# Application of Decision Tree Algorithm in Early Entrepreneurial Project Screening

Authors: Yu Min Wang; Lin Xue
Journal: Scientific Programming
 (2022)

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## Abstract

Venture capital firms are always faced with insufficient information and insufficient time when evaluating whether startups are worth investing in. This paper focuses on how to combine the public information of startups with the decision tree algorithm to assist investors in project screening. By extracting the public information of 1104 AI and big data companies from January 2016 to June 2017 and the financing progress in the following 18 months, this paper finds that: the six indicators of having a working background in well-known companies, being reported by well-known media, having patents and being invested by excellent institutions, working experience is highly related to this venture, and having well-known financing consultants can effectively help investors screen out projects that can obtain sustainable financing, that is, high potential projects. Considering the problem of data availability in the real world, combined with industry experience, this paper makes a more detailed variable mining for semistructured information, such as the team resume of start-ups, and selects a decision tree algorithm that is insensitive to missing values and has strong interpretability to amplify the value of fragmented information. Finally, by the improvement of the algorithm, a project screening model that can meet the needs of investment practice is designed, and the fusion mode of public information and private information is discussed, which has a more complete guiding significance for optimizing investment work.


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## Body

## 1. Introduction

Venture capital is a form of investment that provides capital for start-ups with high growth potential in exchange for equity. Massive entrepreneurial projects and high investment costs pose a great challenge to fund managers. How to screen as many high-quality entrepreneurial companies in the market as possible under the condition of limited manpower and time, identify their development potential, and provide support is the first kind of problem faced by investors, i.e., time constraints. Another consensus is that there is a serious lack of information in the field of early investment. In the face of newly established companies, investors often cannot obtain hard data about the market, products, and finance, and they can only make decisions under imperfect information in combination with their own experience, industry estimates, and the judgment of the start-up team. In this process, which factors should be given a higher weight, and how to reduce the components of subjective assumptions and make decisions more rational are the second kind of problems faced by investors, i.e., information constraints.Facing the problem of insufficient time and information, an intuitive solution is to let the machine assist people to collect and process more information, precipitate the decision-making knowledge in the way of algorithm, and rationalize the investment process. However, a series of questions, such as which channels, what information to extract, what indicators to refine, and what feedback to modify the model, need some definition to be effectively answered. Therefore, this paper extracts the link of early project screening from the whole process of investment activities and simplifies the problem as follows:Assume that an investment institution has obtained the profile of an early-stage company disclosed in the venture capital media, including the endorsement information of relevant parties, such as “past investors,” and the information of team characteristics, such as “profile of team members.” Can this institution predict the company’s potential to obtain the next round of financing based on the financing development of related companies in the same industry over the past period of time? Finally, decide if it is worth the time to research and follow up on the investment.The two types of input required to answer this question have been extensively studied in academia.In terms of team characteristics, the positive effects of industry-related experience [1, 2], entrepreneurial experience [1, 3, 4], and well-known company work background [5, 6] on the development of startups have been supported by most literature. However, studies on demographic factors are currently scarce. In the dimension of stakeholders, the endorsement of investment institutions [7, 8] and social capital [9, 10] are positive to the development of the company. The value of financing consultants, venture capital media, and other relevant parties has not been fully explored.Therefore, this paper attempts to apply the research results of academia on venture capital decision-making and the influencing factors of startups to real investment activities, and based on semistructured public datasets, extract valuable traditional indicators from as many dimensions as possible and novel metrics to test its predictive power.Therefore, this paper attempts to apply the research results of academia on venture capital decision-making and the influencing factors of startups to real investment activities, and based on semistructured public datasets, extract valuable traditional indicators from as many dimensions as possible and novel indicators to test its predictive effect.


## 2. Data Preparation

### 2.1. Data Sources and Extraction Methods

Since the potential of a startup is a latent variable, the effectiveness of the predictive model needs to be validated by explicit indicators. Therefore, this paper selects “whether to obtain the next round of financing,” which is highly related to the company’s potential, as a proxy variable to test the effect of “team capability signal” and “relevant party endorsement signal” in building a predictive model. Considering that companies that have never raised funding have less publicly available information, we target our research on startups that have raised at least one round of funding [11].In practice, different industries have different investment analysis frameworks, and the timing and market enthusiasm have a significant impact on the difficulty of obtaining financing for entrepreneurial projects. Therefore, this paper, firstly, controls the industry and time window, and it selects all investment and financing events in the field of artificial intelligence and big data from January 2016 to December 2018, which is a total of 2527. The source of the data is Xinniu Data (https//:www.xiniudata.com), a third-party venture capital service platform, whose institutional account can provide data export function.The normal financing rhythm for startups is to complete a new round of financing every 12–18 months. Based on this industry experience, this paper splits the dataset and selects all projects that have received early (round A and earlier) financing between January 2016 and June 2017. After the project, a total of 1104 independent companies entered the sample set. Whether these companies receive a new round of funding between July 2017 and December 2018 will be the predictor variable.


