equity crowdfunding success. About one third of the campaigns in our sample were launched by companies which had previously successfully raised external funds (e.g., by grants or business angels). On average, these companies (conditional on raising external funds) were able to secure over £1 million of funds in the form of equity investments and over £250,000 in the form of grants (unreported results). Out of all the campaigns in our sample, 159 were launched by businesses which attended an accelerator program. About 11% of the companies in our sample hold patents or trademarks, and have thereby protected their intellectual property (more so in the case of Crowdcube). The companies in our sample are on average 2 years and 9 months old, but companies on Seedrs tend to be somewhat younger than those on Crowdcube.

The main responsibility for effectively promoting and successfully managing not only the business but also the equity crowdfunding pitch lies largely in the hands of the entrepreneur and his/her team. We, therefore, consider several characteristics relating to the director team and its composition (i.e., the number of directors, the director age, and the presence of non-founding directors). The average director in our sample is 40 years of age and most companies employ more than one director. Almost half of the campaigns are launched by companies which have one or more non-founding directors on board.

As discussed in Section 2.6., the platform itself also plays an important role in the equity crowdfunding market. We include a platform dummy variable (Seedrs) to control for the availability of distinct platform characteristics that may lead to different success outcomes (e.g., Seedrs offers nominee structure). We additionally take into account whether each of the campaigns in our sample represents a first time pitch or a follow-on fundraising. The probability of equity crowdfunding success for follow-on campaigns is likely to be higher, as the business has already undergone the market’s scrutiny.

Table 5 presents the difference in means and medians between successful and unsuccessful campaigns. Successful pitches tend to have higher valuations on average as derived by their higher targets and lower proportions of equity on offer. Companies that launched successful pitches were more often able to secure external funds from other sources prior to their equity crowdfunding campaign, were more often part of an accelerator program and more often protected their intellectual property. Successful companies also employed more and younger directors, as well as more non-founding directors. Most follow-on campaigns ended up being successful.

Table 6 reports the evolution of equity crowdfunding campaign attributes over time (2012–2017). According to Table 5, campaigns launched more recently (2015–2017) are much more likely to be successful than campaigns launched in the early days (2011–2014) of Crowdcube and Seedrs. The average target amounts and equity offered have also steadily increased over time. More recent campaigns are launched by companies, which were more often successful at raising funds externally, attended accelerator programs or protected their intellectual property. In the period of 2015 to 2017, the companies pitching on both equity crowdfunding platforms were older and employed larger boards of directors, including more non-founding directors. Taken together this evidence suggests that the equity crowdfunding market has matured over time and that more established companies have turned to equity crowdfunding as a viable financing alternative. To control for these changing market conditions and for not readily observable changes in the way platforms operate, we include year fixed effects in our regressions on the determinants of success.

Following the standard approach in the forecasting literature (e.g., Demers and Joos 2007), we use only the subset of explanatory variables with significant explanatory power to develop our models. To identify those we first estimate the full original model from the previous section on just the campaigns included in our estimation window. We then retain only those predictors that are significant from the first stage and rerun the regressions. This way we strike a balance between overfitting the model and its ability to describe the data. To develop annual forecasts of success on equity crowdfunding platforms, we use three-year rolling estimation windows in order to ensure that our estimation process is updated. For example, we use data from 2012 to 2014 to predict the likelihood of success in 2015, we then roll the estimation window forward by one year, i.e., use data from 2013 to 2015 to predict the likelihood of success in 2016.¹¹ The parsimonious models for each year are shown in Table 8.

The results of our regressions confirm the importance of the determinants of success from the previous section. Across all three years, the amount of retained equity, the availability of previous external financing, accelerator attendance and follow-on raises have a highly significant positive effect on the probability of having a successful equity crowdfunding campaign. The age of the company and the campaign target amount seem to not predict success in equity crowdfunding. While the number of directors is significantly positively associated with success over all three forecasted years, their age seems to have a negative impact. The last section of Table 8 assesses the models’ performance. The reported Hosmer-Lemeshow goodness-of-fit statistic is insignificant for all of our models, which indicates that the models fit the data well.

