**Outlook:**Proterra Inc. Common Stock assigned short-term Ba1 & long-term B2 forecasted stock rating.

**Dominant Strategy :**Buy

**Time series to forecast n: 11 Dec 2022**for (n+1 year)

**Methodology :**Modular Neural Network (Market Volatility Analysis)

## Abstract

This paper addresses problem of predicting direction of movement of stock and stock price index. The study compares four prediction models, Artificial Neural Network (ANN), Support Vector Machine (SVM), random forest and naive-Bayes with two approaches for input to these models.(Nikou, M., Mansourfar, G. and Bagherzadeh, J., 2019. Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms. Intelligent Systems in Accounting, Finance and Management, 26(4), pp.164-174.)** We evaluate Proterra Inc. Common Stock prediction models with Modular Neural Network (Market Volatility Analysis) and Lasso Regression ^{1,2,3,4} and conclude that the PTRA stock is predictable in the short/long term. **

**According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Buy**

## Key Points

- Game Theory
- Trading Interaction
- Can statistics predict the future?

## PTRA Target Price Prediction Modeling Methodology

We consider Proterra Inc. Common Stock Decision Process with Modular Neural Network (Market Volatility Analysis) where A is the set of discrete actions of PTRA 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(Lasso 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 (Market Volatility Analysis)) X S(n):→ (n+1 year) $\overrightarrow{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of PTRA 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?

## PTRA Stock Forecast (Buy or Sell) for (n+1 year)

**Sample Set:**Neural Network

**Stock/Index:**PTRA Proterra Inc. Common Stock

**Time series to forecast n: 11 Dec 2022**for (n+1 year)

**According to price forecasts for (n+1 year) 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 Proterra Inc. Common Stock

- For purchased or originated credit-impaired financial assets, expected credit losses shall be discounted using the credit-adjusted effective interest rate determined at initial recognition.
- If a put option obligation written by an entity or call option right held by an entity prevents a transferred asset from being derecognised and the entity measures the transferred asset at amortised cost, the associated liability is measured at its cost (ie the consideration received) adjusted for the amortisation of any difference between that cost and the gross carrying amount of the transferred asset at the expiration date of the option. For example, assume that the gross carrying amount of the asset on the date of the transfer is CU98 and that the consideration received is CU95. The gross carrying amount of the asset on the option exercise date will be CU100. The initial carrying amount of the associated liability is CU95 and the difference between CU95 and CU100 is recognised in profit or loss using the effective interest method. If the option is exercised, any difference between the carrying amount of the associated liability and the exercise price is recognised in profit or loss.
- For the purpose of applying the requirements in paragraphs 6.4.1(c)(i) and B6.4.4–B6.4.6, an entity shall assume that the interest rate benchmark on which the hedged cash flows and/or the hedged risk (contractually or noncontractually specified) are based, or the interest rate benchmark on which the cash flows of the hedging instrument are based, is not altered as a result of interest rate benchmark reform.
- The following are examples of when the objective of the entity's business model may be achieved by both collecting contractual cash flows and selling financial assets. This list of examples is not exhaustive. Furthermore, the examples are not intended to describe all the factors that may be relevant to the assessment of the entity's business model nor specify the relative importance of the factors.

*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

Proterra Inc. Common Stock assigned short-term Ba1 & long-term B2 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) with Lasso Regression ^{1,2,3,4} and conclude that the PTRA stock is predictable in the short/long term.**

**According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Buy**

### Financial State Forecast for PTRA Proterra Inc. Common Stock Options & Futures

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

Outlook* | Ba1 | B2 |

Operational Risk | 75 | 56 |

Market Risk | 81 | 37 |

Technical Analysis | 73 | 42 |

Fundamental Analysis | 88 | 56 |

Risk Unsystematic | 38 | 78 |

### Prediction Confidence Score

## References

- Tibshirani R. 1996. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58:267–88
- K. Boda and J. Filar. Time consistent dynamic risk measures. Mathematical Methods of Operations Research, 63(1):169–186, 2006
- V. Borkar and R. Jain. Risk-constrained Markov decision processes. IEEE Transaction on Automatic Control, 2014
- H. Kushner and G. Yin. Stochastic approximation algorithms and applications. Springer, 1997.
- A. K. Agogino and K. Tumer. Analyzing and visualizing multiagent rewards in dynamic and stochastic environments. Journal of Autonomous Agents and Multi-Agent Systems, 17(2):320–338, 2008
- Athey S, Bayati M, Imbens G, Zhaonan Q. 2019. Ensemble methods for causal effects in panel data settings. NBER Work. Pap. 25675
- Dimakopoulou M, Zhou Z, Athey S, Imbens G. 2018. Balanced linear contextual bandits. arXiv:1812.06227 [cs.LG]

## Frequently Asked Questions

Q: What is the prediction methodology for PTRA stock?A: PTRA stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and Lasso Regression

Q: Is PTRA stock a buy or sell?

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

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

A: The consensus rating for Proterra Inc. Common Stock is Buy and assigned short-term Ba1 & long-term B2 forecasted stock rating.

Q: What is the consensus rating of PTRA stock?

A: The consensus rating for PTRA is Buy.

Q: What is the prediction period for PTRA stock?

A: The prediction period for PTRA is (n+1 year)