### 2.2. Data Field Description

The dataset collected in this paper contains 17 original fields: company name, industry field, one-sentence introduction, the current round of financing time, current round, the current round of financing amount, the current round of investor, company introduction, contact information, establishment time, region, company advantages, industry label, team members and introduction, activity performance, and business information [12]. After desensitizing the company name to the company ID, there are 4 remaining fields related to this modeling, all of which are semistructured or unstructured information.By manually labeling the original dataset, we split the above four fields into the following two categories, with a total of 10 binary variables. See Table1 for specific field extraction and processing.Table 1
Field extraction and processing.
Field classificationProcessed field nameData SourcesRelevant party endorsement signalInvested by excellent institutionsInvestors in the current roundWell-known financing advisors (FA)Company advantageWell-known media reportsCompany advantageTeam ability signalHave patentsCompany advantage + team members and profilesWorking background in a well-known companyTeam members and profilesCore members have a well-known university education backgroundTeam members and profilesCore members have entrepreneurial experienceTeam members and profilesThe members have the same school or work experienceTeam members and profilesThe core team has diverse professional skillsTeam members and profilesPrevious work is highly relevant to this ventureTeam members and profiles + company profileThe structured processing of information and the processing of missing values are explained as follows:(1)
Invested by excellent institutions (GoodInvestor): If the current round of investors includes excellent investment institutions, the variable is 1, otherwise, it is 0. The definition of an excellent institution is that more than 45% of the projects invested in 2015 have the next round of financing (this proportion is an empirical value and can be adjusted in practice). The information comes from the venture capital institution database of Enniu. If the investment institution does not disclose it, the variable is a missing value, marked with “?”(2)
Have well-known financing advisors (GoodFA): The variable is 1 if the company advantage contains “well-known FA”; otherwise, it is 0. The definition of well-known financing consultants by Enniu Data includes well-known financing consultants in the industry, such as Huaxing Capital and Xiaofan Table (this range can be dynamically adjusted in practice). There are no missing values for this item.(3)
Well-known media reports (GoodCoverage): The variable is 1 if the company advantage contains “famous media coverage,” and 0 otherwise. The definition of this label by Enniu Data is that platforms, such as Pencil Road and Entrepreneurship, have an exclusive interview with the target company, rather than a simple financing news disclosure. In practice, this range can be dynamically adjusted. There are no missing values for this item.(4)
Have patents (Patent): The variable is 1 if the company or its members hold patents, otherwise 0. Among them, patents only refer to the company’s invention patents, excluding trademarks, and so on. There are no missing values for this item.(5)
Working background in a well-known company (CompanyHalo): If the team members and their profiles mention well-known internet companies (Tencent, Baidu, Alibaba, JD, Xiaomi, Netease, 360, Sina, Google, etc.) or technology enterprises (Microsoft, IBM, Huawei, Lenovo, sap, Oracle, etc.), then the variable is 1, otherwise, it is 0. If the source field is missing, it is marked as “?”(6)
Core members have a well-known university education background (EduHalo): If the team members and the experience of famous universities in the world are mentioned in the profile, the variable is 1, otherwise, it is 0. If the source field is missing, it is marked as “?”(7)
Core members have entrepreneurial experience (StartupExps): If “continuous entrepreneurship” is mentioned in team members and profiles, the variable is 1, otherwise, it is 0. If the source field is missing, it is marked as “?”(8)
The members have the same school or work experience (CoworkExps): In the team members and profiles, it can be inferred that the core members have studied in the same university or the same company. Then, the variable is 1. If there is no obvious intersection in the main experience, it is 0. If the source field is missing or difficult to judge, it is marked as “?”(9)
The core team has diverse professional skills (DiverseExps): In the team members and profiles, the core members’ experience covers at least two items in technology, product, management, marketing, and finance. Then, the variable is 1. If the background of core members is similar, then the variable is 0 (for example, CEO and CTO are fromR & D background). If the source field is missing or difficult to judge, it is marked as “?”(10)
Previous work is highly relevant to this venture (WorkRelated): If the team members and the work background mentioned in the profile are highly correlated with the industry or business of the start-up company, the variable is 1, and if it is obviously irrelevant, it is 0. If the source field is missing or difficult to judge, it is marked as “?”


## 2.1. Data Sources and Extraction Methods

Since the potential of a startup is a latent variable, the effectiveness of the predictive model needs to be validated by explicit indicators. Therefore, this paper selects “whether to obtain the next round of financing,” which is highly related to the company’s potential, as a proxy variable to test the effect of “team capability signal” and “relevant party endorsement signal” in building a predictive model. Considering that companies that have never raised funding have less publicly available information, we target our research on startups that have raised at least one round of funding [11].In practice, different industries have different investment analysis frameworks, and the timing and market enthusiasm have a significant impact on the difficulty of obtaining financing for entrepreneurial projects. Therefore, this paper, firstly, controls the industry and time window, and it selects all investment and financing events in the field of artificial intelligence and big data from January 2016 to December 2018, which is a total of 2527. The source of the data is Xinniu Data (https//:www.xiniudata.com), a third-party venture capital service platform, whose institutional account can provide data export function.The normal financing rhythm for startups is to complete a new round of financing every 12–18 months. Based on this industry experience, this paper splits the dataset and selects all projects that have received early (round A and earlier) financing between January 2016 and June 2017. After the project, a total of 1104 independent companies entered the sample set. Whether these companies receive a new round of funding between July 2017 and December 2018 will be the predictor variable.


