Outlook: Kimbell Tiger Acquisition Corporation Class A Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating.
Time series to forecast n: 13 Mar 2023 for (n+1 year)
Methodology : Deductive Inference (ML)

## Abstract

Kimbell Tiger Acquisition Corporation Class A Common Stock prediction model is evaluated with Deductive Inference (ML) and Statistical Hypothesis Testing1,2,3,4 and it is concluded that the TGR stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Buy

## Key Points

1. Why do we need predictive models?
2. How accurate is machine learning in stock market?
3. Investment Risk

## TGR Target Price Prediction Modeling Methodology

We consider Kimbell Tiger Acquisition Corporation Class A Common Stock Decision Process with Deductive Inference (ML) where A is the set of discrete actions of TGR 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(Statistical Hypothesis Testing)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Deductive Inference (ML)) X S(n):→ (n+1 year) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of TGR 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?

## TGR Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: TGR Kimbell Tiger Acquisition Corporation Class A Common Stock
Time series to forecast n: 13 Mar 2023 for (n+1 year)

According to price forecasts for (n+1 year) 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 Kimbell Tiger Acquisition Corporation Class A Common Stock

1. Lifetime expected credit losses are generally expected to be recognised before a financial instrument becomes past due. Typically, credit risk increases significantly before a financial instrument becomes past due or other lagging borrower-specific factors (for example, a modification or restructuring) are observed. Consequently when reasonable and supportable information that is more forward-looking than past due information is available without undue cost or effort, it must be used to assess changes in credit risk.
2. An entity is not required to restate prior periods to reflect the application of these amendments. The entity may restate prior periods if, and only if, it is possible without the use of hindsight and the restated financial statements reflect all the requirements in this Standard. If an entity does not restate prior periods, the entity shall recognise any difference between the previous carrying amount and the carrying amount at the beginning of the annual reporting period that includes the date of initial application of these amendments in the opening retained earnings (or other component of equity, as appropriate) of the annual reporting period that includes the date of initial application of these amendments.
3. IFRS 15, issued in May 2014, amended paragraphs 3.1.1, 4.2.1, 5.1.1, 5.2.1, 5.7.6, B3.2.13, B5.7.1, C5 and C42 and deleted paragraph C16 and its related heading. Paragraphs 5.1.3 and 5.7.1A, and a definition to Appendix A, were added. An entity shall apply those amendments when it applies IFRS 15.
4. 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.

*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

Kimbell Tiger Acquisition Corporation Class A Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating. Kimbell Tiger Acquisition Corporation Class A Common Stock prediction model is evaluated with Deductive Inference (ML) and Statistical Hypothesis Testing1,2,3,4 and it is concluded that the TGR stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Buy

### TGR Kimbell Tiger Acquisition Corporation Class A Common Stock Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementBa3Baa2
Balance SheetBa2Caa2
Leverage RatiosB2Ba3
Cash FlowB1Ba2
Rates of Return and ProfitabilityCBaa2

*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: 87 out of 100 with 846 signals.

## References

1. M. Petrik and D. Subramanian. An approximate solution method for large risk-averse Markov decision processes. In Proceedings of the 28th International Conference on Uncertainty in Artificial Intelligence, 2012.
2. Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85
3. J. Z. Leibo, V. Zambaldi, M. Lanctot, J. Marecki, and T. Graepel. Multi-agent Reinforcement Learning in Sequential Social Dilemmas. In Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2017), Sao Paulo, Brazil, 2017
4. V. Borkar. A sensitivity formula for the risk-sensitive cost and the actor-critic algorithm. Systems & Control Letters, 44:339–346, 2001
5. Mullainathan S, Spiess J. 2017. Machine learning: an applied econometric approach. J. Econ. Perspect. 31:87–106
6. G. Shani, R. Brafman, and D. Heckerman. An MDP-based recommender system. In Proceedings of the Eigh- teenth conference on Uncertainty in artificial intelligence, pages 453–460. Morgan Kaufmann Publishers Inc., 2002
7. Scholkopf B, Smola AJ. 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press
Frequently Asked QuestionsQ: What is the prediction methodology for TGR stock?
A: TGR stock prediction methodology: We evaluate the prediction models Deductive Inference (ML) and Statistical Hypothesis Testing
Q: Is TGR stock a buy or sell?
A: The dominant strategy among neural network is to Buy TGR Stock.
Q: Is Kimbell Tiger Acquisition Corporation Class A Common Stock stock a good investment?
A: The consensus rating for Kimbell Tiger Acquisition Corporation Class A Common Stock is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of TGR stock?
A: The consensus rating for TGR is Buy.
Q: What is the prediction period for TGR stock?
A: The prediction period for TGR is (n+1 year)