Outlook: Tsakos Energy Navigation Ltd Series F Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Shares par value \$1.00 is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Wait until speculative trend diminishes
Time series to forecast n: 31 Jan 2023 for (n+8 weeks)
Methodology : Statistical Inference (ML)

## Abstract

Tsakos Energy Navigation Ltd Series F Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Shares par value \$1.00 prediction model is evaluated with Statistical Inference (ML) and Paired T-Test1,2,3,4 and it is concluded that the TNP^F stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Wait until speculative trend diminishes

## Key Points

1. What is the best way to predict stock prices?
2. What is a prediction confidence?
3. Is it better to buy and sell or hold?

## TNP^F Target Price Prediction Modeling Methodology

We consider Tsakos Energy Navigation Ltd Series F Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Shares par value \$1.00 Decision Process with Statistical Inference (ML) where A is the set of discrete actions of TNP^F 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(Paired T-Test)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(Statistical Inference (ML)) X S(n):→ (n+8 weeks) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of TNP^F 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?

## TNP^F Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: TNP^F Tsakos Energy Navigation Ltd Series F Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Shares par value \$1.00
Time series to forecast n: 31 Jan 2023 for (n+8 weeks)

According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Wait until speculative trend diminishes

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 Tsakos Energy Navigation Ltd Series F Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Shares par value \$1.00

1. 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).
2. If, at the date of initial application, it is impracticable (as defined in IAS 8) for an entity to assess whether the fair value of a prepayment feature was insignificant in accordance with paragraph B4.1.12(c) on the basis of the facts and circumstances that existed at the initial recognition of the financial asset, an entity shall assess the contractual cash flow characteristics of that financial asset on the basis of the facts and circumstances that existed at the initial recognition of the financial asset without taking into account the exception for prepayment features in paragraph B4.1.12. (See also paragraph 42S of IFRS 7.)
3. For a financial guarantee contract, the entity is required to make payments only in the event of a default by the debtor in accordance with the terms of the instrument that is guaranteed. Accordingly, cash shortfalls are the expected payments to reimburse the holder for a credit loss that it incurs less any amounts that the entity expects to receive from the holder, the debtor or any other party. If the asset is fully guaranteed, the estimation of cash shortfalls for a financial guarantee contract would be consistent with the estimations of cash shortfalls for the asset subject to the guarantee
4. In some cases, the qualitative and non-statistical quantitative information available may be sufficient to determine that a financial instrument has met the criterion for the recognition of a loss allowance at an amount equal to lifetime expected credit losses. That is, the information does not need to flow through a statistical model or credit ratings process in order to determine whether there has been a significant increase in the credit risk of the financial instrument. In other cases, an entity may need to consider other information, including information from its statistical models or credit ratings processes.

*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

Tsakos Energy Navigation Ltd Series F Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Shares par value \$1.00 is assigned short-term Ba1 & long-term Ba1 estimated rating. Tsakos Energy Navigation Ltd Series F Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Shares par value \$1.00 prediction model is evaluated with Statistical Inference (ML) and Paired T-Test1,2,3,4 and it is concluded that the TNP^F stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Wait until speculative trend diminishes

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

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementBa2C
Balance SheetCaa2C
Leverage RatiosBaa2Caa2
Cash FlowB2Caa2
Rates of Return and ProfitabilityCaa2Baa2

*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: 84 out of 100 with 565 signals.

## References

1. D. Bertsekas. Min common/max crossing duality: A geometric view of conjugacy in convex optimization. Lab. for Information and Decision Systems, MIT, Tech. Rep. Report LIDS-P-2796, 2009
2. Bengio Y, Schwenk H, Senécal JS, Morin F, Gauvain JL. 2006. Neural probabilistic language models. In Innovations in Machine Learning: Theory and Applications, ed. DE Holmes, pp. 137–86. Berlin: Springer
3. Knox SW. 2018. Machine Learning: A Concise Introduction. Hoboken, NJ: Wiley
4. Van der Vaart AW. 2000. Asymptotic Statistics. Cambridge, UK: Cambridge Univ. Press
5. Clements, M. P. D. F. Hendry (1995), "Forecasting in cointegrated systems," Journal of Applied Econometrics, 10, 127–146.
6. T. Shardlow and A. Stuart. A perturbation theory for ergodic Markov chains and application to numerical approximations. SIAM journal on numerical analysis, 37(4):1120–1137, 2000
7. Matzkin RL. 1994. Restrictions of economic theory in nonparametric methods. In Handbook of Econometrics, Vol. 4, ed. R Engle, D McFadden, pp. 2523–58. Amsterdam: Elsevier
Frequently Asked QuestionsQ: What is the prediction methodology for TNP^F stock?
A: TNP^F stock prediction methodology: We evaluate the prediction models Statistical Inference (ML) and Paired T-Test
Q: Is TNP^F stock a buy or sell?
A: The dominant strategy among neural network is to Wait until speculative trend diminishes TNP^F Stock.
Q: Is Tsakos Energy Navigation Ltd Series F 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 F Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Shares par value \$1.00 is Wait until speculative trend diminishes and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of TNP^F stock?
A: The consensus rating for TNP^F is Wait until speculative trend diminishes.
Q: What is the prediction period for TNP^F stock?
A: The prediction period for TNP^F is (n+8 weeks)

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