**Outlook:**JOHNS LYNG GROUP LIMITED is assigned short-term B2 & long-term B1 estimated rating.

**AUC Score :**

**Short-Term Revised**

^{1}:**Dominant Strategy :**Speculative Trend

**Time series to forecast n:** for

^{2}

**Methodology :**Supervised Machine Learning (ML)

**Hypothesis Testing :**Linear Regression

**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.

JOHNS LYNG GROUP LIMITED prediction model is evaluated with Supervised Machine Learning (ML) and Linear Regression

^{1,2,3,4}and it is concluded that the JLG stock is predictable in the short/long term. Supervised machine learning (ML) is a type of machine learning where a model is trained on labeled data. This means that the data has been tagged with the correct output for the input data. The model learns to predict the output for new input data based on the labeled data. Supervised ML is a powerful tool that can be used for a variety of tasks, including classification, regression, and forecasting. Classification tasks involve predicting the category of an input data, such as whether an email is spam or not. Regression tasks involve predicting a numerical value for an input data, such as the price of a house. Forecasting tasks involve predicting future values for a time series, such as the sales of a product.

**According to price forecasts for 16 Weeks period, the dominant strategy among neural network is: Speculative Trend**

## Key Points

- Trading Signals
- Stock Forecast Based On a Predictive Algorithm
- Market Outlook

## JLG Target Price Prediction Modeling Methodology

We consider JOHNS LYNG GROUP LIMITED Decision Process with Supervised Machine Learning (ML) where A is the set of discrete actions of JLG 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(Linear Regression)

^{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(Supervised Machine Learning (ML)) X S(n):→ 16 Weeks $\overrightarrow{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

p:Price signals of JLG stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

### Supervised Machine Learning (ML)

Supervised machine learning (ML) is a type of machine learning where a model is trained on labeled data. This means that the data has been tagged with the correct output for the input data. The model learns to predict the output for new input data based on the labeled data. Supervised ML is a powerful tool that can be used for a variety of tasks, including classification, regression, and forecasting. Classification tasks involve predicting the category of an input data, such as whether an email is spam or not. Regression tasks involve predicting a numerical value for an input data, such as the price of a house. Forecasting tasks involve predicting future values for a time series, such as the sales of a product.### Linear Regression

In statistics, linear regression is a method for estimating the relationship between a dependent variable and one or more independent variables. The dependent variable is the variable that is being predicted, and the independent variables are the variables that are used to predict the dependent variable. Linear regression assumes that the relationship between the dependent variable and the independent variables is linear. This means that the dependent variable can be represented as a straight line function of the independent variables.

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?

## JLG Stock Forecast (Buy or Sell)

**Sample Set:**Neural Network

**Stock/Index:**JLG JOHNS LYNG GROUP LIMITED

**Time series to forecast:**16 Weeks

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

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 Supervised Machine Learning (ML) based JLG Stock Prediction Model

- In the reporting period that includes the date of initial application of these amendments, an entity is not required to present the quantitative information required by paragraph 28(f) of IAS 8.
- In applying the effective interest method, an entity identifies fees that are an integral part of the effective interest rate of a financial instrument. The description of fees for financial services may not be indicative of the nature and substance of the services provided. Fees that are an integral part of the effective interest rate of a financial instrument are treated as an adjustment to the effective interest rate, unless the financial instrument is measured at fair value, with the change in fair value being recognised in profit or loss. In those cases, the fees are recognised as revenue or expense when the instrument is initially recognised.
- Subject to the conditions in paragraphs 4.1.5 and 4.2.2, this Standard allows an entity to designate a financial asset, a financial liability, or a group of financial instruments (financial assets, financial liabilities or both) as at fair value through profit or loss provided that doing so results in more relevant information.
- The requirement that an economic relationship exists means that the hedging instrument and the hedged item have values that generally move in the opposite direction because of the same risk, which is the hedged risk. Hence, there must be an expectation that the value of the hedging instrument and the value of the hedged item will systematically change in response to movements in either the same underlying or underlyings that are economically related in such a way that they respond in a similar way to the risk that is being hedged (for example, Brent and WTI crude oil).

*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.

### JLG JOHNS LYNG GROUP LIMITED Financial Analysis*

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

Outlook* | B2 | B1 |

Income Statement | B2 | B1 |

Balance Sheet | Caa2 | B2 |

Leverage Ratios | Baa2 | B3 |

Cash Flow | Caa2 | Ba3 |

Rates of Return and Profitability | Caa2 | B2 |

*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

- V. Borkar. Stochastic approximation: a dynamical systems viewpoint. Cambridge University Press, 2008
- M. Puterman. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York, 1994.
- J. N. Foerster, Y. M. Assael, N. de Freitas, and S. Whiteson. Learning to communicate with deep multi-agent reinforcement learning. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, pages 2137–2145, 2016.
- Chernozhukov V, Newey W, Robins J. 2018c. Double/de-biased machine learning using regularized Riesz representers. arXiv:1802.08667 [stat.ML]
- Canova, F. B. E. Hansen (1995), "Are seasonal patterns constant over time? A test for seasonal stability," Journal of Business and Economic Statistics, 13, 237–252.
- Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, et al. 2008. Top 10 algorithms in data mining. Knowl. Inform. Syst. 14:1–37
- Breiman L. 1993. Better subset selection using the non-negative garotte. Tech. Rep., Univ. Calif., Berkeley

## Frequently Asked Questions

Q: What is the prediction methodology for JLG stock?A: JLG stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Linear Regression

Q: Is JLG stock a buy or sell?

A: The dominant strategy among neural network is to Speculative Trend JLG Stock.

Q: Is JOHNS LYNG GROUP LIMITED stock a good investment?

A: The consensus rating for JOHNS LYNG GROUP LIMITED is Speculative Trend and is assigned short-term B2 & long-term B1 estimated rating.

Q: What is the consensus rating of JLG stock?

A: The consensus rating for JLG is Speculative Trend.

Q: What is the prediction period for JLG stock?

A: The prediction period for JLG is 16 Weeks

- Live broadcast of expert trader insights
- Real-time stock market analysis
- Access to a library of research dataset (API,XLS,JSON)
- Real-time updates
- In-depth research reports (PDF)