**Outlook:**WAM LEADERS LIMITED is assigned short-term B1 & long-term Ba1 estimated rating.

**AUC Score :**

**Short-Term Revised**

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

**Time series to forecast n:** for

^{2}

**Methodology :**Statistical Inference (ML)

**Hypothesis Testing :**Wilcoxon Rank-Sum Test

**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

WAM LEADERS LIMITED prediction model is evaluated with Statistical Inference (ML) and Wilcoxon Rank-Sum Test^{1,2,3,4}and it is concluded that the WLE 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.

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

## Key Points

- What are main components of Markov decision process?
- Investment Risk
- How accurate is machine learning in stock market?

## WLE Target Price Prediction Modeling Methodology

We consider WAM LEADERS LIMITED Decision Process with Statistical Inference (ML) where A is the set of discrete actions of WLE 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(Wilcoxon Rank-Sum Test)

^{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(Statistical Inference (ML)) X S(n):→ 3 Month $\overrightarrow{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

p:Price signals of WLE stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

### Statistical Inference (ML)

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.### Wilcoxon Rank-Sum Test

The Wilcoxon rank-sum test, also known as the Mann-Whitney U test, is a non-parametric test that is used to compare the medians of two independent samples. It is a rank-based test, which means that it does not assume that the data is normally distributed. The Wilcoxon rank-sum test is calculated by first ranking the data from both samples, and then finding the sum of the ranks for one of the samples. The Wilcoxon rank-sum test statistic is then calculated by subtracting the sum of the ranks for one sample from the sum of the ranks for the other sample. The p-value for the Wilcoxon rank-sum test is calculated using a table of critical values. The p-value is the probability of obtaining a test statistic at least as extreme as the one observed, assuming that 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?

## WLE Stock Forecast (Buy or Sell)

**Sample Set:**Neural Network

**Stock/Index:**WLE WAM LEADERS LIMITED

**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 Statistical Inference (ML) based WLE Stock Prediction Model

- When measuring the fair values of the part that continues to be recognised and the part that is derecognised for the purposes of applying paragraph 3.2.13, an entity applies the fair value measurement requirements in IFRS 13 Fair Value Measurement in addition to paragraph 3.2.14.
- When using historical credit loss experience in estimating expected credit losses, it is important that information about historical credit loss rates is applied to groups that are defined in a manner that is consistent with the groups for which the historical credit loss rates were observed. Consequently, the method used shall enable each group of financial assets to be associated with information about past credit loss experience in groups of financial assets with similar risk characteristics and with relevant observable data that reflects current conditions.
- Interest Rate Benchmark Reform—Phase 2, which amended IFRS 9, IAS 39, IFRS 7, IFRS 4 and IFRS 16, issued in August 2020, added paragraphs 5.4.5–5.4.9, 6.8.13, Section 6.9 and paragraphs 7.2.43–7.2.46. An entity shall apply these amendments for annual periods beginning on or after 1 January 2021. Earlier application is permitted. If an entity applies these amendments for an earlier period, it shall disclose that fact.
- 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.

### WLE WAM LEADERS LIMITED Financial Analysis*

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

Outlook* | B1 | Ba1 |

Income Statement | C | B2 |

Balance Sheet | B3 | Baa2 |

Leverage Ratios | Baa2 | B2 |

Cash Flow | Caa2 | Baa2 |

Rates of Return and Profitability | Baa2 | Baa2 |

*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

WAM LEADERS LIMITED is assigned short-term B1 & long-term Ba1 estimated rating. WAM LEADERS LIMITED prediction model is evaluated with Statistical Inference (ML) and Wilcoxon Rank-Sum Test^{1,2,3,4} and it is concluded that the WLE 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

- Cheung, Y. M.D. Chinn (1997), "Further investigation of the uncertain unit root in GNP," Journal of Business and Economic Statistics, 15, 68–73.
- Imbens GW, Lemieux T. 2008. Regression discontinuity designs: a guide to practice. J. Econom. 142:615–35
- Doudchenko N, Imbens GW. 2016. Balancing, regression, difference-in-differences and synthetic control methods: a synthesis. NBER Work. Pap. 22791
- Knox SW. 2018. Machine Learning: A Concise Introduction. Hoboken, NJ: Wiley
- T. Morimura, M. Sugiyama, M. Kashima, H. Hachiya, and T. Tanaka. Nonparametric return distribution ap- proximation for reinforcement learning. In Proceedings of the 27th International Conference on Machine Learning, pages 799–806, 2010
- Bera, A. M. L. Higgins (1997), "ARCH and bilinearity as competing models for nonlinear dependence," Journal of Business Economic Statistics, 15, 43–50.
- Barrett, C. B. (1997), "Heteroscedastic price forecasting for food security management in developing countries," Oxford Development Studies, 25, 225–236.

## Frequently Asked Questions

Q: What is the prediction methodology for WLE stock?A: WLE stock prediction methodology: We evaluate the prediction models Statistical Inference (ML) and Wilcoxon Rank-Sum Test

Q: Is WLE stock a buy or sell?

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

Q: Is WAM LEADERS LIMITED stock a good investment?

A: The consensus rating for WAM LEADERS LIMITED is Buy and is assigned short-term B1 & long-term Ba1 estimated rating.

Q: What is the consensus rating of WLE stock?

A: The consensus rating for WLE is Buy.

Q: What is the prediction period for WLE stock?

A: The prediction period for WLE is 3 Month

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