## 2.2. Data Field Description

The dataset collected in this paper contains 17 original fields: company name, industry field, one-sentence introduction, the current round of financing time, current round, the current round of financing amount, the current round of investor, company introduction, contact information, establishment time, region, company advantages, industry label, team members and introduction, activity performance, and business information [12]. After desensitizing the company name to the company ID, there are 4 remaining fields related to this modeling, all of which are semistructured or unstructured information.By manually labeling the original dataset, we split the above four fields into the following two categories, with a total of 10 binary variables. See Table1 for specific field extraction and processing.Table 1
Field extraction and processing.
Field classificationProcessed field nameData SourcesRelevant party endorsement signalInvested by excellent institutionsInvestors in the current roundWell-known financing advisors (FA)Company advantageWell-known media reportsCompany advantageTeam ability signalHave patentsCompany advantage + team members and profilesWorking background in a well-known companyTeam members and profilesCore members have a well-known university education backgroundTeam members and profilesCore members have entrepreneurial experienceTeam members and profilesThe members have the same school or work experienceTeam members and profilesThe core team has diverse professional skillsTeam members and profilesPrevious work is highly relevant to this ventureTeam members and profiles + company profileThe structured processing of information and the processing of missing values are explained as follows:(1)
Invested by excellent institutions (GoodInvestor): If the current round of investors includes excellent investment institutions, the variable is 1, otherwise, it is 0. The definition of an excellent institution is that more than 45% of the projects invested in 2015 have the next round of financing (this proportion is an empirical value and can be adjusted in practice). The information comes from the venture capital institution database of Enniu. If the investment institution does not disclose it, the variable is a missing value, marked with “?”(2)
Have well-known financing advisors (GoodFA): The variable is 1 if the company advantage contains “well-known FA”; otherwise, it is 0. The definition of well-known financing consultants by Enniu Data includes well-known financing consultants in the industry, such as Huaxing Capital and Xiaofan Table (this range can be dynamically adjusted in practice). There are no missing values for this item.(3)
Well-known media reports (GoodCoverage): The variable is 1 if the company advantage contains “famous media coverage,” and 0 otherwise. The definition of this label by Enniu Data is that platforms, such as Pencil Road and Entrepreneurship, have an exclusive interview with the target company, rather than a simple financing news disclosure. In practice, this range can be dynamically adjusted. There are no missing values for this item.(4)
Have patents (Patent): The variable is 1 if the company or its members hold patents, otherwise 0. Among them, patents only refer to the company’s invention patents, excluding trademarks, and so on. There are no missing values for this item.(5)
Working background in a well-known company (CompanyHalo): If the team members and their profiles mention well-known internet companies (Tencent, Baidu, Alibaba, JD, Xiaomi, Netease, 360, Sina, Google, etc.) or technology enterprises (Microsoft, IBM, Huawei, Lenovo, sap, Oracle, etc.), then the variable is 1, otherwise, it is 0. If the source field is missing, it is marked as “?”(6)
Core members have a well-known university education background (EduHalo): If the team members and the experience of famous universities in the world are mentioned in the profile, the variable is 1, otherwise, it is 0. If the source field is missing, it is marked as “?”(7)
Core members have entrepreneurial experience (StartupExps): If “continuous entrepreneurship” is mentioned in team members and profiles, the variable is 1, otherwise, it is 0. If the source field is missing, it is marked as “?”(8)
The members have the same school or work experience (CoworkExps): In the team members and profiles, it can be inferred that the core members have studied in the same university or the same company. Then, the variable is 1. If there is no obvious intersection in the main experience, it is 0. If the source field is missing or difficult to judge, it is marked as “?”(9)
The core team has diverse professional skills (DiverseExps): In the team members and profiles, the core members’ experience covers at least two items in technology, product, management, marketing, and finance. Then, the variable is 1. If the background of core members is similar, then the variable is 0 (for example, CEO and CTO are fromR & D background). If the source field is missing or difficult to judge, it is marked as “?”(10)
Previous work is highly relevant to this venture (WorkRelated): If the team members and the work background mentioned in the profile are highly correlated with the industry or business of the start-up company, the variable is 1, and if it is obviously irrelevant, it is 0. If the source field is missing or difficult to judge, it is marked as “?”


## 3. Research Design and Model Validation

To make a relatively independent judgment and comparison on the predictive value of the two types of signals, this paper, firstly, constructs two basic models using the endorsement signal of relevant parties and the team ability signal, respectively, and compares the Bayesian network and decision tree C4.5 and random forest. Then, the two kinds of signals are fused to build a comprehensive model to test the improvement of key indicators. Finally, considering the significant difference in the cost of “rejecting the true and accepting the false” in investment practice, this paper introduces a cost matrix to improve the recall rate of the model for high potential projects.


### 3.1. Basic Model 1: Based on Endorsement Signals of Related Parties

We, firstly, select the endorsement signals of three interested parties—invested by excellent institutions, well-known financing consultants, and well-known media reports—as the model input, and take whether we obtain the next round of financing within 18 months after the first disclosure of financing information as the classification label (yes is 1, no is 0). There are 1104 records in the complete dataset, of which 414 have the next round of financing (defined as a positive sample), accounting for 37.5%.Under 10 10-fold cross-checks, the average performance of the Bayesian network, decision tree C4.5, and random forest algorithms was tested. The experimental environment is the open-source data mining software Weka, and the algorithm uses the default parameters. Considering the problem situation abstracted from investment practice and how to select a small proportion of samples that cover as many real positive samples as possible, we give the overall classification performance as a reference in Table2 but focus on comparing the recall rate and accuracy rate of positive samples (i.e., continuous financing projects).Table 2
Basic model 1: endorsement signals of related parties.
Bayesian networkDecision tree C4.5Random forestOverall accuracy64.2%63.2%61.6%Overall recall65.8%64.8%63.7%The number of positive samples judged210281256The actual number of positive samples123153135Positive sample precision58.7%54.6%52.5%Positive sample recall29.7%36.9%32.5%It can be seen from the above table that the Bayesian network is the model with the highest positive sample precision, and the decision tree C4.5 is the model with the highest positive sample recall rate.Although the decision tree C4.5 model is slightly lower than the Bayesian network in accuracy, it predicts 30 projects that can get the next round of financing more than the latter on average (153 vs. 123), while only 71 additional projects need to be reviewed (281 vs. 210), which has significant value in practice. Because for investors, compared with the original 1,104 projects, the number of projects has been reduced to 281, which has significantly saved working time (corresponding to a 75% compression rate). 24% more project coverage is desirable in exchange for some additional project viewing time, and the recall of high-potential projects is a major consideration at this time.In summary, decision tree C4.5 is the best performing model for this requirement.


