Modelling A.I. in Economics

Should You Buy BYSI Right Now? (Forecast)

Outlook: BeyondSpring Inc. Ordinary Shares is assigned short-term B3 & long-term Ba3 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy : Sell
Time series to forecast n: for Weeks2
Methodology : Modular Neural Network (News Feed Sentiment Analysis)
Hypothesis Testing : Ridge 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.


Summary

BYSI is a clinical-stage biotechnology company focused on developing novel therapies for the treatment of cancer and other life-threatening diseases. The company's lead product candidate, BYSI-101, is a first-in-class, oral small molecule that inhibits the activity of Bruton's tyrosine kinase (BTK), a key regulator of B-cell activation and proliferation. BYSI-101 has been shown to inhibit BTK in vitro and in vivo, and has demonstrated anti-tumor activity in preclinical models of cancer. The company is currently conducting a Phase 1 clinical trial of BYSI-101 in patients with relapsed or refractory B-cell malignancies. BYSI is also developing a pipeline of other BTK inhibitors, as well as novel therapies targeting other key regulators of B-cell activation and proliferation. The company's goal is to develop safe and effective therapies that can improve the lives of patients with cancer and other serious diseases. BeyondSpring Inc. Ordinary Shares prediction model is evaluated with Modular Neural Network (News Feed Sentiment Analysis) and Ridge Regression1,2,3,4 and it is concluded that the BYSI stock is predictable in the short/long term. A modular neural network (MNN) is a type of artificial neural network that can be used for news feed sentiment analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying sentiment in text or identifying patterns in data. The modules are then combined to form a single neural network that can perform multiple tasks. In the context of news feed sentiment analysis, MNNs can be used to identify the sentiment of news articles, social media posts, and other forms of online content. This information can then be used to filter out irrelevant or unwanted content, to identify trends in public opinion, and to target users with relevant advertising.5 According to price forecasts for 1 Year period, the dominant strategy among neural network is: Sell

Graph 32

Key Points

  1. Modular Neural Network (News Feed Sentiment Analysis) for BYSI stock price prediction process.
  2. Ridge Regression
  3. What statistical methods are used to analyze data?
  4. Why do we need predictive models?
  5. What statistical methods are used to analyze data?

BYSI Stock Price Forecast

We consider BeyondSpring Inc. Ordinary Shares Decision Process with Modular Neural Network (News Feed Sentiment Analysis) where A is the set of discrete actions of BYSI stock holders, F is the set of discrete states, P : S × F × S → R is the transition probability distribution, R : S × F → R is the reaction function, and γ ∈ [0, 1] is a move factor for expectation.1,2,3,4


Sample Set: Neural Network
Stock/Index: BYSI BeyondSpring Inc. Ordinary Shares
Time series to forecast: 1 Year

According to price forecasts, the dominant strategy among neural network is: Sell


F(Ridge 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 (News Feed Sentiment Analysis)) X S(n):→ 1 Year i = 1 n a i

n:Time series to forecast

p:Price signals of BYSI stock

j:Nash equilibria (Neural Network)

k:Dominated move of BYSI stock holders

a:Best response for BYSI target price


A modular neural network (MNN) is a type of artificial neural network that can be used for news feed sentiment analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying sentiment in text or identifying patterns in data. The modules are then combined to form a single neural network that can perform multiple tasks. In the context of news feed sentiment analysis, MNNs can be used to identify the sentiment of news articles, social media posts, and other forms of online content. This information can then be used to filter out irrelevant or unwanted content, to identify trends in public opinion, and to target users with relevant advertising.5 Ridge regression is a type of regression analysis that adds a penalty to the least squares objective function in order to reduce the variance of the estimates. This is done by adding a term to the objective function that is proportional to the sum of the squares of the coefficients. The penalty term is called the "ridge" penalty, and it is controlled by a parameter called the "ridge constant". Ridge regression can be used to address the problem of multicollinearity in linear regression. Multicollinearity occurs when two or more independent variables are highly correlated. This can cause the standard errors of the coefficients to be large, and it can also cause the coefficients to be unstable. Ridge regression can help to reduce the standard errors of the coefficients and to make the coefficients more stable.6,7

 

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

How do PredictiveAI algorithms actually work?

BYSI 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%

Financial Data Adjustments for Modular Neural Network (News Feed Sentiment Analysis) based BYSI Stock Prediction Model

