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ALLURING AND ENGAGING THE CROWD:

A LOOK AT UNITED STATES EQUITY CROWDFUNDING OFFERINGS USING SENTIMENT ANALYSIS

______

By

SARAH J. BORCHERS

______

A DISSERTATION

Submitted to the faculty of the Graduate School of the Creighton University in Partial Fulfillment of the Requirements for the degree of Doctor of Business Administration ______

Omaha, NE April 16, 2020

Copyright (2020) Sarah J. Borchers

This document is copyrighted material. Under copyright law, no part of this document may be reproduced without the expressed permission of the author.

ABSTRACT

Equity crowdfunding has grown exponentially in the United States since the passage of the JOBS Act in 2013, yet it continues to be an area that is relatively unexplored in the United States due to the limited availability of data. Given the limited amount of information available to potential investors about these highly risky, early-stage crowdfunding investment opportunities, qualitative factors, such as the sentiment or readability of the language used to describe the campaign, is likely to play an important role in the decision to invest, yet this area has not been explored in the equity crowdfunding literature. My dissertation examines the impact of both the sentiment and readability of the language used in a campaign description as well as an entrepreneur’s engagement with the crowd during a campaign. I first examine the role sentiment, as measured tone, and readability, play in influencing an investor’s decision to invest via equity crowdfunding platforms and whether this ultimately impacts the success of the offering. I employ various logit, Tobit, and beta regressions to determine the impact sentiment and readability play in campaign success. Further, I examine equity crowdfunding offerings both before and after the passage of Title III of the JOBS Act and the differences in investor motivations for both accredited and non-accredited investors. I find that readability and success have an inverse u-shaped relationship. Further, I find that the use of greater positive tone in the crowdfunding description increases the success of the campaigns for a subset of offerings prior to the passage of Title III only. Second, I examine an entrepreneur’s engagement with the crowd via questions and responses, comments, and testimonials and whether engagement ultimately impacts the success of the offering. I find that these on-platform engagement tactics positively impact the success of the campaign for a subset of offerings after the passage of Title III of the JOBS Act in the United States.

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ACKNOWLEDGEMENTS

The completion of this dissertation and the DBA program would not have been possible without my mentors, my family, and my friends who are like family. Words feel extremely inadequate to express the deep sense of gratitude I have for all of you. To my mentors, I was so incredibly lucky to work with some of the most talented faculty throughout the DBA program and appreciated their guidance and expertise. In addition, I am so fortunate to work among such a wonderful group of individuals who also offered me countless hours of help and support. To my cohort, I never knew how much I was going to need all of you when I embarked on this journey. I am blessed to have been placed in a cohort with such talented and incredible human beings. To my dissertation committee members, Dr. Dunham and Dr. Raju, I will never be able to repay you or fully express my gratitude. Your countless hours of guidance, suggestions, and proofreading (let’s face it, rewriting) were invaluable. Serving on a committee has to be one of the most thankless roles around, yet you sacrificed your own time and energy without hesitation. To my family, you have all stayed by my side from day one and never tried to talk me out of embarking on this journey. You were there for advice, guidance, and to talk me off the ledge on multiple occasions. To my husband, thank you for dropping everything to help out and for being a constant pillar of support. Mom and dad, thank you for raising me to be crazy enough to think I could accomplish anything I set my mind to. To my kids, I hope you see that you can do anything and I hope you realize the sacrifices I made in the time I was not able to spend with you were all done out of love and with you in mind. To my friends who are also my family, you saved me on multiple occasions. From being able to call you last minute to pick kids up from school, or grab something I forgot, or watch my kids so I could work, I always knew things were taken care of and everyone was in good hands. Thank you for being selfless and always willing to lend a helping hand. I will never be able to repay you.

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Table of Contents ABSTRACT ...... iii ACKNOWLEDGEMENTS ...... iv LIST OF FIGURES ...... vii LIST OF TABLES ...... viii CHAPTER 1: INTRODUCTION ...... 1 1.1 Dissertation Overview...... 1 1.2 Dissertation Organization ...... 4 CHAPTER 2: LITERATURE REVIEW ...... 5 2.1 The JOBS Act ...... 6 2.2 Signaling Theory ...... 7 2.3 Pecking Order Theory ...... 9 2.4 Social Identity Theory ...... 10 2.5 The Decision to List and Invest ...... 10 2.6 Crowdfunding Success ...... 12 2.6.1 Campaign Characteristics ...... 13 2.6.2 Understandability, Tone and Sentiment ...... 15 2.6.3 Use of Networks...... 18 CHAPTER 3: HYPOTHESES DEVELOPMENT ...... 21 CHAPTER 4: METHODOLOGY ...... 24 4.1 Impact of Sentiment on Campaign Success ...... 24 4.2 Impact of Crowd Engagement on Campaign Success ...... 27 CHAPTER 5: DATA AND DESCRIPTIVE STATISTICS ...... 29 CHAPTER 6: RESULTS ...... 36 6.1 The Effects of Sentiment and Readability on Campaign Success ...... 36 6.2 Effects of Sentiment and Readability After Passage of Title III ...... 38 6.3 Impact of Engagement with the Crowd on Campaign Success ...... 39 6.4 Robustness Analyses ...... 42 6.4.1 Beta Regression Robustness Results ...... 42 6.4.2 SMOG Readability Index ...... 43 CHAPTER 7: CONCLUSION...... 46 7.1 Contributions ...... 46 7.2 Limitations ...... 49

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7.3 Future Research Ideas ...... 50 VARIABLE DEFINITIONS ...... 53 FIGURES ...... 54 TABLES ...... 67 BIBLIOGRAPHY ...... 92

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LIST OF FIGURES Figure 1 Campaign Information Presented on Wefunder Home Page ...... 55 Figure 2 Campaign Information Presented on Wefunder for World Tree ...... 56 Figure 3 Campaign Information Presented on Wefunder for World Tree ...... 57 Figure 4 Histogram of the Percent of the Campaign Amount Raised ...... 58 Figure 5 Unsuccessful and Successful Offerings ...... 59 Figure 6 Unsuccessful and Successful Campaign Offerings by Industry ...... 60 Figure 7 Top Five Platforms by Number of Offerings ...... 61 Figure 8 Top Five States by Number of Offerings ...... 62 Figure 9 Top Five States by Total Campaign Dollars Requested ...... 63 Figure 10 Percentage of Total Offerings by Region ...... 64 Figure 11 Top 50 Words Used in Campaign Descriptions ...... 65 Figure 12 Top 50 Words Used in Successful Campaign Descriptions ...... 66

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LIST OF TABLES Table 1 Crowdfunding Options in the United States ...... 68 Table 2 Summary Statistics ...... 69 Table 3 Tests for Differences in Means ...... 72 Table 4 Mood's Test for Differences in Medians ...... 73 Table 5 Correlation Matrix ...... 74 Table 6 Impact of Sentiment on Campaign Success - Logit Model ...... 77 Table 7 Impact of Sentiment on Campaign Success - Logit Model with Balanced Panel ..... 78 Table 8 Impact of Sentiment on Campaign Success - Tobit Model ...... 79 Table 9 Impact of Sentiment on Campaign Success with Title III Interaction - Logit Model 80 Table 10 Impact of Sentiment on Campaign Success - Logit Model with Balanced Panel ..... 81 Table 11 Impact of Sentiment on Campaign Success with Title III Interaction - Tobit Model 82 Table 12 Impact of Engagement with the Crowd on Campaign Success - Logit Results ...... 83 Table 13 Impact of Engagement with the Crowd on Campaign Success - Tobit Results ...... 84 Table 14 Impact of Sentiment on Campaign Success - Beta Regression Model ...... 85 Table 15 Impact of Sentiment on Campaign Success with Title III Interaction - Beta Regression Model ...... 86 Table 16 Model Comparisons with Title III Interaction - Full Dataset ...... 87 Table 17 Model Comparisons - Full Dataset ...... 88 Table 18 Model Comparisons - Title II Subsample ...... 89 Table 19 Model Comparisons - Title III Subset ...... 90 Table 20 Impact of Sentiment on Campaign Success -Tobit Model Using the SMOG Readability Index ...... 91

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CHAPTER 1: INTRODUCTION

1.1 Dissertation Overview Bringing new business ventures to life can be a costly endeavor for entrepreneurs, most of which require outside capital for success. Companies often undergo several financing rounds to meet their target goal (Sherman, 2003), and entrepreneurs traditionally have had to rely on venture capitalists or angel investors to meet their financing needs. Over the past decade, however, an emerging form of financing called crowdfunding has gained in popularity as an additional source of financing. Crowdfunding has been defined as “an open call, essentially through the Internet, for the provision of financial resources either in the form of donation or in exchange for some form of reward and/or voting rights in order to support initiatives for specific purposes” (Schwienbacher

& Larralde, 2010). Crowdfunding as a source of financing has surged in recent years, primarily due to the passage of Title II of the Jumpstart Our Business Startups (JOBS) Act in September

2013, which allowed public solicitation of new ventures. The passage of Title II made it easier for entrepreneurs to seek funds from accredited investors, as they were previously not able to gain funding through internet-based equity crowdfunding (Mamonov & Malaga, 2019). Then, in March

2016, the passage of Title III of the JOBS Act opened the door to crowdfunding opportunities to non-accredited investors. In 2017 alone, over $17.2 billion was raised through crowdfunding platforms in North America. This accounts for approximately half of the total $28.8 billion raised globally (Szmigiera, 2019). Of the total amount raised through crowdfunding globally, approximately $2.5 billion was raised via equity crowdfunding campaigns (United States

Securities and Exchange Commission [SEC], 2019).

Despite the surge in popularity, most of the research on crowdfunding to date has examined crowdfunding activities outside of the United States. To my knowledge, only a few studies have

1 examined equity crowdfunding activities using data from the United States (Agrawal et al., 2016;

Mamonov & Malaga, 2019; Mamonov & Malaga, 2018; Mamonov et al., 2017). Regardless, most of the crowdfunding studies to date have sought to identify the key determinants of fundraising success, generally measured by whether or not the entrepreneur meets his or her funding goal on the crowdfunding platform. While most studies to date have linked fundraising success to factors such as the availability of financial statements, the amount of funding sought, and the length of time of the fundraising campaign, very few studies have examined whether the tone or sentiment of the language used to describe the startup in a crowdfunding campaign is a determinant of fundraising success. There is good reason to suggest it might: arguably, the first and most important piece of information about a fundraising campaign on a crowdfunding platform is the campaign description: it is essentially the entrepreneur’s opportunity to make that good “first impression” on potential investors. On any given crowdfunding platform where several entrepreneurs are competitively seeking funding for their startups, making a good first impression would seem to be even more critical given very little information is known or available about these companies. When potential investors log onto any one of several crowdfunding platforms, they are presented with varying amounts of information about each campaign, depending on the platform.

Some information, such as the description of the company and the amount of funding sought, is generally consistent from platform to platform.

However, the amount of other information about individual fundraising campaigns on crowdfunding platforms varies significantly across platforms. Some crowdfunding platforms allow for entrepreneurs to interact with potential investors. In these cases, potential investors are able to post questions for the entrepreneur, and the entrepreneur can post responses, all of which is viewable by all potential investors. Some platforms also allow for existing customers to post

2 testimonials about a company’s product or service. These on-platform interactions and testimonials are likely to provide potential investors with additional key information that affects their decision to invest. To date, few studies have examined the role that this engagement between the entrepreneur and potential investors play in fundraising success. Although there are a few existing studies that examine the impact of on-platform engagement via interaction and communication on fundraising success in the rewards-based crowdfunding space, to my knowledge there are no studies that examine the impact of on-platform engagement on fundraising success in the equity crowdfunding space.

Again, given the limited amount of information available to potential investors about these highly risky, early-stage crowdfunding investment opportunities, particularly the lack of audited financial information, other qualitative factors, such as the tone or sentiment of the language used to describe the crowdfunding campaign and the presence of on-platform engagement, are likely to play an important role in the decision to invest. That is the focus of my dissertation: I investigate whether crowdfunding campaign success is influenced by 1) the tone or sentiment of the language used by the entrepreneur to describe the crowdfunding company and campaign, and/or 2) the presence of on-platform engagement via interaction and communication with the entrepreneur:

More formally, my primary research questions are stated as follows:

Research Question 1: Does the sentiment (as measured by tone) and readability of the language used in a campaign description impact the success of a US equity crowdfunding offering?

Research Question 2: Does on-platform entrepreneur engagement (via interaction and communication) impact the success of a US equity crowdfunding offering?

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Prior research suggests that investors in different countries view equity crowdfunding differently, partially driven by varying degrees of risk aversion (Kshetri, 2018). Therefore, there is reason to believe that investigating the determinants of equity crowdfunding success using

United States data, particularly in light of the fairly recent passages of Title II and Title III of the

JOBS Act, may yield different results than those studies using data from other countries and other crowdfunding categories. Further, the assumed differences in investor sophistication between accredited and non-accredited investors may result in different investment behavior; yet, this potential behavior discrepancy is an area that has been largely unexplored in the literature. Thus, my dissertation seeks to fill this gap by examining the effect of sentiment and on-platform engagement on equity crowdfunding success in the United States. My study allows for potential differences in success outcomes of equity crowdfunding offerings that took place after the passage of Title II but before the passage of Title III when non-accredited investors entered the landscape, and those that took place after the passage of Title III.

1.2 Dissertation Organization The remainder of this dissertation is organized as follows. In Section II, I review the crowdfunding literature and identify gaps in the literature. Section III outlines five testable hypotheses. In Section IV, I outline the research methodology that is utilized to test the hypotheses.

Section V describes the data sources for the study and the variables used to test the hypotheses.

The section concludes with a summary of descriptive statistics. Section VI presents the empirical results of the various models. Finally, Section VII concludes by discussing implications for theory and practice and offers future research ideas.

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CHAPTER 2: LITERATURE REVIEW

Crowdfunding is typically segmented into four categories: donation-based, rewards-based, debt-based, and equity-based. Although all four forms have similar characteristics, each has its own unique features as well. Regardless of category, crowdfunding transactions take place on an internet platform and can be completed virtually anywhere. Entrepreneurs or others seeking funding simply post a request for funding on one of several crowdfunding platforms, and potential investors can access the platform, browse the various offers and, if interested, invest money in return for a personal benefit (Ahlers et al., 2015).

While the logistics of each category of crowdfunding are similar, the motivations behind,

or the personal benefits derived, vary by crowdfunding category. For example, rewards-based

crowdfunding investors are incentivized by various types of rewards, such as advance tickets to a performance, a discount on a new project, or future royalties on music sales (Mamonov & Malaga,

2019; Lukkarinen et al., 2016). A donation-based crowdfunding platform facilitates donations for a variety of philanthropic endeavors without an expectation of anything in return. An example of a popular donation-based crowdfunding platform is GoFundMe, where investors can make donations to a variety of causes. These investors are motivated by personal philanthropic reasons.

