AUC Score :
Short-Term Revised1 :
Dominant Strategy : Sell
Time series to forecast n:
Methodology : Multi-Instance Learning (ML)
Hypothesis Testing : Stepwise 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
Edify Acquisition Corp. Class A Common Stock prediction model is evaluated with Multi-Instance Learning (ML) and Stepwise Regression1,2,3,4 and it is concluded that the EAC stock is predictable in the short/long term. Multi-instance learning (MIL) is a machine learning (ML) problem where a dataset consists of multiple instances, and each instance is associated with a single label. The goal of MIL is to learn a model that can predict the label of a new instance based on the labels of the instances that it is similar to. MIL is a challenging problem because the instances in a dataset are not labeled individually. This means that the model cannot simply learn a mapping from the features of an instance to its label. Instead, the model must learn a way to combine the features of multiple instances to predict the label of a new instance. According to price forecasts for 3 Month period, the dominant strategy among neural network is: Sell
Key Points
- What are buy sell or hold recommendations?
- Stock Forecast Based On a Predictive Algorithm
- Is now good time to invest?
EAC Target Price Prediction Modeling Methodology
We consider Edify Acquisition Corp. Class A Common Stock Decision Process with Multi-Instance Learning (ML) where A is the set of discrete actions of EAC 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(Stepwise Regression)5,6,7= X R(Multi-Instance Learning (ML)) X S(n):→ 3 Month
n:Time series to forecast
p:Price signals of EAC stock
j:Nash equilibria (Neural Network)
k:Dominated move
a:Best response for target price
Multi-Instance Learning (ML)
Multi-instance learning (MIL) is a machine learning (ML) problem where a dataset consists of multiple instances, and each instance is associated with a single label. The goal of MIL is to learn a model that can predict the label of a new instance based on the labels of the instances that it is similar to. MIL is a challenging problem because the instances in a dataset are not labeled individually. This means that the model cannot simply learn a mapping from the features of an instance to its label. Instead, the model must learn a way to combine the features of multiple instances to predict the label of a new instance.Stepwise Regression
Stepwise regression is a method of variable selection in which variables are added or removed from a model one at a time, based on their statistical significance. There are two main types of stepwise regression: forward selection and backward elimination. In forward selection, variables are added to the model one at a time, starting with the variable with the highest F-statistic. The F-statistic is a measure of how much improvement in the model is gained by adding the variable. Variables are added to the model until no variable adds a statistically significant improvement to the model.
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?
EAC Stock Forecast (Buy or Sell)
Sample Set: Neural NetworkStock/Index: EAC Edify Acquisition Corp. Class A Common Stock
Time series to forecast: 3 Month
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 Multi-Instance Learning (ML) based EAC Stock Prediction Model
- For some types of fair value hedges, the objective of the hedge is not primarily to offset the fair value change of the hedged item but instead to transform the cash flows of the hedged item. For example, an entity hedges the fair value interest rate risk of a fixed-rate debt instrument using an interest rate swap. The entity's hedge objective is to transform the fixed-interest cash flows into floating interest cash flows. This objective is reflected in the accounting for the hedging relationship by accruing the net interest accrual on the interest rate swap in profit or loss. In the case of a hedge of a net position (for example, a net position of a fixed-rate asset and a fixed-rate liability), this net interest accrual must be presented in a separate line item in the statement of profit or loss and other comprehensive income. This is to avoid the grossing up of a single instrument's net gains or losses into offsetting gross amounts and recognising them in different line items (for example, this avoids grossing up a net interest receipt on a single interest rate swap into gross interest revenue and gross interest expense).
- There is a rebuttable presumption that unless inflation risk is contractually specified, it is not separately identifiable and reliably measurable and hence cannot be designated as a risk component of a financial instrument. However, in limited cases, it is possible to identify a risk component for inflation risk that is separately identifiable and reliably measurable because of the particular circumstances of the inflation environment and the relevant debt market
- Adjusting the hedge ratio by decreasing the volume of the hedged item does not affect how the changes in the fair value of the hedging instrument are measured. The measurement of the changes in the value of the hedged item related to the volume that continues to be designated also remains unaffected. However, from the date of rebalancing, the volume by which the hedged item was decreased is no longer part of the hedging relationship. For example, if an entity originally hedged a volume of 100 tonnes of a commodity at a forward price of CU80 and reduces that volume by 10 tonnes on rebalancing, the hedged item after rebalancing would be 90 tonnes hedged at CU80. The 10 tonnes of the hedged item that are no longer part of the hedging relationship would be accounted for in accordance with the requirements for the discontinuation of hedge accounting (see paragraphs 6.5.6–6.5.7 and B6.5.22–B6.5.28).
- For the purpose of applying the requirements in paragraphs 6.4.1(c)(i) and B6.4.4–B6.4.6, an entity shall assume that the interest rate benchmark on which the hedged cash flows and/or the hedged risk (contractually or noncontractually specified) are based, or the interest rate benchmark on which the cash flows of the hedging instrument are based, is not altered as a result of interest rate benchmark reform.
*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.
EAC Edify Acquisition Corp. Class A Common Stock Financial Analysis*
Rating | Short-Term | Long-Term Senior |
---|---|---|
Outlook* | Ba2 | Ba3 |
Income Statement | Baa2 | Caa2 |
Balance Sheet | Baa2 | Baa2 |
Leverage Ratios | Baa2 | Baa2 |
Cash Flow | C | Ba3 |
Rates of Return and Profitability | Baa2 | C |
*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?
Conclusions
Edify Acquisition Corp. Class A Common Stock is assigned short-term Ba2 & long-term Ba3 estimated rating. Edify Acquisition Corp. Class A Common Stock prediction model is evaluated with Multi-Instance Learning (ML) and Stepwise Regression1,2,3,4 and it is concluded that the EAC stock is predictable in the short/long term. According to price forecasts for 3 Month period, the dominant strategy among neural network is: Sell
Prediction Confidence Score
References
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- Hartigan JA, Wong MA. 1979. Algorithm as 136: a k-means clustering algorithm. J. R. Stat. Soc. Ser. C 28:100–8
- Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85
- Bottou L. 1998. Online learning and stochastic approximations. In On-Line Learning in Neural Networks, ed. D Saad, pp. 9–42. New York: ACM
- 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
- Bamler R, Mandt S. 2017. Dynamic word embeddings via skip-gram filtering. In Proceedings of the 34th Inter- national Conference on Machine Learning, pp. 380–89. La Jolla, CA: Int. Mach. Learn. Soc.
- ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. Can Neural Networks Predict Stock Market? (No. Stock Analysis). AC Investment Research.
Frequently Asked Questions
Q: What is the prediction methodology for EAC stock?A: EAC stock prediction methodology: We evaluate the prediction models Multi-Instance Learning (ML) and Stepwise Regression
Q: Is EAC stock a buy or sell?
A: The dominant strategy among neural network is to Sell EAC Stock.
Q: Is Edify Acquisition Corp. Class A Common Stock stock a good investment?
A: The consensus rating for Edify Acquisition Corp. Class A Common Stock is Sell and is assigned short-term Ba2 & long-term Ba3 estimated rating.
Q: What is the consensus rating of EAC stock?
A: The consensus rating for EAC is Sell.
Q: What is the prediction period for EAC stock?
A: The prediction period for EAC is 3 Month
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