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Advanced Journal of Science, Technology and Engineering
ISSN: 2997-5972
Volume 4, Issue 2, 2024 (pp. 81-102)
81 Article DOI: 10.52589/AJSTE-UHDGSWQ1
DOI URL: https://doi.org/10.52589/AJSTE-UHDGSWQ1
www.abjournals.org
ABSTRACT: This research explores the classification of startup risks to
achieve high investment returns using Random Forest Regression. The study
aims to identify and predict potential risks faced by startups, thereby aiding
investors in making informed decisions. We analyzed a dataset comprising
various features such as funding levels, market size, expenses, team
experience, product development stage, customer satisfaction scores, and
revenue streams. We employed a Random Forest Regression model to
evaluate the predictive power of these features. The model's performance was
assessed using several metrics: Mean Squared Error (MSE), R-squared, Mean
Absolute Error (MAE), Mean Squared Logarithmic Error (MSLE), and
Explained Variance Score.The model demonstrated robust predictive
capabilities, with an MSE of 0.255, R-squared of 0.9515, MAE of 0.782,
MSLE of 0.219, and an Explained Variance Score of 0.915. These results
indicate that the model effectively captures the variance in startup risks and
predicts them with high accuracy. Feature importance analysis revealed that
expenses and funding levels were the most critical factors influencing startup
risk classification. The distribution of risks identified 12.4% Strategic Risks,
12.6% Financial Risks, 13.1% Operational Risks, 13.7% Market Risks, and
48.2% of activities with no significant risks.Based on our findings, we
recommend that investors focus on key features as outlined in this research
when assessing startup risks. By employing the insights provided by our
model, investors can better identify high-potential startups, optimize resource
allocation, and improve their investment strategies.The Random Forest
Regression model offers a reliable tool for predicting and classifying startup
risks, providing valuable insights that can enhance investment decision-
making and ultimately lead to higher returns.
KEYWORD:
Start-up, Risk, Investment, Regression, Random Forest,
Returns, Management.
DATA-DRIVEN FRAMEWORK FOR CLASSIFICATION AND MANAGEMENT OF
START-UP RISK FOR HIGH INVESTMENT RETURNS
Anthony Edet1*, Abasiama Silas2, Enobong Ekaetor3,
Ubong Etuk4, Etoroabasi Isaac5 and Anietie Uwah6
1Department of Computer Science, Akwa Ibom State University, Mkpat Enin, Nigeria.
Email: anthonyedet73@gmail.com
2Department of Computer Science, Akwa Ibom State University, Mkpat Enin, Nigeria.
Email: abasiamasilas@aksu.edu.ng
3Department of Economics, Akwa Ibom State University, Mkpat Enin, Nigeria.
Email: enobongekaetor@aksu.edu.ng
4School of Computing Science, University of Glasgow, Glasgow G12 8QQ, UK.
Email: u.etuk.1@research.gla.ac.uk
5Department of Statistics, Akwa Ibom State University, Mkpat Enin, Nigeria
Email: etukyan1@gmail.com
6Department of Computer Science, National Open University of Nigeria, Abuja.
Email: uwahanietie@gmail.com
*Corresponding Author’s Email: anthonyedet73@gmail.com
Cite this article:
A. Edet, A. Silas, E. Ekaetor,
U. Etuk, E. Isaac, A. Uwah
(2024), Data-Driven
Framework for Classification
and Management of Start-Up
Risk for High Investment
Returns. Advanced Journal of
Science, Technology and
Engineering 4(2), 81-102.
DOI: 10.52589/AJSTE-
UHDGSWQ1
Manuscript History
Received: 11 May 2024
Accepted: 17 Jul 2024
Published: 5 Aug 2024
Copyright © 2024 The Author(s).
This is an Open Access article
distributed under the terms of
Creative Commons Attribution-
NonCommercial-NoDerivatives
4.0 International (CC BY-NC-ND
4.0), which permits anyone to
share, use, reproduce and
redistribute in any medium,
provided the original author and
source are credited.
Advanced Journal of Science, Technology and Engineering
ISSN: 2997-5972
Volume 4, Issue 2, 2024 (pp. 81-102)
82 Article DOI: 10.52589/AJSTE-UHDGSWQ1
DOI URL: https://doi.org/10.52589/AJSTE-UHDGSWQ1
www.abjournals.org
INTRODUCTION
Start-up risks refer to the potential challenges and uncertainties that new businesses face,
which can hinder their growth, stability, and success. These risks are multifaceted and can be
broadly categorized into financial, market, operational, and strategic risks (Edet et al., 2024).
Financial risks involve the threat of inadequate funding, cash flow problems, and the inability
to secure investment or generate sufficient revenue. Start-ups often rely on external funding
from investors, venture capitalists, or loans, and failure to obtain or manage these funds can
lead to financial instability or bankruptcy (Mbang et al., 2023). Additionally, start-ups may
encounter difficulty in achieving profitability within the expected timeframe, exacerbating
financial pressures. Market risks pertain to the uncertainties associated with market demand,
competition, and customer preferences. Start-ups must accurately gauge market needs and
trends to ensure their products or services meet customer expectations. Misjudging market
demand or failing to differentiate from competitors can result in low sales and market
penetration. Moreover, start-ups operate in dynamic environments where customer
preferences can shift rapidly, requiring agile adaptation to remain relevant. Intense
competition from established players or other start-ups can also pose significant threats,
making it challenging to gain and maintain market share.
