**Outlook:**HARVEY NORMAN HOLDINGS LIMITED assigned short-term B1 & long-term Ba3 forecasted stock rating.

**Dominant Strategy :**Sell

**Time series to forecast n: 07 Dec 2022**for (n+6 month)

**Methodology :**Deductive Inference (ML)

## Abstract

Stock prediction with data mining techniques is one of the most important issues in finance being investigated by researchers across the globe. Data mining techniques can be used extensively in the financial markets to help investors make qualitative decision. One of the techniques is artificial neural network (ANN). However, in the application of ANN for predicting the financial market the use of technical analysis variables for stock prediction is predominant. In this paper, we present a hybridized approach which combines the use of the variables of technical and fundamental analysis of stock market indicators for prediction of future price of stock in order to improve on the existing approaches. (Mehtab, S., Sen, J. and Dutta, A., 2021. Stock price prediction using machine learning and LSTM-based deep learning models. In Symposium on Machine Learning and Metaheuristics Algorithms, and Applications (pp. 88-106). Springer, Singapore.)** We evaluate HARVEY NORMAN HOLDINGS LIMITED prediction models with Deductive Inference (ML) and Polynomial Regression ^{1,2,3,4} and conclude that the HVN stock is predictable in the short/long term. **

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Sell HVN stock.**

## Key Points

- Understanding Buy, Sell, and Hold Ratings
- Which neural network is best for prediction?
- What is prediction in deep learning?

## HVN Target Price Prediction Modeling Methodology

We consider HARVEY NORMAN HOLDINGS LIMITED Decision Process with Deductive Inference (ML) where A is the set of discrete actions of HVN 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(Polynomial 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(Deductive Inference (ML)) X S(n):→ (n+6 month) $\overrightarrow{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

p:Price signals of HVN 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?

## HVN Stock Forecast (Buy or Sell) for (n+6 month)

**Sample Set:**Neural Network

**Stock/Index:**HVN HARVEY NORMAN HOLDINGS LIMITED

**Time series to forecast n: 07 Dec 2022**for (n+6 month)

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Sell HVN 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 HARVEY NORMAN HOLDINGS LIMITED

- Annual Improvements to IFRSs 2010–2012 Cycle, issued in December 2013, amended paragraphs 4.2.1 and 5.7.5 as a consequential amendment derived from the amendment to IFRS 3. An entity shall apply that amendment prospectively to business combinations to which the amendment to IFRS 3 applies.
- The change in the value of the hedged item determined using a hypothetical derivative may also be used for the purpose of assessing whether a hedging relationship meets the hedge effectiveness requirements.
- 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.
- 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.

*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

HARVEY NORMAN HOLDINGS LIMITED assigned short-term B1 & long-term Ba3 forecasted stock rating.** We evaluate the prediction models Deductive Inference (ML) with Polynomial Regression ^{1,2,3,4} and conclude that the HVN stock is predictable in the short/long term.**

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Sell HVN stock.**

### Financial State Forecast for HVN HARVEY NORMAN HOLDINGS LIMITED Options & Futures

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

Outlook* | B1 | Ba3 |

Operational Risk | 48 | 72 |

Market Risk | 58 | 49 |

Technical Analysis | 56 | 68 |

Fundamental Analysis | 81 | 73 |

Risk Unsystematic | 55 | 69 |

### Prediction Confidence Score

## References

- Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., GXO Options & Futures Prediction. AC Investment Research Journal, 101(3).
- R. Sutton and A. Barto. Reinforcement Learning. The MIT Press, 1998
- Robins J, Rotnitzky A. 1995. Semiparametric efficiency in multivariate regression models with missing data. J. Am. Stat. Assoc. 90:122–29
- Bamler R, Mandt S. 2017. Dynamic word embeddings via skip-gram filtering. In Proceedings of the 34th Inter- national Conference on Machine Learning, pp. 380–89. La Jolla, CA: Int. Mach. Learn. Soc.
- Blei DM, Lafferty JD. 2009. Topic models. In Text Mining: Classification, Clustering, and Applications, ed. A Srivastava, M Sahami, pp. 101–24. Boca Raton, FL: CRC Press
- Burgess, D. F. (1975), "Duality theory and pitfalls in the specification of technologies," Journal of Econometrics, 3, 105–121.
- Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., Can neural networks predict stock market?(ATVI Stock Forecast). AC Investment Research Journal, 101(3).

## Frequently Asked Questions

Q: What is the prediction methodology for HVN stock?A: HVN stock prediction methodology: We evaluate the prediction models Deductive Inference (ML) and Polynomial Regression

Q: Is HVN stock a buy or sell?

A: The dominant strategy among neural network is to Sell HVN Stock.

Q: Is HARVEY NORMAN HOLDINGS LIMITED stock a good investment?

A: The consensus rating for HARVEY NORMAN HOLDINGS LIMITED is Sell and assigned short-term B1 & long-term Ba3 forecasted stock rating.

Q: What is the consensus rating of HVN stock?

A: The consensus rating for HVN is Sell.

Q: What is the prediction period for HVN stock?

A: The prediction period for HVN is (n+6 month)