### 3.2. Basic Model 2: Based on Team Ability Signal

In the basic model 2, we select seven founding team ability signals as the model input, including having patents, working background in well-known companies, core members having an educational background in well-known universities at home and abroad, core members having entrepreneurial experience, members having the same school or work experience, core teams having diversified professional skills, and previous work is highly related to this entrepreneurship. Then, repeat the aforementioned experimental process.It can be seen from Table3 that at this time, random forest is the model with the highest classification accuracy of positive samples, while the Bayesian network is the model with the highest recall rate of positive samples. The difference in precision is slightly lower than the difference in recall. In addition, compared with basic model 1, the three types of algorithms have improved in key indicators. Among them, the positive sample recall rate of the Bayesian network improved the most significantly (43.5% vs. 29.7%). The positive sample precision rate of the random forest algorithm improved the most significantly (65.0% vs. 52.5%).Table 3
Basic model 2: team ability signal.
Bayesian networkDecision tree C4.5Random forestOverall accuracy68.3%68.0%68.2%Overall recall69.3%68.9%69.0%The number of positive samples judged286249237The actual number of positive samples180160154Positive sample precision63.0%64.2%65.0%Positive sample recall43.5%38.6%37.3%A possible explanation is that, compared with the related party endorsement signal, the dimension of the team capability signal is richer, however, the proportion of missing values is also higher. Under the characteristics of this dataset, the Bayesian network can learn the rules existing in a small number of samples from the combination of indicators, and the pruned decision tree algorithm is more inclined to learn the negative samples with a higher proportion in the dataset. In the discrimination method, although the size of the selected samples is smaller (the compression rate is above 77%), the recall ability of positive samples is weak.To sum up, in investment practice, if one encounters a situation with rich attributes but many missing values, the Bayesian network may be the better choice among the three.


### 3.3. Fusion Model

For base models 1 and 2, the sample compression and precision are acceptable in practice, however, the optimal model does not exceed 44% in terms of recall on positive samples. For investment institutions, the cost of omitting star projects is huge. Only when the recall rate of positive samples is high enough, the model has application value.To this end, we tried to fuse the endorsement signal of related parties with fewer dimensions and the team capability signal with more dimensions as the model input and repeated the above modeling and testing process. The results are summarized in Table4.Table 4
Fusion model.
Bayesian networkDecision tree C4.5Random forestOverall accuracy69.7%67.6%67.0%Overall recall70.5%68.5%67.9%The number of positive samples judged327339332The actual number of positive samples207203196Positive sample precision63.5%59.7%59.0%Positive sample recall50.1%48.9%47.4%It can be seen from the above table that compared with the basic models 1 and 2, the three types of algorithms have a large proportion of the improvement in the recall rate of positive samples, and the value of the combination of the two types of indicators for improving the prediction effectiveness of the model has been verified. Among them, the Bayesian network has the best performance. The performance of decision tree C4.5 is in the middle, and the performance of random forest is second. Compared with model 2, the recall rate of positive samples for decision tree C4.5 is improved by 10 percentage points.It is of great significance for investment practice. Take decision tree C4.5 as an example. Its practical meaning is as follows: after 1104 projects are screened by the model, 339 will be judged as possible for the next round of financing, of which 203 can indeed get the next round of financing. From the perspective of project coverage, if investors screen projects according to this method, they can cover 48.9% of high potential projects under 30.7% of the workload. From the perspective of time cost, investors can spend 59.7% of their working time on valuable projects, which is significantly improved compared with the positive sample rate of 37.5% when looking at projects at random.However, it is worth noting that even under the optimal model, half of the high potential projects will not be screened out by the algorithm. By tracing back the characteristics of samples and their classification results, it can be inferred that there are three reasons, which are as follows:(1)
Incomplete information. For example, 72 positive samples lack team members’ profiles and lack positive signals in the dimension of endorsement of related parties, which objectively limits the upper limit of the prediction ability of the model.(2)
The positive and negative proportions of the samples are uneven. The proportion of negative samples is 62.5%, which is 1.7 times that of positive samples, which makes the model with the goal of reducing the error rate more inclined to learn the discriminative method of negative samples, while the mining of positive samples is insufficient.(3)
There are unobserved influencing factors. Important factors affecting a company’s ability to secure its next round of funding may not be limited to the 10 metrics used in the model.


## 3.1. Basic Model 1: Based on Endorsement Signals of Related Parties

We, firstly, select the endorsement signals of three interested parties—invested by excellent institutions, well-known financing consultants, and well-known media reports—as the model input, and take whether we obtain the next round of financing within 18 months after the first disclosure of financing information as the classification label (yes is 1, no is 0). There are 1104 records in the complete dataset, of which 414 have the next round of financing (defined as a positive sample), accounting for 37.5%.Under 10 10-fold cross-checks, the average performance of the Bayesian network, decision tree C4.5, and random forest algorithms was tested. The experimental environment is the open-source data mining software Weka, and the algorithm uses the default parameters. Considering the problem situation abstracted from investment practice and how to select a small proportion of samples that cover as many real positive samples as possible, we give the overall classification performance as a reference in Table2 but focus on comparing the recall rate and accuracy rate of positive samples (i.e., continuous financing projects).Table 2
Basic model 1: endorsement signals of related parties.
Bayesian networkDecision tree C4.5Random forestOverall accuracy64.2%63.2%61.6%Overall recall65.8%64.8%63.7%The number of positive samples judged210281256The actual number of positive samples123153135Positive sample precision58.7%54.6%52.5%Positive sample recall29.7%36.9%32.5%It can be seen from the above table that the Bayesian network is the model with the highest positive sample precision, and the decision tree C4.5 is the model with the highest positive sample recall rate.Although the decision tree C4.5 model is slightly lower than the Bayesian network in accuracy, it predicts 30 projects that can get the next round of financing more than the latter on average (153 vs. 123), while only 71 additional projects need to be reviewed (281 vs. 210), which has significant value in practice. Because for investors, compared with the original 1,104 projects, the number of projects has been reduced to 281, which has significantly saved working time (corresponding to a 75% compression rate). 24% more project coverage is desirable in exchange for some additional project viewing time, and the recall of high-potential projects is a major consideration at this time.In summary, decision tree C4.5 is the best performing model for this requirement.