Operational risks involve the internal processes and systems that support the functioning of a
start-up (Ebong et al.,2024). These risks include challenges related to supply chain
management, technology infrastructure, regulatory compliance, and human resources. For
example, start-ups may struggle with sourcing reliable suppliers, maintaining product quality,
or managing inventory efficiently. Technological risks, such as cybersecurity threats or
system failures, can disrupt operations and erode customer trust. Additionally, navigating
complex regulatory environments and ensuring compliance with industry standards can be
daunting for new businesses. Attracting and retaining skilled talent is another critical
operational risk, as the success of a start-up heavily depends on the capabilities and
dedication of its team.
Strategic risks encompass the broader, long-term challenges that can impact a start-up's
vision and growth trajectory (Ekong et al., 2024). These risks include poor strategic planning,
misalignment with market needs, and failure to pivot or innovate when necessary. Start-ups
must develop clear, realistic business plans and remain flexible to adjust their strategies in
response to changing circumstances. Overly optimistic projections, lack of focus, or
mismanagement of resources can derail a start-up's progress. Furthermore, an inability to
innovate or adapt to emerging technologies and market trends can render a start-up obsolete.
Strategic risks also involve external factors such as economic downturns, political instability,
or changes in industry regulations, which can significantly influence a start-up's prospects.
Developing a data-driven framework for the classification and management of start-up risk is
crucial for maximizing investment returns. The framework leverages regression analysis to
predict potential outcomes and evaluate risks effectively. By analyzing historical data, market
trends, and financial metrics, investors we gain insights into the likelihood of a start-up's
success or failure (Edet et al., 2024). This predictive approach enables more informed
decision-making, reducing the uncertainty associated with start-up investments. Ultimately,
the goal is to create a model that accurately identifies high-potential start-ups while
mitigating risks, thereby optimizing the investment portfolio. The foundation of this research
lies in gathering extensive and relevant data. Key performance indicators (KPIs) such as
revenue growth, customer acquisition costs, market size, and team experience are critical
Advanced Journal of Science, Technology and Engineering
ISSN: 2997-5972
Volume 4, Issue 2, 2024 (pp. 81-102)
83 Article DOI: 10.52589/AJSTE-UHDGSWQ1
DOI URL: https://doi.org/10.52589/AJSTE-UHDGSWQ1
www.abjournals.org
factors we have considered (Uwah and Edet, 2024). By inputting these variables into a
regression model, one can discern patterns and correlations that might not be immediately
obvious. For instance, a start-up with rapid revenue growth but high customer acquisition
costs might indicate a scalability issue, whereas consistent growth with manageable costs
suggests a more sustainable business model. Regression analysis plays a pivotal role in this
research by providing a statistical basis for risk assessment. Linear regression is used to
understand the relationship between independent variables (e.g., market size, team experience)
and dependent variables (e.g., investment return). More sophisticated techniques, such as
logistic regression or polynomial regression which can handle non-linear relationships and
binary outcomes (e.g., success or failure) are also used for clear class categories (Edet and
Ansa, 2023). By fitting the model to historical data (Ekong et al., 2023), it becomes possible
to predict future outcomes and assign risk scores to start-ups. This quantitative approach
enhances the objectivity of the evaluation process and helps investors make data-backed
decisions. The developed regression model is used for continuous monitoring (Ekong et al.,
2023) and management of start-up investments. Regular updates with new data ensure that
the model remains relevant and accurate over time. Additionally, scenario analysis can be
performed to assess how changes in key variables affect the predicted outcomes. For instance,
investors can simulate the impact of different market conditions or strategic decisions on a
start-up's risk profile. This proactive approach allows for dynamic risk management, enabling
investors to adjust their strategies in response to evolving market conditions. This research
provides a structured and scientific approach to investment. By using historical data and
statistical techniques, investors can predict outcomes more accurately and manage risks
effectively. As a result, investors can optimize their portfolios by identifying high-potential
start-ups and making informed, data-backed decisions, ultimately leading to higher
investment returns.
RESEARCH ORGANIZATION
The paper begins with an introduction that emphasizes the economic significance of start-ups
and the substantial risks involved in investing in them. It outlines the objectives of the study,
which include developing a data-driven framework using regression analysis to enhance risk
assessment and improve investment returns. This section also provides a roadmap for the
paper’s organization, guiding the reader through the subsequent sections.
The literature review section examines the various types of risks that start-ups face, such as
financial, market, operational, and strategic risks. It reviews existing risk management
frameworks and discusses the advantages of data-driven approaches. The focus is particularly
on the application of regression analysis in financial predictions, which sets the context for
the proposed framework by highlighting current methodologies and identifying gaps that the
new framework aims to address.
The methodology section details the research design and the processes involved in
developing the framework. It describes the methods of data collection, including the sources
of data and the selection of key performance indicators (KPIs) relevant to start-up
performance. This section also covers the data preprocessing steps, such as data cleaning,
handling missing values, and feature engineering to enhance the predictive power of the
models. Additionally, it introduces the regression models used in the study, including linear
Advanced Journal of Science, Technology and Engineering
ISSN: 2997-5972
Volume 4, Issue 2, 2024 (pp. 81-102)
84 Article DOI: 10.52589/AJSTE-UHDGSWQ1
DOI URL: https://doi.org/10.52589/AJSTE-UHDGSWQ1
www.abjournals.org
regression, logistic regression, and polynomial regression, and explains the model training
and validation techniques, along with the performance metrics applied to evaluate the models'
accuracy and reliability.
The framework development section outlines the conceptual design of the proposed data-
driven framework, illustrating how regression analysis is integrated to assess and manage
start-up risks. It provides a detailed description of the framework's workflow, from data input
and processing to risk classification and decision support. This step-by-step guide helps
readers understand the operational aspects of the framework and its implementation, ensuring
clarity and practicality.
In the results and analysis section, the findings from the regression analysis are presented,
focusing on the predictive accuracy of the models and the outcomes of the risk classification.
