**Outlook:**AvePoint Inc. Class A Common Stock is assigned short-term Baa2 & long-term B2 estimated rating.

**AUC Score :**

**Short-Term Revised**

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

**Time series to forecast n:** for

^{2}

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

**Hypothesis Testing :**Chi-Square

**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

AvePoint Inc. Class A Common Stock prediction model is evaluated with Supervised Machine Learning (ML) and Chi-Square^{1,2,3,4}and it is concluded that the AVPT 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.

^{5}

**According to price forecasts for 8 Weeks period, the dominant strategy among neural network is: Hold**

## Key Points

- Supervised Machine Learning (ML) for AVPT stock price prediction process.
- Chi-Square
- What is neural prediction?
- What is statistical models in machine learning?
- How do predictive algorithms actually work?

## AVPT Stock Price Forecast

We consider AvePoint Inc. Class A Common Stock Decision Process with Supervised Machine Learning (ML) where A is the set of discrete actions of AVPT 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}

**Sample Set:**Neural Network

**Stock/Index:**AVPT AvePoint Inc. Class A Common Stock

**Time series to forecast:**8 Weeks

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

^{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):→ 8 Weeks $\overrightarrow{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

p:Price signals of AVPT stock

j:Nash equilibria (Neural Network)

k:Dominated move of AVPT stock holders

a:Best response for AVPT target price

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.

^{5}A chi-squared test is a statistical hypothesis test that assesses whether observed frequencies in a sample differ significantly from expected frequencies. It is one of the most widely used statistical tests in the social sciences and in many areas of observational research. The chi-squared test is a non-parametric test, meaning that it does not assume that the data is normally distributed. This makes it a versatile tool that can be used to analyze a wide variety of data. There are two main types of chi-squared tests: the chi-squared goodness of fit test and the chi-squared test of independence.

^{6,7}

For further technical information as per how our model work we invite you to visit the article below:

### AVPT Stock Forecast (Buy or Sell) Strategic Interaction Table

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 AVPT Stock Prediction Model

- In some jurisdictions, the government or a regulatory authority sets interest rates. For example, such government regulation of interest rates may be part of a broad macroeconomic policy or it may be introduced to encourage entities to invest in a particular sector of the economy. In some of these cases, the objective of the time value of money element is not to provide consideration for only the passage of time. However, despite paragraphs B4.1.9A–B4.1.9D, a regulated interest rate shall be considered a proxy for the time value of money element for the purpose of applying the condition in paragraphs 4.1.2(b) and 4.1.2A(b) if that regulated interest rate provides consideration that is broadly consistent with the passage of time and does not provide exposure to risks or volatility in the contractual cash flows that are inconsistent with a basic lending arrangement.
- An entity's estimate of expected credit losses on loan commitments shall be consistent with its expectations of drawdowns on that loan commitment, ie it shall consider the expected portion of the loan commitment that will be drawn down within 12 months of the reporting date when estimating 12-month expected credit losses, and the expected portion of the loan commitment that will be drawn down over the expected life of the loan commitment when estimating lifetime expected credit losses.
- An example of a fair value hedge is a hedge of exposure to changes in the fair value of a fixed-rate debt instrument arising from changes in interest rates. Such a hedge could be entered into by the issuer or by the holder.
- An entity is not required to incorporate forecasts of future conditions over the entire expected life of a financial instrument. The degree of judgement that is required to estimate expected credit losses depends on the availability of detailed information. As the forecast horizon increases, the availability of detailed information decreases and the degree of judgement required to estimate expected credit losses increases. The estimate of expected credit losses does not require a detailed estimate for periods that are far in the future—for such periods, an entity may extrapolate projections from available, detailed information.

*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.

### AVPT AvePoint Inc. Class A Common Stock Financial Analysis*

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

Outlook* | Baa2 | B2 |

Income Statement | Baa2 | C |

Balance Sheet | Baa2 | B3 |

Leverage Ratios | Baa2 | B2 |

Cash Flow | Baa2 | Baa2 |

Rates of Return and Profitability | B1 | 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?

## References

- Jorgenson, D.W., Weitzman, M.L., ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. Apple's Stock Price: How News Affects Volatility. AC Investment Research Journal, 220(44).
- Zou H, Hastie T. 2005. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67:301–20
- Chernozhukov V, Escanciano JC, Ichimura H, Newey WK. 2016b. Locally robust semiparametric estimation. arXiv:1608.00033 [math.ST]
- A. Eck, L. Soh, S. Devlin, and D. Kudenko. Potential-based reward shaping for finite horizon online POMDP planning. Autonomous Agents and Multi-Agent Systems, 30(3):403–445, 2016
- P. Milgrom and I. Segal. Envelope theorems for arbitrary choice sets. Econometrica, 70(2):583–601, 2002
- White H. 1992. Artificial Neural Networks: Approximation and Learning Theory. Oxford, UK: Blackwell
- Athey S, Imbens GW. 2017a. The econometrics of randomized experiments. In Handbook of Economic Field Experiments, Vol. 1, ed. E Duflo, A Banerjee, pp. 73–140. Amsterdam: Elsevier

## Frequently Asked Questions

Q: Is AVPT stock expected to rise?A: AVPT stock prediction model is evaluated with Supervised Machine Learning (ML) and Chi-Square and it is concluded that dominant strategy for AVPT stock is Hold

Q: Is AVPT stock a buy or sell?

A: The dominant strategy among neural network is to Hold AVPT Stock.

Q: Is AvePoint Inc. Class A Common Stock stock a good investment?

A: The consensus rating for AvePoint Inc. Class A Common Stock is Hold and is assigned short-term Baa2 & long-term B2 estimated rating.

Q: What is the consensus rating of AVPT stock?

A: The consensus rating for AVPT is Hold.

Q: What is the forecast for AVPT stock?

A: AVPT target price forecast: Hold

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