## 3.2. Basic Model 2: Based on Team Ability Signal

In the basic model 2, we select seven founding team ability signals as the model input, including having patents, working background in well-known companies, core members having an educational background in well-known universities at home and abroad, core members having entrepreneurial experience, members having the same school or work experience, core teams having diversified professional skills, and previous work is highly related to this entrepreneurship. Then, repeat the aforementioned experimental process.It can be seen from Table3 that at this time, random forest is the model with the highest classification accuracy of positive samples, while the Bayesian network is the model with the highest recall rate of positive samples. The difference in precision is slightly lower than the difference in recall. In addition, compared with basic model 1, the three types of algorithms have improved in key indicators. Among them, the positive sample recall rate of the Bayesian network improved the most significantly (43.5% vs. 29.7%). The positive sample precision rate of the random forest algorithm improved the most significantly (65.0% vs. 52.5%).Table 3
Basic model 2: team ability signal.
Bayesian networkDecision tree C4.5Random forestOverall accuracy68.3%68.0%68.2%Overall recall69.3%68.9%69.0%The number of positive samples judged286249237The actual number of positive samples180160154Positive sample precision63.0%64.2%65.0%Positive sample recall43.5%38.6%37.3%A possible explanation is that, compared with the related party endorsement signal, the dimension of the team capability signal is richer, however, the proportion of missing values is also higher. Under the characteristics of this dataset, the Bayesian network can learn the rules existing in a small number of samples from the combination of indicators, and the pruned decision tree algorithm is more inclined to learn the negative samples with a higher proportion in the dataset. In the discrimination method, although the size of the selected samples is smaller (the compression rate is above 77%), the recall ability of positive samples is weak.To sum up, in investment practice, if one encounters a situation with rich attributes but many missing values, the Bayesian network may be the better choice among the three.


## 3.3. Fusion Model

For base models 1 and 2, the sample compression and precision are acceptable in practice, however, the optimal model does not exceed 44% in terms of recall on positive samples. For investment institutions, the cost of omitting star projects is huge. Only when the recall rate of positive samples is high enough, the model has application value.To this end, we tried to fuse the endorsement signal of related parties with fewer dimensions and the team capability signal with more dimensions as the model input and repeated the above modeling and testing process. The results are summarized in Table4.Table 4
Fusion model.
Bayesian networkDecision tree C4.5Random forestOverall accuracy69.7%67.6%67.0%Overall recall70.5%68.5%67.9%The number of positive samples judged327339332The actual number of positive samples207203196Positive sample precision63.5%59.7%59.0%Positive sample recall50.1%48.9%47.4%It can be seen from the above table that compared with the basic models 1 and 2, the three types of algorithms have a large proportion of the improvement in the recall rate of positive samples, and the value of the combination of the two types of indicators for improving the prediction effectiveness of the model has been verified. Among them, the Bayesian network has the best performance. The performance of decision tree C4.5 is in the middle, and the performance of random forest is second. Compared with model 2, the recall rate of positive samples for decision tree C4.5 is improved by 10 percentage points.It is of great significance for investment practice. Take decision tree C4.5 as an example. Its practical meaning is as follows: after 1104 projects are screened by the model, 339 will be judged as possible for the next round of financing, of which 203 can indeed get the next round of financing. From the perspective of project coverage, if investors screen projects according to this method, they can cover 48.9% of high potential projects under 30.7% of the workload. From the perspective of time cost, investors can spend 59.7% of their working time on valuable projects, which is significantly improved compared with the positive sample rate of 37.5% when looking at projects at random.However, it is worth noting that even under the optimal model, half of the high potential projects will not be screened out by the algorithm. By tracing back the characteristics of samples and their classification results, it can be inferred that there are three reasons, which are as follows:(1)
Incomplete information. For example, 72 positive samples lack team members’ profiles and lack positive signals in the dimension of endorsement of related parties, which objectively limits the upper limit of the prediction ability of the model.(2)
The positive and negative proportions of the samples are uneven. The proportion of negative samples is 62.5%, which is 1.7 times that of positive samples, which makes the model with the goal of reducing the error rate more inclined to learn the discriminative method of negative samples, while the mining of positive samples is insufficient.(3)
There are unobserved influencing factors. Important factors affecting a company’s ability to secure its next round of funding may not be limited to the 10 metrics used in the model.


## 4. Model Discussion and Extension

### 4.1. Factor Analysis and Rules Summary

This paper designs and validates a predictive model that can be used for early-stage project screening, but in investment practice, prediction results alone are not enough to support project screening. Investors need to understand the decision-making basis behind the model to confirm each other with their past investment experience, correct possible wrong judgments, or add new screening rules. At the same time, investors also need to know which factors contribute more to the prediction to consciously collect this information when contacting entrepreneurs or improve the information quality of these dimensions through other channels.Considering the interpretability of the rules, this section selects the pruned decision tree C4.5 model as the analysis object, as shown in Figure1.Figure 1
Visualization of decision tree rules (after pruning).As can be seen from the above figure, there are six indicators in the four layers of the decision tree, which are team capability signals (work background in well-known companies, patents, previous work experience is highly related to this venture) and relevant party endorsement signals (reported by well-known media, invested by excellent institutions and well-known financing consultants), three each. They are the most discriminative for judging whether a project can get the next round of financing [3].There are also observable differences in the 0–1 distribution of these indicators in the whole sample and the positive sample, as shown in Table5 for simplicity (the proportion of missing values is hidden). Except for “well-known financing consultants,” the proportion of the remaining five indicators with a value of 1 in the positive sample is 12–19 percentage points higher than that in the whole sample.Table 5
0–1 distribution difference of typical elements in all samples and positive samples.
VariableAll samplesPositive samples only1010CompanyHalo31978519521929%71%47%53%Patent58352127214253%47%66%34%WorkRelated474682571843%6%62%4%GoodCoverage27283215525925%75%37%63%GoodInvestor22272613223920%66%32%58%GoodFA1349707134312%88%17%83%Combined with the classification results output from Figure1 and the decision tree model, the following four representative investment logics can be summarized from right to left:(1)
76% of entrepreneurial teams with a working background in well-known companies can obtain the next round of financing (characteristics: strong team ability signal).(2)
69% of the teams without working background of well-known companies but with well-known media reports and patents can obtain the next round of financing (characteristics: screened by stakeholders and strong technical ability of the team).(3)
64% of the teams that have no working background in well-known companies and no well-known media reports but have been invested by excellent institutions and whose previous work is highly related to this entrepreneurship can obtain the next round of financing (characteristics: they have been screened by stakeholders and their team ability matches the entrepreneurship).(4)
55% of the entrepreneurial teams without working background of well-known companies, well-known media reports, and investment by excellent institutions but endorsed by well-known financing consultants can obtain the next round of financing. If the enterprise does not even have the signal of a well-known financing consultant, there is a 70% probability that it will not be able to obtain the next round of financing (feature: when it does not have the signal of team ability, the signal of stakeholders can play a certain screening role).From the above typical rules, it can be found that the working background of well-known companies is the most differentiated signal. If it does not have this feature, it needs to have at least one of the ability signals of other teams and the endorsement signals of relevant parties to have a higher probability of obtaining the next round of financing.The language to be converted into investment practice is as follows: if the team has outstanding highlights in its work history, it is worthy of investors’ research and follow-up. If the team is average, it needs the investment of other excellent institutions or the information reported by well-known media to be considered.


