CREC Stock: A Spark That Could Fizzle

Outlook: Crescera Capital Acquisition Corp Class A Ordinary Shares is assigned short-term B1 & long-term B2 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised :
Dominant Strategy : Sell
Time series to forecast n: for 4 Weeks
Methodology : Inductive Learning (ML)
Hypothesis Testing : Ridge Regression
Surveillance : Major exchange and OTC

Abstract

Crescera Capital Acquisition Corp Class A Ordinary Shares prediction model is evaluated with Inductive Learning (ML) and Ridge Regression^1,2,3,4 and it is concluded that the CREC stock is predictable in the short/long term. Inductive learning is a type of machine learning in which the model learns from a set of labeled data and makes predictions about new, unlabeled data. The model is trained on the labeled data and then used to make predictions on new data. Inductive learning is a supervised learning algorithm, which means that it requires labeled data to train. The labeled data is used to train the model to make predictions about new data. There are many different types of inductive learning algorithms, including decision trees, support vector machines, and neural networks. Each type of algorithm has its own strengths and weaknesses. According to price forecasts for 4 Weeks period, the dominant strategy among neural network is: Sell

Key Points

1. How do you know when a stock will go up or down?
2. What are buy sell or hold recommendations?
3. Can statistics predict the future?

CREC Target Price Prediction Modeling Methodology

We consider Crescera Capital Acquisition Corp Class A Ordinary Shares Decision Process with Inductive Learning (ML) where A is the set of discrete actions of CREC 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(Ridge 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(Inductive Learning (ML)) X S(n):→ 4 Weeks $\begin{array}{l}\int e^x\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of CREC stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Inductive Learning (ML)

Inductive learning is a type of machine learning in which the model learns from a set of labeled data and makes predictions about new, unlabeled data. The model is trained on the labeled data and then used to make predictions on new data. Inductive learning is a supervised learning algorithm, which means that it requires labeled data to train. The labeled data is used to train the model to make predictions about new data. There are many different types of inductive learning algorithms, including decision trees, support vector machines, and neural networks. Each type of algorithm has its own strengths and weaknesses.

Ridge Regression

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.

 

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?

CREC Stock Forecast (Buy or Sell) for 4 Weeks

Sample Set: Neural Network
Stock/Index: CREC Crescera Capital Acquisition Corp Class A Ordinary Shares
Time series to forecast: 4 Weeks

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

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 Crescera Capital Acquisition Corp Class A Ordinary Shares

1. An entity shall assess at the inception of the hedging relationship, and on an ongoing basis, whether a hedging relationship meets the hedge effectiveness requirements. At a minimum, an entity shall perform the ongoing assessment at each reporting date or upon a significant change in the circumstances affecting the hedge effectiveness requirements, whichever comes first. The assessment relates to expectations about hedge effectiveness and is therefore only forward-looking.
2. Compared to a business model whose objective is to hold financial assets to collect contractual cash flows, this business model will typically involve greater frequency and value of sales. This is because selling financial assets is integral to achieving the business model's objective instead of being only incidental to it. However, there is no threshold for the frequency or value of sales that must occur in this business model because both collecting contractual cash flows and selling financial assets are integral to achieving its objective.
3. An alternative benchmark rate designated as a non-contractually specified risk component that is not separately identifiable (see paragraphs 6.3.7(a) and B6.3.8) at the date it is designated shall be deemed to have met that requirement at that date, if, and only if, the entity reasonably expects the alternative benchmark rate will be separately identifiable within 24 months. The 24-month period applies to each alternative benchmark rate separately and starts from the date the entity designates the alternative benchmark rate as a non-contractually specified risk component for the first time (ie the 24- month period applies on a rate-by-rate basis).
4. Interest Rate Benchmark Reform—Phase 2, which amended IFRS 9, IAS 39, IFRS 7, IFRS 4 and IFRS 16, issued in August 2020, added paragraphs 5.4.5–5.4.9, 6.8.13, Section 6.9 and paragraphs 7.2.43–7.2.46. An entity shall apply these amendments for annual periods beginning on or after 1 January 2021. Earlier application is permitted. If an entity applies these amendments for an earlier period, it shall disclose that fact.

*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

Crescera Capital Acquisition Corp Class A Ordinary Shares is assigned short-term B1 & long-term B2 estimated rating. Crescera Capital Acquisition Corp Class A Ordinary Shares prediction model is evaluated with Inductive Learning (ML) and Ridge Regression^1,2,3,4 and it is concluded that the CREC stock is predictable in the short/long term. According to price forecasts for 4 Weeks period, the dominant strategy among neural network is: Sell

CREC Crescera Capital Acquisition Corp Class A Ordinary Shares Financial Analysis*

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

*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: 76 out of 100 with 652 signals.

References

1. F. A. Oliehoek, M. T. J. Spaan, and N. A. Vlassis. Optimal and approximate q-value functions for decentralized pomdps. J. Artif. Intell. Res. (JAIR), 32:289–353, 2008
2. E. van der Pol and F. A. Oliehoek. Coordinated deep reinforcement learners for traffic light control. NIPS Workshop on Learning, Inference and Control of Multi-Agent Systems, 2016.
3. Hornik K, Stinchcombe M, White H. 1989. Multilayer feedforward networks are universal approximators. Neural Netw. 2:359–66
4. Bennett J, Lanning S. 2007. The Netflix prize. In Proceedings of KDD Cup and Workshop 2007, p. 35. New York: ACM
5. E. Altman, K. Avrachenkov, and R. N ́u ̃nez-Queija. Perturbation analysis for denumerable Markov chains with application to queueing models. Advances in Applied Probability, pages 839–853, 2004
6. Burgess, D. F. (1975), "Duality theory and pitfalls in the specification of technologies," Journal of Econometrics, 3, 105–121.
7. Scholkopf B, Smola AJ. 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press

Frequently Asked Questions

Q: What is the prediction methodology for CREC stock?
A: CREC stock prediction methodology: We evaluate the prediction models Inductive Learning (ML) and Ridge Regression
Q: Is CREC stock a buy or sell?
A: The dominant strategy among neural network is to Sell CREC Stock.
Q: Is Crescera Capital Acquisition Corp Class A Ordinary Shares stock a good investment?
A: The consensus rating for Crescera Capital Acquisition Corp Class A Ordinary Shares is Sell and is assigned short-term B1 & long-term B2 estimated rating.
Q: What is the consensus rating of CREC stock?
A: The consensus rating for CREC is Sell.
Q: What is the prediction period for CREC stock?
A: The prediction period for CREC is 4 Weeks

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