Harvard Case - New Constructs: Disrupting Fundamental Analysis with Robo-Analysts
"New Constructs: Disrupting Fundamental Analysis with Robo-Analysts" Harvard business case study is written by Charles C.Y. Wang, Kyle Thomas. It deals with the challenges in the field of Accounting. The case study is 18 page(s) long and it was first published on : Feb 8, 2018
At Fern Fort University, we recommend New Constructs adopt a phased approach to market expansion, focusing on attracting institutional investors and financial advisors as early adopters. This strategy involves leveraging the strengths of their robo-analyst technology while addressing potential challenges related to market acceptance, data integrity, and regulatory compliance.
2. Background
New Constructs is a financial research firm that utilizes 'robo-analysts,' AI-powered algorithms, to generate financial reports and analysis. These reports are designed to be more accurate and unbiased than traditional analyst reports, which can be influenced by conflicts of interest and biases. The company aims to disrupt the traditional financial research industry by providing a more transparent and reliable source of information.
The case study focuses on New Constructs' challenges in gaining market traction and overcoming skepticism from the investment community. The company faces competition from established financial research firms and must navigate the complexities of regulatory compliance and data integrity.
3. Analysis of the Case Study
This case study can be analyzed through the lens of several frameworks:
- Porter's Five Forces: New Constructs operates in a competitive industry with high barriers to entry due to the need for significant capital investment in technology and data infrastructure. The threat of substitutes is moderate, as investors can access alternative research sources. However, the bargaining power of buyers (investors) is high, as they have many options for financial research.
- Value Chain Analysis: New Constructs' core competency lies in its proprietary robo-analyst technology, which provides a unique value proposition to investors by offering unbiased and accurate financial analysis. This technology is supported by a robust data infrastructure and a team of experienced financial professionals.
- Growth Strategy: New Constructs is pursuing a disruptive innovation strategy, aiming to replace traditional financial research methods with its AI-powered approach. This strategy requires a careful balance between innovation and market acceptance.
4. Recommendations
- Target Institutional Investors and Financial Advisors: New Constructs should focus its marketing efforts on institutional investors and financial advisors, who are more likely to appreciate the value of unbiased and accurate financial analysis.
- Develop a Phased Market Expansion Strategy: The company should adopt a phased approach to market expansion, starting with a pilot program for a select group of institutional investors. This will allow New Constructs to gather feedback and refine its product offering before expanding to a wider audience.
- Build Trust and Credibility: New Constructs must build trust and credibility with potential clients by demonstrating the accuracy and reliability of its robo-analysts. This can be achieved through rigorous testing, independent audits, and transparent disclosure of methodologies.
- Address Regulatory Compliance: The company should proactively engage with regulators to ensure compliance with relevant rules and regulations. This includes addressing concerns about data privacy, security, and the potential for algorithmic bias.
- Develop a Strong Brand Identity: New Constructs should develop a strong brand identity that communicates its core values of transparency, accuracy, and unbiased analysis. This can be achieved through effective marketing campaigns and public relations efforts.
5. Basis of Recommendations
These recommendations are based on the following considerations:
- Core Competencies and Consistency with Mission: The recommendations align with New Constructs' core competency in AI-powered financial analysis and its mission to provide unbiased and accurate financial research.
- External Customers and Internal Clients: The target audience of institutional investors and financial advisors aligns with the company's value proposition.
- Competitors: The recommendations address the competitive landscape by focusing on differentiation and building trust.
- Attractiveness - Quantitative Measures: The phased approach to market expansion allows for iterative improvement and reduces the risk of significant financial losses.
6. Conclusion
New Constructs has the potential to revolutionize the financial research industry with its innovative robo-analyst technology. However, the company must navigate the challenges of market acceptance, regulatory compliance, and data integrity. By adopting a phased approach to market expansion, building trust and credibility, and engaging with regulators, New Constructs can achieve its goals and become a leading provider of unbiased and accurate financial analysis.
7. Discussion
Alternative strategies include:
- Direct-to-consumer approach: Targeting individual investors through online platforms and social media. This strategy could be less profitable due to lower average revenue per customer.
- Partnership with existing financial research firms: Collaborating with established firms to integrate robo-analysts into their existing products and services. This strategy could accelerate market penetration but may require compromising on control and brand identity.
Key risks and assumptions:
- Risk of regulatory changes: Changes in regulations could impact New Constructs' business model.
- Risk of data breaches: Data security breaches could damage the company's reputation and erode trust.
- Assumption of market acceptance: The success of the strategy depends on investors' willingness to embrace AI-powered financial analysis.
8. Next Steps
- Develop a pilot program for institutional investors: Select a small group of institutional investors to test the robo-analyst platform and gather feedback. (Timeline: 3-6 months)
- Engage with regulators: Proactively communicate with regulatory bodies to address concerns and ensure compliance. (Timeline: Ongoing)
- Refine marketing strategy: Develop targeted marketing campaigns for institutional investors and financial advisors. (Timeline: 6-12 months)
- Expand market reach: Gradually expand the platform's availability to a wider audience based on the success of the pilot program. (Timeline: 12-18 months)
By taking these steps, New Constructs can position itself for success in the rapidly evolving financial research landscape.
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Case Description
This case highlights the business challenges associated with a financial technology firm, New Constructs, that created a technology that can quickly parse complicated public firm financials to paint a clearer economic picture of firms, remove accounting distortions, and aid investment analyses. Combining machine learning with human financial analysts, this technology automated the collection and classification of comprehensive quantitative data from annual (10-Ks) and quarterly reports (10-Qs). The technology parsed information from the face of the financials and from disclosures in the management discussion and analysis or the footnotes sections to the financial statements (e.g., gains and losses that are transitory in nature or off-balance-sheet liabilities like future operating lease payments). Using these data, New Constructs applied a set of transparent and clearly-defined adjustments to a firm's income statement and balance sheet to create alternative measures of its sustainable operating profits, invested capital, and return on invested capital. This case highlights the complexity of financial disclosures in 10-Ks and 10-Qs and how machine learning and human analysts can work together to improve the quality of investment research. This case also highlights how analysts may adjust income statements and balance sheets to understand firm performance better or facilitate forecasting. However, could the CEO of New Constructs convince market participants of the technology's (and the data's) value? What else could the company do to establish a toehold in an increasingly competitive market for financial and alternative databases?
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