BRAEMAR SHIPPING SERVICES PLC Research Report

## Summary

Understanding the pattern of financial activities and predicting their development and changes are research hotspots in academic and financial circles. Because financial data contain complex, incomplete and fuzzy information, predicting their development trends is an extremely difficult challenge. Fluctuations in financial data depend on a myriad of correlated constantly changing factors. Therefore, predicting and analysing financial data are a nonlinear, time-dependent problem. Deep neural networks (DNNs) combine the advantages of deep learning (DL) and neural networks and can be used to solve nonlinear problems more satisfactorily compared to conventional machine learning algorithms. We evaluate BRAEMAR SHIPPING SERVICES PLC prediction models with Modular Neural Network (Market News Sentiment Analysis) and Stepwise Regression1,2,3,4 and conclude that the LON:BMS stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold LON:BMS stock.

## Key Points

1. Can stock prices be predicted?
2. What is neural prediction?
3. Game Theory

## LON:BMS Target Price Prediction Modeling Methodology

We consider BRAEMAR SHIPPING SERVICES PLC Stock Decision Process with Modular Neural Network (Market News Sentiment Analysis) where A is the set of discrete actions of LON:BMS 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(Stepwise Regression)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(Modular Neural Network (Market News Sentiment Analysis)) X S(n):→ (n+4 weeks) $∑ i = 1 n s i$

n:Time series to forecast

p:Price signals of LON:BMS 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?

## LON:BMS Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: LON:BMS BRAEMAR SHIPPING SERVICES PLC
Time series to forecast n: 25 Nov 2022 for (n+4 weeks)

According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold LON:BMS stock.

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 (Yellow to Green): *Technical Analysis%

## Adjusted IFRS* Prediction Methods for BRAEMAR SHIPPING SERVICES PLC

1. If an entity previously accounted for a derivative liability that is linked to, and must be settled by, delivery of an equity instrument that does not have a quoted price in an active market for an identical instrument (ie a Level 1 input) at cost in accordance with IAS 39, it shall measure that derivative liability at fair value at the date of initial application. Any difference between the previous carrying amount and the fair value shall be recognised in the opening retained earnings of the reporting period that includes the date of initial application.
2. For loan commitments, an entity considers changes in the risk of a default occurring on the loan to which a loan commitment relates. For financial guarantee contracts, an entity considers the changes in the risk that the specified debtor will default on the contract.
3. IFRS 7 defines credit risk as 'the risk that one party to a financial instrument will cause a financial loss for the other party by failing to discharge an obligation'. The requirement in paragraph 5.7.7(a) relates to the risk that the issuer will fail to perform on that particular liability. It does not necessarily relate to the creditworthiness of the issuer. For example, if an entity issues a collateralised liability and a non-collateralised liability that are otherwise identical, the credit risk of those two liabilities will be different, even though they are issued by the same entity. The credit risk on the collateralised liability will be less than the credit risk of the non-collateralised liability. The credit risk for a collateralised liability may be close to zero.
4. All investments in equity instruments and contracts on those instruments must be measured at fair value. However, in limited circumstances, cost may be an appropriate estimate of fair value. That may be the case if insufficient more recent information is available to measure fair value, or if there is a wide range of possible fair value measurements and cost represents the best estimate of fair value within that range.

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

## Conclusions

BRAEMAR SHIPPING SERVICES PLC assigned short-term B2 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) with Stepwise Regression1,2,3,4 and conclude that the LON:BMS stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold LON:BMS stock.

### Financial State Forecast for LON:BMS BRAEMAR SHIPPING SERVICES PLC Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B2B1
Operational Risk 6356
Market Risk6885
Technical Analysis3081
Fundamental Analysis7335
Risk Unsystematic4647

### Prediction Confidence Score

Trust metric by Neural Network: 83 out of 100 with 537 signals.

## References

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3. Byron, R. P. O. Ashenfelter (1995), "Predicting the quality of an unborn grange," Economic Record, 71, 40–53.
4. Robins J, Rotnitzky A. 1995. Semiparametric efficiency in multivariate regression models with missing data. J. Am. Stat. Assoc. 90:122–29
5. Li L, Chu W, Langford J, Moon T, Wang X. 2012. An unbiased offline evaluation of contextual bandit algo- rithms with generalized linear models. In Proceedings of 4th ACM International Conference on Web Search and Data Mining, pp. 297–306. New York: ACM
6. Athey S, Imbens GW. 2017b. The state of applied econometrics: causality and policy evaluation. J. Econ. Perspect. 31:3–32
7. 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
Frequently Asked QuestionsQ: What is the prediction methodology for LON:BMS stock?
A: LON:BMS stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) and Stepwise Regression
Q: Is LON:BMS stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:BMS Stock.
Q: Is BRAEMAR SHIPPING SERVICES PLC stock a good investment?
A: The consensus rating for BRAEMAR SHIPPING SERVICES PLC is Hold and assigned short-term B2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:BMS stock?
A: The consensus rating for LON:BMS is Hold.
Q: What is the prediction period for LON:BMS stock?
A: The prediction period for LON:BMS is (n+4 weeks)