JSPR Stock Forecast

Outlook: JSPR is assigned short-term B1 & long-term B1 estimated rating.
AUC Score : What is AUC Score?
Short-term Tactic1 :
Dominant Strategy :
Time series to forecast n: for Weeks2
ML Model Testing : Supervised Machine Learning (ML)
Hypothesis Testing : Wilcoxon Sign-Rank Test
Surveillance : Major exchange and OTC

1Short-term revised.

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


Key Points

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About JSPR

Jasper Therapeutics Inc. is a clinical-stage biopharmaceutical company focused on developing novel cell therapies for patients with serious autoimmune diseases and cancer. The company's lead product candidate, Jasper-100, is an investigational gene-edited cell therapy designed to selectively eliminate pathogenic immune cells responsible for driving autoimmune disorders. Jasper Therapeutics is pursuing a targeted approach to address the underlying causes of these debilitating conditions, aiming to provide durable and potentially curative treatment options.


The company's research and development efforts are underpinned by a proprietary cell engineering platform that allows for precise modification of immune cells to achieve therapeutic effects. Jasper Therapeutics is advancing its pipeline through rigorous clinical trials, with a strategic focus on demonstrating the safety and efficacy of its innovative therapies. The company's commitment to scientific advancement and patient well-being positions it as a significant player in the evolving landscape of cell-based medicine.

JSPR
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ML Model Testing

F(Wilcoxon Sign-Rank Test)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(Supervised Machine Learning (ML))3,4,5 X S(n):→ 16 Weeks R = 1 0 0 0 1 0 0 0 1

n:Time series to forecast

p:Price signals of JSPR stock

j:Nash equilibria (Neural Network)

k:Dominated move of JSPR stock holders

a:Best response for JSPR target price

 

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

How do KappaSignal algorithms actually work?

JSPR 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%

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Rating Short-Term Long-Term Senior
OutlookB1B1
Income StatementB2Caa2
Balance SheetB2Caa2
Leverage RatiosB1Ba3
Cash FlowB1B1
Rates of Return and ProfitabilityB2B1

*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?

References

  1. D. Bertsekas. Min common/max crossing duality: A geometric view of conjugacy in convex optimization. Lab. for Information and Decision Systems, MIT, Tech. Rep. Report LIDS-P-2796, 2009
  2. S. Bhatnagar, H. Prasad, and L. Prashanth. Stochastic recursive algorithms for optimization, volume 434. Springer, 2013
  3. M. Benaim, J. Hofbauer, and S. Sorin. Stochastic approximations and differential inclusions, Part II: Appli- cations. Mathematics of Operations Research, 31(4):673–695, 2006
  4. Babula, R. A. (1988), "Contemporaneous correlation and modeling Canada's imports of U.S. crops," Journal of Agricultural Economics Research, 41, 33–38.
  5. S. Bhatnagar and K. Lakshmanan. An online actor-critic algorithm with function approximation for con- strained Markov decision processes. Journal of Optimization Theory and Applications, 153(3):688–708, 2012.
  6. L. Busoniu, R. Babuska, and B. D. Schutter. A comprehensive survey of multiagent reinforcement learning. IEEE Transactions of Systems, Man, and Cybernetics Part C: Applications and Reviews, 38(2), 2008.
  7. Jacobs B, Donkers B, Fok D. 2014. Product Recommendations Based on Latent Purchase Motivations. Rotterdam, Neth.: ERIM

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