In debt-based crowdfunding, investors loan money to individuals or businesses, and these lenders expect to receive a return in the form of interest. Finally, through equity-based crowdfunding, investors receive an equity stake in the companies they choose to invest (Ahlers et al., 2015;

Mamonov & Malaga, 2019). Of course, these investors expect to earn a return on their equity investment, which mirrors the motives behind conventional forms of equity investing. My dissertation is focused on the last category – equity crowdfunding – which is a relatively new

5 phenomenon in the United States due to the recent passages of Title II and Title III of the Jumpstart

Our Business Startups (JOBS) Act.

Investors in mainstream forms of financing tend to be risk averse, which makes it tough

for early stage start-ups to gain funding through venture capital or angel investing. Further, endless

competition for funds exists between early stage new ventures due to the limited supply of venture

capitalists and angel investors. Equity crowdfunding has many similarities to venture capital and

angel investing, and it is increasingly being used as a new form of financing for start-ups

(Lukkarinen et al., 2016). Similarities to more traditional forms of investing include the

motivation, the funding through shares, and the absence of active intermediaries. Although equity

crowdfunding has been established in other countries for over a decade, until recently, equity

crowdfunding was not an option in the United States. While venture capital, angel investing, and

even rewards-based crowdfunding were legal, equity crowdfunding was prohibited by the

Securities and Exchange Act of 1933 and 1934 until September 2013.

2.1 The JOBS Act Although it is always very difficult for entrepreneurs to raise capital for new business ventures, the 2007-2008 financial crisis made it exponentially more difficult. In response to the financial crisis, the JOBS Act was signed into law in the US in April 2012. In general, the JOBS

Act aimed to stimulate economic growth by improving access to public capital markets and eliminating listing requirements for emerging growth companies (Colombo et al., 2016; United

States Securities and Exchange Commission [SEC], 2013). As it relates specifically to crowdfunding, Title II of the JOBS Act went into effect in September 2013, which relaxed the rules for public solicitation of investment capital from accredited investors. Prior to the passage of Title II, general solicitation (e.g., public advertising) for participation in a securities offering

6 was illegal for early stage private companies. In short, the passage of Title II made it easier for start-ups to seek funds from accredited investors via crowdfunding1. Then, in May 2016, Title III

of the JOBS Act went into effect, opening crowdfunding investment opportunities to non-

accredited investors, albeit with restrictions. Under Title II, an early stage company can raise an

unlimited amount of money from accredited investors. However, under Title III, early stage

companies can only raise $1.07 million in any twelve-month period from non-accredited investors,

and there are also limits on the amount individual non-accredited investors can invest across all

crowdfunding offerings in a twelve-month period. Table 1 provides a summary of the terms of

each Title of the JOBS Act. Since the passage of the JOBS Act in 2013, it is estimated that $1.4 billion has been raised by entrepreneurs from accredited investors via equity crowdfunding platforms (Mamonov & Malaga, 2019). Of this total, approximately $108.2 million was raised between May 16, 2016 and December 31, 2018 after the passage of Title III of the JOBS Act (SEC,

2019).

2.2 Signaling Theory Investing in equity offerings is a risky endeavor, particularly investing in early-stage companies. Thousands of entrepreneurs are out there seeking investment capital, and prospective investors gather as much information as they can in order to make their investment decisions. Of course, similar to public equity markets, there is an information asymmetry problem in early-stage equity offerings: these entrepreneurs likely have superior information about their firm’s prospects than prospective investors, and the information asymmetry problem is further exacerbated by the lack of operating history and financial information for these early stage companies. Thus, potential

1 In the United States, an accredited investor is someone who has an annual income exceeding $200,000 or has assets in excess of $1 million, excluding their primary residence (SEC, 2013). 7 investors are forced to make decisions with incomplete information (Courtney et al., 2016; Ahlers et al., 2015; Agrawal et al., 2014). In the context of equity crowdfunding, information asymmetry problems may be exacerbated by equity crowdfunding platforms that only allow prospective

investors to see very limited information about the companies listed (Agrawal et al., 2014).

One potential way for entrepreneurs to improve their chances of getting their equity

crowdfunding campaign funded is to take steps that help mitigate the information asymmetry via

signaling (Spence, 1973, 2002; Connelly et al., 2011). To help mitigate this information

asymmetry in equity crowdfunding campaigns, a variety of solutions have been proposed.

Recently, investors have been increasingly looking to social media content and other signals to

reduce this information asymmetry (Belleflamme et al., 2014; Connelly et al., 2011; Vismara,

2016; Agrawal et al., 2014; Kaminski et al., 2018). For example, the use of networks (such as

Facebook, Linkedin, and Twitter) has proven to be an effective tool to help reduce information

asymmetry and to signal a start-up’s value to potential investors (Vismara, 2016; Ahlers et al.,

2015). Along with the use of social networks, other proposed methods to signal a start-up’s value

include using financial roadmaps, taking actions to decrease the perception of risk, and increasing board presence (Ahlers et al., 2015).

Further, Mamonov et al. (2017) suggest the use of clear and appropriate descriptions of the

funding opportunity to reduce information asymmetry. Parhankangas and Ehrlich (2014) find that

the language used in a business plan impacts an angel investor’s decision to invest. Further, Chen

et al. (2009) studied persuasion theory regarding venture capitalist behavior and found that potential investors gauge an entrepreneur’s passion for their product or service inferred from the

language of the business plan, which impacts their decision to invest. Following this approach,

Zhou et al. (2018) applied persuasion theory to crowdfunding descriptions in their rewards-based

8 study. Finally, prior research has explored the use of syndicates to reduce information asymmetry in equity crowdfunding ventures (Agrawal et al., 2016). Syndicates are groups of investors that are typically led by an experienced venture capitalist who performs due diligence on the potential investments. Syndicates help to align the incentives of both issuers and investors and encourage the flow of information, thereby reducing information asymmetry (Agrawal et al., 2016).

2.3 Pecking Order Theory The pecking order of capital structure (Myers & Majluf, 1984) provides some theoretical reasoning as to why entrepreneurs seek to solicit equity financing. Pecking order theory suggests a firm will issue debt if possible, rather than issue equity, when internal cash is not sufficient to fund projects. Firms prefer to use debt financing first because it has lower agency costs than equity financing due to it representing a contractual obligation and investors having a clearer understanding of its risk/return profile. The steep agency costs associated with equity financing, again due to the information asymmetry problem, predict that firms will typically look to the equity markets for financing as a last resort. While it is probably the case that early-stage companies would have difficulty seeking debt financing and equity financing may be their only option, the notion of whether firms seek equity financing via crowdfunding as a first or last resort is unclear.

Walthoff-Borm et al. (2018) examine equity crowdfunding in the United Kingdom and find evidence that firms indeed follow the pecking order: they first rely on internal financing, followed by debt financing, and thirdly, raise external equity via crowdfunding. However, Bellavitis et al.

(2017) argue equity crowdfunding takes place in opposition to pecking order theory and suggest there is a possibility that the recent emergence of equity crowdfunding reverts or distorts traditional pecking order.

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2.4 Social Identity Theory A variety of theories have emerged within the information systems literature relating to an individual willingness to participate in equity crowdfunding offerings. Tajfel and Turner’s (1979) social identity theory, which suggests a person’s identity and sense of self is based on the groups that they are involved in or members thereof, has been widely used to explain motivations behind crowdfunding contributions. In later work, Aaker and Akutsu (2009) applied social identity theory to an individual’s giving patterns and determined that choices, including giving, are often based on one’s identity and that individuals will invest more for ideas or products that support their social identity. Not only has the literature used social identity theory in explaining motivations in crowdfunding campaigns, prior research has also found that social capital plays an important role.

Zheng et al. (2014) develop the idea of multidimensional social capital: they argue that the social networks that an individual is embedded in can facilitate resource exchanges and knowledge sharing through structural dimensions, relational dimensions, and cognitive dimensions. This bond creates a felt obligation to help fund the campaign.

2.5 The Decision to List and Invest Based on in-depth interviews with individuals who had sought funds using crowdfunding,

Gerber et al. (2012) found that the main reason that individuals use a crowdfunding platform is to raise funds while maintaining full control over their project. In doing so, entrepreneurs can receive validation, connect with others, and expand awareness of their projects via social media. Similar results suggest an entrepreneur’s decision to use crowdfunding platforms includes raising money, gaining public attention, and obtaining feedback on the product or service offering (Bellefamme et al., 2014). Although entrepreneurs may want to remain in complete control of their project,

Walthoff-Borm et al. (2018) suggest firms that list on equity crowdfunding platforms may not

10 have any other financing alternative. Firms listed on equity crowdfunding platforms tend to be less profitable, have more debt, and own more intangible assets than similar firms who raise capital

using other means – which suggests they have very few financing options.

Although crowdfunding campaigns are attractive for some start-ups, several factors deter

some individuals from raising funds via a crowdfunding campaign. Using raw data from articles, blogs, reports, consultants, and videos, Kshetri (2018) found two key factors that deter entrepreneurs from raising capital via equity crowdfunding: 1) the intrinsic costs associated with the offering, and 2) the possibility of the funding request failing. Specifically, the higher the degree of stigmatization associated with failure in a particular country, the lower the likelihood of the entrepreneur to solicit funds via equity crowdfunding.

After choosing to solicit capital via equity crowdfunding, founders face the decision of which crowdfunding platform to use. Loher (2017) conducted interviews with crowdfunding platform operators in Germany and found that crowdfunding platforms play a heavy role in the screening process of new ventures wanting to list to make sure they are in line with investors’ preferences. These platforms also help market and promote campaign listings to potential investors. Platform choice seems to be a determinant of funding success, as there are significant differences across platforms in terms of mission, positioning, and helping to facilitate the breakdown of information asymmetry between investors and start-ups (Loher, 2017).

After an entrepreneur has decided to solicit funds via an equity crowdfunding platform, arguably the most crucial step in success is attracting investors. In evaluating the decision to invest, Lukkarinen et al. (2016) document that investors in equity crowdfunding campaigns use different investment criteria than those traditionally used by venture capitalists and angel investors.

While extant literature has documented a vast array of reasons why investors choose to invest in

11 equity crowdfunding offerings, including both intrinsic motivations (control or use of an innovation or enjoyment) and extrinsic motivations (financial rewards), the primary reason for investing in equity crowdfunding offerings is the potential financial benefit (Lukkarinen et al.,

2016). Ordanni et al. (2011) find that feelings of patronage and participation may drive some individuals to invest. Trust with current investors, as well as with the platform, may also motivate investment; Kshetri (2018) found that high degrees of trust are related to an individual’s propensity to invest in equity crowdfunding. Consistent with this idea, results from a survey among crowdfunding investors in the United Kingdom indicated that other investors’ browsing on the equity crowdfunding platform was the most popular method of discovering investment opportunities. Further, potential investors were swayed by current investors’ investments in various projects as well as comments made by investors on the platform (Baeck et al., 2014).

Finally, Mamonov and Malaga (2019) find that investors also appear to focus on the market and agency risks associated with the venture when screening a potential investment.

2.6 Crowdfunding Success Equity crowdfunding success is arguably the most widely researched topic in the equity crowdfunding literature. Success has been defined consistently across the literature as the founder’s ability to attract a funding amount greater than or equal to the minimum amount of funding sought by the entrepreneur in the crowdfunding listing (Mamonov et al., 2017; Malaga et al., 2017; Horvat & Papamarkou, 2017; Colombo et al., 2015). However, other measures of success have also been considered, such as the number of investors attracted, the percentage of the minimum amount of funding sought that is actually raised, and the amount of capital pledged on a given day of a funding campaign (Vismara, 2016; Colombo et al., 2015; Block et al., 2018).

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What are the key determinants of an entrepreneur’s ability to raise funds in the equity crowdfunding space? Previous literature has identified some key factors of equity crowdfunding success, which can be generally classified into three distinct categories: campaign characteristics, understandability of the company’s concept and offering, and use of networks (Lukkarinen et al.,

2016).

2.6.1 Campaign Characteristics When an entrepreneur posts a listing seeking capital on any equity crowdfunding platform, he or she will provide basic information about the company and offering. Such information typically includes campaign-specific factors such as the funding target amount (the funding goal), the minimum investment amount, the length of the campaign, and the availability of financial statements (Lukkarinen et al., 2016). Figure 1 shows the information a potential investor is presented with when logging onto a popular crowdfunding platform, Wefunder. Investors can browse campaigns by name, tagline, and a brief description of the product or service. Figure 2 presents a closer look at the information presented on the initial Wefunder page about a campaign called, World Tree. When potential investors click on a campaign, they are presented with additional information about the campaign. Figure 3 presents the information available to the investor after clicking on a specific crowdfunding campaign. The description piece varies by campaign as some campaigns contain more information and others contain less. Wefunder allows for updates throughout the campaign where investors can stay up to date throughout the campaign by the entrepreneur. Further, the grapevine tab allows potential investors, current and past investors, or acquaintances to write testimonials about the product or service offering or about the entrepreneurs themselves. Finally, potential and current investors may ask questions or post

13 comments to the entrepreneur and the entrepreneur has the ability to respond to them. The updates, grapevine, comments and responses are referred to as on-platform engagement.

Prior literature documents that the probability of crowdfunding success is inversely related to the amount of the funding goal target, the minimum investment amount, and the duration of the crowdfunding campaign (Block et al., 2018; Lukkarinen et al., 2016; Cordova et al., 2015).

Further, entrepreneurs who offer a smaller fraction of their company’s equity at listing, and those that have more social capital, experience a higher likelihood of success (Vismara, 2016). Over the duration of a crowdfunding campaign, investments made tend to exhibit a barbell, with most investments being made at the beginning and the end of the campaign. This finding suggests that the middle, inactive stage of the campaign should be shortened to increase the probability of success (Cordova et al., 2015). Finally, the availability of financial statements has a positive impact on crowdfunding success; however, an external certification of the financial statements (e.g. audited statements) does not improve the probability of success (Ahlers et al., 2015).

Other studies suggest that entrepreneur gender may play a role in crowdfunding success.

Malaga et al. (2017) study the impact of gender on crowdfunding campaigns in the United States.

Their findings suggest that women entrepreneurs are underrepresented in equity crowdfunding platforms as compared to their participation through traditional angel investments. In a study of

UK-based, rewards-based and equity crowdfunding campaigns, Horvat and Papamarkou (2017) also find that women are underrepresented on crowdfunding platforms and also document that female founders experience higher success rates in fundraising than men.

Further, a stream of the equity crowdfunding literature has examined the role that an offering’s product type and stage of development plays in fundraising success. Lukkarinen et al.