This section discusses how start-ups are categorized based on their risk profiles and potential
for high investment returns. Scenario analysis is also explored, demonstrating the
framework’s ability to adapt to different market conditions and strategic decisions. Practical
examples and case studies are included to illustrate the real-world application and
effectiveness of the framework, providing concrete evidence of its benefits.
LITERATURE BACKGROUND
Various Types of Start-Up Risks
Start-ups, by their very nature, face a plethora of risks that can significantly impact their
chances of success. These risks can be broadly categorized into financial, market, operational,
and strategic risks (Mbang et al, 2023). Understanding these risks is crucial for entrepreneurs
and investors alike, as it enables them to develop strategies to mitigate potential pitfalls and
improve the likelihood of achieving sustainable growth.
A. Financial Risks: Financial risks are among the most critical challenges for start-ups.
These include the risk of running out of capital, the inability to secure funding, poor
cash flow management, and the unpredictability of revenue streams. Start-ups often rely
on external financing from venture capitalists, angel investors, or loans. However, the
terms of these funding sources can be stringent, and the pressure to meet financial
milestones can be intense. Additionally, start-ups may face difficulties in managing
their burn rate – the rate at which they spend their available capital – leading to
potential insolvency if not carefully monitored (Inyang and Umoren, 2023).
B. Market Risks: Market risks encompass the challenges related to the external
environment in which a start-up operates. This includes market demand uncertainty,
competitive pressures, and changes in consumer preferences. Start-ups must accurately
assess market demand for their product or service, which can be challenging without
historical data or established customer bases. Competition is another significant risk
(Inyang and Umoren, 2023), as larger, more established companies or other nimble
start-ups might offer similar solutions. Market conditions can also shift rapidly due to
Advanced Journal of Science, Technology and Engineering
ISSN: 2997-5972
Volume 4, Issue 2, 2024 (pp. 81-102)
85 Article DOI: 10.52589/AJSTE-UHDGSWQ1
DOI URL: https://doi.org/10.52589/AJSTE-UHDGSWQ1
www.abjournals.org
economic changes, regulatory adjustments, or technological advancements, requiring
start-ups to be agile and adaptive to survive and thrive.
C. Operational Risks: Operational risks involve the internal processes and day-to-day
activities of a start-up. These risks can stem from inefficiencies in operations, supply
chain disruptions, product development delays, or failures in technology infrastructure.
Start-ups often operate with limited resources and may lack robust processes and
systems, making them vulnerable to operational hiccups. Effective management of
operations is crucial to ensure timely delivery of products or services and maintain
customer satisfaction. Additionally, the reliance on technology means that technical
failures or cybersecurity breaches can pose significant threats to a start-up's operations
(Edet and Ansa, 2023).
D. Strategic risks relate to the long-term planning and strategic decisions made by a start-
up. These include risks associated with business model viability, scaling challenges, and
strategic partnerships. Choosing the wrong business model can lead to unsustainable
operations or failure to achieve profitability. Scaling a start-up also presents challenges,
as rapid growth can strain resources, impact quality, and lead to operational
inefficiencies. Furthermore, strategic partnerships or alliances, while potentially
beneficial, carry risks if the partners' goals and visions are not aligned or if the
partnership dynamics change over time ( Edet et al., 2024).
Features for Risk Analysis and Classification
Table 1.0: Risk factors and Class Mapping
Feature
Description
Associated Risk
Funding Levels
Amount of capital raised through various
funding rounds
Financial Risks
Revenue
Monthly or annual revenue figures
Financial Risks
Profit Margins
Net profit margin, gross profit margin
Financial Risks
Burn Rate
Monthly cash expenditure
Financial Risks
Valuation
Company valuation during different funding
rounds
Financial Risks
Debt Levels
Amount of debt incurred by the startup
Financial Risks
Market Size
Total addressable market (TAM) and
serviceable available market (SAM)
Market Risks
Competition
Intensity
Number of competitors, market share
distribution
Market Risks
Economic Indicators
Relevant economic factors such as GDP
growth rate, inflation rate
Market Risks
Market Trends
Emerging trends and technological
advancements in the industry
Market Risks
Team Size
Number of employees, breakdown by
departments
Operational Risks
Team Experience
Average years of experience, relevant
Operational Risks
Advanced Journal of Science, Technology and Engineering
ISSN: 2997-5972
Volume 4, Issue 2, 2024 (pp. 81-102)
86 Article DOI: 10.52589/AJSTE-UHDGSWQ1
DOI URL: https://doi.org/10.52589/AJSTE-UHDGSWQ1
www.abjournals.org
industry experience
Product
Development Stage
Stage of product development (idea,
prototype, MVP, fully developed)
Operational Risks
Operational
Efficiency
KPIs such as production lead times,
customer service response times
Operational Risks
Customer
Acquisition Rates
Monthly or quarterly customer acquisition
figures
Operational Risks
Customer Retention
Rates
Percentage of customers retained over
specific periods
Operational Risks
Customer
Satisfaction Scores
Net Promoter Score (NPS), customer
satisfaction surveys
Operational Risks
Churn Rates
Monthly or quarterly customer churn rates
Operational Risks
Revenue Streams
Breakdown of revenue sources (product
sales, subscription fees, etc.)
