Outlook: Textainer Group Holdings Limited Depositary Shares each representing a 1/1000th interest in a share of 7.000% Series A Cumulative Redeemable Perpetual Preference Shares is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Sell
Time series to forecast n: 09 Feb 2023 for (n+16 weeks)
Methodology : Modular Neural Network (CNN Layer)

## Abstract

Textainer Group Holdings Limited Depositary Shares each representing a 1/1000th interest in a share of 7.000% Series A Cumulative Redeemable Perpetual Preference Shares prediction model is evaluated with Modular Neural Network (CNN Layer) and Multiple Regression1,2,3,4 and it is concluded that the TGH^A stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Sell

## Key Points

1. Decision Making
2. Market Signals
3. What is Markov decision process in reinforcement learning?

## TGH^A Target Price Prediction Modeling Methodology

We consider Textainer Group Holdings Limited Depositary Shares each representing a 1/1000th interest in a share of 7.000% Series A Cumulative Redeemable Perpetual Preference Shares Decision Process with Modular Neural Network (CNN Layer) where A is the set of discrete actions of TGH^A 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(Multiple Regression)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(Modular Neural Network (CNN Layer)) X S(n):→ (n+16 weeks) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of TGH^A 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?

## TGH^A Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: TGH^A Textainer Group Holdings Limited Depositary Shares each representing a 1/1000th interest in a share of 7.000% Series A Cumulative Redeemable Perpetual Preference Shares
Time series to forecast n: 09 Feb 2023 for (n+16 weeks)

According to price forecasts for (n+16 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 Textainer Group Holdings Limited Depositary Shares each representing a 1/1000th interest in a share of 7.000% Series A Cumulative Redeemable Perpetual Preference Shares

1. If subsequently an entity reasonably expects that the alternative benchmark rate will not be separately identifiable within 24 months from the date the entity designated it as a non-contractually specified risk component for the first time, the entity shall cease applying the requirement in paragraph 6.9.11 to that alternative benchmark rate and discontinue hedge accounting prospectively from the date of that reassessment for all hedging relationships in which the alternative benchmark rate was designated as a noncontractually specified risk component.
2. IFRS 17, issued in May 2017, amended paragraphs 2.1, B2.1, B2.4, B2.5 and B4.1.30, and added paragraph 3.3.5. Amendments to IFRS 17, issued in June 2020, further amended paragraph 2.1 and added paragraphs 7.2.36‒7.2.42. An entity shall apply those amendments when it applies IFRS 17.
3. Accordingly the date of the modification shall be treated as the date of initial recognition of that financial asset when applying the impairment requirements to the modified financial asset. This typically means measuring the loss allowance at an amount equal to 12-month expected credit losses until the requirements for the recognition of lifetime expected credit losses in paragraph 5.5.3 are met. However, in some unusual circumstances following a modification that results in derecognition of the original financial asset, there may be evidence that the modified financial asset is credit-impaired at initial recognition, and thus, the financial asset should be recognised as an originated credit-impaired financial asset. This might occur, for example, in a situation in which there was a substantial modification of a distressed asset that resulted in the derecognition of the original financial asset. In such a case, it may be possible for the modification to result in a new financial asset which is credit-impaired at initial recognition.
4. An entity need not undertake an exhaustive search for information but shall consider all reasonable and supportable information that is available without undue cost or effort and that is relevant to the estimate of expected credit losses, including the effect of expected prepayments. The information used shall include factors that are specific to the borrower, general economic conditions and an assessment of both the current as well as the forecast direction of conditions at the reporting date. An entity may use various sources of data, that may be both internal (entity-specific) and external. Possible data sources include internal historical credit loss experience, internal ratings, credit loss experience of other entities and external ratings, reports and statistics. Entities that have no, or insufficient, sources of entityspecific data may use peer group experience for the comparable financial instrument (or groups of financial instruments).

*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

Textainer Group Holdings Limited Depositary Shares each representing a 1/1000th interest in a share of 7.000% Series A Cumulative Redeemable Perpetual Preference Shares is assigned short-term Ba1 & long-term Ba1 estimated rating. Textainer Group Holdings Limited Depositary Shares each representing a 1/1000th interest in a share of 7.000% Series A Cumulative Redeemable Perpetual Preference Shares prediction model is evaluated with Modular Neural Network (CNN Layer) and Multiple Regression1,2,3,4 and it is concluded that the TGH^A stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Sell

### TGH^A Textainer Group Holdings Limited Depositary Shares each representing a 1/1000th interest in a share of 7.000% Series A Cumulative Redeemable Perpetual Preference Shares Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementCaa2Baa2
Balance SheetBa3Baa2
Leverage RatiosCCaa2
Cash FlowB2C
Rates of Return and ProfitabilityBa1Baa2

*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: 89 out of 100 with 867 signals.

## References

1. V. Borkar. Q-learning for risk-sensitive control. Mathematics of Operations Research, 27:294–311, 2002.
2. Scholkopf B, Smola AJ. 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press
3. J. Ott. A Markov decision model for a surveillance application and risk-sensitive Markov decision processes. PhD thesis, Karlsruhe Institute of Technology, 2010.
4. Mnih A, Hinton GE. 2007. Three new graphical models for statistical language modelling. In International Conference on Machine Learning, pp. 641–48. La Jolla, CA: Int. Mach. Learn. Soc.
5. 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
6. Friedman JH. 2002. Stochastic gradient boosting. Comput. Stat. Data Anal. 38:367–78
7. Vilnis L, McCallum A. 2015. Word representations via Gaussian embedding. arXiv:1412.6623 [cs.CL]
Frequently Asked QuestionsQ: What is the prediction methodology for TGH^A stock?
A: TGH^A stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Multiple Regression
Q: Is TGH^A stock a buy or sell?
A: The dominant strategy among neural network is to Sell TGH^A Stock.
Q: Is Textainer Group Holdings Limited Depositary Shares each representing a 1/1000th interest in a share of 7.000% Series A Cumulative Redeemable Perpetual Preference Shares stock a good investment?
A: The consensus rating for Textainer Group Holdings Limited Depositary Shares each representing a 1/1000th interest in a share of 7.000% Series A Cumulative Redeemable Perpetual Preference Shares is Sell and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of TGH^A stock?
A: The consensus rating for TGH^A is Sell.
Q: What is the prediction period for TGH^A stock?
A: The prediction period for TGH^A is (n+16 weeks)