(2016) find that campaigns targeted at consumers are often more successful than those targeted at

14 businesses. Horvat et al. (2018) measure the novelty of crowdfunding campaigns and find that campaigns that are classified as more innovative and distinctive actually attract less funding. This finding suggests there is a trade-off between product innovativeness and conventionality. In a similar vein, Mamonov and Malaga (2018) find products and services that are in the beta or prototype state of development are less likely to attract funding, suggesting that investors are drawn to campaigns with well-developed, later-stage products. Consistent with this idea, Nitani et al. (2019) find that entrepreneurs are more successful in attracting funds for market expansion or marketing, suggesting such products are well beyond the prototype stage.

Investor characteristics, such as the stage of the campaign in which the investor chooses to contribute (Lukkarinen et al., 2016; Vismara, 2018), the types of investors (Vulkan et al., 2016; Li et al., 2019), and geographic concentration (Horvat et al., 2018), also play a role in crowdfunding success. Horvat et al. (2018) find that campaign fundraising success is positively related to the geographic concentration of investors. Early investing as well as investing by private investors during the hidden phase has been shown to increase funding success (Lukkarinen et al., 2016;

Vismara, 2018).

2.6.2 Understandability, Tone and Sentiment Studies in the behavioral finance literature suggest that investor decisions may be affected by tone or sentiment of language, how information is framed, or personal emotions (Tversky &

Kahneman, 1974, 1979; Lowenstein et al., 2001; Baker & Wurgler, 2006). That is, an individual’s

investment decisions may not be solely based on objective financial analysis but may also be

affected by emotional influences and subjective sentiment. Prior empirical research supports this

notion: studies have shown that investors’ decisions are driven not only by quantitative factors but

also qualitative factors such as the tone of earnings announcements or management’s discussion

15 and analysis, and the tone of media coverage (Li, 2008; Biddle et al., 2009; Li, 2010a; Li, 2010b;

DeFranco et al., 2015; Henry, 2008) or the readability of certain information or disclosures (Nowak et al., 2018; Zhou et al., 2018; Block et al., 2018).

As noted previously, the campaign description is perhaps the most important content of a crowdfunding campaign, as it is essentially the entrepreneur’s best opportunity to grab the attention of potential investors. A small stream of research has used sentiment analysis to examine the language used by the entrepreneur to describe the company in the crowdfunding campaign to see if it has a meaningful impact on campaign success. In their exploratory study of real estate crowdfunding offerings, Mamonov et al. (2017) use text mining of project descriptions of real estate ventures on the Patch of Land real estate crowdfunding platform and find that a more positive description leads to an increased probability of campaign investment, although their results were limited to real estate offerings and included mostly debt-based crowdfunding offerings. In a recent survey of the Entrepreneurship Theory and Practice Editorial Board on how crowdfunding should evolve, the second most highly rated potential research question revolved around the content presented on the platform and the various media used to influence crowdfunding success (McKenny et al., 2017).

Further, in other related work on language complexity, Block et al. (2018) use a readability index to assess the readability of crowdfunding campaign description texts and find that an update to the description text with easier to understand language during an equity crowdfunding campaign positively impacts crowdfunding investor participation in terms of both the number of investments made and the investment amount; however, the length of the text update has no incremental effect.

In a related study on debt crowdfunding, Gao et al. (2018) use automated linguistic feature extraction and find that lenders bid more aggressively, are more likely to fund, and charge lower

16 interest rates to borrowers when a borrower’s campaign description is more readable, more positive, and contains fewer deception cues. In another debt crowdfunding study, Dorfleitner et al. (2016) find an increased probability of campaign success when campaign descriptions contain fewer spelling errors, are shorter in text length, and have more positive keywords.

Although studies of text sentiment are sparse in the crowdfunding space, related research on language understandability is more widely available. Belleflamme et al. (2013) find that companies that offer products are more successful in attracting investors than those that offer services. Such a finding suggests that an investment in tangible products may be more understandable and more well received by investors than investments associated with intangible assets. Further, Mamonov and Malaga (2019) find a strong relationship between an entrepreneur’s use of video content to communicate information to potential investors and the success of a campaign. Use of video might enhance the delivery of, and improve the understanding of, the campaign or company’s description. Finally, two studies of German-based crowdfunding examine readability as measured through length or the number of words in an update (Block et al., 2018) or in campaign descriptions (Horvat et al., 2018).

To my knowledge, there has only been one paper the examined the impact of readability on success in the equity crowdfunding realm. Using German equity crowdfunding data, Horvat et al. (2018) examine the impact of readability on success and found a counterintuitive negative relationship. Looking to other categories of crowdfunding for guidance, overall results are mixed.

In the loan-based crowdfunding literature, some studies find an inverse u-shaped relationship

(Dorfleitner et al., 2016), or a u-shaped relationship (Novak et al., 2018). The inverse u-shaped relationship is consistent with the idea that providing a thorough campaign description is positive up to a point before it gets too long and starts to have a negative impact on success. In rewards-

17 based crowdfunding research some studies found the relationship is positive (Aprilia & Wibowo,

2017; Zhou et al., 2018), while others found an inverse u-shaped relationship (Moy et al., 2018).

Collectively, these few studies on readability suggest that investors’ decisions to invest in companies in specific crowdfunding offerings may be influenced by non-financial factors such as the tone and sentiment of the language used to describe the campaign and company. To my knowledge, there have been no similar readability studies using United States equity crowdfunding data.

2.6.3 Use of Networks A small number of studies, mainly in the rewards-based crowdfunding arena, have found a link between crowdfunding success and the use of networks, which includes both private networks and social media networks. Entrepreneurs use these networks as a means of communicating with current investors and attracting potential investors. For equity campaigns,

Agrawal et al. (2016) stress the importance of an entrepreneur’s own personal connections in both early-stage contributions as well as overall campaign success. Also, as noted previously, investors often look to those who have already invested for guidance in the decision to invest (Baeck et al.,

2014). Others have found that overall campaign success is related to the utilization of public and private networks, and those entrepreneurs with more connections have a greater probability of success (Lukkarinen et al., 2016; Vismara, 2016; Ahlers et al., 2015; Mamonov & Malaga, 2019).

Finally, the activation of a personal network outside of the crowdfunding platform allows the entrepreneur to interact with potential investors, helping to reduce information asymmetry and increasing the probability of crowdfunding success (Agrawal et al., 2014; Giudici et al., 2013).

In diving deeper into the crowdfunding literature relating to networks, engagement with investors and potential investors can occur in two distinct ways; on-page (directly on the platform)

18 and off-page, which refers to off platform communication via social media networks such as

Facebook, Instagram, or Twitter, or even via a company’s website (Beier & Wagner, 2015; Qiu,

2013). Extant literature surrounding equity crowdfunding and the use of networks is sparse; in fact, to my knowledge, very few studies have been done in this area, none of which study United

States crowdfunding data (Lukkarinen et al., 2016; Nevin et al., 2017; Vismara, 2016). Studying a sample of crowdfunding campaigns in Europe, Lukkarinen et al. (2016) examine social media connectivity and find companies who leverage social media networks have higher chances of campaign success. Nevin et al. (2017) also find that entrepreneurs who are more engaged with the crowd on-platform and via social media networks are more successful than those who are less engaged. In studying campaigns in the United Kingdom, Vismara (2016) argues an entrepreneur’s network ties and connections help to promote innovation and reduce uncertainty, leading to greater campaign success.

Looking to existing rewards-based crowdfunding literature relating to on-page communications, researchers have found that the quality of a project presentation, the application of online videos on the platform, and the frequency of platform updates tend to increase the likelihood of success (Mollick, 2014; Kuppuswamy & Bayus, 2013). Through their on-page communication, entrepreneurs as well as prospective investors, not only exchange capital, but they also contribute positive or negative word of mouth. This word of mouth can impact campaign success by reducing uncertainty, increasing awareness, and answering questions about the product or service being offered and has proven to be an effective means of interacting with the crowd

(Kaminski et al., 2018, Belleflamme et al., 2014, Giudici et al., 2013).

Several rewards-based studies have found that individuals seeking funds on crowdfunding platforms that are more engaged with the crowd via comments and interaction are more successful

19 in their crowdfunding campaigns and collect greater pledges (Gleasure & Feller, 2016; Rapp et al.,

2013; Kaur & Gera, 2017; Kromidha & Robson, 2016; Thies et al., 2014). Engagement centers around the two-way interaction between the project initiator and the investor. These studies show that campaign success depends not only on connectedness to the crowd via one-way interaction, but also engagement with the crowd via the two-way interaction (Nevin et al., 2017; Kromidha &

Robson, 2016). Engagement with the crowd leads to positive brand performance, retailer performance, and consumer-retailer loyalty (Rapp et al., 2013). Finally, rewards-based crowdfunding research has found that online connections of the campaign page with social media channels increase success (Mollick, 2014). Although rewards-based literature has examined the use of networks to engage investors and potential investors, to my knowledge, there have been no similar studies to date on US equity crowdfunding. Since platform features are similar across various types of crowdfunding, ie. rewards and equity, I posit that engagement may play a crucial role for entrepreneurs seeking funds via equity crowdfunding campaigns as well.

Collectively, this comprehensive review of the crowdfunding literature shows there is a gap in the literature relating to the few studies examining US equity crowdfunding offerings, the role of readability and investor sentiment, the overall success of equity crowdfunding campaigns, and the role of on-page network communication in equity crowdfunding success. To my knowledge, there have been no studies examining the role of language tone on campaign success using equity crowdfunding data, and only one study using German crowdfunding data examines the role of readability in equity crowdfunding success. Further, there have been no studies that examine the role that an entrepreneur’s engagement with the crowd via comments, updates, and other investor testimonies plays in the funding success of US crowdfunding campaigns. My dissertation seeks to answer questions that fills these gaps.

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CHAPTER 3: HYPOTHESES DEVELOPMENT

As previously noted, the main two research questions of my dissertation link equity crowdfunding success to the sentiment (as measured by tone) and readability of the language used in a crowdfunding campaign description (RQ1), and to the level of on-platform engagement with the entrepreneur (RQ2). In addressing RQ1, I develop four testable hypotheses: Hypothesis 1 and

Hypothesis 3 relate to tone, and Hypothesis 2 and Hypothesis 4 relate to readability.

As noted in the literature review, there is a lack of research specifically relating language tone to equity crowdfunding offerings. While it is not clear whether tone will affect the success of an equity crowdfunding campaign, recent evidence from debt crowdfunding campaigns (Gao et al., 2018), real estate crowdfunding campaigns (Mamonov et al., 2017) and rewards-based campaigns (Zhou et al., 2018) suggest there may be a link. Thus, I hypothesize the following:

H1: There is a positive relationship between the success of a US equity crowdfunding campaign and the positivity of the tone of the language used in the campaign description.

Readability encompasses the ability of potential investors to capture the message intended by the entrepreneur. The readability measure factors in the length of the text, the complexity of the

language used, and spelling and grammatical errors. One method of measuring readability in the

literature is the total number of words used in a passage of text. Since start-ups typically feature

new products, services or unproven technologies, there is little background information available

to potential investors. Therefore, entrepreneurs must provide sufficient description information to

increase potential investor confidence and trust (Zhou et al., 2018). However, there is also

evidence that providing too much information can have a negative impact on funding success.

21

Again, the literature to date on readability and campaign success in the equity

crowdfunding space is sparse, but the few related studies in the other categories of crowdfunding

that document a link between readability and funding success (Dorfleitner et al., 2016; Nowak et

al., 2018; Moy et al., 2018; Aprilia & Wibowo, 2017; Zhou et al., 2018) suggest that equity

crowdfunding campaign success may indeed be linked to readability. Following the consensus of

these previous studies, I hypothesize the following:

H2: There is an inverse u-shaped relationship between the success of a US equity crowdfunding campaign and the readability of the campaign description.

The equity crowdfunding arena has evolved over the past several years in the United States

since the passage of Title II of the JOBS Act in 2013. Again, passage of Title II permitted only

accredited investors to invest in crowdfunding opportunities whereas the passage of Title III

opened the door for non-accredited investors to participate. There is reason to suspect there may be differences in knowledge and investor sophistication between accredited and non-accredited

investors, as non-accredited investors are likely to have less wealth and investment experience.

Although behavioral research suggests that all investor types are susceptible to sentiment, the

lower level of investor sophistication among non-accredited investors might lead their investment

decisions to be more influenced by language, tone, and sentiment than accredited investors.

Therefore, I hypothesize the following:

H3: The positivity of the tone of the language used in the campaign description of a US equity crowdfunding listing will have a greater impact on campaign success for non-accredited investors than accredited investors.

H4: The readability of the campaign description of a US equity crowdfunding listing will have a greater impact on campaign success for non-accredited investors than accredited investors.

22

Finally, with respect to RQ2, I investigate the role of engagement between the entrepreneur and potential investors on equity crowdfunding success. As supported by several studies showing that engagement with potential investors is favorable for campaign success in other categories

(besides equity) of crowdfunding (Gleasure & Feller, 2016; Rapp et al., 2013; Kaur & Gera, 2017;

Kromidha & Robson, 2016; Thies et al., 2014), I hypothesize the following:

H5: There is a positive relationship between the success of a US equity crowdfunding campaign and the level of engagement with potential investors via comments, updates, and testimonials.

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CHAPTER 4: METHODOLOGY

In this section, I provide a summary of the estimation methods used to test the five hypotheses.

4.1 Impact of Sentiment on Campaign Success My first four hypotheses relate to the relation between campaign success and the sentiment

of the language, as measured by tone, used in a crowdfunding campaign description. As noted previously, success of a crowdfunding campaign is typically measured two ways in the

crowdfunding literature: 1) whether or not the entrepreneur raised the minimum asking (or target)

amount, and 2) the percent of the minimum asking amount raised. Therefore, a logit model and

Tobit model will be used to test H1 and H2 using both success measures:

Logit model: (0,1) = + + + + ′ + (1)

Tobit model: = + + + + ′ + (2)

Where:  Success (0,1) is a dummy variable that takes the value of one if the entrepreneur of campaign i raised the minimum funding goal amount and zero otherwise;  Percent raised is the amount of funding raised by the entrepreneur, as a proportion of the minimum funding goal amount, for campaign i;  Tone is the sentiment score of the language used in the description for campaign i, as calculated in Equation 3;  Readability is the word count (after removing stopwords) of the description field for firm i, and  X is a vector of company specific control variables for campaign i.

Additionally, all regressions include quarter-year fixed effects to capture potential variations in

market-wide economic performance that might affect fundraising conditions. Further, all

24 regressions include platform, sector, and region fixed effects. All variables used in the study are defined in the Appendix.

Given that a nontrivial number of companies in the sample raise no funds at all (0% of their campaign ask) and the percent raised ranges from 0% to over 100% (some entrepreneurs raise an amount above their ask), a Tobit model (Tobin, 1956) is appropriate for estimating Equation 2. A

Tobit model is used to model a corner solution dependent variable, as is the case with the percent raised variable (Wooldridge, 2002). The logit and Tobit frameworks are consistent with prior crowdfunding studies (Vismara, 2016; Colombo et al., 2015). In addition to using Tobit regressions, I also run logit regressions where I balance the regressions to include an equal number of successful and unsuccessful campaigns.