To examine the strictly out-of-sample predictive performance of our models, we first derive the forecasted probability of success for each equity crowdfunding campaign (between 2015 and 2017) by fitting the relevant year’s estimated coefficients to the campaign’s variables. In other words, for each campaign we generate an ex-ante probability of success based upon the equity crowdfunding experience of the previous three years. Based on these out-of-sample predicted probabilities, we generate a binary prediction of equity crowdfunding success (i.e., whether equity crowdfunding success equals 0 or 1) using different cutoff values (e.g., 0.5).¹² To test the predictive ability of our models, we cross-classify these binary predictions with the binary success variable for each year, i.e., we compare the true success outcome of each campaign in 2015, 2016, and 2017 to its out-of-sample forecasted probability (for similar approach, see Meoli et al. 2014). The classification results are reported in Table 9.

Sensitivity (the rate of correct predictions or true positive rate) refers to the proportion of successful campaigns that are correctly identified as such based on the out-of-sample forecasting model. In other words, it refers to the number of times the out-of-sample campaigns are correctly identified as successful divided by the total number of successful campaigns. The specificity (the true negative rate) is measured as the proportion of correctly specified unsuccessful campaigns. In that sense, 1-specificity (the ratio between the predicted successful campaigns that were not successful in reality and the total number of unsuccessful campaigns) depicts a type II error. The overall correct rate refers to the overall rate of correct classification. Our models correctly classify around 60 to 64% of all campaigns at different cutoff values. This result implies that an entrepreneur or a platform employee using our forecasting model to predict equity crowdfunding success would be on average more than 60% correct.

We additionally compute an adjusted success rate, which adjusts our rate of correct predictions for type II errors by estimating the ratio of correct predictions to the total number of predicted successful campaigns. This adjusted success rate has a more important and perhaps more interesting interpretation. If the decision to go live with an equity crowdfunding campaign was based solely on our model (i.e., all predicted successful campaigns launch and all predicted failed campaigns do not), the adjusted success rate for each year would range between 65% and 71% (for a cutoff value of 0.5). This is especially informative for platform organizers, who in 2015 were able to facilitate the success of only 52% of the campaigns, in 2016, 50% of the campaigns, and in 2017, 62% of the campaigns. Our simple forecasting model outperforms these outcomes for all years at different cutoff values.

Although informative, the classification method for evaluating forecasting performance suffers from a number of limitations (Demers and Joos 2007). First, the choice of a cutoff threshold is arbitrary, however, has a direct impact for measuring the forecasting ability of the model. Second, the continuous predicted probabilities estimated based on the forecasting model are collapsed into a binary outcome. And third, the classification is highly sensitive to the relative proportions of successful and unsuccessful campaigns in the sample. An alternative and perhaps more intuitive approach to presenting the model accuracy is provided by the ROC curve (Hosmer et al. 2013), which generalizes the classification table analysis by providing a graphical representation of the model performance for all possible cutoff values. The ROC curve plots the sensitivity against the type II error (or 1-specificity) of the logit model predictions, while the area under the ROC curve provides a measure of the model’s ability to discriminate between successes and failures. We adopt this methodology as an additional assessment of our prediction models’ accuracy. Figure 1 presents the ROC curves generated out-of-sample based on our forecasting models for each year, as well as the corresponding values for the areas under the ROC curves. A ROC value of 1 describes the best possible prediction model, which yields a point in the upper left corner of the graph corresponding to 100% specificity and 100% sensitivity. A ROC value of 0.5, which corresponds to a 45-degree line (used as a benchmark in Fig. 1), represents a model with a predictive power equivalent to a coin toss. As evident from the figure, the areas under the ROC curves are 0.671, 0.689, and 0.648 for the year 2015, 2016, and 2017 models respectively, all of which are considered as good levels of discrimination power (Hosmer et al., 2013). Taken together, the out-of-sample tests we perform suggest that our forecasting models have sound predictive ability.