**Outlook:**LANSDOWNE OIL & GAS PLC is assigned short-term B2 & long-term Ba3 estimated rating.

**AUC Score :**

**Short-Term Revised**

^{1}:**Dominant Strategy :**Hold

**Time series to forecast n:** for

^{2}

**Methodology :**Statistical Inference (ML)

**Hypothesis Testing :**Spearman Correlation

**Surveillance :**Major exchange and OTC

^{1}The accuracy of the model is being monitored on a regular basis.(15-minute period)

^{2}Time series is updated based on short-term trends.

LANSDOWNE OIL & GAS PLC prediction model is evaluated with Statistical Inference (ML) and Spearman Correlation

^{1,2,3,4}and it is concluded that the LON:LOGP stock is predictable in the short/long term. Statistical inference is a process of drawing conclusions about a population based on data from a sample of that population. In machine learning (ML), statistical inference is used to make predictions about new data based on data that has already been seen.

^{5}

**According to price forecasts for 3 Month period, the dominant strategy among neural network is: Hold**

## Key Points

- How accurate is machine learning in stock market?
- Market Signals
- Should I buy stocks now or wait amid such uncertainty?

## LON:LOGP Stock Price Forecast

We consider LANSDOWNE OIL & GAS PLC Decision Process with Statistical Inference (ML) where A is the set of discrete actions of LON:LOGP 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:**LON:LOGP LANSDOWNE OIL & GAS PLC

**Time series to forecast:**3 Month

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

^{6,7}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Statistical Inference (ML)) X S(n):→ 3 Month $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of LON:LOGP stock

j:Nash equilibria (Neural Network)

k:Dominated move of LON:LOGP stock holders

a:Best response for LON:LOGP target price

Statistical inference is a process of drawing conclusions about a population based on data from a sample of that population. In machine learning (ML), statistical inference is used to make predictions about new data based on data that has already been seen.

^{5}Spearman correlation is a nonparametric measure of the strength and direction of association between two variables. It is a rank-based correlation, which means that it does not assume that the data is normally distributed. Spearman correlation is calculated by first ranking the data for each variable, and then calculating the Pearson correlation between the ranks.

^{6,7}

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:LOGP 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 Statistical Inference (ML) based LON:LOGP Stock Prediction Model

- An entity shall assess at the inception of the hedging relationship, and on an ongoing basis, whether a hedging relationship meets the hedge effectiveness requirements. At a minimum, an entity shall perform the ongoing assessment at each reporting date or upon a significant change in the circumstances affecting the hedge effectiveness requirements, whichever comes first. The assessment relates to expectations about hedge effectiveness and is therefore only forward-looking.
- An entity may manage and evaluate the performance of a group of financial liabilities or financial assets and financial liabilities in such a way that measuring that group at fair value through profit or loss results in more relevant information. The focus in this instance is on the way the entity manages and evaluates performance, instead of on the nature of its financial instruments.
- An entity must look through until it can identify the underlying pool of instruments that are creating (instead of passing through) the cash flows. This is the underlying pool of financial instruments.
- A portfolio of financial assets that is managed and whose performance is evaluated on a fair value basis (as described in paragraph 4.2.2(b)) is neither held to collect contractual cash flows nor held both to collect contractual cash flows and to sell financial assets. The entity is primarily focused on fair value information and uses that information to assess the assets' performance and to make decisions. In addition, a portfolio of financial assets that meets the definition of held for trading is not held to collect contractual cash flows or held both to collect contractual cash flows and to sell financial assets. For such portfolios, the collection of contractual cash flows is only incidental to achieving the business model's objective. Consequently, such portfolios of financial assets must be measured at fair value through profit or loss.

*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.

### LON:LOGP LANSDOWNE OIL & GAS PLC Financial Analysis*

Rating | Short-Term | Long-Term Senior |
---|---|---|

Outlook* | B2 | Ba3 |

Income Statement | Caa2 | B2 |

Balance Sheet | B3 | B2 |

Leverage Ratios | B3 | Baa2 |

Cash Flow | Ba3 | Baa2 |

Rates of Return and Profitability | B2 | C |

*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

- E. Altman. Constrained Markov decision processes, volume 7. CRC Press, 1999
- D. Bertsekas and J. Tsitsiklis. Neuro-dynamic programming. Athena Scientific, 1996.
- Cheung, Y. M.D. Chinn (1997), "Further investigation of the uncertain unit root in GNP," Journal of Business and Economic Statistics, 15, 68–73.
- Abadir, K. M., K. Hadri E. Tzavalis (1999), "The influence of VAR dimensions on estimator biases," Econometrica, 67, 163–181.
- Van der Vaart AW. 2000. Asymptotic Statistics. Cambridge, UK: Cambridge Univ. Press
- Alexander, J. C. Jr. (1995), "Refining the degree of earnings surprise: A comparison of statistical and analysts' forecasts," Financial Review, 30, 469–506.
- Brailsford, T.J. R.W. Faff (1996), "An evaluation of volatility forecasting techniques," Journal of Banking Finance, 20, 419–438.

## Frequently Asked Questions

Q: What is the prediction methodology for LON:LOGP stock?A: LON:LOGP stock prediction methodology: We evaluate the prediction models Statistical Inference (ML) and Spearman Correlation

Q: Is LON:LOGP stock a buy or sell?

A: The dominant strategy among neural network is to Hold LON:LOGP Stock.

Q: Is LANSDOWNE OIL & GAS PLC stock a good investment?

A: The consensus rating for LANSDOWNE OIL & GAS PLC is Hold and is assigned short-term B2 & long-term Ba3 estimated rating.

Q: What is the consensus rating of LON:LOGP stock?

A: The consensus rating for LON:LOGP is Hold.

Q: What is the prediction period for LON:LOGP stock?

A: The prediction period for LON:LOGP is 3 Month

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