Modelling A.I. in Economics

Where Will CDAK Stock Be in 4 Weeks? (Forecast)

Outlook: Codiak BioSciences Inc. Common Stock is assigned short-term B1 & long-term B1 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy : Sell
Time series to forecast n: for Weeks2
Methodology : Statistical Inference (ML)
Hypothesis Testing : Linear 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.


Codiak BioSciences Inc. Common Stock prediction model is evaluated with Statistical Inference (ML) and Linear Regression1,2,3,4 and it is concluded that the CDAK stock is predictable in the short/long term. Statistical inference is a process of drawing conclusions about a population based on data from a sample of that population. In machine learning (ML), statistical inference is used to make predictions about new data based on data that has already been seen. According to price forecasts for 4 Weeks period, the dominant strategy among neural network is: Sell

Graph 28

Key Points

  1. What is prediction model?
  2. Technical Analysis with Algorithmic Trading
  3. What is a prediction confidence?

CDAK Target Price Prediction Modeling Methodology

We consider Codiak BioSciences Inc. Common Stock Decision Process with Statistical Inference (ML) where A is the set of discrete actions of CDAK 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


F(Linear Regression)5,6,7= 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(Statistical Inference (ML)) X S(n):→ 4 Weeks i = 1 n r i

n:Time series to forecast

p:Price signals of CDAK stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Statistical Inference (ML)

Statistical inference is a process of drawing conclusions about a population based on data from a sample of that population. In machine learning (ML), statistical inference is used to make predictions about new data based on data that has already been seen.

Linear Regression

In statistics, linear regression is a method for estimating the relationship between a dependent variable and one or more independent variables. The dependent variable is the variable that is being predicted, and the independent variables are the variables that are used to predict the dependent variable. Linear regression assumes that the relationship between the dependent variable and the independent variables is linear. This means that the dependent variable can be represented as a straight line function of the independent variables.

 

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

How do AC Investment Research machine learning (predictive) algorithms actually work?

CDAK Stock Forecast (Buy or Sell)

Sample Set: Neural Network
Stock/Index: CDAK Codiak BioSciences Inc. Common Stock
Time series to forecast: 4 Weeks

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

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 Statistical Inference (ML) based CDAK Stock Prediction Model

  1. However, depending on the nature of the financial instruments and the credit risk information available for particular groups of financial instruments, an entity may not be able to identify significant changes in credit risk for individual financial instruments before the financial instrument becomes past due. This may be the case for financial instruments such as retail loans for which there is little or no updated credit risk information that is routinely obtained and monitored on an individual instrument until a customer breaches the contractual terms. If changes in the credit risk for individual financial instruments are not captured before they become past due, a loss allowance based only on credit information at an individual financial instrument level would not faithfully represent the changes in credit risk since initial recognition.
  2. An entity may use practical expedients when measuring expected credit losses if they are consistent with the principles in paragraph 5.5.17. An example of a practical expedient is the calculation of the expected credit losses on trade receivables using a provision matrix. The entity would use its historical credit loss experience (adjusted as appropriate in accordance with paragraphs B5.5.51–B5.5.52) for trade receivables to estimate the 12-month expected credit losses or the lifetime expected credit losses on the financial assets as relevant. A provision matrix might, for example, specify fixed provision rates depending on the number of days that a trade receivable is past due (for example, 1 per cent if not past due, 2 per cent if less than 30 days past due, 3 per cent if more than 30 days but less than 90 days past due, 20 per cent if 90–180 days past due etc). Depending on the diversity of its customer base, the entity would use appropriate groupings if its historical credit loss experience shows significantly different loss patterns for different customer segments. Examples of criteria that might be used to group assets include geographical region, product type, customer rating, collateral or trade credit insurance and type of customer (such as wholesale or retail)
  3. The fact that a derivative is in or out of the money when it is designated as a hedging instrument does not in itself mean that a qualitative assessment is inappropriate. It depends on the circumstances whether hedge ineffectiveness arising from that fact could have a magnitude that a qualitative assessment would not adequately capture.
  4. If, in applying paragraph 7.2.44, an entity reinstates a discontinued hedging relationship, the entity shall read references in paragraphs 6.9.11 and 6.9.12 to the date the alternative benchmark rate is designated as a noncontractually specified risk component for the first time as referring to the date of initial application of these amendments (ie the 24-month period for that alternative benchmark rate designated as a non-contractually specified risk component begins from the date of initial application of these amendments).

*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.

CDAK Codiak BioSciences Inc. Common Stock Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*B1B1
Income StatementCaa2Baa2
Balance SheetB2B2
Leverage RatiosBaa2B2
Cash FlowCaa2B2
Rates of Return and ProfitabilityBaa2C

*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. Hartigan JA, Wong MA. 1979. Algorithm as 136: a k-means clustering algorithm. J. R. Stat. Soc. Ser. C 28:100–8
  2. Andrews, D. W. K. W. Ploberger (1994), "Optimal tests when a nuisance parameter is present only under the alternative," Econometrica, 62, 1383–1414.
  3. R. Howard and J. Matheson. Risk sensitive Markov decision processes. Management Science, 18(7):356– 369, 1972
  4. Kitagawa T, Tetenov A. 2015. Who should be treated? Empirical welfare maximization methods for treatment choice. Tech. Rep., Cent. Microdata Methods Pract., Inst. Fiscal Stud., London
  5. Friedberg R, Tibshirani J, Athey S, Wager S. 2018. Local linear forests. arXiv:1807.11408 [stat.ML]
  6. K. Tuyls and G. Weiss. Multiagent learning: Basics, challenges, and prospects. AI Magazine, 33(3): 41–52, 2012
  7. Andrews, D. W. K. W. Ploberger (1994), "Optimal tests when a nuisance parameter is present only under the alternative," Econometrica, 62, 1383–1414.
Frequently Asked QuestionsQ: What is the prediction methodology for CDAK stock?
A: CDAK stock prediction methodology: We evaluate the prediction models Statistical Inference (ML) and Linear Regression
Q: Is CDAK stock a buy or sell?
A: The dominant strategy among neural network is to Sell CDAK Stock.
Q: Is Codiak BioSciences Inc. Common Stock stock a good investment?
A: The consensus rating for Codiak BioSciences Inc. Common Stock is Sell and is assigned short-term B1 & long-term B1 estimated rating.
Q: What is the consensus rating of CDAK stock?
A: The consensus rating for CDAK is Sell.
Q: What is the prediction period for CDAK stock?
A: The prediction period for CDAK is 4 Weeks

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