To test for differences in the impact of sentiment on campaign success for campaigns that

listed before and after the passage of Title III in H3 and H4, I include a dummy variable in the

regressions that is equal to one for offerings that listed after the passage of Title III and zero

otherwise, as well as an interaction term that interacts the Title III dummy variable with the

sentiment (tone) and readability measures.

Variables of Interest

The first variable of interest, sentiment, is measured using the tone of the language used in

the campaign description. Although several sentiment word dictionaries have been developed for

use in textual analysis, the Loughran and McDonald (LM) Sentiment Word Lists (Loughran &

McDonald, 2011) are the most widely used in sentiment research and were created with financial

communication in mind. The LM Sentiment Word Lists are list of words classified to be in one of

the following categories: Negative, Positive, Uncertainty, Litigious, Strong Modal, Weak Modal,

25 and Constraining. I apply the LM Sentiment Word Lists to my sample of equity crowdfunding campaign descriptions to create the following sentiment-related variables:

 Word Count, which is the total number of words in the description of the offering after removing stopwords, such as “a,” “I,” “and,” “the,” and “of”;  Positive Word Count, which is the number of words from the LM positive word list that appear in a campaign description;  Negative Word Count, which is the number of words from the LM negative word list that appear in a campaign description.

Following the method of Courtney et al. (2016) for measuring sentiment in rewards-based

crowdfunding, I first estimate an overall sentiment score, as measured by the tone of the language

used in an equity crowdfunding description, as follows:

= (3)

Thus, tone is a continuous variable ranging from -1 to 1 that captures the overall net tone of the campaign description.

The second variable of interest, readability, captures the ability of the potential investor to decipher the intended message presented in the campaign description. Readability can be affected by the length of the passage of text, spelling and grammatical errors, and the complexity of the language used (Zhou et al., 2018; Block et al., 2018). I follow previous literature and measure readability as the total number of words in a campaign description after removing stopwords.

Control Variables

I control for a host of firm and campaign characteristics that are previously documented to

explain variation in success in the crowdfunding literature. As previously noted in the literature

review, prior research has found that the funding target amount (the funding goal), the minimum

26 investment amount, and the availability of financial statements (Block et al., 2018; Lukkarinen et al., 2016; Cordova et al., 2015; Ahlers et al., 2015) are related to campaign success. Thus, in all regressions, I control for the minimum investment amount and whether prior financial information was reported by the company. I also control for public market condition as measured by the CBOE

Volatility Index (VIX). It can be assumed that as the market becomes more volatile, investors, as a whole, are not investing in riskier opportunities like equity crowdfunding. Finally, standard errors are clustered by sector in most regressions to allow for any unobserved effects within industry when obtaining error covariances.

4.2 Impact of Crowd Engagement on Campaign Success The fifth hypothesis relates to the relation between campaign success and crowd engagement, as measured by comments, updates and testimonials posted on the crowdfunding platform by entrepreneurs and potential investors. Crowd engagement data are only available for sample crowdfunding campaign offerings that occurred after the passage of Title III, so a subset of the full sample will be used to test RQ2 and H5. The specific equations to test H5 are modified versions of Equations 1 and 2:

Logit model: (0,1) = + + + + + + + ′ + (4)

Tobit model: = + + + + + + + ′ + (5)

Where:  Comments is the total number of on-page comments and responses between the potential investor and start-up, for campaign i;

27

 Updates is the total number of on-page updates the entrepreneur posts during the campaign, for campaign i;  Testimonials is the total number of on-page testimonials about the offering or entrepreneur, for campaign i;  X is a vector of company specific control variables for campaign i.

Again, all regressions include quarter-year, platform, sector, and region fixed effects. Further,

Equations 4 and 5 include additional firm and campaign controls due to data availability.

Specifically, the additional controls include the number of employees, the length of the campaign, whether the company extended its deadline, whether a video was included, and dummy variables for whether the crowdfunding campaign has a Facebook or Twitter presence. All variables used in the study are defined in the Appendix.

28

CHAPTER 5: DATA AND DESCRIPTIVE STATISTICS

The sample of crowdfunding data is comprised of US equity crowdfunding offerings covering the October 1, 2013 to June 30, 2019 time period. The sample includes crowdfunding offerings that took place after the passage of Title II but before the passage of Title III (defined as

Title II offerings), and crowdfunding offerings that took place after the passage of Title III when non-accredited investors entered the landscape (defined as Title III offerings).

Data on Title II offerings are from a proprietary dataset obtained from FinMkt (formerly known as Crowdnetic). The FinMkt dataset aggregates crowdfunding campaign-level data across

17 United States crowdfunding platforms; each platform provides their respective crowdfunding data directly to FinMkt. The dataset contains information on over 3,600 Title II closed equity crowdfunding offerings for the period starting October 1, 2013 and ending September 30, 2016 and covers eight sectors. Again, the date range of the FinMkt dataset corresponds to the timeframe before Title III of the JOBS Act was passed. After cleaning the data, the final sample includes

3,216 Title II equity crowdfunding offerings over the October 2013–September 2016 time period.

The FinMkt Title II data is then supplemented with Title III offerings that were hand collected from various sources. After the passage of Title III, entrepreneurs that want to raise funds via a crowdfunding campaign are required to file a Form C with the United States Securities and

Exchange Commission (SEC), which provides specific details relating to a particular crowdfunding campaign (platform used, target funding amount, etc.). I gather data from all Form

C filings filed between May 2016 and June 2019 from the SEC EDGAR web site. In some cases, an entrepreneur who filed an original Form C relating to a particular campaign may submit subsequent Form C/U filings relating to the same crowdfunding campaign as a means to provide

29 updated information. I consolidate all data from multiple Form C filings relating to the same crowdfunding offering into a single observation, based on its accession number.

For each Title III crowdfunding campaign, I then collect other campaign-level data from the various platform web sites where the campaigns were hosted. Data collected directly from the equity-crowdfunding platforms include the offering description, tagline, the number of investors, whether or not the campaign uses video, whether or not there is an on-page Twitter presence, whether or not there is an on-page Facebook presence, the number of testimonials, the number of comments and responses, and the number of on-page updates. Of course, these data are not all available for all campaigns, as there is significant variation in campaign information across platforms. The final sample of Title III campaigns includes 580 common stock and preferred stock

equity crowdfunding offerings in the Title III portion of the dataset. So, in total, the final sample

consists of 3,796 equity crowdfunding campaigns over the October 2013–June 2019 time period:

3,216 Title II offerings over the October 2013–September 2016 time period, and 580 Title III

offerings over the May 2016–June 2019 time period.

Table 2 presents summary statistics for the complete crowdfunding dataset. Panels A, B

and C report summary statistics for the full sample, the Title II subsample, and the Title III

subsample, respectively. For the full sample, Panel A shows that the average equity crowdfunding

entrepreneur sought to raise just under $2 million, with entrepreneurs successfully raising an

average of 119.3% of their ask amount. However, these averages are misleading; the average percent raised is highly skewed toward a few, very successful campaigns. To clarify, 78.4% of all

sample campaigns failed to raise any funding, so the median ask amount is much lower at $300k,

the median percent raised is 0%, and the percent of campaigns that are successful (as measured by

raising 100% or more of their campaign ask) is only 9.9%. In terms of the sentiment and readability

30 variables, the average campaign description length is 67 words, and, not surprising, is written with a slightly positive tone. A little over 50% of campaigns provide prior financial information, and only 16% of equity crowdfunding entrepreneurs are female.

Panels B and C of Table 2 provide summary statistics for the subsamples of Title II and

Title III campaigns, respectively. Panel B shows that, relative to the full sample, the average Title

II campaign has a much lower success rate at 7.9% and only 2% of entrepreneurs are successful in raising their ask amount. However, with the exception of a much larger ask amount ($2.3 million) than the full sample, the other firm characteristics are similar to that of the full sample. Panel C shows that the average Title III campaign entrepreneur raises their ask amount 57% of the time and raises an average of 736.7% of their ask amount. Interesting, the readability measure is approximately the same across both Panels B and C at 67 words. As noted earlier, the Title III subsample in Panel C has additional data not available in the Title II subsample. The average Title

III campaign runs for 135 days with entrepreneurs extending the deadline at least once 26% of the time. Interestingly, of the 153 campaigns that extended their deadline, 116, or 76%, raised 100% or greater of their campaign ask, suggesting it may be in an entrepreneur’s best interest to extend the deadline of the campaign in order to be successful. Title III campaigns rarely provide prior financial information, and the average campaign start date is a little less than 3 years from the date of incorporation. As previously mentioned, entrepreneurs will often update campaign information by filing a Form C/U. Over 56% of Title III campaigns include a video on their crowdfunding campaign page.

In terms of engagement, Panel C shows that the average campaign receives a combined 39 comments and responses and 12 testimonials. Further, entrepreneurs post an average of nine updates throughout a typical campaign. Finally, 39% of campaigns mention or link to the

31 company’s Facebook page on the crowdfunding platform, and 33% mention or link the company’s

Twitter handle.

To evaluate differences between the Title II and Title III subsample summary statistics in

Panels B and C, univariate results comparing means and medians2 of the two subsamples are presented in Tables 3 and 4. Both Tables 3 and 4 show that, on average and at the median, entrepreneurs raised a significantly larger amount of funding, and were significantly more likely to raise their requested amounts, after the passage of Title III. Table 3 shows that only 1.52% of

Title II crowdfunding entrepreneurs raised their target funding amount, and they raised an average and median of 7.9% and 0.0% of that amount, respectively. In comparison, 56% of Title III crowdfunding entrepreneurs raised their target funding amount and raised an average and median of 736.69% and 100.0% of that amount, respectively. The primary reason for these significantly larger means and medians in the Title III subsample is that the average and median amounts requested by entrepreneurs were significantly lower after the passage of Title III. The mean amount requested by Title II entrepreneurs is $2.3 million whereas the mean amount requested by Title III entrepreneurs is only $55,866. Further, there is a significantly larger number of female entrepreneurs in the Title II subsample, and prior financial information is significantly more often in the Title III subsample. It is not surprising to see this latter result given the significantly higher ask amount in the Title II subsample. In terms of sentiment, both Title II and Title III campaign descriptions are presented with a slightly positive tone with Title III campaigns being significantly more positive than Title II campaign descriptions, raising the question of whether this more positive tone may contribute to greater likelihood of success. There is no significant difference in

2 The Mood (1950) median test is used for differences in medians between the two subsamples. Mood, A.M. (1950). Introduction to the Theory of Statistics. McGraw-Hill, 394-399. 32 means for the readability variables; however, the median Title II campaign description is significantly longer than the median Title II campaign description. A correlation matrix for the full sample is presented in Table 5 Panel A followed by a subset of Title II offerings presented in Panel

B and a subset of Title III offerings in Panel C.

Figure 4 presents the distribution of the amount raised by entrepreneurs, stated as a percent of their ask amounts, in the full sample. As might be expected, the funding outcome is quite binary, with most entrepreneurs raising either a minimal amount or more than their ask. More than 80% of equity crowdfunding campaigns end up raising less than a third of their ask amount, and although not shown, more than 77% raise less than 10%. At the other end of the distribution, just under 10% of campaigns ended up receiving more than 100% of their ask amount. Figure 5 presents the distribution of unsuccessful and successful campaign offerings for the entire dataset

as well as for the Title II and Title III subsamples. In the full sample, of the 378 total successful

campaigns, 49 campaigns were from the Title II subsample and 329 from the Title III subsample.

Again, this result is not surprising given that the Title III subsample has a much lower average and

median ask amount.

Figure 6 presents the total number of successful and unsuccessful campaigns by industry

for the full sample. The services and technology industries comprise the majority of the

crowdfunding campaigns, representing 36.5% and 32.6% of the sample, respectively. Other

industries represented include consumer goods at 9.5%, commerce and industry at 8.3%, and

financial at 6.0%. The remaining industries of energy, healthcare and materials account for a

combined total of 7.2% of sample crowdfunding campaigns. While the services sector has the

greatest representation in the dataset, only 67 or 4.8% of the services-related campaigns were

33 successful. In contrast, the industry with the highest success rate is commerce and industry at

35.0%.

The platform utilized by the entrepreneur often dictates the type of information presented about the campaign and the ease of engagement with the crowd. Figure 7 presents the five platforms represented with the highest number of equity crowdfunding campaigns in the sample.

In the full sample, there are 19 platforms that hosted five or more campaigns. Of the 19 total platforms used by the full sample of campaigns, the top five platforms account for 92.3% of all campaigns. Angel is the platform of choice for the majority of equity crowdfunding campaigns, representing 2,708 campaigns or 71.3% of all sample campaigns. Angel is followed by Equity net at 6.8%, Start Engine Capital at 6.8%, Crowdfunder at 6.8% and Wefunder Portal at 2.0%.

The full sample includes crowdfunding campaigns from all 50 states, and all but 10 states have at least 10 campaigns. Figure 8 presents the total number of offerings for the top states. The state with the most equity crowdfunding campaigns in the sample is California, which accounts for 1,101 (29%) of the full sample. California is followed by New York and Florida, accounting for 11.5% and 7.0%, respectively, of the sample. Texas ranks fourth at 6.2% and Illinois, fifth, at

3.7% of the total number of offerings. Figure 9 presents the top five states by the amount of funding requested. In the full sample, a total of $7.49 billion was sought by entrepreneurs.

Although California was the state with the highest number of individual equity campaigns, it came in second in total campaign dollars requested at $1.8 billion (24%). Texas requested the highest total campaign dollars at $2.4 billion (32%). New York campaigns requested $557 million (7%), followed by Florida with $428 million (6%), and finally Ohio with $252 million (3%) requested.

Campaigns from these five states accounted for 73% of the total amount of funding sought by entrepreneurs in the full sample.

34

Figure 10 presents the distribution of equity crowdfunding campaigns into four regions:

Midwest, Northeast, South, and West. The West accounted for the highest number at 1,570 campaigns, representing 41.4% of the sample. This is not surprising as California was the state with the highest number of campaigns. The South accounts for 26.7% of the sample, followed by the Northeast at 20.9% and the Midwest at 11.0%.

Finally, to gain early insight into the possible relation between sentiment and equity crowdfunding success, a wordcloud of the top 50 most frequently used words in all sample campaign descriptions is presented in Figure 11. A similar wordcloud for the subsample of successful campaigns is presented in Figure 12. Figure 11 shows that the most frequently used words for all campaigns are platform, social, company, market, and users, and Figure 12 shows that the most frequently used words in successful campaigns include can, company, new, people and technology.

35

CHAPTER 6: RESULTS

I begin my analysis of results by examining the relationship between sentiment, as measured by tone, and campaign success as well as the relationship between readability and campaign success. I then investigate the relation between entrepreneur engagement and campaign success.

