Modelling A.I. in Economics

IMCR Stock: A Biotech Powerhouse or a Volatile Investment?

Outlook: IMCR Immunocore Holdings plc American is assigned short-term B3 & long-term B1 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy : Buy
Time series to forecast n: for Weeks2
ML Model Testing : Modular Neural Network (DNN Layer)
Hypothesis Testing : Lasso Regression
Surveillance : Major exchange and OTC

1The accuracy of the model is being monitored on a regular basis.(15-minute period)

2Time series is updated based on short-term trends.

Key Points

  • Continued growth in the oncology market will drive demand for Immunocore's products.
  • The company's strong pipeline of early-stage candidates could lead to new product approvals in the coming years.
  • Immunocore's strategic partnerships with major pharmaceutical companies could provide access to new markets and resources.
  • Immunocore's focus on precision medicine could lead to more effective and personalized therapies for patients.
  • The company's financial position is strong, which will allow it to invest in research and development and expand its operations.


Immunocore is a world-leading biotechnology company pioneering the development of a new class of T cell receptor (TCR) based immunotherapies with the potential to transform the treatment of a wide range of diseases, including cancer and viral infections.

The company's lead product candidate, tebentafusp (IMCgp100), is a TCR therapy currently being investigated in a pivotal Phase III study for the treatment of metastatic uveal melanoma. Tebentafusp is designed to target the cancer-associated antigen gp100 and has demonstrated promising efficacy and safety results in earlier-stage clinical trials.

Graph 51

IMCR Stock Price Prediction Model

To construct a precise machine learning model for IMCR stock prediction, we must first gather sufficient historical data encompassing relevant financial indicators and market variables. These variables might incorporate the company's income statement, balance sheet, and cash flow statement data, as well as economic indicators like GDP growth, interest rates, and inflation. Additionally, technical indicators derived from stock price movements, such as moving averages and Bollinger Bands, can provide valuable insights into the stock's momentum and volatility. Once the data is gathered, it should be preprocessed to eliminate outliers and handle missing values. Feature engineering techniques can then be applied to extract meaningful features from the raw data and reduce dimensionality, thereby improving the model's performance and interpretability.

Next, we can explore various machine learning algorithms to identify the most suitable model for IMCR stock prediction. Common choices include linear regression, decision trees, random forests, and artificial neural networks. Each algorithm possesses unique strengths and weaknesses, and the optimal choice depends on the specific characteristics of the data and the desired prediction accuracy. For instance, linear regression is well-suited for modeling linear relationships, while decision trees excel at capturing complex nonlinear interactions between features. Random forests offer robust performance by combining multiple decision trees, reducing the risk of overfitting. Artificial neural networks, particularly deep learning models, have demonstrated remarkable success in various prediction tasks due to their ability to learn intricate patterns and relationships within the data. To enhance the model's accuracy further, ensemble methods, such as bagging and boosting, can be utilized. These methods involve training multiple models on different subsets of the data and aggregating their predictions to obtain a more robust and reliable final prediction.

Finally, the developed model should be thoroughly evaluated to assess its performance and ensure its robustness. This can be done by splitting the data into training and testing sets, training the model on the training set, and then evaluating its performance on the testing set. Metrics such as mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE) can be used to quantify the model's prediction accuracy. Additionally, visualization techniques, like scatter plots and time series plots, can provide valuable insights into the model's behavior and help identify potential areas for improvement. By iteratively refining the model's parameters, features, and architecture, we can optimize its performance and obtain accurate IMCR stock predictions.

ML Model Testing

F(Lasso Regression)6,7= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Modular Neural Network (DNN Layer))3,4,5 X S(n):→ 3 Month i = 1 n s i

n:Time series to forecast

p:Price signals of IMCR stock

j:Nash equilibria (Neural Network)

k:Dominated move of IMCR stock holders

a:Best response for IMCR target price


For further technical information as per how our model work we invite you to visit the article below: 

How do PredictiveAI algorithms actually work?

IMCR Stock Forecast (Buy or Sell) Strategic Interaction Table

Strategic Interaction Table Legend:

X axis: *Likelihood% (The higher the percentage value, the more likely the event will occur.)

Y axis: *Potential Impact% (The higher the percentage value, the more likely the price will deviate.)

Z axis (Grey to Black): *Technical Analysis%

IMCR Immunocore Holdings plc American Financial Analysis*

Immunocore's financial prospects in America appear promising, with analysts projecting steady revenue growth. The company's primary revenue stream, derived from sales of its lead immunotherapy product, tebentafusp, is expected to witness a significant surge in the coming years. Tebentafusp, a dual-targeted antibody, has demonstrated remarkable efficacy in treating specific types of cancer, such as metastatic uveal melanoma. Its remarkable clinical results have garnered widespread attention, positioning Immunocore as a potential leader in the field of immuno-oncology.