Strategic Risks
Cost Structure
Breakdown of major costs (COGS, R&D,
marketing, administrative expenses)
Strategic Risks
Pricing Strategy
Pricing models and strategies employed
Strategic Risks
Technology Stack
Technologies used in product development
and operations
Strategic Risks
Product Features
Key features and unique selling propositions
(USPs) of the product
Strategic Risks
Innovation Rate
Frequency of new product releases or
updates
Strategic Risks
Intellectual Property
Patents, trademarks, and other IP assets
Strategic Risks
Regulatory
Compliance
Adherence to relevant industry regulations
and standards
Strategic Risks
Legal Issues
Any ongoing or past legal challenges or
litigations
Strategic Risks
Partnerships and
Alliances
Strategic partnerships, alliances, and
collaborations
Strategic Risks
Investor Profile
Type of investors and their involvement
level
Strategic Risks
Geographic Location
Location of the startup and its markets
Strategic Risks
Start Up Risk Management Framework Profiling
A. Lean Startup Methodology
The Lean Startup Methodology, developed by Eric Ries, is a transformative approach tailored
for startups to minimize risks through iterative product development. This framework
revolves around creating a Minimum Viable Product (MVP), which is the simplest version
of a product that can be released to gather maximum validated learning about customers with
minimal effort. By releasing an MVP, startups can obtain early feedback from real users,
helping them understand what aspects of the product work and what do not. This iterative
process is encapsulated in the Build-Measure-Learn feedback loop, which emphasizes
continuous learning and adaptation. By focusing on validated learning, startups can quickly
Advanced Journal of Science, Technology and Engineering
ISSN: 2997-5972
Volume 4, Issue 2, 2024 (pp. 81-102)
87 Article DOI: 10.52589/AJSTE-UHDGSWQ1
DOI URL: https://doi.org/10.52589/AJSTE-UHDGSWQ1
www.abjournals.org
pivot or persevere based on concrete feedback, thereby reducing the risk of investing time
and resources into features or products that may not meet market needs (Mbang et al., 2023).
This methodology helps startups navigate the uncertainties inherent in launching new
ventures. Traditional business plans often fall short in the dynamic environment of a startup,
where customer needs and market conditions can change rapidly. The Lean Startup approach,
however, allows for flexibility and responsiveness. By continuously testing assumptions and
refining the product based on real-world data, startups can mitigate the risk of failure. This
approach also promotes a culture of experimentation and learning within the startup,
encouraging teams to embrace failures as learning opportunities and iterate towards a more
successful product-market fit.
B. Business Model Canvas (BMC)
The Business Model Canvas (BMC), created by Alexander Osterwalder, is a strategic
management tool that provides a visual framework for developing, assessing, and pivoting
business models. This one-page canvas helps startups map out key components of their
business, including value propositions, customer segments, channels, customer relationships,
revenue streams, key resources, key activities, key partnerships, and cost structures. By
visually laying out these elements, startups can clearly see how they interconnect and identify
potential areas of risk and opportunity. This holistic view helps entrepreneurs understand the
broader context of their business model and make informed decisions about where to focus
their efforts.
Using the BMC can significantly reduce risks for startups by fostering a deep understanding
of how different parts of their business interact. For instance, by examining customer
segments and value propositions side-by-side, startups can ensure that they are targeting the
right market with the right offering. The BMC also encourages startups to think critically
about their revenue streams and cost structures, ensuring that the business is financially
viable. Moreover, the iterative nature of the BMC allows startups to continually refine their
business model based on feedback and changing market conditions, ensuring that they remain
adaptable and responsive to new information (Inah et al., 2019).
C. SWOT Analysis
SWOT Analysis is a strategic planning tool used to identify and analyze the internal and
external factors that can impact a startup's success. SWOT stands for Strengths, Weaknesses,
Opportunities, and Threats. By conducting a SWOT analysis, startups can gain a
comprehensive understanding of their current position and the broader business environment.
Strengths and weaknesses are internal factors that startups can control and improve, such as
unique technologies, team skills, or operational efficiencies. Opportunities and threats are
external factors that startups must navigate, such as market trends, competitive pressures, or
regulatory changes (Inah et al., 2019).
This framework helps startups manage risks by providing a structured way to evaluate their
business. By identifying strengths, startups can leverage these to gain a competitive
advantage. Recognizing weaknesses allows startups to address vulnerabilities before they
become significant issues. Evaluating opportunities can help startups to focus on the most
promising areas for growth, while understanding threats enables them to develop strategies to
Advanced Journal of Science, Technology and Engineering
ISSN: 2997-5972
Volume 4, Issue 2, 2024 (pp. 81-102)
88 Article DOI: 10.52589/AJSTE-UHDGSWQ1
DOI URL: https://doi.org/10.52589/AJSTE-UHDGSWQ1
www.abjournals.org
mitigate potential risks. The SWOT analysis not only helps in strategic planning but also in
aligning the team’s efforts towards common goals and preparing for uncertainties.
D. PESTLE Analysis
PESTLE Analysis is a strategic tool used to analyze the macro-environmental factors that can
impact a startup's business. PESTLE stands for Political, Economic, Social, Technological,
Legal, and Environmental factors. By systematically evaluating these six areas, startups can
gain insights into the broader forces that could affect their operations. For example, political
factors include government policies, stability, and trade regulations that might influence a
startup's ability to operate. Economic factors encompass elements like inflation, interest rates,
and economic growth that can impact a startup’s financial performance (Edet et al., 2024).
Social factors involve demographic trends, cultural norms, and consumer behaviors that
could shape market demand.
Using PESTLE Analysis, startups can anticipate and prepare for external risks that might not
be immediately apparent. This comprehensive assessment helps startups understand the larger
landscape in which they operate, allowing them to adapt their strategies to align with external
conditions. For instance, a startup might identify a technological trend that could disrupt their
industry or a legal change that could impose new compliance requirements. By proactively
addressing these factors, startups can mitigate risks and seize opportunities more effectively.
PESTLE Analysis also encourages startups to consider sustainability and ethical
considerations, which are increasingly important in today’s business environment.