### 4.2. Multiclassification Problem

The so-called multiclassification problem refers to using an empirical loss function to predict the classification results of different categories [13]. From start-up to listing, companies usually need to go through more than four rounds of financing (angel round, round A, round B, and pre-IPO round). However, companies that receive continuous financing in the early stage may not be able to go public or be acquired. For investment institutions, if an investment cannot be successfully exited, there will be no return on investment. Therefore, the more valuable the predictive model is to the investment institution, the more the predicted variable can be closer to the long-term potential of the company.In this regard, an intuitive idea is as follows: after the first financing, companies that can obtain two or more rounds of financing have higher potential and value than companies that have only received one round of financing. In other words, we can classify start-ups into three categories based on the number of follow-up rounds a company has received after their initial funding round, which are as follows: low-value (no follow-on financing, recorded as L), medium value (1 follow-on round, recorded as class M), and high value (2 or more rounds of follow-up financing, recorded as class H).It should be noted that this multiclassification problem has certain particularities: on the one hand, the three categories are not semantically independent but have a certain degree of progressive relationship. In terms of value, class L < class M < class H. On the other hand, from the perspective of investment practice, both M and H categories should be selected into the project library to be followed up, however, the H category deserves more attention. Therefore, the misclassification between class M and class H does not incur excessive costs.The original decision tree C4.5 model can handle multiclassification problems but does not pay much attention to the above two problems. Therefore, this section, firstly, uses it as a reference model to measure the general classification effect of the algorithm and then discusses how to improve it.In terms of datasets, this section follows all the projects that have received early financing from January 2016 to June 2017 in the artificial intelligence and big data industries, which is a total of 1104 projects. The independent variable is consistent with the training set. However, the predictor variable is changed to the number of subsequent financings, with a value of 0 representing no subsequent financing (type L), a value of 1 representing a subsequent round of financing (type M), and a value of 2 represents 2 or more follow-on financing rounds (Class H). Repeating the modeling process and 10-fold cross-checking, the classification performance of the three categories is shown in Tables6 and 7.Table 6
Three classification performance: based on decision tree C4.5 model.
LMHSample frequency and proportion690(62.5%)294(26.6%)120(10.9%)Quantity determined as this category93113439Actual quantity of this category6274513Accuracy67.3%33.6%33.3%Recall90.9%15.3%10.8%Table 7
Confusion matrix: based on decision tree C4.5 model.
Classified into LClassified into MClassified into HL6275211M2344515H703713It can be seen from the above table that the decision tree C4.5 algorithm is more biased toward negative samples with a higher proportion (i.e., samples that have not obtained subsequent financing). 84.3% (931/1104) of the samples were classified into the low-value L class, and the identification of medium-value (M-class) and high-value (H-class) samples was severely underidentified. Among the M and H classes, the algorithm also prefers to discriminate as the conservative M class (134 vs. 39). From the perspective of investment practice, this classification effect is not satisfactory.


### 4.3. Scalability Discussion

It is worth noting that the above models we designed and tested only consider the public information provided by third-party venture capital service institutions, however, in practice, investment institutions can also obtain private information from multiple channels, such as reading project business plans, communicate with founders, communicate with financing consultants, communicate with peers, communicate with colleagues with relevant experience, obtain third-party research data, etc.The potential value of this private information is fourfold:(1)
Supplements the missing values of each indicator in the original model, and corrects the wrong labels.(2)
Adds new observations to the model to improve the predictive efficiency of the model.(3)
Provides more information about the research subjects (i.e., startups), such as funding amounts and valuations, to design proxy variables that are closer to the company’s potential.(4)
Builds a closed loop between forecasts and observations earlier than the market, and iterates models earlier to improve forecasts and gain a competitive advantage.Therefore, this section focuses on how to combine public information with private information to form a dynamically updated project screening mechanism. Figure2 presents a conceptual framework considering practical feasibility.Figure 2
Project screening framework that integrates private information and public information.As shown in the figure, after the public information about the project is obtained, it can be analyzed and processed through the following five steps:(1)
Clean and label the data. The dimensions of the labeling can include the ten dimensions proposed in this article, and it can also continue to expand with the enrichment of practical experience.(2)
For projects with relatively complete information, use decision trees or other machine learning algorithms to generate predictions and make corrections based on expert knowledge in investment institutions, thereby generating two sets—projects worthy of follow-up and projects that are not to be followed up for the time being.(3)
Projects that have not been followed up for the time being will enter the waiting pool together with projects with incomplete information before. After investors obtain more information about the team and projects, they will enter the data cleaning process for remarking.(4)
Projects worthy of follow-up and projects not to be followed up enter the observation pool together. Combined with the financing and development information obtained by investors through industry exchanges, projects are divided into four categories: TP (true positive) refers to projects that are determined by institutions to be worthy of follow-up and have real potential through follow-up observation, FP (false positive) refers to the project that the institution determines is worthy of follow-up, however, it is found that it has no development potential and sustainable financing ability, TN (true negative) refers to the project determined by the organization not to follow up temporarily and found to have no potential in the follow-up, and FN (false negative) refers to the project that the organization determines not to follow up temporarily but actually finds potential.(5)
The misclassified samples (i.e., FP and FN) are included in the check pool, and the dataset is used to modify the previous algorithm.


