Modelling A.I. in Economics

Is OPINL Stock Expected to Go Up? (Forecast)

Outlook: Office Properties Income Trust 6.375% Senior Notes due 2050 is assigned short-term B1 & long-term Ba3 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy : Sell
Time series to forecast n: for Weeks2
Methodology : Modular Neural Network (Speculative Sentiment Analysis)
Hypothesis Testing : Linear 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

Office Properties Income Trust 6.375% Senior Notes due 2050 prediction model is evaluated with Modular Neural Network (Speculative Sentiment Analysis) and Linear Regression1,2,3,4 and it is concluded that the OPINL 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 speculative 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 speculative sentiment analysis, MNNs can be used to identify the sentiment of people who are speculating about the future value of an asset, such as a stock or a cryptocurrency. This information can then be used to make investment decisions, to identify trends in the market, and to target investors with relevant advertising.5 According to price forecasts for 3 Month period, the dominant strategy among neural network is: Sell

Graph 2

Key Points

  1. Modular Neural Network (Speculative Sentiment Analysis) for OPINL stock price prediction process.
  2. Linear Regression
  3. What are the most successful trading algorithms?
  4. How can neural networks improve predictions?
  5. Stock Rating

OPINL Stock Price Forecast

We consider Office Properties Income Trust 6.375% Senior Notes due 2050 Decision Process with Modular Neural Network (Speculative Sentiment Analysis) where A is the set of discrete actions of OPINL 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


Sample Set: Neural Network
Stock/Index: OPINL Office Properties Income Trust 6.375% Senior Notes due 2050
Time series to forecast: 3 Month

According to price forecasts, the dominant strategy among neural network is: Sell


F(Linear Regression)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(Modular Neural Network (Speculative Sentiment Analysis)) X S(n):→ 3 Month i = 1 n s i

n:Time series to forecast

p:Price signals of OPINL stock

j:Nash equilibria (Neural Network)

k:Dominated move of OPINL stock holders

a:Best response for OPINL target price


A modular neural network (MNN) is a type of artificial neural network that can be used for speculative 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 speculative sentiment analysis, MNNs can be used to identify the sentiment of people who are speculating about the future value of an asset, such as a stock or a cryptocurrency. This information can then be used to make investment decisions, to identify trends in the market, and to target investors with relevant advertising.5 In statistics, linear regression is a method for estimating the relationship between a dependent variable and one or more independent variables. The dependent variable is the variable that is being predicted, and the independent variables are the variables that are used to predict the dependent variable. Linear regression assumes that the relationship between the dependent variable and the independent variables is linear. This means that the dependent variable can be represented as a straight line function of the independent variables.6,7

 

For further technical information as per how our model work we invite you to visit the article below: 

How do PredictiveAI algorithms actually work?

OPINL Stock Forecast (Buy or Sell) Strategic Interaction Table

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 Modular Neural Network (Speculative Sentiment Analysis) based OPINL Stock Prediction Model

  1. Hedging relationships that qualified for hedge accounting in accordance with IAS 39 that also qualify for hedge accounting in accordance with the criteria of this Standard (see paragraph 6.4.1), after taking into account any rebalancing of the hedging relationship on transition (see paragraph 7.2.25(b)), shall be regarded as continuing hedging relationships.
  2. IFRS 16, issued in January 2016, amended paragraphs 2.1, 5.5.15, B4.3.8, B5.5.34 and B5.5.46. An entity shall apply those amendments when it applies IFRS 16.
  3. An entity shall apply Annual Improvements to IFRS Standards 2018–2020 to financial liabilities that are modified or exchanged on or after the beginning of the annual reporting period in which the entity first applies the amendment.
  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.

OPINL Office Properties Income Trust 6.375% Senior Notes due 2050 Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*B1Ba3
Income StatementB2Caa2
Balance SheetBaa2Baa2
Leverage RatiosB2Baa2
Cash FlowBa1B1
Rates of Return and ProfitabilityCB1

*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?

References

  1. Chernozhukov V, Escanciano JC, Ichimura H, Newey WK. 2016b. Locally robust semiparametric estimation. arXiv:1608.00033 [math.ST]
  2. Jorgenson, D.W., Weitzman, M.L., ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. Can Neural Networks Predict Stock Market?. AC Investment Research Journal, 220(44).
  3. Varian HR. 2014. Big data: new tricks for econometrics. J. Econ. Perspect. 28:3–28
  4. Holland PW. 1986. Statistics and causal inference. J. Am. Stat. Assoc. 81:945–60
  5. Athey S, Bayati M, Doudchenko N, Imbens G, Khosravi K. 2017a. Matrix completion methods for causal panel data models. arXiv:1710.10251 [math.ST]
  6. Krizhevsky A, Sutskever I, Hinton GE. 2012. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, Vol. 25, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 1097–105. San Diego, CA: Neural Inf. Process. Syst. Found.
  7. Pennington J, Socher R, Manning CD. 2014. GloVe: global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods on Natural Language Processing, pp. 1532–43. New York: Assoc. Comput. Linguist.
Frequently Asked QuestionsQ: Is OPINL stock expected to rise?
A: OPINL stock prediction model is evaluated with Modular Neural Network (Speculative Sentiment Analysis) and Linear Regression and it is concluded that dominant strategy for OPINL stock is Sell
Q: Is OPINL stock a buy or sell?
A: The dominant strategy among neural network is to Sell OPINL Stock.
Q: Is Office Properties Income Trust 6.375% Senior Notes due 2050 stock a good investment?
A: The consensus rating for Office Properties Income Trust 6.375% Senior Notes due 2050 is Sell and is assigned short-term B1 & long-term Ba3 estimated rating.
Q: What is the consensus rating of OPINL stock?
A: The consensus rating for OPINL is Sell.
Q: What is the forecast for OPINL stock?
A: OPINL target price forecast: Sell

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