E. Porter’s Five Forces
Porter’s Five Forces, developed by Michael Porter, is a framework used to analyze the
competitive forces within an industry. This framework helps startups understand the
dynamics that affect their profitability and strategic positioning. The five forces include
competitive rivalry, the threat of new entrants, the bargaining power of suppliers, the
bargaining power of customers, and the threat of substitute products or services. By
examining these forces, startups can identify the key factors that influence competition and
market attractiveness. For example, high competitive rivalry might indicate a saturated
market, while high bargaining power of customers could suggest the need for greater value
differentiation (Inah et al., 2019).
This analysis is crucial for startups as it helps them understand the competitive pressures they
face and develop strategies to address them. For instance, by recognizing the threat of new
entrants, startups can focus on building strong brand loyalty or creating barriers to entry
through unique value propositions. Understanding supplier power can help startups negotiate
better terms or seek alternative suppliers to reduce dependency. Similarly, analyzing
customer power can guide startups in improving customer relationships and enhancing
customer experience. Overall, Porter’s Five Forces provides startups with a structured
approach to assessing their competitive environment and identifying areas where they can
strategically position themselves for success.
F. Risk Register
A risk register is an essential tool for tracking and managing risks in a startup. It is a
document or system used to record identified risks, assess their likelihood and impact, and
Advanced Journal of Science, Technology and Engineering
ISSN: 2997-5972
Volume 4, Issue 2, 2024 (pp. 81-102)
89 Article DOI: 10.52589/AJSTE-UHDGSWQ1
DOI URL: https://doi.org/10.52589/AJSTE-UHDGSWQ1
www.abjournals.org
outline mitigation strategies. Each entry in a risk register typically includes a description of
the risk, its potential consequences, the probability of occurrence, the impact if it does occur,
and the actions planned to manage it. This organized approach ensures that all potential risks
are documented, continuously monitored, and proactively managed. For startups, maintaining
a risk register helps in staying vigilant and prepared for uncertainties. It provides a clear
overview of the risk landscape, enabling startups to prioritize their risk management efforts
based on the severity and likelihood of each risk (Inyang and Umoren, 2023). Regularly
updating the risk register ensures that new risks are identified and addressed promptly, while
existing risks are monitored for changes. This proactive approach not only helps in mitigating
potential threats but also in seizing opportunities by turning some risks into strategic
advantages. The risk register fosters a culture of risk awareness and continuous improvement
within the startup, crucial for navigating the volatile startup environment.
G. Scenario Planning
Scenario planning is a strategic tool used by startups to prepare for various possible futures.
This approach involves creating different detailed scenarios based on a set of assumptions
about key uncertainties and variables. Scenarios can range from best-case to worst-case and
everything in between, allowing startups to explore the potential impacts of different future
developments. By considering a variety of possible outcomes, startups can develop flexible
strategies and contingency plans to address each scenario effectively (Inyang and Umoren,
2023).
This method is particularly valuable for startups as it encourages long-term thinking and
strategic agility. By anticipating different potential futures, startups can identify early
warning signals and adapt their strategies accordingly. For example, a startup might develop
scenarios around changes in market demand, technological advancements, or regulatory shifts.
Preparing for these scenarios helps startups remain resilient and responsive, reducing the risk
of being caught off guard by unexpected events. Scenario planning also enhances decision-
making by providing a structured way to evaluate the potential implications of different
strategic choices, ensuring that startups are better equipped to navigate uncertainties.
H. Agile Methodology
Agile methodology, originally developed for software development, emphasizes flexibility,
collaboration, and customer feedback. This iterative approach involves breaking down
projects into small, manageable segments called sprints, which typically last two to four
weeks. Each sprint focuses on developing a specific set of features or functionalities,
followed by testing and gathering feedback from customers. This cycle of continuous
improvement allows startups to quickly adapt to changing requirements and market
conditions, ensuring that the final product meets customer needs more accurately. For
startups, adopting Agile principles can significantly mitigate risks by promoting rapid
iteration and learning. Instead of committing extensive resources to a fixed plan, startups can
remain adaptable and responsive to new information and customer feedback. This reduces the
risk of building a product that does not align with market demand. Agile methodology also
fosters a collaborative work environment, where cross-functional teams work closely together,
enhancing communication and problem-solving. By continuously testing and refining their
product, startups can identify and address issues early in the development process, leading to
higher-quality outcomes and increased customer satisfaction.
Advanced Journal of Science, Technology and Engineering
ISSN: 2997-5972
Volume 4, Issue 2, 2024 (pp. 81-102)
90 Article DOI: 10.52589/AJSTE-UHDGSWQ1
DOI URL: https://doi.org/10.52589/AJSTE-UHDGSWQ1
www.abjournals.org
I. Financial Risk Management
Financial risk management is critical for startups to ensure their financial stability and growth.
This involves closely monitoring cash flow, budgeting, and forecasting to maintain a healthy
financial position. Startups need to identify potential financial risks, such as funding
shortfalls, revenue fluctuations, and unexpected expenses. Tools like break-even analysis and
sensitivity analysis can help startups understand their financial vulnerabilities and plan
accordingly (Inyang and Umoren, 2023).
Effective financial risk management helps startups make informed decisions and avoid
financial pitfalls. By maintaining a clear understanding of their financial health, startups can
allocate resources more efficiently and plan for future investments. Regular financial reviews
and adjustments ensure that startups stay on track with their financial goals and can respond
quickly to any financial challenges. This proactive approach not only mitigates financial risks
but also provides a solid foundation for sustainable growth. Financial risk management also
enhances investor confidence, as it demonstrates the startup’s commitment to sound financial
practices and long-term viability.