## 4.1. Factor Analysis and Rules Summary

This paper designs and validates a predictive model that can be used for early-stage project screening, but in investment practice, prediction results alone are not enough to support project screening. Investors need to understand the decision-making basis behind the model to confirm each other with their past investment experience, correct possible wrong judgments, or add new screening rules. At the same time, investors also need to know which factors contribute more to the prediction to consciously collect this information when contacting entrepreneurs or improve the information quality of these dimensions through other channels.Considering the interpretability of the rules, this section selects the pruned decision tree C4.5 model as the analysis object, as shown in Figure1.Figure 1
Visualization of decision tree rules (after pruning).As can be seen from the above figure, there are six indicators in the four layers of the decision tree, which are team capability signals (work background in well-known companies, patents, previous work experience is highly related to this venture) and relevant party endorsement signals (reported by well-known media, invested by excellent institutions and well-known financing consultants), three each. They are the most discriminative for judging whether a project can get the next round of financing [3].There are also observable differences in the 0–1 distribution of these indicators in the whole sample and the positive sample, as shown in Table5 for simplicity (the proportion of missing values is hidden). Except for “well-known financing consultants,” the proportion of the remaining five indicators with a value of 1 in the positive sample is 12–19 percentage points higher than that in the whole sample.Table 5
0–1 distribution difference of typical elements in all samples and positive samples.
VariableAll samplesPositive samples only1010CompanyHalo31978519521929%71%47%53%Patent58352127214253%47%66%34%WorkRelated474682571843%6%62%4%GoodCoverage27283215525925%75%37%63%GoodInvestor22272613223920%66%32%58%GoodFA1349707134312%88%17%83%Combined with the classification results output from Figure1 and the decision tree model, the following four representative investment logics can be summarized from right to left:(1)
76% of entrepreneurial teams with a working background in well-known companies can obtain the next round of financing (characteristics: strong team ability signal).(2)
69% of the teams without working background of well-known companies but with well-known media reports and patents can obtain the next round of financing (characteristics: screened by stakeholders and strong technical ability of the team).(3)
64% of the teams that have no working background in well-known companies and no well-known media reports but have been invested by excellent institutions and whose previous work is highly related to this entrepreneurship can obtain the next round of financing (characteristics: they have been screened by stakeholders and their team ability matches the entrepreneurship).(4)
55% of the entrepreneurial teams without working background of well-known companies, well-known media reports, and investment by excellent institutions but endorsed by well-known financing consultants can obtain the next round of financing. If the enterprise does not even have the signal of a well-known financing consultant, there is a 70% probability that it will not be able to obtain the next round of financing (feature: when it does not have the signal of team ability, the signal of stakeholders can play a certain screening role).From the above typical rules, it can be found that the working background of well-known companies is the most differentiated signal. If it does not have this feature, it needs to have at least one of the ability signals of other teams and the endorsement signals of relevant parties to have a higher probability of obtaining the next round of financing.The language to be converted into investment practice is as follows: if the team has outstanding highlights in its work history, it is worthy of investors’ research and follow-up. If the team is average, it needs the investment of other excellent institutions or the information reported by well-known media to be considered.


## 4.2. Multiclassification Problem

The so-called multiclassification problem refers to using an empirical loss function to predict the classification results of different categories [13]. From start-up to listing, companies usually need to go through more than four rounds of financing (angel round, round A, round B, and pre-IPO round). However, companies that receive continuous financing in the early stage may not be able to go public or be acquired. For investment institutions, if an investment cannot be successfully exited, there will be no return on investment. Therefore, the more valuable the predictive model is to the investment institution, the more the predicted variable can be closer to the long-term potential of the company.In this regard, an intuitive idea is as follows: after the first financing, companies that can obtain two or more rounds of financing have higher potential and value than companies that have only received one round of financing. In other words, we can classify start-ups into three categories based on the number of follow-up rounds a company has received after their initial funding round, which are as follows: low-value (no follow-on financing, recorded as L), medium value (1 follow-on round, recorded as class M), and high value (2 or more rounds of follow-up financing, recorded as class H).It should be noted that this multiclassification problem has certain particularities: on the one hand, the three categories are not semantically independent but have a certain degree of progressive relationship. In terms of value, class L < class M < class H. On the other hand, from the perspective of investment practice, both M and H categories should be selected into the project library to be followed up, however, the H category deserves more attention. Therefore, the misclassification between class M and class H does not incur excessive costs.The original decision tree C4.5 model can handle multiclassification problems but does not pay much attention to the above two problems. Therefore, this section, firstly, uses it as a reference model to measure the general classification effect of the algorithm and then discusses how to improve it.In terms of datasets, this section follows all the projects that have received early financing from January 2016 to June 2017 in the artificial intelligence and big data industries, which is a total of 1104 projects. The independent variable is consistent with the training set. However, the predictor variable is changed to the number of subsequent financings, with a value of 0 representing no subsequent financing (type L), a value of 1 representing a subsequent round of financing (type M), and a value of 2 represents 2 or more follow-on financing rounds (Class H). Repeating the modeling process and 10-fold cross-checking, the classification performance of the three categories is shown in Tables6 and 7.Table 6
Three classification performance: based on decision tree C4.5 model.
LMHSample frequency and proportion690(62.5%)294(26.6%)120(10.9%)Quantity determined as this category93113439Actual quantity of this category6274513Accuracy67.3%33.6%33.3%Recall90.9%15.3%10.8%Table 7
Confusion matrix: based on decision tree C4.5 model.
Classified into LClassified into MClassified into HL6275211M2344515H703713It can be seen from the above table that the decision tree C4.5 algorithm is more biased toward negative samples with a higher proportion (i.e., samples that have not obtained subsequent financing). 84.3% (931/1104) of the samples were classified into the low-value L class, and the identification of medium-value (M-class) and high-value (H-class) samples was severely underidentified. Among the M and H classes, the algorithm also prefers to discriminate as the conservative M class (134 vs. 39). From the perspective of investment practice, this classification effect is not satisfactory.


