Modelling A.I. in Economics

TNP^E Stock: A Bright Investment or a Bubble Waiting to Burst? (Forecast)

Outlook: Tsakos Energy Navigation Ltd Series E Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Shares par value $1.00 is assigned short-term B3 & long-term Ba3 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy : Hold
Time series to forecast n: for Weeks2
Methodology : Modular Neural Network (Financial Sentiment Analysis)
Hypothesis Testing : Polynomial 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

Tsakos Energy Navigation Ltd Series E Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Shares par value $1.00 prediction model is evaluated with Modular Neural Network (Financial Sentiment Analysis) and Polynomial Regression1,2,3,4 and it is concluded that the TNP^E stock is predictable in the short/long term. Modular neural networks (MNNs) are a type of artificial neural network that can be used for financial 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 financial sentiment analysis, MNNs can be used to identify the sentiment of financial news articles, social media posts, and other forms of online content. 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 8 Weeks period, the dominant strategy among neural network is: Hold

Graph 31

Key Points

  1. Modular Neural Network (Financial Sentiment Analysis) for TNP^E stock price prediction process.
  2. Polynomial Regression
  3. What is statistical models in machine learning?
  4. Stock Forecast Based On a Predictive Algorithm
  5. Trading Interaction

TNP^E Stock Price Forecast

We consider Tsakos Energy Navigation Ltd Series E Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Shares par value $1.00 Decision Process with Modular Neural Network (Financial Sentiment Analysis) where A is the set of discrete actions of TNP^E 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: TNP^E Tsakos Energy Navigation Ltd Series E Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Shares par value $1.00
Time series to forecast: 8 Weeks

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


F(Polynomial 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 (Financial Sentiment Analysis)) X S(n):→ 8 Weeks R = r 1 r 2 r 3

n:Time series to forecast

p:Price signals of TNP^E stock

j:Nash equilibria (Neural Network)

k:Dominated move of TNP^E stock holders

a:Best response for TNP^E target price


Modular neural networks (MNNs) are a type of artificial neural network that can be used for financial 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 financial sentiment analysis, MNNs can be used to identify the sentiment of financial news articles, social media posts, and other forms of online content. This information can then be used to make investment decisions, to identify trends in the market, and to target investors with relevant advertising.5 Polynomial regression is a type of regression analysis that uses a polynomial function to model the relationship between a dependent variable and one or more independent variables. Polynomial functions are mathematical functions that have a polynomial term, which is a term that is raised to a power greater than 1. In polynomial regression, the dependent variable is modeled as a polynomial function of the independent variables. The degree of the polynomial function is determined by the researcher. The higher the degree of the polynomial function, the more complex the model will be.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?

TNP^E 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 (Financial Sentiment Analysis) based TNP^E Stock Prediction Model

  1. For the purpose of applying the requirements in paragraphs 6.4.1(c)(i) and B6.4.4–B6.4.6, an entity shall assume that the interest rate benchmark on which the hedged cash flows and/or the hedged risk (contractually or noncontractually specified) are based, or the interest rate benchmark on which the cash flows of the hedging instrument are based, is not altered as a result of interest rate benchmark reform.
  2. If, in applying paragraph 7.2.44, an entity reinstates a discontinued hedging relationship, the entity shall read references in paragraphs 6.9.11 and 6.9.12 to the date the alternative benchmark rate is designated as a noncontractually specified risk component for the first time as referring to the date of initial application of these amendments (ie the 24-month period for that alternative benchmark rate designated as a non-contractually specified risk component begins from the date of initial application of these amendments).
  3. For the purpose of this Standard, reasonable and supportable information is that which is reasonably available at the reporting date without undue cost or effort, including information about past events, current conditions and forecasts of future economic conditions. Information that is available for financial reporting purposes is considered to be available without undue cost or effort.
  4. Conversely, if changes in the extent of offset indicate that the fluctuation is around a hedge ratio that is different from the hedge ratio that is currently used for that hedging relationship, or that there is a trend leading away from that hedge ratio, hedge ineffectiveness can be reduced by adjusting the hedge ratio, whereas retaining the hedge ratio would increasingly produce hedge ineffectiveness. Hence, in such circumstances, an entity must evaluate whether the hedging relationship reflects an imbalance between the weightings of the hedged item and the hedging instrument that would create hedge ineffectiveness (irrespective of whether recognised or not) that could result in an accounting outcome that would be inconsistent with the purpose of hedge accounting. If the hedge ratio is adjusted, it also affects the measurement and recognition of hedge ineffectiveness because, on rebalancing, the hedge ineffectiveness of the hedging relationship must be determined and recognised immediately before adjusting the hedging relationship in accordance with paragraph B6.5.8.

*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.

TNP^E Tsakos Energy Navigation Ltd Series E Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Shares par value $1.00 Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*B3Ba3
Income StatementCCaa2
Balance SheetB3Baa2
Leverage RatiosB1Ba2
Cash FlowB3Baa2
Rates of Return and ProfitabilityB3C

*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. D. Bertsekas. Nonlinear programming. Athena Scientific, 1999.
  2. Arjovsky M, Bottou L. 2017. Towards principled methods for training generative adversarial networks. arXiv:1701.04862 [stat.ML]
  3. Jorgenson, D.W., Weitzman, M.L., ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. MRNA: The Next Big Thing in mRNA Vaccines. AC Investment Research Journal, 220(44).
  4. Mullainathan S, Spiess J. 2017. Machine learning: an applied econometric approach. J. Econ. Perspect. 31:87–106
  5. Bessler, D. A. T. Covey (1991), "Cointegration: Some results on U.S. cattle prices," Journal of Futures Markets, 11, 461–474.
  6. Z. Wang, T. Schaul, M. Hessel, H. van Hasselt, M. Lanctot, and N. de Freitas. Dueling network architectures for deep reinforcement learning. In Proceedings of the International Conference on Machine Learning (ICML), pages 1995–2003, 2016.
  7. 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.
Frequently Asked QuestionsQ: Is TNP^E stock expected to rise?
A: TNP^E stock prediction model is evaluated with Modular Neural Network (Financial Sentiment Analysis) and Polynomial Regression and it is concluded that dominant strategy for TNP^E stock is Hold
Q: Is TNP^E stock a buy or sell?
A: The dominant strategy among neural network is to Hold TNP^E Stock.
Q: Is Tsakos Energy Navigation Ltd Series E Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Shares par value $1.00 stock a good investment?
A: The consensus rating for Tsakos Energy Navigation Ltd Series E Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Shares par value $1.00 is Hold and is assigned short-term B3 & long-term Ba3 estimated rating.
Q: What is the consensus rating of TNP^E stock?
A: The consensus rating for TNP^E is Hold.
Q: What is the forecast for TNP^E stock?
A: TNP^E target price forecast: Hold

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