6.1 The Effects of Sentiment and Readability on Campaign Success Table 6 presents the results of the logit model. The dependent variable is a binary variable indicating whether or not the entrepreneur raised 100% or more of the minimum funding goal amount. Column 1 shows results for the full sample, and Columns 2 and 3 show results for the

Title II and Title III subsamples, respectively. The results in all three columns of Table 6 are inconsistent with H1 but consistent with H2. Specifically, the results of the logit model indicate that, in direct contradiction to H1, the tone of the language used in the campaign description is negatively, albeit weakly, related to equity crowdfunding success. However, the word count and squared word count coefficients are positive and negative, respectively, which is consistent with the H2 prediction of an inverse U-shaped relation between readability of the campaign description and campaign success. The word count coefficients are highly statistically significant for both the full sample and the Title III subsample. Although not significant except for the amount requested for the Title III subsample, the firm characteristics are consistent in sign with prior literature: campaign success is negative related to the amount requested and positively related to whether or not the entrepreneur provided prior financial information. Finally, the VIX coefficient is statistically insignificant, suggesting that market volatility plays little role in equity crowdfunding campaign success. Based on the results of the logit model in Table 6, there is little evidence that

36 the tone of the language used in equity crowdfunding campaign descriptions plays a role in campaign success but strong evidence that the readability of the description is highly related to campaign success.

Given the very large number of unsuccessful campaigns in the dataset relative to the very small number of successes, in using the logit model, the logit results may be biased (Peduzzi et al.,

1996). So, in Table 7 I rerun the same regressions from Table 6 but with a balanced panel of successful and unsuccessful campaigns. For each successful campaign, I identify a peer unsuccessful campaign that was requesting a similar ask amount (based on a quarter sort) on the same platform in the same year-quarter. Interestingly, the two of the three coefficients on the tone sentiment are now positive, consistent with H1, but only the coefficient for the Title II subsample is statistically significant. The readability coefficients, word count and word count squared, remain significantly positive and negative, respectively, in the full sample and the Title III subsample.

Further, the firm characteristics coefficients continue to be signed as in Table 6. Finally, as expected, the VIX is negatively and statistically related to crowdfunding success in the Title II subsample. In summary, the balanced logit results lend weak support for H1 and continue to lend strong support for H2.

Table 8 presents the results of a Tobit model where the dependent variable is the amount

of funding raised by the entrepreneur, stated as a proportion of the minimum funding goal amount.

Again, Column 1 shows results for the full sample, and Columns 2 and 3 show results for the Title

II and Title III subsamples, respectively. The results in Table 8 show that the tone sentiment

measure is positive in all three columns, consistent with H1, and is significant at the 5% level for

the Title II subsample. Further, the word count and word count squared readability measures

continue to be significantly positive and negative, respectively. Further, the firm characteristics

37 coefficients continue to be signed as in Tables 6 and 7 but now have stronger statistical significance, and the VIX coefficients are all negative and are statistically significant in the full sample and the Title II subsample. Finally, the reporting of prior financial information is positive and significant at the 1% level for the full sample and the Title II subsample. Collectively, the results in Table 8 are generally much stronger than those in Tables 6 and 7 and are consistent with

H1 and H2.

6.2 Effects of Sentiment and Readability After Passage of Title III H3 purports that the effect of tone on campaign success will be greater after the passage of

Title III due to the pool of potential investors including non-accredited investors. Splitting the full

sample into the Title II and Title III subsamples in Columns 2 and 3 of Tables 6, 7 and 8 allows

for a first look at differences in equity crowdfunding success before and after the passage of Title

III. A review of the collective results from those three tables shows some interesting differences

in the two subsamples. Specifically, in Tables 7 and 8, the tone coefficients in the Title II

subsample are statistically significant but they are insignificant in the Title III subsample. In the

logit results of Tables 6 and 7, the word count and word count squared readability coefficients are

significant for the full sample but when subset, only statistically significant in the Title III

subsample.

Table 9 presents the results of the logit model with an additional Title III dummy variable,

equal to one for Title III campaigns and zero for Title II campaigns, added to the model that is also

interacted with the tone and readability variables. Generally consistent with the logit results in

Tables 6 and 7, the coefficients on the tone and readability are consistent in sign with H1 and H2

albeit statistically insignificant. Further, while the Title III dummy variable is statistically

significant suggesting that Title III campaigns are more successful even after controlling for a host

38 of factors, the interaction terms are not significant. Further, the tone interaction coefficient is negative, opposite to the sign predicted by H3; however, the readability interaction is positive as predicted by H4. Table 10 shows the results of a balanced panel logit regression with the Title III dummy variable and related interaction terms. The results are largely similar to those in Table 9 but now the sentiment tone variable and the tone interaction term are statistically significant.

Further, the readability interaction term is again positive as predicted by H4.

Table 11 presents the results of a Tobit model that includes the Title III dummy variable and related interaction terms. The results are similar to those in Table 8 that lend support to both

H1 and H2, but the Title III dummy coefficient is negative and significant – opposite from the results of the logit regression in Table 10. The tone interaction term continues to be insignificant, but the readability interaction term is positive and significant – consistent with H4. Collectively, the results in Tables 9, 10 and 11 provide little support for H3 but are generally consistent with

H4.

6.3 Impact of Engagement with the Crowd on Campaign Success Table 12 presents the results of logit regressions run on the Title III subsample where additional campaign characteristics and engagement data are available. Again, campaign success, the dependent variable, is a binary variable indicating whether or not the campaign received 100% or more of the minimum funding goal amount. Columns 1 and 2 include the variables of interest along with controls for firm, market, and campaign characteristics, and columns 3 and 4 show results when engagement and social media controls are added to the regression. First, consistent with prior results, the amount requested is statistically and negatively related to campaign success.

However, a review of the additional firm/campaign characteristics in Table 12 reveals mixed

39 results: the number of employees is positive and significant only in column 1 and extending the campaign deadline or including a campaign video on the platform also are only positive and significant in column 2. Further, in all regressions, in contradiction to H1, the sentiment tone variable is negative, and is statistically significant in the models that include the engagement and social media controls. The coefficients on the readability terms are consistent with sign with H2 in columns 1 and 2 but then flip signs in columns 3 and 4. However, most of the readability coefficients are statistically insignificant. Thus, these results show little support for H1 or H2.

However, the coefficients on the engagement are variables in columns 3 and 4 are highly positive and significant. Specifically, consistent with H5, results indicate a positive and significant impact on campaign success for all three engagement variables: the number of comments and responses between the entrepreneur and potential investors, the number of campaign updates posted by the entrepreneur during the campaign, and the number of on-page testimonials from others about the entrepreneur or the product. Further, the coefficients on the two social media coefficients are also both positive, and the Twitter coefficient is statistically significant.

Collectively, the results in Table 12 strongly support H5, which predicts a positive relationship between the success of a US equity crowdfunding campaign and the level of engagement with potential investors via comments, questions, and testimonials. Since the regressions in Table 12 are run on the more balanced Title III subsample (57% successful, 43% unsuccessful), a subsequent regression using a balanced panel similar to that in Table 7 is not warranted.

Table 13 presents the results of the same regressions from Table 12 but using a Tobit model that includes the engagement and social media variables. The coefficients on the sentiment tone variable are positive in three of the four models but are statistically insignificant. Further, the coefficients on the readability word count and word count squared measures are statistically

40 positive and negative, respectively, in columns 1 and 2 – consistent with H2. However, the word count squared coefficients flip signs and become insignificant in columns 3 and 4. Further, consistent with the logit results in Table 12, campaign length is negatively related to campaign success, and extending the campaign deadline is positively related to campaign success. Finally, the coefficients on the engagement measures are mostly consistent with H5: the number of comments and responses and the number of campaign updates by the entrepreneur are positive and statistically related to campaign success; however, the coefficients on the number of testimonies are negative but statistically insignificant. Collectively, the results in Tables 12 and 13 provide fairly strong support for H5.

Taken together, the empirical results in Tables 6-13 provide little support for H1 relating to sentiment, moderate support for H2 relating to readability, and strong support for H5 relating to engagement with the crowd. Notably, the Tobit model results tend to be much more supportive of

H1, H2, and H5 than the logit model results, and the balanced panel logit model results bridge the base logit model results closer to those of the Tobit model. Given that the largely unbalanced composition of unsuccessful to successful campaigns in the full sample which is driven by a large and unbalanced Title II subsample (2% success rate as measured by whether or not the entrepreneur raised 100% or more of the minimum funding goal amount), the Tobit model is a more appropriate model than the logit or balanced panel logit model given the concerns of biased coefficients (Peduzzi et al., 1996). That is, the Tobit model is designed to model corner solution dependent variables and produces consistent coefficient estimates (Wooldridge 2002). So, based solely on the Tobit results, the results in Tables 8, 11, and 13 lend fairly strong support for H1 and

H2 and H5. With respect to the effects of the passage of Title III on crowdfunding success, the

41 results in Tables 9-11 collectively provide mixed evidence in support for H3 and H4. However, based solely on the Tobit results in Table 11, H3 is not supported but H4 is supported.

6.4 Robustness Analyses

6.4.1 Beta Regression Robustness Results The dependent variable in the earlier Tobit regressions was the amount of funding raised by the entrepreneur, stated as a proportion of the minimum funding goal amount. For some observations, this measure takes a value greater than 1 as some campaigns raised amounts greater than their asking amount. An additional regression model, the beta regression model, was developed by Ferrari and Cribari-Neto (2004) for proportion data that assumes values in a standard unit interval between 0 and 1 (Cribari-Neto & Zeileis, 2010). Under the beta regression model, the distribution of the dependent variable can take many shapes, including one that is left or right- skewed as is the case with the percent raised variable. Within the beta regression model, the dependent variable must take on a value that is >0 and <1, therefore, a useful and accepted transformation for the dependent variable in practice is (y * (n-1) +.5) / n, where n is the sample size (Smithson & Verkuilen, 2006).

For robustness, I run beta regressions where the percent raised dependent variable is capped at a value of 1 and the percent raised value is transformed to take values >0 and <1, via the aforementioned transformation. Results of the beta regressions are presented in Tables 14 and 15.

In Table 14, I run the same regressions as those in Tables 6-8, and Table 15 presents results for the same regression specifications as in Tables 9-11. The results in Table 14 are generally in line with the results of those from the Tobit regressions in Table 8: the sentiment tone variable is positive and significant in the Title II subsample and insignificant in the Title III subsample.

42

Furthermore, the readability word count and word count squared coefficients are positive and negative, respectively, and all are statistically significant at the 1% level. In Table 15, the sentiment tone coefficient is positive and significant, and the word count and word count squared readability coefficients remain positively and negatively statistically significant, respectively.

Further, consistent with the Tobit results of Table 11, the coefficient on the Title III dummy variable is negative and significant, and only the word count interaction term is positive and significant. Collectively, the results in Tables 14 and 15 are generally supportive of H1, H2, H4 and H5 but not H3.

A side by side comparison of the logit, Tobit, and beta regression models can be seen in

Tables 16 through 19. For the full dataset including the Title III dummy variable and the interaction terms, the Tobit and beta regression models show identical signs and very similar levels of significance. With very little variation, the results hold for the full dataset without the interaction

effects as well as at the Title II and Title III subset levels. The consistency of the beta regression

results with the Tobit results suggest the Tobit model may be a better suited model for the data

than the logit model.

6.4.2 SMOG Readability Index Finally, an additional proxy for readability was added to the Tobit regression models.

Research surrounding the readability of campaign descriptions within the crowdfunding literature is still in its early stages of exploration. Although the length of the project description has been used as a proxy for readability, it captures only one aspect of readability in the amount of information provided by the entrepreneur. However, an additional measure of readability such as the ease of understandability of the passage may be important to potential investors as well.

Effective communication is important for entrepreneurs as the project description is one of very

43 few ways to communicate campaign characteristics to potential investors. Various readability scores have been used throughout the literature to capture the ease of reading a passage of text. A well-known index that measures readability is the SMOG index, an acronym for Simple Measure of Gobbledygook, created by G. Harry McLaughlin in 1969. The SMOG index value assigned to a passage of text estimates the number of years of education needed to understand a piece of writing and is derived through sentence length and polysyllable (more than three syllables) words.

The SMOG index is argued by McLaughlin to be a more accurate and more easily calculated substitute for another well-known readability index, the Gunning fog index (McLaughlin, 1969).

The average SMOG index value for the full sample of equity crowdfunding campaigns in the sample is 14.94 for the full dataset, 15.01 for a subset of Title II offerings, and 14.54 for a subset of Title III offering meaning the average investor would need approximately 15 years of education to understand the passage of text. This length of time equates to a little over a high school education. A t-test of the differences of means for the two groups is significant. Univariate results suggest offerings after the passage of Title III use less sophisticated language than offerings

from before the passage. Results using the SMOG index are presented in Table 20. For the full

dataset, the readability of the crowdfunding description does not impact the percent of the

campaign raised. The same is true for a subset of offerings after the passage of Title III. There is,

however, a positive relationship between the SMOG index and the percent of the campaign raised

for the subset including accredited investors only before the passage of Title III. This positive

relationship implies that investors need a higher level of education to interpret the passage of text.

This relationship is unexpected since one would expect a more readable passage of text to be easier to understand by potential investors. However, project descriptions are often written in informal language. Because Title II investors are accredited and thought to be more sophisticated, they may

44 be looking to the project description as a signal of preparedness and professionalism of the entrepreneur. This same positive relationship was found in prior rewards-based crowdfunding literature as well (Zhou et al., 2018).

45

CHAPTER 7: CONCLUSION

7.1 Contributions I examine the role sentiment, readability and entrepreneur engagement play in United

States equity-based crowdfunding success. I explore the differences between campaigns prior to and after the passage of Title III of the JOBS Act and includes data from over 3,800 crowdfunding offerings from a period from September 2013 to June 2019. I first employ logit regressions to estimate the impact of tone and readability on overall campaign success. Further, Tobit regressions are used to estimate the impact of tone and readability on the percent of the campaign raised. In addition, logit regressions are fun on a balanced panel of successful and unsuccessful campaigns.

Finally, using a subset of offerings after the passage of Title III, I examine the impact of entrepreneur engagement on campaign success using both logit and Tobit regressions.

Although I hypothesize that sentiment positively impacts success, tone only positively impacts success for a subset of campaign offerings prior to the passage of Title III of the JOBS

Act. Sentiment has no impact on the subset of campaigns open to both accredited and non- accredited investors after the passage of Title III. Although sentiment does not impact all campaign offerings consistently, findings suggest readability does in that, for the most part, there is an inverse u-shaped relationship between readability and success for all types of United States equity crowdfunding campaigns.