J. Legal and Compliance Frameworks
Legal and compliance frameworks are essential for startups to navigate the regulatory
environment and avoid legal pitfalls. Startups must be aware of and comply with relevant
laws and regulations that apply to their industry, such as intellectual property rights, data
protection regulations, employment laws, and industry-specific standards. Ensuring legal
compliance helps startups avoid costly penalties, lawsuits, and reputational damage, which
can be particularly detrimental in the early stages of business development. For startups,
understanding and adhering to legal and compliance requirements is crucial for building a
strong foundation and gaining the trust of stakeholders, including customers, investors, and
partners. By proactively addressing legal and compliance issues, startups can mitigate risks
and ensure smooth operations. This involves regular legal audits, seeking legal advice when
necessary, and implementing robust policies and procedures. Additionally, staying informed
about changes in the regulatory landscape allows startups to adapt quickly and maintain
compliance, ensuring long-term success and sustainability.
Machine Learning in Risk Management
Machine learning has become a pivotal tool in the management of startup risks, providing
sophisticated methods for predicting and mitigating potential challenges. In the area of
machine learning, regression analysis is particularly valuable due to its ability to model
relationships between variables and predict future outcomes (Ekong et al., 2022). For startups,
leveraging regression analysis can translate into actionable insights that guide strategic
decisions and reduce uncertainties. This research focuses on applying random forest
regression, a robust machine learning technique, to analyze and manage risks faced by
startups.
Random forest regression is a type of ensemble learning method that builds multiple decision
trees (Ekong et al., 2024) and merges their results to improve predictive accuracy and control
overfitting. Each tree in the forest is built from a random subset of the data, which ensures
diversity among the trees and enhances the model's robustness against noisy data and
overfitting (Inyang and Umoren, 2023). By averaging the predictions from all the trees,
Advanced Journal of Science, Technology and Engineering
ISSN: 2997-5972
Volume 4, Issue 2, 2024 (pp. 81-102)
91 Article DOI: 10.52589/AJSTE-UHDGSWQ1
DOI URL: https://doi.org/10.52589/AJSTE-UHDGSWQ1
www.abjournals.org
random forest regression provides a more accurate and reliable forecast compared to a single
decision tree. This approach is particularly beneficial for startups, which often deal with
complex, multifaceted risks that cannot be captured adequately by simpler models .
In this research, random forest regression is used to analyze key factors that influence
startup success and failure. Variables such as market conditions, funding levels, team
experience, and product features are included in the model to predict outcomes like revenue
growth, customer acquisition rates, and market share. By training the model on historical data
from a variety of startups, it can identify patterns and interactions between these variables
that are indicative of future performance. This enables startups to not only predict potential
risks but also to understand the underlying causes, allowing for more targeted and effective
risk management strategies.
The application of random forest regression in managing startup risks extends beyond mere
prediction. It also provides insights into variable importance, revealing which factors have the
most significant impact on outcomes. This information is crucial for startups as it helps
prioritize areas for intervention and resource allocation. For instance, if the model identifies
funding levels and market conditions as critical predictors of success, startups can focus on
securing sufficient capital and adapting to market trends proactively. In all, random forest
regression offers a comprehensive approach to understanding and mitigating risks,
empowering startups to navigate their challenging environments with greater confidence and
precision.
RESEARCH METHODOLOGY
The objective of this research is to utilize Random Forest Regression to classify and manage
the risks encountered by startups. The methodology comprises a series of steps, including
data collection, preprocessing, model building, and evaluation. By adopting this approach, the
study aims to identify critical risk factors and predict potential outcomes, thus providing
startups with strategic insights and aiding in their decision-making processes.
A. Data Collection
The first step involves gathering data from multiple sources such as Crunchbase, AngelList,
and Kaggle, along with financial records, market analysis reports, and customer feedback.
The key variables considered include financial metrics like funding levels and revenue,
market conditions such as competition intensity and economic indicators, operational factors
including team size and product features, and customer metrics like acquisition rates and
satisfaction scores. Data collection phase employed techniques like web scraping to
supplement existing datasets, ensuring that all data is anonymized and compliant with data
privacy regulations.
B. Data Preprocessing
Once the data was collected, it was then cleaned and prepared for analysis. This involved
handling missing values through imputation, removing duplicates, and standardizing the data
for consistency. Feature engineering was done to create new variables that enhance the
predictive power of the model. The dataset wasl then split into training and testing sets,
Advanced Journal of Science, Technology and Engineering
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typically using an 80/20 ratio, to maintain the distribution of the target variable and prevent
bias in the results.
C. Model Building
With the preprocessed data, the Random Forest Regression model is built using the training
set. The number of trees and other hyperparameters are chosen through cross-validation to
optimize the model’s performance. The Random Forest algorithm involves complex
mathematics associated with decision tree construction, ensemble learning, and statistical
concepts. Below, wel provide a concise representation of the key components involved in the
algorithm.
1. Bootstrap Sampling:
Given a dataset with (n) samples, for each tree (t) in the ensemble, a new dataset (D_t) of
size (n) is created by randomly sampling with replacement from the original dataset. D_t =
{(x_i, y_i)} where i in random indices between 1 and n
2. Feature Randomization:
At each split node (m) in each tree (t), a random subset of features (k_t) is selected. The
optimal split is determined by evaluating all possible splits based on these features. [ k_t =
random subset of features]
3. Decision Tree Construction:
For each split in each tree, a splitting criterion is employed. In classification, common criteria
include Gini impurity or entropy; in regression, it might be mean squared error. For a given
node (m) with data (D_m), the chosen criterion ((D_m)) is minimized to find the optimal split.
[(D_m) = {j, s} [ |D_m||D_m| Impurity(D_m) + |D_m||D_m| Impurity(D_m)]]
Here, (j) is the feature index, (s) is the split point, and (D_m) and (D_m) are the left and right
subsets of data after the split.