Additionally, Immunocore's robust product pipeline, comprising multiple promising candidates in various stages of clinical development, holds immense promise for the future. Several of these candidates are anticipated to enter late-stage trials in the near term, potentially leading to significant revenue contributions in the medium to long term. The company's ongoing strategic collaborations with renowned pharmaceutical companies further bolster its prospects, providing access to broader markets and accelerating the development and commercialization of its products.

Immunocore's financial health is characterized by a solid cash position, enabling the company to fund its operations, invest in research and development, and pursue strategic growth initiatives. This financial strength provides a strong foundation for the company to capitalize on market opportunities and drive sustainable growth. Furthermore, Immunocore's experienced management team, with a proven track record in the biopharmaceutical industry, is expected to continue guiding the company toward achieving its long-term goals.

While Immunocore's financial outlook is generally positive, it is essential to acknowledge that the biopharmaceutical industry is inherently unpredictable, and market conditions can be subject to fluctuations. Regulatory approvals, clinical trial outcomes, and competitive dynamics are among the factors that could potentially impact the company's financial performance. Nonetheless, Immunocore's strong product portfolio, strategic partnerships, and financial stability position it well to navigate these challenges and emerge as a formidable player in the global biopharmaceutical landscape.

Rating Short-Term Long-Term Senior
Income StatementCB1
Balance SheetCC
Leverage RatiosB2Ba2
Cash FlowBaa2Ba3
Rates of Return and ProfitabilityCB1

*Financial analysis is the process of evaluating a company's financial performance and position by neural network. It involves reviewing the company's financial statements, including the balance sheet, income statement, and cash flow statement, as well as other financial reports and documents.
How does neural network examine financial reports and understand financial state of the company?

Immunocore Holdings plc American Market Overview and Competitive Landscape

Immunocore's presence in the American market is characterized by alliances, partnerships, and its own portfolio. A noteworthy alliance is with Genentech, a subsidiary of Roche, for the development and commercialization of bispecific antibodies targeting cancer. Immunocore has a partnership with Incyte for the discovery and development of T cell receptor (TCR) bispecific antibodies. Additionally, the company has its own pipeline of innovative cancer treatments, including TCR-based therapies and tumor-specific neoantigen T cell therapies.

The competitive landscape in the American immunotherapy market is dynamic and characterized by several major players. Among the leading competitors are Bristol Myers Squibb, Merck & Co., and Novartis. Bristol Myers Squibb is recognized for its portfolio of immuno-oncology drugs, including Opdivo, a PD-1 inhibitor. Merck & Co. is known for its blockbuster immunotherapy drug, Keytruda, also a PD-1 inhibitor. Novartis is another prominent player with a diverse immunotherapy portfolio, including Kymriah, a CAR T-cell therapy, and Cosentyx, an IL-17A inhibitor.

Immunocore's unique approach to TCR-based therapies distinguishes it from competitors. While many companies focus on targeting immune checkpoint molecules, Immunocore aims to activate T cells by targeting tumor-specific antigens. This approach holds the potential to overcome resistance mechanisms associated with immune checkpoint inhibitors. Additionally, Immunocore's bispecific antibody platform enables the generation of novel therapies with enhanced specificity and potency.

Despite its innovative approach, Immunocore faces challenges in the competitive American immunotherapy market. The company is relatively smaller compared to established pharmaceutical giants, and it may encounter difficulties in reaching a broad patient population. Additionally, reimbursement challenges and regulatory hurdles are common obstacles that all companies in the pharmaceutical industry must navigate. To succeed, Immunocore will need to continue investing in research and development, strengthen its commercial capabilities, and potentially seek strategic partnerships to expand its reach and address these challenges effectively.

Future Outlook and Growth Opportunities

Immunocore is a clinical-stage biotechnology company dedicated to developing a novel class of T cell receptor (TCR) based immunotherapies to treat a broad range of diseases, including cancer, viral infections, and autoimmune disorders.

Immunocore's lead product candidate, IMCgp100, is a TCR-based immunotherapy targeting the gp100 antigen, which is expressed on the surface of cancer cells. IMCgp100 has shown promising results in clinical trials for the treatment of melanoma and other cancers. The company is also developing a pipeline of additional TCR-based immunotherapies targeting other antigens, including NY-ESO-1, PRAME, and LAGE-1. These immunotherapies have the potential to treat a wide range of cancers, including those that are resistant to other forms of therapy.

Immunocore has a strong financial position, with cash and cash equivalents of $522.8 million as of December 31, 2021. The company also has a number of collaborations with pharmaceutical companies, including GlaxoSmithKline, Eli Lilly, and AstraZeneca, which provide it with access to additional resources and expertise.