Overall results from the various models indicate the importance of the description field of a crowdfunding offering on campaign success; however, for the most part, campaign descriptions that are too long have a negative impact on campaign success. This result suggests that entrepreneurs should be thorough, yet concise in writing the descriptions of their campaign offerings and use a slightly positive tone. Further, not surprising, campaigns that solicit smaller

46 amounts are more successful than those that raise larger amounts suggesting entrepreneurs may have better luck raising smaller amounts of capital through equity crowdfunding campaigns than larger amounts.

Although there are a few similarities in the factors that drive campaign success between investments prior to and after the passage of Title III; there appear to be more differences than similarities. For example, for those campaigns open to accredited, more sophisticated investors only, results indicate a positive impact of tone on success and an inverse u-shaped relationship between readability and success. When drafting campaign descriptions targeted at accredited investors, results would suggest a greater chance of success when using clear, positive, concise language in the campaign descriptions. Further, entrepreneurs should include the more traditional investment criteria such as prior financial information when trying to attract this group of investors as they appear to be motivated to invest when prior financial information is reported.

Further, accredited investors seem to follow the volatility of the market, indicating in times of market volatility, crowdfunding campaigns may have less of a chance to raise campaign funds.

During these volatile times, sophisticated investors shy away from riskier investment opportunities like equity crowdfunding. Therefore, not surprising, entrepreneurs may have greater luck raising funds via crowdfunding in times of market stability. Further, Title II investors appear to appreciate more complex language used in crowdfunding descriptions as it may be a signal for the preparedness and professionalism of the entrepreneur.

Overall results on the sample of campaigns after the passage of Title III suggest the group of investors that includes both accredited and non-accredited investors appears to be driven more by qualitative factors than quantitative factors. For campaigns targeted at this group of investors, engagement with the crowd appears to be extremely important. Results suggest this group of

47 investors is not driven by the more traditional investment criteria such as financial information and market activity. For these campaigns to be successful, entrepreneurs should be committed to interacting with the potential investors, keeping them updated throughout the campaign and answering any questions that may arise as the campaign persists. To have the greatest chance of success, campaigns open to both accredited and non-accredited investors should market the campaign through social media channels such as twitter and focus on their engagement with the crowd. Entrepreneurs should take an active role during the campaign by engaging with the crowd through the answering of questions and posting multiple updates during the campaign to keep the potential investor abreast of opportunities and information about the campaign status. They should

reach out and encourage testimonials on their campaign page as well to boost campaign interest

and ultimately, success.

By better understanding the factors that drive equity crowdfunding campaign success, entrepreneurs can work to combat the information asymmetry that exists between themselves and potential investors. Results indicate there is no “one size fits all” approach to marketing an equity crowdfunding campaign in the US and entrepreneurs must be mindful of the types of investors they are offering their product or service to and what factors drive those investors’ decisions in order to conduct a successful equity crowdfunding campaign.

My dissertation contributes to the literature surrounding equity crowdfunding in several

ways. This is the first study, to my knowledge, that compares the success factors between

campaigns prior to and after the passage of Title III, shedding light on the differences in preferences between accredited and non-accredited investors. Further, this is the first study that

examines a subset of equity crowdfunding campaigns after the passage of Title III to study the

impact of an entrepreneur’s engagement with the crowd and finds a significant and positive

48 relationship indicating the importance of an entrepreneur being active throughout the campaign.

This study contributes to the sparse literature on equity crowdfunding in the US and offers suggestions to entrepreneurs who are looking to compete for funding for new projects. Unlike prior studies that look at data from only one platform, these results are aggregated across several platforms and are robust to the differences between them. Overall results contribute to the lacking literature on US equity crowdfunding campaigns and help to fill a crucial gap in what makes these

US equity campaigns successful.

7.2 Limitations My dissertation is not without limitations. Arguably the most hindering limitation is that the control variables for regressions on the full dataset as well as the Title II subsets are limited to the original variables within the dataset. Because crowdfunding platform information is updated in real time, as the crowdfunding campaigns pass, the live data often goes away making it too hard to hand collect any additional variables. Entrepreneurs were not required to file a Form C with the

SEC prior to the passage of Title III of the JOBS Act leaving this resource unavailable as well.

Further, although the data used in this dissertation are robust to a significant number of platforms, results may not be generalizable to countries outside of the US due to differences in regulation and platform preferences.

Finally, additional limitations center around the dictionary used for the tone variable.

Results are limited to the Loughran and McDonald sentiment dictionary and are not replicated with other well-known dictionaries. However, the Loughran and McDonald word lists are the most commonly used wordlists in the finance literature and were developed specifically with financial context in mind. Other dictionaries have more relevance in other disciplines and may not have the same meaning in my study.

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7.3 Future Research Ideas By better understanding the factors that increase success of an equity-crowdfunding offering, investors are better able to attract a potential investor and investors are better prepared to

make an informed decision. Additional work that can be explored includes using a data analytics

approach to the data by creating a balanced training set to train the model and then evaluating the

results using a test set. By developing a predictive model with the factors that contribute to a

successful campaign, entrepreneurs may be better able to tailor their campaigns for success

depending on their target investors.

An additional stream of research could focus on a subset of successful campaigns and

follow these campaigns after their successful crowdfunding campaign. Work could be done to

study the behavior of these entrepreneurs after success. Do these companies go public? Do they participate in additional crowdfunding rounds? Do they raise funds via different crowdfunding

types? Further, a subset of unsuccessful firms could be examined to determine whether these firms

try to raise funding through different types of crowdfunding as well.

Further, for Title III campaigns, an entrepreneur must choose whether to issue common stock or preferred stock. In the subset of Title III offerings, 125 campaigns, or 22% offer preferred stock while the remaining 455 offer common stock. There is room to explore the motives behind offering one type of stock over the other and whether one type of stock leads to a more successful campaign than the other.

Finally, for Title III campaigns, updates as well as comments and responses during the campaign had a significant impact on success. Additional sentiment research could be done on the narratives of the updates as well as the questions asked and answered to determine whether the sentiment of these narratives impacted success or whether just the mere engagement with the

50 crowd is driving the success. Overall, this stream of research appears to be positive with significant room for contribution.

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APPENDIX

52

VARIABLE DEFINITIONS

Variable Description Success Measures Total amount raised divided by target capital amount, Percent Raised expressed as a percent Raised Amount Requested Binary success (1 if 100% raised, 0 if < 100%)

Sentiment Variables Loughran and McDonald: Percentage of positive words Tone minus percentage of negative words (excluding stopwords) The number of words in the description excluding Readability (Word Count) stopwords

Firm Characteristics Amount Requested Target Offering Amount (in thousands) Woman Owned Binary (1 if the company is owned by a female) Prior Financial Information Binary (1 if company includes prior financial information) Reported Number of Employees Number of employees at the time of the Form C filing

Market Characteristics Average monthly Chicago Board Options Exchange Monthly VIX Value Volatility Index (VIX) value

Campaign Characteristics Campaign Length Days from Form C filing to deadline date Extended Deadline Binary (1 if company extended their deadline) Video Included Binary (1 if company uses a video on the platform)

Engagement Variables Comments/Responses Number of comments and responses on platform page Updates Number of updates during the campaign Testimonies Number of testimonial posts on platform page

Social Media Presence Binary (1 if company links or mentions Facebook page on Facebook Mention platform) Binary (1 if company links or mentions twitter page on Twitter Handle Mention platform)

53

FIGURES

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Figure 1 Campaign Information Presented on Wefunder Home Page

Figure 1 presents what a potential investor sees when they log onto the popular crowdfunding platform, Wefunder.

55

Figure 2 Campaign Information Presented on Wefunder for World Tree

Figure 2 presents a closer look at a specific campaign, World Tree, on the popular crowdfunding platform, Wefunder.

56

Figure 3 Campaign Information Presented on Wefunder for World Tree

Figure 3 presents the information presented when a potential investor clicks on a specific campaign, World Tree, within the platform, Wefunder.

57

Figure 4 Histogram of the Percent of the Campaign Amount Raised

Figure 4 presents a histogram of the distribution of the amount raised by entrepreneurs in equity crowdfunding campaigns, stated as a percent of their ask amounts, for the full sample of US equity crowdfunding campaigns over the 2013-2019 time period.

58

Figure 5 Unsuccessful and Successful Offerings

Figure 5 presents the distribution of unsuccessful and successful campaign offerings for the full sample, as well as for the Title II and Title III subsamples, of US equity crowdfunding campaigns over the 2013-2019 time period.

3,418 3,167 Number ofOfferings Number 378 251 329 49

Full Dataset Title II Subset Title III Subset Unsuccessful Successful

59

Figure 6 Unsuccessful and Successful Campaign Offerings by Industry

Figure 6 reports the number of unsuccessful and successful campaign offerings in each industry group for the full sample of US equity crowdfunding campaigns over the 2013-2019 time period.

1,317

1,132

318 204 210 Number ofOfferings Number 143 110 106 41 71 67 16 17 20 23 1

Commerce & Consumer Energy Financial Healthcare Materials Services Technology Industry Goods Unsuccessful Successful

60

Figure 7 Top Five Platforms by Number of Offerings

Figure 7 reports the five platforms with the highest number of equity campaign offerings for the full sample of US equity crowdfunding campaigns over the 2013-2019 time period.

2,708

259 257 205 75 Number ofOfferings Number

61

Figure 8 Top Five States by Number of Offerings

Figure 8 reports the five states with the highest number of equity campaign offerings for the full sample of US equity crowdfunding campaigns over the 2013-2019 time period.

1,101

435

Number ofOfferings Number 265 235

142

California New York Florida Texas Illinois

62

Figure 9 Top Five States by Total Campaign Dollars Requested

Figure 9 reports the five states with the highest total dollar amount requested for the full sample of US equity crowdfunding campaigns over the 2013-2019 time period.

$2,402,784

$1,797,659 (in thousands)

$557,344 $428,586

Total Campaign Dollars Requested Requested Dollars Campaign Total $252,315

Texas California New York Florida Ohio

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Figure 10 Percentage of Total Offerings by Region

Figure 10 presents the distribution of crowdfunding campaigns by US region, as determined by state of incorporation, for the full sample of US equity crowdfunding campaigns over the 2013- 2019 time period.

Midwest 11%

West 41% Northeast 21%

South 27%

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Figure 11 Top 50 Words Used in Campaign Descriptions

Figure 11 presents a wordcloud of the top 50 words used in campaign descriptions for the full sample of US equity crowdfunding campaigns over the 2013-2019 time period.

65

Figure 12 Top 50 Words Used in Successful Campaign Descriptions

Figure 12 presents the wordcloud of the top 50 words used in campaign descriptions for successful campaigns in the full sample of US equity crowdfunding campaigns over the 2013- 2019 time period. .

66

TABLES

67

Table 1 Crowdfunding Options in the United States

Table 1 presents a summary of Title II, Title III and Title IV crowdfunding options of the JOBS Act. Regulation D, Regulation A+ Regulation Rule 506(c) Crowdfunding Title of the JOBS Act Title II Title IV Title III Effective Date September 23, 2013 June 19, 2015 May 16, 2016 Investors Allowed Accredited investors Accredited and non- Accredited and non- only accredited investors accredited investors See also note [A] Maximum Offering Unlimited Tier I: limit of $20 $1.07 million within a million within a 12- 12-month period month period See also note [B] Tier II: limit of $50 million within a 12- month period Marketing of General solicitation General solicitation Limited to registered Securities or internet platform or internet platform funding portals only Security Type Restricted Unrestricted Generally restricted Review None Tier I: SEC and Disclosure by issuers is Requirements state-level review required (Form C) Tier II: SEC Ongoing Reporting None Tier I: None Progress updates (Form Requirements Tier II: Semiannual C-U) and annual and annual reports reports (Form C-AR) with the SEC after the offering

[A] An individual “accredited investor” has: (1) A minimum of $200,000 in earned income ($300,000 when combined with a spouse) in each of the two prior years and a “reasonable expectation” of a repeat in the current year, or (2) $1 million or more in net worth, not including primary residence.

[B] There are also limits on the amount individual investors can invest across all crowdfunding offerings in a 12-month period: (1) If an investor’s annual income or net worth is less than $100,000, the limit is the greater of: (a) $2,000 or (b) 5 percent of the lesser of their annual income or net worth, (2) If an investor’s annual income and net worth are equal to or more than $100,000, the limit is 10 percent of the lesser of their annual income or net worth, and (3) During the 12- month period, the aggregate amount of securities sold to an investor through all crowdfunding offerings may not exceed $100,000. Source: U.S. Securities and Exchange Commission

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Table 2 Summary Statistics

Table 2 reports summary statistics for the sample of United States equity crowdfunding campaigns from 2013 to 2019. Panel A reports summary statistics for the full data set, Panel B reports summary statistics for the subset of Title II campaigns, and Title III reports summary statistics for the subset of Title III campaigns. All variables are defined in the Appendix.

Panel A: Full Sample Variable N Mean Median Q1 Q3 Std. Dev.

Success Measures Percent Raised 3,796 119.26 0.00 0.00 5.00 812.83 Fraction That Raised Amount Requested 3,796 0.10 0.00 0.00 0.00 0.30

Variables of Interest Tone (Sentiment) 3,796 0.01 0.01 (0.01) 0.04 0.05 Word Count (Readability) 3,796 67.34 61.00 36.00 88.00 48.52

Firm Characteristics Amount Requested (in thousands) 3,796 1,971.97 300.00 100.00 1,000.00 36,236.55 Woman Owned? 3,796 0.16 0.00 0.00 0.00 0.37 Prior Financial Information Reported? 3,796 0.56 1.00 0.00 1.00 0.50 Number of Employees 580 6.50 3.00 2.00 7.00 13.15

Market Characteristics Monthly VIX Value 3,796 14.29 13.75 13.70 14.00 2.48

Campaign Characteristics Campaign Length (Days) 580 135.05 92.00 76.75 164.25 94.94 Extended Deadline (0/1) 580 0.26 0.00 0.00 1.00 0.44 Video Included (0/1) 580 0.56 1.00 0.00 1.00 0.50 Days from Incorporation to Filing 578 1,031.30 532.50 178.00 1,253.00 1,572.78

Engagement Variables Comments/Responses 383 38.92 14.00 2.00 36.00 98.08 Updates 382 9.14 4.00 0.00 11.00 14.05 Testimonies 382 12.72 0.00 0.00 1.00 57.44

Social Media Characteristics Facebook Mention (0/1) 580 0.39 0.00 0.00 1.00 0.49 Twitter Handle Mention (0/1) 580 0.33 0.00 0.00 1.00 0.47

69

Panel B: Title II Crowdfunding Offerings Only Variable N Mean Median Q1 Q3 Std. Dev.

