**Outlook:**PubMatic Inc. Class A Common Stock is assigned short-term B3 & long-term B2 estimated rating.

**AUC Score :**

**Short-Term Revised**

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

**Time series to forecast n:** for

^{2}

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

**Hypothesis Testing :**Polynomial 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.

## Abstract

PubMatic Inc. Class A Common Stock prediction model is evaluated with Modular Neural Network (DNN Layer) and Polynomial Regression^{1,2,3,4}and it is concluded that the PUBM stock is predictable in the short/long term. In a modular neural network (MNN), a DNN layer is a type of module that is used to learn complex relationships between input and output data. DNN layers are made up of a series of artificial neurons, which are connected to each other by weighted edges. The weights of the edges are adjusted during training to minimize the error between the network's predictions and the desired output. DNN layers are used in a variety of MNN applications, including natural language processing, speech recognition, and machine translation. In natural language processing, DNN layers are used to extract features from text data, such as the sentiment of a sentence or the topic of a conversation. In speech recognition, DNN layers are used to convert audio data into text data. In machine translation, DNN layers are used to translate text from one language to another.

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

## Key Points

- How do predictive algorithms actually work?
- Probability Distribution
- Decision Making

## PUBM Target Price Prediction Modeling Methodology

We consider PubMatic Inc. Class A Common Stock Decision Process with Modular Neural Network (DNN Layer) where A is the set of discrete actions of PUBM 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 (DNN Layer)) X S(n):→ 8 Weeks $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of PUBM stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

### Modular Neural Network (DNN Layer)

In a modular neural network (MNN), a DNN layer is a type of module that is used to learn complex relationships between input and output data. DNN layers are made up of a series of artificial neurons, which are connected to each other by weighted edges. The weights of the edges are adjusted during training to minimize the error between the network's predictions and the desired output. DNN layers are used in a variety of MNN applications, including natural language processing, speech recognition, and machine translation. In natural language processing, DNN layers are used to extract features from text data, such as the sentiment of a sentence or the topic of a conversation. In speech recognition, DNN layers are used to convert audio data into text data. In machine translation, DNN layers are used to translate text from one language to another.### Polynomial Regression

Polynomial regression is a type of regression analysis that uses a polynomial function to model the relationship between a dependent variable and one or more independent variables. Polynomial functions are mathematical functions that have a polynomial term, which is a term that is raised to a power greater than 1. In polynomial regression, the dependent variable is modeled as a polynomial function of the independent variables. The degree of the polynomial function is determined by the researcher. The higher the degree of the polynomial function, the more complex the model will be.

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?

## PUBM Stock Forecast (Buy or Sell)

**Sample Set:**Neural Network

**Stock/Index:**PUBM PubMatic Inc. Class A Common Stock

**Time series to forecast:**8 Weeks

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

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 Modular Neural Network (DNN Layer) based PUBM Stock Prediction Model

- Expected credit losses reflect an entity's own expectations of credit losses. However, when considering all reasonable and supportable information that is available without undue cost or effort in estimating expected credit losses, an entity should also consider observable market information about the credit risk of the particular financial instrument or similar financial instruments.
- If there is a hedging relationship between a non-derivative monetary asset and a non-derivative monetary liability, changes in the foreign currency component of those financial instruments are presented in profit or loss.
- Such designation may be used whether paragraph 4.3.3 requires the embedded derivatives to be separated from the host contract or prohibits such separation. However, paragraph 4.3.5 would not justify designating the hybrid contract as at fair value through profit or loss in the cases set out in paragraph 4.3.5(a) and (b) because doing so would not reduce complexity or increase reliability.
- The purpose of estimating expected credit losses is neither to estimate a worstcase scenario nor to estimate the best-case scenario. Instead, an estimate of expected credit losses shall always reflect the possibility that a credit loss occurs and the possibility that no credit loss occurs even if the most likely outcome is no credit 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.

### PUBM PubMatic Inc. Class A Common Stock Financial Analysis*

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

Outlook* | B3 | B2 |

Income Statement | B3 | Caa2 |

Balance Sheet | Caa2 | Caa2 |

Leverage Ratios | Caa2 | Ba1 |

Cash Flow | Caa2 | Caa2 |

Rates of Return and Profitability | B1 | 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?

## Conclusions

PubMatic Inc. Class A Common Stock is assigned short-term B3 & long-term B2 estimated rating. PubMatic Inc. Class A Common Stock prediction model is evaluated with Modular Neural Network (DNN Layer) and Polynomial Regression^{1,2,3,4} and it is concluded that the PUBM stock is predictable in the short/long term. ** According to price forecasts for 8 Weeks period, the dominant strategy among neural network is: Sell**

### Prediction Confidence Score

## References

- V. Borkar. An actor-critic algorithm for constrained Markov decision processes. Systems & Control Letters, 54(3):207–213, 2005.
- M. Puterman. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York, 1994.
- Varian HR. 2014. Big data: new tricks for econometrics. J. Econ. Perspect. 28:3–28
- R. Sutton and A. Barto. Reinforcement Learning. The MIT Press, 1998
- Barkan O. 2016. Bayesian neural word embedding. arXiv:1603.06571 [math.ST]
- V. Borkar. Stochastic approximation: a dynamical systems viewpoint. Cambridge University Press, 2008
- J. Z. Leibo, V. Zambaldi, M. Lanctot, J. Marecki, and T. Graepel. Multi-agent Reinforcement Learning in Sequential Social Dilemmas. In Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2017), Sao Paulo, Brazil, 2017

## Frequently Asked Questions

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

Q: Is PUBM stock a buy or sell?

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

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

A: The consensus rating for PubMatic Inc. Class A Common Stock is Sell and is assigned short-term B3 & long-term B2 estimated rating.

Q: What is the consensus rating of PUBM stock?

A: The consensus rating for PUBM is Sell.

Q: What is the prediction period for PUBM stock?

A: The prediction period for PUBM is 8 Weeks

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