Modelling A.I. in Economics

Where Will PSA^R Stock Be in 4 Weeks?

Outlook: Public Storage Depositary Shares Each Representing 1/1000 of a 4.00% Cumulative Preferred Share of Bene cial Interest Series R is assigned short-term B3 & long-term B1 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy : Buy
Time series to forecast n: for Weeks2
Methodology : Modular Neural Network (Market News Sentiment Analysis)
Hypothesis Testing : Lasso 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

Public Storage Depositary Shares Each Representing 1/1000 of a 4.00% Cumulative Preferred Share of Bene cial Interest Series R prediction model is evaluated with Modular Neural Network (Market News Sentiment Analysis) and Lasso Regression1,2,3,4 and it is concluded that the PSA^R 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.5 According to price forecasts for 4 Weeks period, the dominant strategy among neural network is: Buy

Graph 47

Key Points

  1. Modular Neural Network (Market News Sentiment Analysis) for PSA^R stock price prediction process.
  2. Lasso Regression
  3. Short/Long Term Stocks
  4. What is the best way to predict stock prices?
  5. How useful are statistical predictions?

PSA^R Stock Price Forecast

We consider Public Storage Depositary Shares Each Representing 1/1000 of a 4.00% Cumulative Preferred Share of Bene cial Interest Series R Decision Process with Modular Neural Network (Market News Sentiment Analysis) where A is the set of discrete actions of PSA^R 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: PSA^R Public Storage Depositary Shares Each Representing 1/1000 of a 4.00% Cumulative Preferred Share of Bene cial Interest Series R
Time series to forecast: 4 Weeks

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


F(Lasso 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 (Market News Sentiment Analysis)) X S(n):→ 4 Weeks S = s 1 s 2 s 3

n:Time series to forecast

p:Price signals of PSA^R stock

j:Nash equilibria (Neural Network)

k:Dominated move of PSA^R stock holders

a:Best response for PSA^R target price


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.5 Lasso regression, also known as L1 regularization, is a type of regression analysis that adds a penalty to the least squares objective function in order to reduce the variance of the estimates and to induce sparsity in the model. This is done by adding a term to the objective function that is proportional to the sum of the absolute values of the coefficients. The penalty term is called the "lasso" penalty, and it is controlled by a parameter called the "lasso constant". Lasso regression can be used to address the problem of multicollinearity in linear regression, as well as the problem of overfitting. Multicollinearity occurs when two or more independent variables are highly correlated. This can cause the standard errors of the coefficients to be large, and it can also cause the coefficients to be unstable. Overfitting occurs when a model is too closely fit to the training data, and as a result, it does not generalize well to new data.6,7

 

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How do PredictiveAI algorithms actually work?

PSA^R 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 (Market News Sentiment Analysis) based PSA^R Stock Prediction Model

  1. An entity shall apply Prepayment Features with Negative Compensation (Amendments to IFRS 9) retrospectively in accordance with IAS 8, except as specified in paragraphs 7.2.30–7.2.34
  2. At the date of initial application, an entity shall determine whether the treatment in paragraph 5.7.7 would create or enlarge an accounting mismatch in profit or loss on the basis of the facts and circumstances that exist at the date of initial application. This Standard shall be applied retrospectively on the basis of that determination.
  3. When a group of items that constitute a net position is designated as a hedged item, an entity shall designate the overall group of items that includes the items that can make up the net position. An entity is not permitted to designate a non-specific abstract amount of a net position. For example, an entity has a group of firm sale commitments in nine months' time for FC100 and a group of firm purchase commitments in 18 months' time for FC120. The entity cannot designate an abstract amount of a net position up to FC20. Instead, it must designate a gross amount of purchases and a gross amount of sales that together give rise to the hedged net position. An entity shall designate gross positions that give rise to the net position so that the entity is able to comply with the requirements for the accounting for qualifying hedging relationships.
  4. An entity shall apply the impairment requirements in Section 5.5 retrospectively in accordance with IAS 8 subject to paragraphs 7.2.15 and 7.2.18–7.2.20.

*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.

PSA^R Public Storage Depositary Shares Each Representing 1/1000 of a 4.00% Cumulative Preferred Share of Bene cial Interest Series R Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*B3B1
Income StatementBa1Baa2
Balance SheetCaa2Baa2
Leverage RatiosB3C
Cash FlowB2B3
Rates of Return and ProfitabilityCB3

*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. Athey S, Bayati M, Imbens G, Zhaonan Q. 2019. Ensemble methods for causal effects in panel data settings. NBER Work. Pap. 25675
  2. Bessler, D. A. T. Covey (1991), "Cointegration: Some results on U.S. cattle prices," Journal of Futures Markets, 11, 461–474.
  3. Athey S, Imbens G. 2016. Recursive partitioning for heterogeneous causal effects. PNAS 113:7353–60
  4. H. Khalil and J. Grizzle. Nonlinear systems, volume 3. Prentice hall Upper Saddle River, 2002.
  5. J. G. Schneider, W. Wong, A. W. Moore, and M. A. Riedmiller. Distributed value functions. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pages 371–378, 1999.
  6. Jorgenson, D.W., Weitzman, M.L., ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. Apple's Stock Price: How News Affects Volatility. AC Investment Research Journal, 220(44).
  7. Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85
Frequently Asked QuestionsQ: Is PSA^R stock expected to rise?
A: PSA^R stock prediction model is evaluated with Modular Neural Network (Market News Sentiment Analysis) and Lasso Regression and it is concluded that dominant strategy for PSA^R stock is Buy
Q: Is PSA^R stock a buy or sell?
A: The dominant strategy among neural network is to Buy PSA^R Stock.
Q: Is Public Storage Depositary Shares Each Representing 1/1000 of a 4.00% Cumulative Preferred Share of Bene cial Interest Series R stock a good investment?
A: The consensus rating for Public Storage Depositary Shares Each Representing 1/1000 of a 4.00% Cumulative Preferred Share of Bene cial Interest Series R is Buy and is assigned short-term B3 & long-term B1 estimated rating.
Q: What is the consensus rating of PSA^R stock?
A: The consensus rating for PSA^R is Buy.
Q: What is the forecast for PSA^R stock?
A: PSA^R target price forecast: Buy

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