Success Measures Percent Raised 3,216 7.91 0.00 0.00 0.00 23.53 Fraction That Raised Amount Requested 3,216 0.02 0.00 0.00 0.00 0.12

Variables of Interest Tone (Sentiment) 3,216 0.01 0.01 (0.01) 0.04 0.05 Word Count (Readability) 3,216 67.31 62.00 37.00 88.00 48.15

Firm Characteristics Amount Requested (in thousands) 3,216 2,317.54 500.00 150.00 1,000.00 39,359.75 Woman Owned? 3,216 0.17 0.00 0.00 0.00 0.38 Prior Financial Information Reported? 3,216 0.64 1.00 0.00 1.00 0.48

Market Characteristics Monthly VIX Value 3,216 14.34 13.75 13.70 13.95 2.23

70

Panel C: Title III Crowdfunding Offerings Only Variable N Mean Median Q1 Q3 Std. Dev.

Success Measures Percent Raised 580 736.69 134.00 0.00 474.50 1,968.92 Fraction That Raised Amount Requested 580 0.57 1.00 0.00 1.00 0.50

Variables of Interest Tone (Sentiment) 580 0.02 0.01 0.00 0.04 0.05 Word Count (Readability) 580 67.49 52.00 32.00 90.00 50.55

Firm Characteristics Amount Requested (in thousands) 580 55.87 10.00 10.00 50.00 113.36 Woman Owned? 580 0.10 0.00 0.00 0.00 0.30 Prior Financial Information Reported? 580 0.13 0.00 0.00 0.00 0.33 Number of Employees 580 6.50 3.00 2.00 7.00 13.15

Market Characteristics Monthly VIX Value 580 14.05 13.12 11.18 15.93 3.57

Campaign Characteristics Campaign Length (Days) 580 135.05 92.00 76.75 164.25 94.94 Extended Deadline (0/1) 580 0.26 0.00 0.00 1.00 0.44 Video Included (0/1) 580 0.56 1.00 0.00 1.00 0.50 Days from Incorporation to Filing 578 1,031.30 532.50 178.00 1,253.00 1,572.58

Engagement Variables Comments/Responses 383 38.92 14.00 2.00 36.00 98.08 Number of Updates 382 9.14 4.00 0.00 11.00 14.05 Testimonies 382 12.72 0.00 0.00 1.00 57.44

Social Media Characteristics Facebook Mention (0/1) 580 0.39 0.00 0.00 1.00 0.49 Twitter Handle Mention (0/1) 580 0.33 0.00 0.00 1.00 0.47

71

Table 3 Tests for Differences in Means

Table 3 reports the results of differences in means tests between the Title II subsample and the Title III subsample. All variables are defined in the Appendix. ***, **, and * denotes significant at the 1%, 5%, and 10% level, respectively.

Title II Title III Subsample Subsample Difference Variable Mean Mean in Means

Success Measures Percent Raised 7.91 736.69 728.78*** Fraction that Raised Amount Requested 0.02 0.56 0.55***

Variables of Interest Tone (Sentiment) 0.01 0.02 0.01** Word Count (Readability) 67.31 67.49 0.18

Firm Characteristics Amount Requested (in thousands) 2,317.54 55.87 2,261.67*** Woman Owned? 0.17 0.10 -0.07*** Prior Financial Information Reported? 0.64 0.13 -0.51***

Market Characteristics Monthly VIX Value 14.34 14.05 -0.29*

72

Table 4 Mood's Test for Differences in Medians

Table 4 reports the results of differences in medians tests between the Title II subsample and the Title III subsample using Mood’s median test. Mood’s median test counts how many observations in each group are greater than the median for all groups combined, and then tests for a significant difference in this proportion among groups. All variables are defined in the Appendix. ***, **, and * denotes significant at the 1%, 5%, and 10% level, respectively.

Title II Title III Subset Subset Difference Variable Median Median in Medians

Success Measures Percent Raised 0.00 134.0 134.00*** Fraction That Raised Amount Requested 0.00 1.00 1.00***

Variables of Interest) Tone (Sentiment) 0.01 0.01 0.00 Word Count (Readability) 62.00 52.00 -10.00***

Firm Characteristics: Amount Requested (in thousands) 500.00 10.00 -490.00*** Woman Owned? 0.00 0.00 0.00*** Prior Financial Information Reported? 1.00 0.00 -1.00***

Market Characteristics: Monthly VIX Value 13.75 13.12 -0.63***

73

Table 5 Correlation Matrix

Table 5 reports a correlation matrix for relevant variables of the sample of United States equity crowdfunding campaigns from 2013 to 2019. Panel A reports the correlation matrix for the full data set, Panel B reports the correlation matrix for the subset of Title II campaigns, and Title III reports the correlation matrix for the subset of Title III campaigns. All variables are defined in the Appendix.

Panel A: Full Sample

Percent Word Amount Monthly Raised Tone Count Requested VIX Quarter SMOG Percent Raised 1.0000 Tone 0.0041 1.0000 Word Count 0.0578 -0.0758 1.0000 Amount Requested -0.0074 -0.0067 -0.0150 1.0000 Monthly VIX 0.0128 -0.0007 0.1210 -0.0006 1.0000 Quarter 0.3226 0.0220 0.1452 -0.0217 0.1005 1.0000 SMOG 0.0084 0.0740 0.1611 -0.0086 0.0429 -0.0107 1.0000

74

Panel B: Title II Crowdfunding Offerings Only Percent Word Amount Monthly Raised Tone Count Requested VIX Quarter SMOG Percent Raised 1.0000 Tone 0.0035 1.0000 Word Count 0.1729 -0.0700 1.0000 Amount Requested -0.0020 -0.0065 -0.0163 1.0000 Monthly VIX 0.0399 0.0147 0.1752 -0.0019 1.0000 Quarter 0.2474 -0.0107 0.4632 -0.0028 0.3454 1.0000 SMOG 0.0732 0.0831 0.1826 -0.0101 0.0343 0.0883 1.0000

75

Panel C: Title III Crowdfunding Offerings Only (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) Percent Raised (1) 1.00 Tone (2) -0.01 1.00 Word Count (3) 0.12 -0.07 1.00 Amount Requested (4) -0.14 0.01 -0.16 1.00 Monthly VIX (5) 0.05 -0.07 -0.05 0.04 1.00 Quarter (6) 0.08 -0.08 0.16 -0.04 0.4234 1.00 SMOG (7) 0.08 -0.02 0.01 -0.13 0.0300 0.13 1.00 Number of Employees (8) 0.16 0.09 -0.02 -0.00 -0.0225 0.08 0.01 1.00 Campaign Length (9) -0.05 0.08 -0.03 0.09 -0.1951 -0.21 -0.00 -0.07 1.00 Comments (10) 0.30 -0.06 0.04 -0.01 -0.0532 -0.14 -0.14 0.04 0.02 1.00 Updates (11) 0.30 -0.10 0.24 -0.10 0.0212 0.07 -0.07 0.02 0.08 0.30 1.00 Testimonials (12) 0.01 -0.00 -0.13 0.10 -0.0048 0.04 -0.10 0.60 0.01 0.08 0.05 1.00

76

Table 6 Impact of Sentiment on Campaign Success - Logit Model

Table 6 reports results from a logit regression where the dependent variable is a binary variable indicating whether or not the entrepreneur raised 100% or more of the minimum funding goal amount. Columns 1, 2 and 3 reports results for the full sample, the Title II subsample and the Title III subsample, respectfully. Standard errors are clustered by sector. All variables are defined in the Appendix. ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.

(1) (2) (3) Full Sample Title II Subsample Title III Subsample Logit Model Logit Model Logit Model Variable Coefficient t-stat Coefficient t-stat Coefficient t-stat Intercept -5.60*** -24.74 -7.00*** -10.43 -10.53*** -5.14

Variables of Interest Tone (Sentiment) -0.11* -1.69 -0.01 -0.04 -0.13 -1.27 Word Count (Readability) 0.65*** 3.76 0.27 0.87 0.74*** 5.52 Word Count Squared -0.49*** -3.25 -0.22 -0.63 -0.45*** -2.90

Firm Characteristics Amount Requested -1.56 -0.81 -0.95 -0.65 -179.52*** -5.41 Prior Financial Information Reported? 0.42 1.55 0.67 1.47 0.34 1.54

Market Characteristics Monthly VIX Level -0.06 -0.54 -0.21 -1.26 0.09 0.98

Platform Controls Yes Yes Yes Quarter Year Controls Yes Yes Yes Region Controls Yes Yes Yes Sector Controls Yes Yes Yes

# of Observations 3,796 3,216 580 AIC 1,213 480 740 Pseudo R2 0.55 0.19 0.17

77

Table 7 Impact of Sentiment on Campaign Success - Logit Model with Balanced Panel

Table 7 reports results from a logit regression on a balanced panel of an equal number of successful and unsuccessful offerings. The dependent variable is a binary variable indicating whether or not the entrepreneur raised 100% or more of the minimum funding goal amount. Columns 1, 2 and 3 report results for the full sample, the Title II subsample and the Title III subsample, respectfully. Standard errors are clustered by sector. All variables are defined in the Appendix. ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.

(1) (2) (3) Full Balanced Title II Balanced Title III Balanced Sample Subsample Subsample Logit Model Logit Model Logit Model Variable Coefficient t-stats Coefficient t-stats Coefficient t-stats Intercept -0.58 -1.49 2.25 1.41 0.18 0.16

Variables of Interest Tone (Sentiment) 0.04 0.23 0.89** 2.39 -0.16 -0.95 Word Count (Readability) 0.86** 2.53 0.47 0.54 1.24*** 3.00 Word Count Squared -0.77* -1.76 -0.18 -0.22 -1.44* -1.69

Firm Characteristics Issue Amount -1.32 -0.20 -1.87 -0.27 -19.71 -0.44 Prior financials reported 0.44 0.97 -1.43** -2.44 0.55 1.28

Market Characteristics VIX Level -0.14 -1.27 -0.47* -1.79 -0.01 -0.10

Platform Dummies Yes Yes Yes Quarter Year Dummies Yes Yes Yes Region Dummies Yes Yes Yes Sector Dummies Yes Yes Yes

# of Observations 406 96 310 AIC 639 163 483 Pseudo R2 0.05 0.23 0.06

78

Table 8 Impact of Sentiment on Campaign Success - Tobit Model

Table 8 reports results from a Tobit regression where the dependent variable is the amount of funding raised by the entrepreneur, stated as a proportion of the minimum funding goal amount. Columns 1, 2 and 3 reports results for the full sample, the Title II subsample and the Title III subsample, respectfully. Standard errors are clustered by sector. All variables are defined in the Appendix. ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.

(1) (2) (3) Full Sample Title II Subsample Title III Subsample Tobit Model Tobit Model Tobit Model Variable Coefficient t-stats Coefficient t-stats Coefficient t-stats Intercept -2,399.40*** -7.14 -92.74*** -12.53 -1,134.86 -2.83

Variables of Interest Tone (Sentiment) 16.37 0.41 1.14** 2.31 9.68 0.08 Word Count 280.98*** 5.43 6.78*** 3.75 783.35*** 3.54 (Readability) Word Count Squared -226.25*** -8.27 -6.95*** -3.07 -510.75** -1.97

Firm Characteristics Amount Requested -5.81 -0.71 -0.71** -2.52 -3.23*** -3.07 Prior Financial 449.27*** 4.65 25.02*** 4.72 146.53 0.41 Information Reported?

Market Characteristics Monthly VIX Level -70.03*** -3.75 -6.16*** -6.87 -31.15 -0.49

Platform Controls Yes Yes Yes Quarter Year Controls Yes Yes Yes Region Controls Yes Yes Yes Sector Controls Yes Yes Yes

# of Observations 3,796 3,216 580 Left-censored 2,689 2,476 213 Observations Log-Likelihood -10,312 -4,708 -3,508

79

Table 9 Impact of Sentiment on Campaign Success with Title III Interaction - Logit Model

Table 9 reports results from a logit regression for the full sample where the dependent variable is a binary variable indicating whether or not the entrepreneur raised 100% or more of the minimum funding goal amount. All variables are defined in the Appendix. ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.

Full Sample Variable Logit Model Coefficient t-stats Intercept -5.61*** -23.82

Variables of Interest Tone (Sentiment) 0.01 0.05 Word Count (Readability) 0.42 1.62 Word Count Squared -0.37 -1.53

Firm Characteristics Amount Requested -1.32 -0.77 Prior Financial Information Reported? 0.43 1.60

Market Characteristics Monthly VIX Level -0.06 -0.54

Interaction Variables Title III dummy 14.28*** 10.56 Tone * Title III dummy -0.14 -0.64 Readability * Title III dummy 0.25 1.39

Platform Controls Yes Quarter Year Controls Yes Region Controls Yes Sector Controls Yes

# of Observations 3,796 AIC 1,214 Pseudo R2 0.56

80

Table 10 Impact of Sentiment on Campaign Success - Logit Model with Balanced Panel

Table 10 reports results from a logit regression on a balanced panel of an equal number of successful and unsuccessful offerings. The dependent variable is a binary variable indicating whether or not the entrepreneur raised 100% or more of the minimum funding goal amount. All variables are defined in the Appendix. ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.

Balanced Panel Variable Logit Model Coefficient t-stats Intercept -0.12 -0.15

Variables of Interest Tone 0.77** 2.48 Word Count (Readability) 0.66 1.38 Word Count Squared -0.60 -1.26

Firm Characteristics Issue Amount -3.07 -0.56 Prior Financial Information Reported 0.40 1.11

Market Characteristics Monthly VIX Level -0.17 -1.05

Interaction Variables Title III dummy 0.07 0.06 Tone * Title III dummy -0.91*** -2.69 Readability * Title III dummy 0.14 0.43

Platform Controls Yes Quarter Year Controls Yes Region Controls Yes Sector Controls Yes

# of Observations 406 AIC 634 2 Pseudo R 0.07

81

Table 11 Impact of Sentiment on Campaign Success with Title III Interaction - Tobit Model

Table 11 reports results from a Tobit regression where the dependent variable is the amount of funding raised by the entrepreneur, stated as a proportion of the minimum funding goal amount. All variables are defined in the Appendix. ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.

Full Sample Variable Tobit Model Coefficient t-stats Intercept -2377.66*** -6.84

Variables of Interest Tone 19.64 0.83 Word Count (Readability) 168.15*** 6.61 Word Count Squared -191.40*** -4.77

Firm Characteristics Amount Requested -8.25 -0.93 Prior Financial Information Reported? 449.43*** 4.99

Market Characteristics Monthly VIX Level -67.49*** -3.46

Interaction Variables Title III dummy -799.02*** -3.53 Tone * Title III dummy -7.74 -0.10 Readability * Title III dummy 310.43*** 2.71

Platform Controls Yes Quarter Year Controls Yes Region Controls Yes Sector Controls Yes

# of Observations 3,796 Left-censored Observations 2,689 Log-Likelihood -10,304

82

Table 12 Impact of Engagement with the Crowd on Campaign Success - Logit Results

Table 12 reports results from a logit regression using the Title III subsample where the dependent variable is a binary variable indicating whether or not the entrepreneur raised 100% or more of the minimum funding goal amount. Specification 1 includes sentiment variables and firm and market characteristic controls only. Specification 2 adds the campaign characteristics to the model. Specification 3 adds in the engagement variables into the model. Specification 4 adds the social media characteristics to the model. All variables are defined in the Appendix. ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.