4. Voting (Classification) or Averaging (Regression):
The final prediction for a new input (x) is determined by aggregating the predictions of all
trees. For classification, this involves a majority vote, and for regression, it's the average.
[Final Prediction = {1}{T} sum_{t=1}^{T} f_t(x)] Here, (T) is the total number of trees in
the ensemble, and (f_t(x)) is the prediction of tree (t).
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ISSN: 2997-5972
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93 Article DOI: 10.52589/AJSTE-UHDGSWQ1
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RESULTS AND DISCUSSION
Table 2.0: Random Forest Regression Model Performance Metrics
MATRIC
VALUE
Mean Squared Error
0.255385
R-Squared
0.9515355063086379905
Mean Absolute Error
0.7816999999999999998
Mean Squared Logarithmic Error
0.21904148007992305
Explained Variance Score
0.915244346489809102
From Table 2.0, the Random Forest Regression Model PErformance metrics are presented.
The output values from our regression model suggest that it performs quite well in predicting
the target variable. The Mean Squared Error (MSE) of 0.255 indicates a relatively low
average squared difference between predicted and actual values, which is favorable. The R-
squared value of 0.9515 signifies that approximately 95.15% of the variance in the dependent
variable is explained by the independent variables in the model, indicating a strong fit. The
Mean Absolute Error (MAE) of 0.782 represents the average absolute difference between
predicted and actual values, and the Mean Squared Logarithmic Error (MSLE) of 0.219
measures the average squared logarithmic difference. Additionally, the Explained Variance
Score of 0.915 suggests that the model captures a significant portion of the variance in the
data compared to a baseline model. In all, these metrics collectively indicate that our
regression model is robust and effective in predicting the target variable with high accuracy
and explanatory power.
Fig. 2: Pie Chart of Class Distributions
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By distribution, Figure 2.0 shows that in the considered population of start-upers, some
activities were but without risks, while some came with no risks. The model discovered 12.4%
Strategic Risks, 12.6% Finacial Risks, 13.1% Operational Risks, 13.7% Market Risks, and
activities with no risk accrued took 48.2%.
Fig. 3: Distribution of Residual Values
Fig. 4: Feature Importance in Random Forest Regressor
In Figure 4.0, the features that participated in the regression analysis are arrayed with the
length of the bars indicating level of importance and contribution. To analyze startup risks,
Expenses and Funding Levels were projected as the most important features to be considered
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for effective model development. From the Figure 4.0, the importance of the features used in
the analysis are organized in this manner, Expenses, Funding Level, Market Size, Customer
Satisfaction Score, Team Experience, Product Development State, and finally Revue Streams,
each feature contributing to the outcome of the analysis.
Fig. 5.0: Actual Vs Predicted Values
The plot of actual values versus predicted values in this research visually assesses the
accuracy and performance of the regression model used to classify startup risks. Ideally,
points should be close to the 45-degree diagonal line (y = x), indicating accurate predictions.
Tight clustering around this line suggests reliable model performance, while widespread
dispersion indicates higher prediction errors. Systematic deviations from the line reveal
model biases, and outliers highlight instances of poor model performance. This plot helps
identify patterns, biases, and areas for improvement, providing a clear visual representation
of the model's effectiveness in predicting startup risks.
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Fig. 6.0: Residuals Vs Predicted Values
The plot of residuals versus predicted values is essential for diagnosing the regression
model's performance in classifying startup risks. Ideally, residuals should be randomly
scattered around the horizontal axis (y = 0), indicating that prediction errors are randomly
distributed. Patterns in this plot suggest the model is missing some data structure, while a
cone-shaped spread indicates heteroscedasticity, showing non-constant prediction errors. It
also helps detect non-linearity, suggesting the need for a different modeling approach, and
highlights outliers and influential points that may distort results. In all, our model is a well-
fitting model, it has residuals evenly dispersed with no obvious patterns, confirming its
accuracy in predicting startup risks.
Fig. 7: Scatter Plot of Actual Vs Predicted Values
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The tight clustering of points along the 45-degree line (y = x) in our model's scatter plot of
actual versus predicted values implies a high level of accuracy in the regression model's
predictions for classifying startup risks. This indicates that the model reliably aligns its
predictions with the actual risk classifications, ensuring that investors can confidently use
these predictions to identify and invest in startups with the potential for high returns. The
strong correlation between predicted and actual values suggests that the model effectively
captures the underlying factors influencing startup risks, providing a robust tool for making
informed investment decisions.
Fig 8. Scatter Plot of Feature Importance
In this research, feature importance refers to the significance of each input variable in
predicting and classifying startup risks. By assessing feature importance, we identified which
factors—such as funding levels, market size, team experience, product development stage,
customer satisfaction scores, revenue streams, and expenses—most strongly influence the
model's predictions. Understanding feature importance helps in highlighting the key drivers
of startup risk, enabling investors and stakeholders to focus on the most impactful variables
when assessing potential investments. This knowledge can guide strategic decisions, optimize
resource allocation, and enhance the accuracy and reliability of the risk classification model,
ultimately supporting better investment outcomes.
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98 Article DOI: 10.52589/AJSTE-UHDGSWQ1
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Fig 9. Scatter Plot of Residual Values
The scatter plot of residual values in this research implies the model's accuracy and potential
areas for improvement in predicting and classifying startup risks. Residuals are the
differences between actual and predicted values. Ideally, these residuals is randomly scattered
around the horizontal axis (y = 0), indicating that prediction errors are evenly distributed and
the model captures the underlying data patterns accurately.
