**Outlook:**NeoVolta Inc. Common Stock assigned short-term Caa2 & long-term Ba3 forecasted stock rating.

**Dominant Strategy :**Buy

**Time series to forecast n: 18 Dec 2022**for (n+6 month)

**Methodology :**Modular Neural Network (CNN Layer)

## Abstract

Prediction of the trend of the stock market is very crucial. If someone has robust forecasting tools, then he/she will increase the return on investment and can get rich easily and quickly. Because there are a lot of factors that can influence the stock market, the stock forecasting problem has always been very complicated. Support Vector Regression is a tool from machine learning that can build a regression model on the historical time series data in the purpose of predicting the future trend of the stock price.(Waqar, M., Dawood, H., Guo, P., Shahnawaz, M.B. and Ghazanfar, M.A., 2017, December. Prediction of stock market by principal component analysis. In 2017 13th International Conference on Computational Intelligence and Security (CIS) (pp. 599-602). IEEE.)** We evaluate NeoVolta Inc. Common Stock prediction models with Modular Neural Network (CNN Layer) and Polynomial Regression ^{1,2,3,4} and conclude that the NEOV stock is predictable in the short/long term. **

**According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Buy**

## Key Points

- Should I buy stocks now or wait amid such uncertainty?
- Understanding Buy, Sell, and Hold Ratings
- What is the use of Markov decision process?

## NEOV Target Price Prediction Modeling Methodology

We consider NeoVolta Inc. Common Stock Decision Process with Modular Neural Network (CNN Layer) where A is the set of discrete actions of NEOV 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(Modular Neural Network (CNN Layer)) X S(n):→ (n+6 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 NEOV 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?

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

**Sample Set:**Neural Network

**Stock/Index:**NEOV NeoVolta Inc. Common Stock

**Time series to forecast n: 18 Dec 2022**for (n+6 month)

**According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Buy**

**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%**

## Adjusted IFRS* Prediction Methods for NeoVolta Inc. Common Stock

- An entity is not required to restate prior periods to reflect the application of these amendments. The entity may restate prior periods if, and only if, it is possible without the use of hindsight and the restated financial statements reflect all the requirements in this Standard. If an entity does not restate prior periods, the entity shall recognise any difference between the previous carrying amount and the carrying amount at the beginning of the annual reporting period that includes the date of initial application of these amendments in the opening retained earnings (or other component of equity, as appropriate) of the annual reporting period that includes the date of initial application of these amendments.
- If, at the date of initial application, determining whether there has been a significant increase in credit risk since initial recognition would require undue cost or effort, an entity shall recognise a loss allowance at an amount equal to lifetime expected credit losses at each reporting date until that financial instrument is derecognised (unless that financial instrument is low credit risk at a reporting date, in which case paragraph 7.2.19(a) applies).
- For a financial guarantee contract, the entity is required to make payments only in the event of a default by the debtor in accordance with the terms of the instrument that is guaranteed. Accordingly, cash shortfalls are the expected payments to reimburse the holder for a credit loss that it incurs less any amounts that the entity expects to receive from the holder, the debtor or any other party. If the asset is fully guaranteed, the estimation of cash shortfalls for a financial guarantee contract would be consistent with the estimations of cash shortfalls for the asset subject to the guarantee
- If a put option written by an entity prevents a transferred asset from being derecognised and the entity measures the transferred asset at fair value, the associated liability is measured at the option exercise price plus the time value of the option. The measurement of the asset at fair value is limited to the lower of the fair value and the option exercise price because the entity has no right to increases in the fair value of the transferred asset above the exercise price of the option. This ensures that the net carrying amount of the asset and the associated liability is the fair value of the put option obligation. For example, if the fair value of the underlying asset is CU120, the option exercise price is CU100 and the time value of the option is CU5, the carrying amount of the associated liability is CU105 (CU100 + CU5) and the carrying amount of the asset is CU100 (in this case the option exercise price).

*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

NeoVolta Inc. Common Stock assigned short-term Caa2 & long-term Ba3 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (CNN Layer) with Polynomial Regression ^{1,2,3,4} and conclude that the NEOV stock is predictable in the short/long term.**

**According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Buy**

### Financial State Forecast for NEOV NeoVolta Inc. Common Stock Options & Futures

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

Outlook* | Caa2 | Ba3 |

Operational Risk | 50 | 66 |

Market Risk | 39 | 33 |

Technical Analysis | 36 | 55 |

Fundamental Analysis | 30 | 90 |

Risk Unsystematic | 59 | 76 |

### Prediction Confidence Score

## References

- Burkov A. 2019. The Hundred-Page Machine Learning Book. Quebec City, Can.: Andriy Burkov
- Y. Chow and M. Ghavamzadeh. Algorithms for CVaR optimization in MDPs. In Advances in Neural Infor- mation Processing Systems, pages 3509–3517, 2014.
- Miller A. 2002. Subset Selection in Regression. New York: CRC Press
- R. Sutton, D. McAllester, S. Singh, and Y. Mansour. Policy gradient methods for reinforcement learning with function approximation. In Proceedings of Advances in Neural Information Processing Systems 12, pages 1057–1063, 2000
- White H. 1992. Artificial Neural Networks: Approximation and Learning Theory. Oxford, UK: Blackwell
- 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.
- Bertsimas D, King A, Mazumder R. 2016. Best subset selection via a modern optimization lens. Ann. Stat. 44:813–52

## Frequently Asked Questions

Q: What is the prediction methodology for NEOV stock?A: NEOV stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Polynomial Regression

Q: Is NEOV stock a buy or sell?

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

Q: Is NeoVolta Inc. Common Stock stock a good investment?

A: The consensus rating for NeoVolta Inc. Common Stock is Buy and assigned short-term Caa2 & long-term Ba3 forecasted stock rating.

Q: What is the consensus rating of NEOV stock?

A: The consensus rating for NEOV is Buy.

Q: What is the prediction period for NEOV stock?

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