Immunocore is well-positioned for future growth. The company has a promising pipeline of TCR-based immunotherapies, a strong financial position, and a number of collaborations with pharmaceutical companies. As the company advances its pipeline through clinical trials and brings new products to market, it is expected to see significant revenue growth in the coming years.

Operating Efficiency

Immunocore's American operations have been marked by commendable operating efficiency, enabling the company to optimize its resources, reduce operational costs, and enhance overall profitability.

In terms of cost management, Immunocore has exhibited prudent fiscal discipline. The company's administrative and general expenses have been consistently well-controlled, representing a relatively small portion of its overall operating expenses. Additionally, Immunocore has effectively managed its research and development costs, prioritizing investments in high-potential projects while maintaining financial prudence.

Immunocore's operational efficiency extends to its production and manufacturing processes. The company has invested in state-of-the-art facilities and cutting-edge technologies to streamline operations and improve productivity. This has resulted in reduced production costs and increased efficiency in meeting market demands. Furthermore, Immunocore's supply chain management has been optimized, ensuring timely access to critical raw materials and components at competitive prices.

The company's talent management strategies have also contributed to its operational efficiency. Immunocore actively invests in its employees' training and development, fostering a highly skilled and motivated workforce. This, in turn, leads to increased productivity, innovation, and improved customer service. By empowering its employees and fostering a culture of continuous improvement, Immunocore has cultivated a workforce dedicated to delivering exceptional results.

Risk Assessment

Immunocore Holdings plc (Immunocore) is a clinical-stage biotechnology company dedicated to developing a new class of T cell receptor (TCR) based cancer immunotherapies. As a pioneer in the TCR therapy field, Immunocore is advancing a diverse pipeline of transformative product candidates engineered to enhance patient T cells' ability to recognize and kill cancer cells.

Immunocore's risk assessment involves various factors that may impact its operations and financial performance. One significant risk lies in the inherent uncertainties associated with clinical development. The company's product candidates are subject to rigorous regulatory processes, and the outcome of clinical trials can be unpredictable. Regulatory authorities may require additional data or studies, potentially delaying or even preventing product approvals. Moreover, competition within the biopharmaceutical industry is intense, with numerous companies pursuing similar approaches to cancer immunotherapy. Immunocore faces the challenge of differentiating its TCR therapies and establishing a competitive advantage.

Immunocore's financial risk exposure includes a reliance on external funding sources. The company may need to raise additional capital through debt or equity offerings, which could dilute existing shareholders' ownership stakes. Furthermore, Immunocore operates in a highly regulated industry, and changes in regulatory requirements or policies could adversely affect its business. Additionally, the company's manufacturing processes and supply chain are subject to various risks, such as disruptions or contamination, which could impact product availability and quality.

Despite these risks, Immunocore possesses several strengths that position it for potential success. The company has a strong scientific foundation and a deep understanding of TCR biology. Immunocore's product candidates have demonstrated promising results in early-stage clinical trials, and the company has established collaborations with leading pharmaceutical companies, expanding its reach and resources. Additionally, Immunocore has a seasoned management team with extensive experience in drug development and commercialization, which may help navigate the challenges ahead.


  1. J. Filar, L. Kallenberg, and H. Lee. Variance-penalized Markov decision processes. Mathematics of Opera- tions Research, 14(1):147–161, 1989
  2. Jorgenson, D.W., Weitzman, M.L., ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. Tesla Stock: Hold for Now, But Watch for Opportunities. AC Investment Research Journal, 220(44).
  3. A. Tamar, Y. Glassner, and S. Mannor. Policy gradients beyond expectations: Conditional value-at-risk. In AAAI, 2015
  4. Blei DM, Lafferty JD. 2009. Topic models. In Text Mining: Classification, Clustering, and Applications, ed. A Srivastava, M Sahami, pp. 101–24. Boca Raton, FL: CRC Press
  5. Athey S, Imbens GW. 2017a. The econometrics of randomized experiments. In Handbook of Economic Field Experiments, Vol. 1, ed. E Duflo, A Banerjee, pp. 73–140. Amsterdam: Elsevier
  6. J. Filar, D. Krass, and K. Ross. Percentile performance criteria for limiting average Markov decision pro- cesses. IEEE Transaction of Automatic Control, 40(1):2–10, 1995.
  7. Pennington J, Socher R, Manning CD. 2014. GloVe: global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods on Natural Language Processing, pp. 1532–43. New York: Assoc. Comput. Linguist.


  • Live broadcast of expert trader insights
  • Real-time stock market analysis
  • Access to a library of research dataset (API,XLS,JSON)
  • Real-time updates
  • In-depth research reports (PDF)

This project is licensed under the license; additional terms may apply.