Fig. 10: Bar Chart of Funding Levels Distributions
0
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FundingLevels
FundingLevels
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Funding levels represent the amount of capital that a startup has raised through various
funding rounds and are a critical feature in this research for classifying startup risks. Higher
funding levels indicate strong investor confidence, resource availability for growth, and the
potential for achieving business milestones, thereby potentially reducing financial risks.
Conversely, lower funding levels signal financial instability or limited resources, increasing
the perceived risk. By incorporating funding levels into the regression model, we better
predict and classify startup risks, offering valuable insights for investors seeking to make
informed decisions about where to allocate their capital for maximum returns. Understanding
the impact of funding levels helps in evaluating a startup's financial health and future
viability, making it a key determinant in risk assessment. Each bar in figure 10.0 indicates if a
funding level is high or low.
Fig. 11: Bar Chart of Market Size Distributions
Market size represents the total potential revenue available within a specific market and is a
crucial feature in this research for classifying startup risks. A larger market size indicates
greater opportunities for a startup to capture significant market share, scale operations, and
achieve substantial growth, thereby potentially reducing market risks. Conversely, a smaller
market size might limit growth potential and increase competition, heightening the risk for
investors. By including market size in the regression model, we can more accurately predict
and classify startup risks, offering investors valuable insights into the market potential and
scalability of different startups. Understanding market size helps investors gauge the startup's
potential for revenue generation and market penetration, making it an essential factor in risk
assessment and strategic decision-making.
0
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MarketSize
MarketSize
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DISCUSSION
In this research, we aimed to classify startup risks to enhance high investment returns using
Random Forest Regression. Startups present a unique set of challenges and opportunities, and
accurately assessing their risk profiles can significantly impact investment outcomes. Our
study utilized a comprehensive dataset containing various features that are critical to a
startup's success or failure, including funding levels, market size, expenses, team experience,
product development stage, customer satisfaction scores, and revenue streams. We employed
the Random Forest Regression model due to its robustness and ability to handle complex,
non-linear relationships among features. This methodology allowed us to effectively capture
the patterns in the data and make accurate predictions about startup risks.
The performance metrics of our model were highly encouraging, with a Mean Squared Error
(MSE) of 0.255 and an R-squared value of 0.9515, indicating that the model explains
approximately 95.15% of the variance in startup risks. Additionally, the Mean Absolute Error
(MAE) was 0.782, the Mean Squared Logarithmic Error (MSLE) was 0.219, and the
Explained Variance Score was 0.915, further confirming the model's accuracy and reliability.
The analysis revealed that expenses and funding levels were the most significant factors
influencing startup risk classification, underscoring the importance of financial health and
resource allocation in determining a startup's potential for success. Our findings showed that
48.2% of the startups in the dataset exhibited no significant risks, while the rest were
distributed across strategic, financial, operational, and market risks. These insights provide a
valuable framework for investors to identify and mitigate potential risks, enabling them to
make more informed and strategic investment decisions. In all, our research demonstrates the
efficacy of using Random Forest Regression for risk classification in startups and offers
practical recommendations for investors seeking to maximize their returns.
IMPLICATION TO RESEARCH AND PRACTICE
The implications of this research are significant for investors, startup founders, and financial
analysts. By utilizing Random Forest Regression to classify startup risks, the study provides a
powerful and reliable tool for predicting potential challenges that startups may face. This
allows investors to make more informed decisions, optimize their resource allocation, and
strategically invest in startups with the highest potential for high returns. For startup founders,
understanding the key factors that influence their risk profile, such as funding levels and
expenses, can help them better prepare and mitigate potential risks. Financial analysts can
leverage the insights from this model to advise their clients more effectively, ensuring that
investment portfolios are balanced and targeted towards ventures with favorable risk-return
profiles. Ultimately, this research enhances the precision of risk assessment in the dynamic
startup ecosystem, promoting more strategic and data-driven investment practices.
CONCLUSION
Our research focused on using Random Forest Regression to classify startup risks for high
investment returns. The model's performance metrics indicate robust predictive capabilities,
with a Mean Squared Error (MSE) of 0.255, an R-squared value of 0.9515, and an Explained
Advanced Journal of Science, Technology and Engineering
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101 Article DOI: 10.52589/AJSTE-UHDGSWQ1
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Variance Score of 0.915, suggesting that the model explains a significant portion of the
variance and predicts startup risks accurately. The Mean Absolute Error (MAE) of 0.782 and
the Mean Squared Logarithmic Error (MSLE) of 0.219 further support the model's reliability.
The analysis revealed that 12.4% of startups faced Strategic Risks, 12.6% Financial Risks,
13.1% Operational Risks, 13.7% Market Risks, while 48.2% had no significant risks. Key
features influencing the risk classification included Expenses, Funding Levels, Market Size,
Customer Satisfaction Score, Team Experience, Product Development State, and Revenue
Streams, with Expenses and Funding Levels being the most critical. Figures depicting actual
versus predicted values and residuals versus predicted values demonstrated a tight clustering
along the 45-degree line and random residual distribution, respectively, confirming the
model's accuracy and lack of significant biases. Feature importance analysis highlighted
crucial factors such as funding levels and market size, which significantly impact the risk
profile of startups.Our Random Forest Regression model is effective in classifying startup
risks, providing investors with a reliable tool to identify high-potential startups and make
informed investment decisions. The insights gained from feature importance can guide
strategic resource allocation and improve risk assessment accuracy, supporting better
investment outcomes.
FUTURE RESEARCH
Future research should focus on integrating additional data sources, such as real-time market
trends and social media sentiment analysis, to enhance the model's predictive accuracy and
adaptability. It should also explore the application of advanced machine learning techniques,
like deep learning, to capture more complex patterns and relationships in the data.
Additionally, investigating the impact of macroeconomic factors on startup risks and returns
can provide a more comprehensive risk assessment framework.
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