## 4.3. Scalability Discussion

It is worth noting that the above models we designed and tested only consider the public information provided by third-party venture capital service institutions, however, in practice, investment institutions can also obtain private information from multiple channels, such as reading project business plans, communicate with founders, communicate with financing consultants, communicate with peers, communicate with colleagues with relevant experience, obtain third-party research data, etc.The potential value of this private information is fourfold:(1)
Supplements the missing values of each indicator in the original model, and corrects the wrong labels.(2)
Adds new observations to the model to improve the predictive efficiency of the model.(3)
Provides more information about the research subjects (i.e., startups), such as funding amounts and valuations, to design proxy variables that are closer to the company’s potential.(4)
Builds a closed loop between forecasts and observations earlier than the market, and iterates models earlier to improve forecasts and gain a competitive advantage.Therefore, this section focuses on how to combine public information with private information to form a dynamically updated project screening mechanism. Figure2 presents a conceptual framework considering practical feasibility.Figure 2
Project screening framework that integrates private information and public information.As shown in the figure, after the public information about the project is obtained, it can be analyzed and processed through the following five steps:(1)
Clean and label the data. The dimensions of the labeling can include the ten dimensions proposed in this article, and it can also continue to expand with the enrichment of practical experience.(2)
For projects with relatively complete information, use decision trees or other machine learning algorithms to generate predictions and make corrections based on expert knowledge in investment institutions, thereby generating two sets—projects worthy of follow-up and projects that are not to be followed up for the time being.(3)
Projects that have not been followed up for the time being will enter the waiting pool together with projects with incomplete information before. After investors obtain more information about the team and projects, they will enter the data cleaning process for remarking.(4)
Projects worthy of follow-up and projects not to be followed up enter the observation pool together. Combined with the financing and development information obtained by investors through industry exchanges, projects are divided into four categories: TP (true positive) refers to projects that are determined by institutions to be worthy of follow-up and have real potential through follow-up observation, FP (false positive) refers to the project that the institution determines is worthy of follow-up, however, it is found that it has no development potential and sustainable financing ability, TN (true negative) refers to the project determined by the organization not to follow up temporarily and found to have no potential in the follow-up, and FN (false negative) refers to the project that the organization determines not to follow up temporarily but actually finds potential.(5)
The misclassified samples (i.e., FP and FN) are included in the check pool, and the dataset is used to modify the previous algorithm.


## 5. Conclusion

This paper mainly studies the application of the machine learning method represented by a decision tree in early entrepreneurial project screening. The research process is divided into four steps.The first step is to establish a prediction model that can be used in investment practice. By processing the real investment and financing event data set in the field of artificial intelligence and big data, this paper extracts two kinds of signals, which are as follows: the endorsement of relevant parties and team ability, with a total of 10 indicators. After establishing the basic model based on the two types of indicators and verifying their prediction effectiveness, this paper combines all indicators to test whether the fusion model is significantly improved compared with the basic model. Then, considering that in practice, investors are more interested in projects that can continuously obtain financing (i.e., positive samples). This paper introduces a heuristic misclassification cost matrix to adjust the weight of the two types of samples and verifies how much the improved model can improve the recall rate of positive samples.The second step is to interpret the law revealed by the model and to explore whether this law is still applicable over time and whether it can be applied to project screening in similar industries. The selected model is the decision tree C4.5 model with more interpretable rules. By interpreting the top-down judgment path of the decision tree, this paper extracts general rules for guiding project selection.The third step is to extend the decision tree model from the binary classification scenario of positive and negative samples to the multiclassification scenario of project value segmentation to improve the usefulness of the model in investment practice.Finally, in view of the private information available to investment institutions in reality, this paper discusses how to incorporate private information into an iterative analysis process based on the model constructed from public information to achieve a more accurate prediction of high-potential projects.The implications of this paper for management are as follows:(1)
The value of the three team ability indicators, including working background in a well-known company, patents, and work experience, which are highly related to this entrepreneurship in predicting the potential of the company, has been supported by data. When communicating with entrepreneurs or conducting project surveys, investors deserve to verify the authenticity and basis of these indicators from more information sources. In addition, based on the working experience and the subsequent development performance of the company, a more accurate division basis can be established for the scope of well-known companies, the quality and quantity of patents, and the correlation between work experience and entrepreneurship to continuously improve the screening and judgment of projects.(2)
The optimal model and cost matrix settings are different for different classification objectives, and the same is true for different data dimensions. In practice, investors should fully consider their own classification needs and the availability of external data and then select the model with the best classification effect. In addition, in the context of not pursuing model interpretability, it is also an optional idea to introduce algorithms such as Bayesian network and random forest or use integrated learning methods, such as Bagging and Boosting, to improve model prediction.(3)
On the issue of early project screening, the dimension of relevant party endorsement, which is less studied by the academic community, actually has a high signal value. It is also consistent with the basic common sense of the investment industry. These experiences should be incorporated into the construction of project screening mechanisms based on machine learning methods.



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*Source: 3584196-2022-03-31.xml*