**Outlook:**Exelixis Inc. Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating.

**Dominant Strategy :**Wait until speculative trend diminishes

**Time series to forecast n: 13 Feb 2023**for (n+1 year)

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

## Abstract

Exelixis Inc. Common Stock prediction model is evaluated with Modular Neural Network (Market Volatility Analysis) and Polynomial Regression^{1,2,3,4}and it is concluded that the EXEL stock is predictable in the short/long term.

**According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Wait until speculative trend diminishes**

## Key Points

- What is neural prediction?
- What is Markov decision process in reinforcement learning?
- Stock Rating

## EXEL Target Price Prediction Modeling Methodology

We consider Exelixis Inc. Common Stock Decision Process with Modular Neural Network (Market Volatility Analysis) where A is the set of discrete actions of EXEL 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 (Market Volatility Analysis)) X S(n):→ (n+1 year) $\sum _{i=1}^{n}\left({a}_{i}\right)$

n:Time series to forecast

p:Price signals of EXEL 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?

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

**Sample Set:**Neural Network

**Stock/Index:**EXEL Exelixis Inc. Common Stock

**Time series to forecast n: 13 Feb 2023**for (n+1 year)

**According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Wait until speculative trend diminishes**

**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%**

## IFRS Reconciliation Adjustments for Exelixis Inc. Common Stock

- If an entity measures a hybrid contract at fair value in accordance with paragraphs 4.1.2A, 4.1.4 or 4.1.5 but the fair value of the hybrid contract had not been measured in comparative reporting periods, the fair value of the hybrid contract in the comparative reporting periods shall be the sum of the fair values of the components (ie the non-derivative host and the embedded derivative) at the end of each comparative reporting period if the entity restates prior periods (see paragraph 7.2.15).
- An entity shall apply Prepayment Features with Negative Compensation (Amendments to IFRS 9) retrospectively in accordance with IAS 8, except as specified in paragraphs 7.2.30–7.2.34
- 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.
- At the date of initial application, an entity shall assess whether a financial asset meets the condition in paragraphs 4.1.2(a) or 4.1.2A(a) on the basis of the facts and circumstances that exist at that date. The resulting classification shall be applied retrospectively irrespective of the entity's business model in prior reporting periods.

*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.

## Conclusions

Exelixis Inc. Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating. Exelixis Inc. Common Stock prediction model is evaluated with Modular Neural Network (Market Volatility Analysis) and Polynomial Regression^{1,2,3,4} and it is concluded that the EXEL stock is predictable in the short/long term. ** According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Wait until speculative trend diminishes**

### EXEL Exelixis Inc. Common Stock Financial Analysis*

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

Outlook* | Ba1 | Ba1 |

Income Statement | Baa2 | C |

Balance Sheet | Caa2 | Caa2 |

Leverage Ratios | B3 | Ba3 |

Cash Flow | Baa2 | B1 |

Rates of Return and Profitability | B3 | Ba3 |

*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?

### Prediction Confidence Score

## References

- S. Bhatnagar. An actor-critic algorithm with function approximation for discounted cost constrained Markov decision processes. Systems & Control Letters, 59(12):760–766, 2010
- V. Borkar. Q-learning for risk-sensitive control. Mathematics of Operations Research, 27:294–311, 2002.
- Rosenbaum PR, Rubin DB. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70:41–55
- G. Shani, R. Brafman, and D. Heckerman. An MDP-based recommender system. In Proceedings of the Eigh- teenth conference on Uncertainty in artificial intelligence, pages 453–460. Morgan Kaufmann Publishers Inc., 2002
- Morris CN. 1983. Parametric empirical Bayes inference: theory and applications. J. Am. Stat. Assoc. 78:47–55
- N. B ̈auerle and A. Mundt. Dynamic mean-risk optimization in a binomial model. Mathematical Methods of Operations Research, 70(2):219–239, 2009.
- Athey S, Imbens G. 2016. Recursive partitioning for heterogeneous causal effects. PNAS 113:7353–60

## Frequently Asked Questions

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

Q: Is EXEL stock a buy or sell?

A: The dominant strategy among neural network is to Wait until speculative trend diminishes EXEL Stock.

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

A: The consensus rating for Exelixis Inc. Common Stock is Wait until speculative trend diminishes and is assigned short-term Ba1 & long-term Ba1 estimated rating.

Q: What is the consensus rating of EXEL stock?

A: The consensus rating for EXEL is Wait until speculative trend diminishes.

Q: What is the prediction period for EXEL stock?

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