(1) (2) (3) (4) Title III Campaigns Title III Campaigns Title III Campaigns Title III Campaigns Variable Logit Model Logit Model Logit Model Logit Model Coefficient t-stats Coefficient t-stats Coefficient t-stats Coefficient t-stats Intercept -11.48*** -3.56 -11.47*** -3.04 -42.58*** -3.90 -49.11*** -4.14

Variables of Interest Tone -0.17 -1.64 -0.09 -0.62 -0.53** -2.02 -0.60** -2.10 Word Count (Readability) 0.71*** 2.96 0.04 0.11 -0.30 -0.61 -0.26 -0.50 Word Count Squared -0.42 -1.19 -0.06 -0.16 0.36 0.63 0.38 0.57 Engagement Variables Comments/Responses 0.14*** 4.06 0.16*** 4.19 Updates 0.11** 2.31 0.08* 1.69 Testimonials 0.18*** 3.47 0.16*** 3.23 Firm Characteristics Prior Financial Information Reported 0.23 0.75 0.08 0.20 0.72 1.09 1.07 1.53 Number of Employees 0.03** 2.42 0.02 0.94 0.05 1.39 0.05 1.18 Market Characteristics Monthly VIX Level 0.05 0.79 -0.03 -0.40 -0.01 -0.06 0.01 0.11 Campaign Characteristics Campaign Length -0.00 -0.97 -0.00 -1.33 -0.00 -1.09 Extended Deadline (0/1) 1.24*** 3.23 0.57 1.06 0.62 1.10 Video Included (0/1) 1.04*** 3.16 0.58 1.04 0.22 0.07 Social Media Characteristics Facebook Mention (0/1) 0.58 0.60 Twitter Handle Mention (0/1) 1.65* 1.68

Platform Controls Yes Yes Yes Yes Quarter Year Controls Yes Yes Yes Yes Region Controls Yes Yes Yes Yes Sector Controls Yes Yes Yes Yes

# of Observations 580 407 364 364 AIC 734 456 263 254 Pseudo R2 0.18 0.54 0.79 0.80

83

Table 13 Impact of Engagement with the Crowd on Campaign Success - Tobit Results

Table 13 reports results from a Tobit regression for the Title III subsample where the dependent variable is the amount of funding raised by the entrepreneur, stated as a proportion of the minimum funding goal amount. Specification 1 includes sentiment variables and firm and market characteristic controls only. Specification 2 adds the campaign characteristics to the model. Specification 3 adds in the engagement variables into the model. Specification 4 adds the social media characteristics to the model. All variables are defined in the Appendix. ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively. (1) (2) (3) (4) Title III Campaigns Title III Campaigns Title III Campaigns Title III Campaigns Variable Tobit Model Tobit Model Tobit Model Tobit Model Coefficient t-stats Coefficient t-stats Coefficient t-stats Coefficient t-stats Intercept -1,081.35*** -5.06 -1,978.09*** -5.73 1,139.56 1.12 762.46 0.89 Variables of Interest

Tone (Sentiment) -30.64 -0.31 24.91 0.26 159.81 1.19 150.89 1.10 Word Count (Readability) 756.02*** 3.72 603.58*** 3.94 70.62 0.31 90.72 0.38 Word Count Squared -458.21** -2.26 -435.59** -2.33 77.53 0.41 73.67 0.39 Engagement Variables Comments/Responses 8.05*** 2.86 44.14*** 4.54

Updates 47.80*** 4.70 7.83** 2.74

Testimonials -6.00 -1.37 -6.28 -1.35

Firm Characteristics

Issue Amount -3.16*** -2.78 -3.14*** -3.44 -2.38*** -3.41 -2.23*** -3.37 Prior Financial Information Reported 6.93 0.02 -87.0 -0.02 74.35 0.19 128.96 0.35 Number of Employees 32.38* 1.7 29.23 1.47 40.50 1.53 41.40 1.54 Market Characteristics Monthly VIX Level 6.04 0.22 -14.77 -0.4 -0.17 0.00 -1.61 -0.04 Campaign Characteristics Campaign Length -1.83* -1.83 -2.56*** -6.93 -2.16*** -10.26

Extended Deadline (0/1) 1202.44*** 4.82 617.65*** 3.21 604.52*** 2.83

Video Included (0/1) 1420.22*** 7.59 -142.73 -0.25 -256.47 -0.44

Social Media Characteristics

Facebook Mention (0/1) 66.23 0.22

Twitter Handle Mention (0/1) 577.72 1.64

Platform Controls Yes Yes Yes Yes Quarter Year Controls Yes Yes Yes Yes Region Controls Yes Yes Yes Yes Sector Controls Yes Yes Yes Yes

# of Observations 580 580 364 364 Left-censored Observations 213 213 64 64 Log-Likelihood -3,499 -3,471 -2,910 -2,907

84

Table 14 Impact of Sentiment on Campaign Success - Beta Regression Model

Table 14 reports results from a beta regression where the dependent variable is the amount of funding raised by the entrepreneur, stated as a proportion of the minimum funding goal amount. The dependent variable is restricted to values between 0 and 1, transformed using the standard beta regression dependent variable transformation. Standard errors are clustered by sector. All variables are defined in the Appendix. ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.

Dependent Variable: Percent Raised (between 0 and 1) Full Sample Title II Subsample Title III Subsample Variable Beta Regression Beta Regression Beta Regression Coefficient t-stats Coefficient t-stats Coefficient t-stats Intercept -1.65*** -18.96 -2.32*** -33.97 -2.79*** -5.69

Variables of Interest Tone (Sentiment) -0.01 -0.74 0.01* 1.71 -0.05 -1.23 Word Count (Readability) 0.15*** 4.44 0.05** 2.41 0.39*** 6.34 Word Count Squared -0.08*** -4.44 -0.04*** -4.83 -0.27*** -3.41

Firm Characteristics Issue Amount 0.00 0.34 -0.00 -.80 -45.06*** -6.09 Prior Financial Information Reported 0.10*** 3.73 0.10*** 3.88 0.11 1.39

Market Characteristics Monthly VIX Level -0.04** -2.14 -0.06*** -3.13 0.02 0.45

Platform Dummies Yes Yes Yes Quarter Year Dummies Yes Yes Yes Region Dummies Yes Yes Yes Sector Dummies Yes Yes Yes

# of Observations 3796 3216 580 Log Likelihood 16,460 14,150 2,762 Pseudo R-squared 0.47 0.33 0.21

85

Table 15 Impact of Sentiment on Campaign Success with Title III Interaction - Beta Regression Model

Table 15 reports results from a beta regression where the dependent variable is the amount of funding raised by the entrepreneur, stated as a proportion of the minimum funding goal amount. The dependent variable is restricted to values between 0 and 1, transformed using the standard beta regression dependent variable transformation. Robust standard errors are clustered by sector. All variables are defined in the Appendix. ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively. Full Dataset Variable Beta Regression Coefficient t-stats Intercept -1.67*** -17.74

Variables of Interest Tone (Sentiment) 0.01** 2.38 Word Count (Readability) 0.06*** 3.32 Word Count Squared -0.06*** -3.60

Firm Characteristics Issue Amount -0.00 -0.45 Prior Financial Information Reported 0.10*** 4.03

Market Characteristics Monthly VIX Level -0.04** -2.10

Interaction Variables Title III dummy -1.18* -1.82 Tone * Title III dummy -0.11 -1.47 Word Count * Title III dummy 0.40*** 6.53

Platform Controls Yes Quarter Year Controls Yes Region Controls Yes Sector Controls Yes

# of Observations 3,796 Log Likelihood 16,490 Pseudo R-squared 0.48

86

Table 16 Model Comparisons with Title III Interaction - Full Dataset

Table 16 presents the results of the logit model, Tobit model, and beta regression model for the full sample, including the Title III dummy and the interaction terms. All variables are defined in the Appendix. ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.

Dependent Variable: Percent Raised (Transformed to be >0 Dependent Variable: Dependent Variable: and Success (0/1) Percent Raised (0 to >100) < 100) Full Sample Full Sample Full Sample Variable Logit Model Tobit Model Beta Regression Coefficient t-stats Coefficient t-stats Coefficient t-stats Intercept -5.61*** -23.82 -2377.66*** -6.84 -1.67*** -17.74 Sentiment Variables Tone 0.01 0.05 19.64 0.83 0.01** 2.38 Word Count (Readability) 0.42 1.62 168.15*** 6.61 0.06*** 3.32 Word Count Squared -0.37 -1.53 -191.40*** -4.77 -0.06*** -3.60 Firm Characteristics Issue Amount -1.32 -0.77 -8.25 -0.93 -0.00 -0.45 Prior Financial Information Reported 0.43 1.60 449.43*** 4.99 0.10*** 4.03 Market Characteristics Monthly VIX Level -0.06 -0.54 -67.49*** -3.46 -0.04** -2.09 Interaction Variables Title III dummy 14.28*** 10.56 -799.02*** -3.54 -1.18* -1.82 Tone * Title III dummy -0.14 -0.64 -7.74 -0.10 -0.11 -1.47 Readability * Title III dummy 0.25 1.39 310.43*** 2.71 0.40*** 6.53

Platform Controls Yes Yes Yes Quarter Year Controls Yes Yes Yes Region Controls Yes Yes Yes Sector Controls Yes Yes Yes

87

Table 17 Model Comparisons - Full Dataset

Table 17 presents the results of the logit model, Tobit model, and beta regression model for the full sample without the Title III dummy and interaction terms. All variables are defined in the Appendix. ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.

Dependent Variable: Percent Raised Dependent Dependent Variable: (Transformed to be >0 Variable: Success Percent Raised (0 to and (0/1) >100) < 100) Full Sample Full Sample Full Sample Variable Logit Model Tobit Model Beta Regression Coefficient t-stats Coefficient t-stats Coefficient t-stats Intercept -5.60*** -24.74 -2,399.40*** -7.14 -1.65*** -18.96

Sentiment Variables Tone -0.11* -1.69 16.37 0.41 -0.01 -0.74 Word Count (Readability) 0.65*** 3.76 280.98*** 5.43 0.15*** 4.44 Word Count Squared -0.49*** -3.25 -226.26*** -8.27 -0.08*** -4.44

Firm Characteristics Issue Amount -1.56 -0.81 -5.81 -0.70 0.00 0.34 Prior financials reported 0.42 1.55 449.27*** 4.65 0.10*** 3.73

Market Characteristics Monthly VIX Level -0.06 -0.54 -70.03*** -3.75 -0.04** -2.14

Platform Dummies Yes Yes Yes Quarter Year Dummies Yes Yes Yes Region Dummies Yes Yes Yes Sector Dummies Yes Yes Yes

88

Table 18 Model Comparisons - Title II Subsample

Table 18 presents the results of the logit model, Tobit model, and beta regression model for a subset of Title II equity campaign offerings. All variables are defined in the Appendix. ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.

Dependent Variable: Dependent Variable: Dependent Variable: Percent Raised Success (0/1) Percent Raised (Transformed) Title II Subsample Title II Subsample Title II Subsample Variable Logit Model Tobit Model Beta Regression Coefficient t-stats Coefficient t-stats Coefficient t-stats Intercept -7.00*** -10.43 -92.74*** -12.53 -2.32*** -33.97

Sentiment Variables Tone -0.01 -0.04 1.14** 2.27 0.01* 1.71 Word Count (Readability) 0.27 0.87 6.78*** 3.75 0.05** 2.41 Word Count Squared -0.22 -0.63 -6.95*** -3.07 -0.04*** -4.83

Firm Characteristics Issue Amount -0.95 -0.65 -0.71** -2.52 -0.00 -0.80 Prior Financial Information Reported 0.67 1.47 25.02*** 4.72 0.10*** 3.88

Market Characteristics Monthly VIX Level -0.21 -1.26 -6.16*** -6.87 -0.06*** -3.13

Platform Dummies Yes Yes Yes Quarter Year Dummies Yes Yes Yes Region Dummies Yes Yes Yes Sector Dummies Yes Yes Yes

89

Table 19 Model Comparisons - Title III Subset

Table 19 presents the results of the logit model, Tobit model, and beta regression model for a subset of Title II equity campaign offerings. All variables are defined in the Appendix. ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.

Dependent Variable: Dependent Variable: Dependent Variable: Percent Raised Success (0/1) Percent Raised (Transformed) Title III Subsample Title III Subsample Title III Subsample Variable Logit Model Tobit Model Beta Regression Coefficient t-stats Coefficient t-stats Coefficient t-stats Intercept -10.53*** -5.14 -1,134.86*** -2.83 -2.79*** -5.69

Sentiment Variables Tone -0.13 -1.27 9.68 0.08 -0.05 -1.23 Word Count (Readability) 0.74*** 5.52 783.35*** 3.54 0.39*** 6.35 Word Count Squared -0.45*** -2.90 -510.75** -1.97 -0.27*** -3.42

Firm Characteristics Issue Amount -179.52*** -5.41 -3.23*** -3.07 -45.06*** -6.10 Prior Financial Information Reported 0.34 1.54 146.53 0.41 0.11 1.40

Market Characteristics Monthly VIX Level 0.09 0.98 -31.15 -0.49 0.02 0.45

Platform Dummies Yes Yes Yes Quarter Year Dummies Yes Yes Yes Region Dummies Yes Yes Yes Sector Dummies Yes Yes Yes

90

Table 20 Impact of Sentiment on Campaign Success - Tobit Model Using the SMOG Readability Index

Table 20 reports results from a Tobit regression where the dependent variable is the amount of funding raised by the entrepreneur, stated as a proportion of the minimum funding goal amount. All variables are defined in the Appendix. ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.

Tobit Regression Tobit Regression Tobit Regression Full Sample Title II Subsample Title III Subsample Coefficient t-stats Coefficient t-stats Coefficient t-stats Intercept -2,476.51*** -5.08 -102.51*** -10.19 -2992.80*** -3.45

Sentiment Variables Tone -1.21 -0.04 0.74* 1.71 -42.58 -0.39 SMOG (Readability) 23.13 1.61 0.65* 1.75 91.83 1.39

Firm Characteristics Issue Amount -0.00 -1.25 -0.00** -2.48 -2.80** -2.58 Prior financials reported 458.84*** 5.57 24.90*** 4.48 178.57 0.55

Market Characteristics VIX Level -72.49*** -4.02 -6.18*** 4.48 -55.14 -0.79

Platform Dummies Yes Yes Yes Quarter Year Dummies Yes Yes Yes Region Dummies Yes Yes Yes Sector Dummies Yes Yes Yes

# of Observations 3796 3216 580 Log Likelihood -10,319 -4,710 -3,213

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