Modelling A.I. in Economics

XPOF Xponential Fitness Inc. Class A Common Stock (Forecast)

Outlook: Xponential Fitness Inc. Class A Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Hold
Time series to forecast n: 11 Feb 2023 for (n+8 weeks)
Methodology : Active Learning (ML)

Abstract

Xponential Fitness Inc. Class A Common Stock prediction model is evaluated with Active Learning (ML) and ElasticNet Regression1,2,3,4 and it is concluded that the XPOF stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Hold

Key Points

  1. Understanding Buy, Sell, and Hold Ratings
  2. What is the use of Markov decision process?
  3. Market Signals

XPOF Target Price Prediction Modeling Methodology

We consider Xponential Fitness Inc. Class A Common Stock Decision Process with Active Learning (ML) where A is the set of discrete actions of XPOF 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(ElasticNet 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(Active Learning (ML)) X S(n):→ (n+8 weeks) R = 1 0 0 0 1 0 0 0 1

n:Time series to forecast

p:Price signals of XPOF stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

 

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?

XPOF Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: XPOF Xponential Fitness Inc. Class A Common Stock
Time series to forecast n: 11 Feb 2023 for (n+8 weeks)

According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Hold

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 Xponential Fitness Inc. Class A Common Stock

  1. When measuring the fair values of the part that continues to be recognised and the part that is derecognised for the purposes of applying paragraph 3.2.13, an entity applies the fair value measurement requirements in IFRS 13 Fair Value Measurement in addition to paragraph 3.2.14.
  2. Leverage is a contractual cash flow characteristic of some financial assets. Leverage increases the variability of the contractual cash flows with the result that they do not have the economic characteristics of interest. Stand-alone option, forward and swap contracts are examples of financial assets that include such leverage. Thus, such contracts do not meet the condition in paragraphs 4.1.2(b) and 4.1.2A(b) and cannot be subsequently measured at amortised cost or fair value through other comprehensive income.
  3. An entity shall apply this Standard retrospectively, in accordance with IAS 8 Accounting Policies, Changes in Accounting Estimates and Errors, except as specified in paragraphs 7.2.4–7.2.26 and 7.2.28. This Standard shall not be applied to items that have already been derecognised at the date of initial application.
  4. For the purposes of measuring expected credit losses, the estimate of expected cash shortfalls shall reflect the cash flows expected from collateral and other credit enhancements that are part of the contractual terms and are not recognised separately by the entity. The estimate of expected cash shortfalls on a collateralised financial instrument reflects the amount and timing of cash flows that are expected from foreclosure on the collateral less the costs of obtaining and selling the collateral, irrespective of whether foreclosure is probable (ie the estimate of expected cash flows considers the probability of a foreclosure and the cash flows that would result from it). Consequently, any cash flows that are expected from the realisation of the collateral beyond the contractual maturity of the contract should be included in this analysis. Any collateral obtained as a result of foreclosure is not recognised as an asset that is separate from the collateralised financial instrument unless it meets the relevant recognition criteria for an asset in this or other Standards.

*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

Xponential Fitness Inc. Class A Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating. Xponential Fitness Inc. Class A Common Stock prediction model is evaluated with Active Learning (ML) and ElasticNet Regression1,2,3,4 and it is concluded that the XPOF stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Hold

XPOF Xponential Fitness Inc. Class A Common Stock Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementBa3B1
Balance SheetBa3Caa2
Leverage RatiosB1C
Cash FlowBaa2Baa2
Rates of Return and ProfitabilityBaa2Baa2

*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: 83 out of 100 with 787 signals.

References

  1. L. Panait and S. Luke. Cooperative multi-agent learning: The state of the art. Autonomous Agents and Multi-Agent Systems, 11(3):387–434, 2005.
  2. Wan M, Wang D, Goldman M, Taddy M, Rao J, et al. 2017. Modeling consumer preferences and price sensitiv- ities from large-scale grocery shopping transaction logs. In Proceedings of the 26th International Conference on the World Wide Web, pp. 1103–12. New York: ACM
  3. Jacobs B, Donkers B, Fok D. 2014. Product Recommendations Based on Latent Purchase Motivations. Rotterdam, Neth.: ERIM
  4. M. Sobel. The variance of discounted Markov decision processes. Applied Probability, pages 794–802, 1982
  5. Athey S, Imbens GW. 2017b. The state of applied econometrics: causality and policy evaluation. J. Econ. Perspect. 31:3–32
  6. J. Hu and M. P. Wellman. Nash q-learning for general-sum stochastic games. Journal of Machine Learning Research, 4:1039–1069, 2003.
  7. Hartford J, Lewis G, Taddy M. 2016. Counterfactual prediction with deep instrumental variables networks. arXiv:1612.09596 [stat.AP]
Frequently Asked QuestionsQ: What is the prediction methodology for XPOF stock?
A: XPOF stock prediction methodology: We evaluate the prediction models Active Learning (ML) and ElasticNet Regression
Q: Is XPOF stock a buy or sell?
A: The dominant strategy among neural network is to Hold XPOF Stock.
Q: Is Xponential Fitness Inc. Class A Common Stock stock a good investment?
A: The consensus rating for Xponential Fitness Inc. Class A Common Stock is Hold and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of XPOF stock?
A: The consensus rating for XPOF is Hold.
Q: What is the prediction period for XPOF stock?
A: The prediction period for XPOF is (n+8 weeks)

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