Reading International Inc Class B Common Stock is assigned short-term B3 & long-term Ba1 estimated rating.

Outlook: Reading International Inc Class B Common Stock is assigned short-term B3 & long-term Ba1 estimated rating.
Dominant Strategy : Speculative Trend
Time series to forecast n: 21 Jun 2023 for 1 Year
Methodology : Modular Neural Network (News Feed Sentiment Analysis)

Summary

Reading International Inc Class B Common Stock prediction model is evaluated with Modular Neural Network (News Feed Sentiment Analysis) and Wilcoxon Sign-Rank Test^1,2,3,4 and it is concluded that the RDIB stock is predictable in the short/long term. A modular neural network (MNN) is a type of artificial neural network that can be used for news feed sentiment analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying sentiment in text or identifying patterns in data. The modules are then combined to form a single neural network that can perform multiple tasks. In the context of news feed sentiment analysis, MNNs can be used to identify the sentiment of news articles, social media posts, and other forms of online content. This information can then be used to filter out irrelevant or unwanted content, to identify trends in public opinion, and to target users with relevant advertising. According to price forecasts for 1 Year period, the dominant strategy among neural network is: Speculative Trend

Key Points

1. Can machine learning predict?
2. What is a prediction confidence?
3. What statistical methods are used to analyze data?

RDIB Target Price Prediction Modeling Methodology

We consider Reading International Inc Class B Common Stock Decision Process with Modular Neural Network (News Feed Sentiment Analysis) where A is the set of discrete actions of RDIB 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(Wilcoxon Sign-Rank Test)^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(Modular Neural Network (News Feed Sentiment Analysis)) X S(n):→ 1 Year $\overrightarrow{R}=\left(r_1,r_2,r_3\right)$

n:Time series to forecast

p:Price signals of RDIB stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Modular Neural Network (News Feed Sentiment Analysis)

A modular neural network (MNN) is a type of artificial neural network that can be used for news feed sentiment analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying sentiment in text or identifying patterns in data. The modules are then combined to form a single neural network that can perform multiple tasks. In the context of news feed sentiment analysis, MNNs can be used to identify the sentiment of news articles, social media posts, and other forms of online content. This information can then be used to filter out irrelevant or unwanted content, to identify trends in public opinion, and to target users with relevant advertising.

Wilcoxon Sign-Rank Test

The Wilcoxon rank-sum test, also known as the Mann-Whitney U test, is a non-parametric test that is used to compare the medians of two independent samples. It is a rank-based test, which means that it does not assume that the data is normally distributed. The Wilcoxon rank-sum test is calculated by first ranking the data from both samples, and then finding the sum of the ranks for one of the samples. The Wilcoxon rank-sum test statistic is then calculated by subtracting the sum of the ranks for one sample from the sum of the ranks for the other sample. The p-value for the Wilcoxon rank-sum test is calculated using a table of critical values. The p-value is the probability of obtaining a test statistic at least as extreme as the one observed, assuming that the null hypothesis is true.

 

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?

RDIB Stock Forecast (Buy or Sell) for 1 Year

Sample Set: Neural Network
Stock/Index: RDIB Reading International Inc Class B Common Stock
Time series to forecast n: 21 Jun 2023 for 1 Year

According to price forecasts for 1 Year period, the dominant strategy among neural network is: Speculative Trend

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 Reading International Inc Class B Common Stock

1. At the date of initial application, an entity shall assess whether a financial asset meets the condition in paragraphs 4.1.2(a) or 4.1.2A(a) on the basis of the facts and circumstances that exist at that date. The resulting classification shall be applied retrospectively irrespective of the entity's business model in prior reporting periods.
2. Although the objective of an entity's business model may be to hold financial assets in order to collect contractual cash flows, the entity need not hold all of those instruments until maturity. Thus an entity's business model can be to hold financial assets to collect contractual cash flows even when sales of financial assets occur or are expected to occur in the future.
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. Paragraphs 6.9.7–6.9.13 provide exceptions to the requirements specified in those paragraphs only. An entity shall apply all other hedge accounting requirements in this Standard, including the qualifying criteria in paragraph 6.4.1, to hedging relationships that were directly affected by 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.

Conclusions

Reading International Inc Class B Common Stock is assigned short-term B3 & long-term Ba1 estimated rating. Reading International Inc Class B Common Stock prediction model is evaluated with Modular Neural Network (News Feed Sentiment Analysis) and Wilcoxon Sign-Rank Test^1,2,3,4 and it is concluded that the RDIB stock is predictable in the short/long term. According to price forecasts for 1 Year period, the dominant strategy among neural network is: Speculative Trend

RDIB Reading International Inc Class B Common Stock Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	B3	Ba1
Income Statement	B1	Baa2
Balance Sheet	C	Baa2
Leverage Ratios	B1	Baa2
Cash Flow	C	B2
Rates of Return and Profitability	B3	Caa2

*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: 72 out of 100 with 733 signals.

References

1. Bennett J, Lanning S. 2007. The Netflix prize. In Proceedings of KDD Cup and Workshop 2007, p. 35. New York: ACM
2. Robins J, Rotnitzky A. 1995. Semiparametric efficiency in multivariate regression models with missing data. J. Am. Stat. Assoc. 90:122–29
3. D. White. Mean, variance, and probabilistic criteria in finite Markov decision processes: A review. Journal of Optimization Theory and Applications, 56(1):1–29, 1988.
4. Dimakopoulou M, Zhou Z, Athey S, Imbens G. 2018. Balanced linear contextual bandits. arXiv:1812.06227 [cs.LG]
5. Dimakopoulou M, Zhou Z, Athey S, Imbens G. 2018. Balanced linear contextual bandits. arXiv:1812.06227 [cs.LG]
6. Hartford J, Lewis G, Taddy M. 2016. Counterfactual prediction with deep instrumental variables networks. arXiv:1612.09596 [stat.AP]
7. M. Sobel. The variance of discounted Markov decision processes. Applied Probability, pages 794–802, 1982

Frequently Asked Questions

Q: What is the prediction methodology for RDIB stock?
A: RDIB stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and Wilcoxon Sign-Rank Test
Q: Is RDIB stock a buy or sell?
A: The dominant strategy among neural network is to Speculative Trend RDIB Stock.
Q: Is Reading International Inc Class B Common Stock stock a good investment?
A: The consensus rating for Reading International Inc Class B Common Stock is Speculative Trend and is assigned short-term B3 & long-term Ba1 estimated rating.
Q: What is the consensus rating of RDIB stock?
A: The consensus rating for RDIB is Speculative Trend.
Q: What is the prediction period for RDIB stock?
A: The prediction period for RDIB is 1 Year