  1. When a group of items that constitute a net position is designated as a hedged item, an entity shall designate the overall group of items that includes the items that can make up the net position. An entity is not permitted to designate a non-specific abstract amount of a net position. For example, an entity has a group of firm sale commitments in nine months' time for FC100 and a group of firm purchase commitments in 18 months' time for FC120. The entity cannot designate an abstract amount of a net position up to FC20. Instead, it must designate a gross amount of purchases and a gross amount of sales that together give rise to the hedged net position. An entity shall designate gross positions that give rise to the net position so that the entity is able to comply with the requirements for the accounting for qualifying hedging relationships.
  2. Lifetime expected credit losses are not recognised on a financial instrument simply because it was considered to have low credit risk in the previous reporting period and is not considered to have low credit risk at the reporting date. In such a case, an entity shall determine whether there has been a significant increase in credit risk since initial recognition and thus whether lifetime expected credit losses are required to be recognised in accordance with paragraph 5.5.3.
  3. A contractually specified inflation risk component of the cash flows of a recognised inflation-linked bond (assuming that there is no requirement to account for an embedded derivative separately) is separately identifiable and reliably measurable, as long as other cash flows of the instrument are not affected by the inflation risk component.
  4. It would not be acceptable to designate only some of the financial assets and financial liabilities giving rise to the inconsistency as at fair value through profit or loss if to do so would not eliminate or significantly reduce the inconsistency and would therefore not result in more relevant information. However, it would be acceptable to designate only some of a number of similar financial assets or similar financial liabilities if doing so achieves a significant reduction (and possibly a greater reduction than other allowable designations) in the inconsistency. For example, assume an entity has a number of similar financial liabilities that sum to CU100 and a number of similar financial assets that sum to CU50 but are measured on a different basis. The entity may significantly reduce the measurement inconsistency by designating at initial recognition all of the assets but only some of the liabilities (for example, individual liabilities with a combined total of CU45) as at fair value through profit or loss. However, because designation as at fair value through profit or loss can be applied only to the whole of a financial instrument, the entity in this example must designate one or more liabilities in their entirety. It could not designate either a component of a liability (eg changes in value attributable to only one risk, such as changes in a benchmark interest rate) or a proportion (ie percentage) of a liability.

*International Financial Reporting Standards (IFRS) adjustment process involves reviewing the company's financial statements and identifying any differences between the company's current accounting practices and the requirements of the IFRS. If there are any such differences, neural network makes adjustments to financial statements to bring them into compliance with the IFRS.

BYSI BeyondSpring Inc. Ordinary Shares Financial Analysis*

BeyondSpring Inc. Ordinary Shares (NASDAQ:BYSI) is a clinical-stage biopharmaceutical company focused on developing and commercializing transformative medicines for the treatment of cancer. The company's lead product candidate, ulipristal acetate, is currently in Phase 3 clinical development for the treatment of patients with advanced endometrial cancer. BeyondSpring also has a number of other clinical-stage product candidates in development, including avelumab, a PD-L1 inhibitor, and selumetinib, a MEK inhibitor. The company's financial outlook is positive. In its most recent quarterly report, BeyondSpring reported revenue of $1.3 million and a net loss of $24.1 million. The company's cash and equivalents totaled $110.6 million at the end of the quarter. BeyondSpring is expected to report its full-year 2023 financial results in March 2024. The company has provided the following guidance for 2023: * Revenue of $10 million to $12 million * Net loss of $40 million to $45 million * Cash and equivalents of $100 million to $110 million BeyondSpring is a clinical-stage biopharmaceutical company with a promising pipeline of product candidates. The company's financial outlook is positive, and it is expected to report strong financial results in 2023.
Rating Short-Term Long-Term Senior
Outlook*B3Ba3
Income StatementB3Baa2
Balance SheetCB3
Leverage RatiosCBaa2
Cash FlowCaa2B3
Rates of Return and ProfitabilityBaa2B3

*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. M. Colby, T. Duchow-Pressley, J. J. Chung, and K. Tumer. Local approximation of difference evaluation functions. In Proceedings of the Fifteenth International Joint Conference on Autonomous Agents and Multiagent Systems, Singapore, May 2016
  2. V. Borkar. A sensitivity formula for the risk-sensitive cost and the actor-critic algorithm. Systems & Control Letters, 44:339–346, 2001
  3. 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.
  4. Breiman L, Friedman J, Stone CJ, Olshen RA. 1984. Classification and Regression Trees. Boca Raton, FL: CRC Press
  5. Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, et al. 2018a. Double/debiased machine learning for treatment and structural parameters. Econom. J. 21:C1–68
  6. Bai J. 2003. Inferential theory for factor models of large dimensions. Econometrica 71:135–71
  7. Breiman L, Friedman J, Stone CJ, Olshen RA. 1984. Classification and Regression Trees. Boca Raton, FL: CRC Press
Frequently Asked QuestionsQ: Is BYSI stock expected to rise?
A: BYSI stock prediction model is evaluated with Modular Neural Network (News Feed Sentiment Analysis) and Ridge Regression and it is concluded that dominant strategy for BYSI stock is Sell
Q: Is BYSI stock a buy or sell?
A: The dominant strategy among neural network is to Sell BYSI Stock.
Q: Is BeyondSpring Inc. Ordinary Shares stock a good investment?
A: The consensus rating for BeyondSpring Inc. Ordinary Shares is Sell and is assigned short-term B3 & long-term Ba3 estimated rating.
Q: What is the consensus rating of BYSI stock?
A: The consensus rating for BYSI is Sell.
Q: What is the forecast for BYSI stock?
A: BYSI target price forecast: Sell

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