Chesapeake Utilities Corporation Common Stock Forecast & Analysis

Outlook: Chesapeake Utilities Corporation Common Stock is assigned short-term B2 & long-term B1 estimated rating.
Dominant Strategy : Buy
Time series to forecast n: 23 Jun 2023 for 4 Weeks
Methodology : Modular Neural Network (Market Direction Analysis)

Summary

Chesapeake Utilities Corporation Common Stock prediction model is evaluated with Modular Neural Network (Market Direction Analysis) and Lasso Regression^1,2,3,4 and it is concluded that the CPK stock is predictable in the short/long term. Modular neural networks (MNNs) are a type of artificial neural network that can be used for market direction analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying patterns in data or predicting future price movements. The modules are then combined to form a single neural network that can perform multiple tasks.In the context of market direction analysis, MNNs can be used to identify patterns in market data that suggest that the market is likely to move in a particular direction. This information can then be used to make predictions about future price movements. According to price forecasts for 4 Weeks period, the dominant strategy among neural network is: Buy

Key Points

1. Market Signals
2. Trading Signals
3. What is prediction model?

CPK Target Price Prediction Modeling Methodology

We consider Chesapeake Utilities Corporation Common Stock Decision Process with Modular Neural Network (Market Direction Analysis) where A is the set of discrete actions of CPK 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(Lasso Regression)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Modular Neural Network (Market Direction Analysis)) X S(n):→ 4 Weeks $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of CPK stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Modular Neural Network (Market Direction Analysis)

Modular neural networks (MNNs) are a type of artificial neural network that can be used for market direction analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying patterns in data or predicting future price movements. The modules are then combined to form a single neural network that can perform multiple tasks.In the context of market direction analysis, MNNs can be used to identify patterns in market data that suggest that the market is likely to move in a particular direction. This information can then be used to make predictions about future price movements.

Lasso Regression

Lasso regression, also known as L1 regularization, 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 and to induce sparsity in the model. This is done by adding a term to the objective function that is proportional to the sum of the absolute values of the coefficients. The penalty term is called the "lasso" penalty, and it is controlled by a parameter called the "lasso constant". Lasso regression can be used to address the problem of multicollinearity in linear regression, as well as the problem of overfitting. 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. Overfitting occurs when a model is too closely fit to the training data, and as a result, it does not generalize well to new data.

 

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?

CPK Stock Forecast (Buy or Sell) for 4 Weeks

Sample Set: Neural Network
Stock/Index: CPK Chesapeake Utilities Corporation Common Stock
Time series to forecast n: 23 Jun 2023 for 4 Weeks

According to price forecasts for 4 Weeks period, the dominant strategy among neural network is: Buy

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%

IFRS Reconciliation Adjustments for Chesapeake Utilities Corporation Common Stock

1. For a financial guarantee contract, the entity is required to make payments only in the event of a default by the debtor in accordance with the terms of the instrument that is guaranteed. Accordingly, cash shortfalls are the expected payments to reimburse the holder for a credit loss that it incurs less any amounts that the entity expects to receive from the holder, the debtor or any other party. If the asset is fully guaranteed, the estimation of cash shortfalls for a financial guarantee contract would be consistent with the estimations of cash shortfalls for the asset subject to the guarantee
2. The assessment of whether lifetime expected credit losses should be recognised is based on significant increases in the likelihood or risk of a default occurring since initial recognition (irrespective of whether a financial instrument has been repriced to reflect an increase in credit risk) instead of on evidence of a financial asset being credit-impaired at the reporting date or an actual default occurring. Generally, there will be a significant increase in credit risk before a financial asset becomes credit-impaired or an actual default occurs.
3. Expected credit losses reflect an entity's own expectations of credit losses. However, when considering all reasonable and supportable information that is available without undue cost or effort in estimating expected credit losses, an entity should also consider observable market information about the credit risk of the particular financial instrument or similar financial instruments.
4. The business model may be to hold assets to collect contractual cash flows even if the entity sells financial assets when there is an increase in the assets' credit risk. To determine whether there has been an increase in the assets' credit risk, the entity considers reasonable and supportable information, including forward looking information. Irrespective of their frequency and value, sales due to an increase in the assets' credit risk are not inconsistent with a business model whose objective is to hold financial assets to collect contractual cash flows because the credit quality of financial assets is relevant to the entity's ability to collect contractual cash flows. Credit risk management activities that are aimed at minimising potential credit losses due to credit deterioration are integral to such a business model. Selling a financial asset because it no longer meets the credit criteria specified in the entity's documented investment policy is an example of a sale that has occurred due to an increase in credit risk. However, in the absence of such a policy, the entity may demonstrate in other ways that the sale occurred due to an increase in credit risk.

*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.

Conclusions

Chesapeake Utilities Corporation Common Stock is assigned short-term B2 & long-term B1 estimated rating. Chesapeake Utilities Corporation Common Stock prediction model is evaluated with Modular Neural Network (Market Direction Analysis) and Lasso Regression^1,2,3,4 and it is concluded that the CPK stock is predictable in the short/long term. According to price forecasts for 4 Weeks period, the dominant strategy among neural network is: Buy

CPK Chesapeake Utilities Corporation Common Stock Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	B2	B1
Income Statement	Caa2	Baa2
Balance Sheet	Caa2	B3
Leverage Ratios	Ba2	B2
Cash Flow	Ba3	Caa2
Rates of Return and Profitability	Baa2	Ba3

*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?

Prediction Confidence Score

Trust metric by Neural Network: 78 out of 100 with 768 signals.

References

1. Friedman JH. 2002. Stochastic gradient boosting. Comput. Stat. Data Anal. 38:367–78
2. N. B ̈auerle and J. Ott. Markov decision processes with average-value-at-risk criteria. Mathematical Methods of Operations Research, 74(3):361–379, 2011
3. Athey S, Imbens GW. 2017b. The state of applied econometrics: causality and policy evaluation. J. Econ. Perspect. 31:3–32
4. Breiman L. 2001a. Random forests. Mach. Learn. 45:5–32
5. K. Tumer and D. Wolpert. A survey of collectives. In K. Tumer and D. Wolpert, editors, Collectives and the Design of Complex Systems, pages 1–42. Springer, 2004.
6. Bennett J, Lanning S. 2007. The Netflix prize. In Proceedings of KDD Cup and Workshop 2007, p. 35. New York: ACM
7. Andrews, D. W. K. (1993), "Tests for parameter instability and structural change with unknown change point," Econometrica, 61, 821–856.

Frequently Asked Questions

Q: What is the prediction methodology for CPK stock?
A: CPK stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Direction Analysis) and Lasso Regression
Q: Is CPK stock a buy or sell?
A: The dominant strategy among neural network is to Buy CPK Stock.
Q: Is Chesapeake Utilities Corporation Common Stock stock a good investment?
A: The consensus rating for Chesapeake Utilities Corporation Common Stock is Buy and is assigned short-term B2 & long-term B1 estimated rating.
Q: What is the consensus rating of CPK stock?
A: The consensus rating for CPK is Buy.
Q: What is the prediction period for CPK stock?
A: The prediction period for CPK is 4 Weeks

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