**Outlook:**SLINGSBY (H.C.) PLC is assigned short-term Baa2 & long-term Ba3 estimated rating.

**AUC Score :**

**Short-Term Revised**

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

**Time series to forecast n:** for

^{2}

**Methodology :**Transductive Learning (ML)

**Hypothesis Testing :**Statistical Hypothesis Testing

**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.

## Summary

SLINGSBY (H.C.) PLC prediction model is evaluated with Transductive Learning (ML) and Statistical Hypothesis Testing^{1,2,3,4}and it is concluded that the LON:SLNG stock is predictable in the short/long term. Transductive learning is a supervised machine learning (ML) method in which the model is trained on both labeled and unlabeled data. The goal of transductive learning is to predict the labels of the unlabeled data. Transductive learning is a hybrid of inductive and semi-supervised learning. Inductive learning algorithms are trained on labeled data only, while semi-supervised learning algorithms are trained on a combination of labeled and unlabeled data. Transductive learning algorithms can achieve better performance than inductive learning algorithms on tasks where there is a small amount of labeled data. This is because transductive learning algorithms can use the unlabeled data to help them learn the relationships between the features and the labels.

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

## Key Points

- Can statistics predict the future?
- Market Signals
- How do predictive algorithms actually work?

## LON:SLNG Target Price Prediction Modeling Methodology

We consider SLINGSBY (H.C.) PLC Decision Process with Transductive Learning (ML) where A is the set of discrete actions of LON:SLNG 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(Statistical Hypothesis Testing)

^{5,6,7}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Transductive Learning (ML)) X S(n):→ 3 Month $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of LON:SLNG stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

### Transductive Learning (ML)

Transductive learning is a supervised machine learning (ML) method in which the model is trained on both labeled and unlabeled data. The goal of transductive learning is to predict the labels of the unlabeled data. Transductive learning is a hybrid of inductive and semi-supervised learning. Inductive learning algorithms are trained on labeled data only, while semi-supervised learning algorithms are trained on a combination of labeled and unlabeled data. Transductive learning algorithms can achieve better performance than inductive learning algorithms on tasks where there is a small amount of labeled data. This is because transductive learning algorithms can use the unlabeled data to help them learn the relationships between the features and the labels.### Statistical Hypothesis Testing

Statistical hypothesis testing is a process used to determine whether there is enough evidence to support a claim about a population based on a sample. The process involves making two hypotheses, a null hypothesis and an alternative hypothesis, and then collecting data and using statistical tests to determine which hypothesis is more likely to be true. The null hypothesis is the statement that there is no difference between the population and the sample. The alternative hypothesis is the statement that there is a difference between the population and the sample. The statistical test is used to calculate a p-value, which is the probability of obtaining the observed data or more extreme data if the null hypothesis is true. A p-value of less than 0.05 is typically considered to be statistically significant, which means that there is less than a 5% chance of obtaining the observed data or more extreme data if the null hypothesis is true.

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:SLNG Stock Forecast (Buy or Sell)

**Sample Set:**Neural Network

**Stock/Index:**LON:SLNG SLINGSBY (H.C.) PLC

**Time series to forecast:**3 Month

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

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 Transductive Learning (ML) based LON:SLNG Stock Prediction Model

- Lifetime expected credit losses are generally expected to be recognised before a financial instrument becomes past due. Typically, credit risk increases significantly before a financial instrument becomes past due or other lagging borrower-specific factors (for example, a modification or restructuring) are observed. Consequently when reasonable and supportable information that is more forward-looking than past due information is available without undue cost or effort, it must be used to assess changes in credit risk.
- 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.
- The fair value of a financial instrument at initial recognition is normally the transaction price (ie the fair value of the consideration given or received, see also paragraph B5.1.2A and IFRS 13). However, if part of the consideration given or received is for something other than the financial instrument, an entity shall measure the fair value of the financial instrument. For example, the fair value of a long-term loan or receivable that carries no interest can be measured as the present value of all future cash receipts discounted using the prevailing market rate(s) of interest for a similar instrument (similar as to currency, term, type of interest rate and other factors) with a similar credit rating. Any additional amount lent is an expense or a reduction of income unless it qualifies for recognition as some other type of asset.
- There is a rebuttable presumption that unless inflation risk is contractually specified, it is not separately identifiable and reliably measurable and hence cannot be designated as a risk component of a financial instrument. However, in limited cases, it is possible to identify a risk component for inflation risk that is separately identifiable and reliably measurable because of the particular circumstances of the inflation environment and the relevant debt market

*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:SLNG SLINGSBY (H.C.) PLC Financial Analysis*

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

Outlook* | Baa2 | Ba3 |

Income Statement | Baa2 | Baa2 |

Balance Sheet | Baa2 | Baa2 |

Leverage Ratios | Baa2 | Caa2 |

Cash Flow | Baa2 | Ba2 |

Rates of Return and Profitability | Caa2 | Caa2 |

*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?

## Conclusions

SLINGSBY (H.C.) PLC is assigned short-term Baa2 & long-term Ba3 estimated rating. SLINGSBY (H.C.) PLC prediction model is evaluated with Transductive Learning (ML) and Statistical Hypothesis Testing^{1,2,3,4} and it is concluded that the LON:SLNG stock is predictable in the short/long term. ** According to price forecasts for 3 Month period, the dominant strategy among neural network is: Buy**

### Prediction Confidence Score

## References

- 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
- Barkan O. 2016. Bayesian neural word embedding. arXiv:1603.06571 [math.ST]
- Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85
- Byron, R. P. O. Ashenfelter (1995), "Predicting the quality of an unborn grange," Economic Record, 71, 40–53.
- R. Sutton and A. Barto. Reinforcement Learning. The MIT Press, 1998
- R. Rockafellar and S. Uryasev. Optimization of conditional value-at-risk. Journal of Risk, 2:21–42, 2000.
- 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:SLNG stock?A: LON:SLNG stock prediction methodology: We evaluate the prediction models Transductive Learning (ML) and Statistical Hypothesis Testing

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

A: The dominant strategy among neural network is to Buy LON:SLNG Stock.

Q: Is SLINGSBY (H.C.) PLC stock a good investment?

A: The consensus rating for SLINGSBY (H.C.) PLC is Buy and is assigned short-term Baa2 & long-term Ba3 estimated rating.

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

A: The consensus rating for LON:SLNG is Buy.

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

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

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