Stock market is a promising financial investment that can generate great wealth. However, the volatile nature of the stock market makes it a very high risk investment. Thus, a lot of researchers have contributed their efforts to forecast the stock market pricing and average movement. Researchers have used various methods in computer science and economics in their quests to gain a piece of this volatile information and make great fortune out of the stock market investment. This paper investigates various techniques for the stock market prediction using artificial neural network (ANN).** We evaluate Exelixis prediction models with Modular Neural Network (Speculative Sentiment Analysis) and Linear Regression ^{1,2,3,4} and conclude that the EXEL stock is predictable in the short/long term. **

**According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold EXEL stock.**

**EXEL, Exelixis, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Operational Risk
- What is the use of Markov decision process?
- Nash Equilibria

## EXEL Target Price Prediction Modeling Methodology

This study presents financial network indicators that can be applied to global stock market investment strategies. We propose to design both undirected and directed volatility networks of global stock market based on simple pair-wise correlation and system-wide connectedness of stock date using a vector auto-regressive model. We consider Exelixis Stock Decision Process with Linear Regression 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(Linear 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 (Speculative Sentiment Analysis)) X S(n):→ (n+16 weeks) $\sum _{i=1}^{n}\left({a}_{i}\right)$

n:Time series to forecast

p:Price signals of EXEL stock

j:Nash equilibria

k:Dominated move

a:Best response for target price

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## EXEL Stock Forecast (Buy or Sell) for (n+16 weeks)

**Sample Set:**Neural Network

**Stock/Index:**EXEL Exelixis

**Time series to forecast n: 09 Oct 2022**for (n+16 weeks)

**According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold EXEL stock.**

**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 (Yellow to Green): *Technical Analysis%**

## Conclusions

Exelixis assigned short-term B3 & long-term B1 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) with Linear Regression ^{1,2,3,4} and conclude that the EXEL stock is predictable in the short/long term.**

**According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold EXEL stock.**

### Financial State Forecast for EXEL Stock Options & Futures

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

Outlook* | B3 | B1 |

Operational Risk | 40 | 71 |

Market Risk | 84 | 31 |

Technical Analysis | 42 | 72 |

Fundamental Analysis | 40 | 81 |

Risk Unsystematic | 42 | 43 |

### Prediction Confidence Score

## References

- White H. 1992. Artificial Neural Networks: Approximation and Learning Theory. Oxford, UK: Blackwell
- M. L. Littman. Markov games as a framework for multi-agent reinforcement learning. In Ma- chine Learning, Proceedings of the Eleventh International Conference, Rutgers University, New Brunswick, NJ, USA, July 10-13, 1994, pages 157–163, 1994
- S. Proper and K. Tumer. Modeling difference rewards for multiagent learning (extended abstract). In Proceedings of the Eleventh International Joint Conference on Autonomous Agents and Multiagent Systems, Valencia, Spain, June 2012
- Keane MP. 2013. Panel data discrete choice models of consumer demand. In The Oxford Handbook of Panel Data, ed. BH Baltagi, pp. 54–102. Oxford, UK: Oxford Univ. Press
- A. Tamar, Y. Glassner, and S. Mannor. Policy gradients beyond expectations: Conditional value-at-risk. In AAAI, 2015
- Barrett, C. B. (1997), "Heteroscedastic price forecasting for food security management in developing countries," Oxford Development Studies, 25, 225–236.
- Gentzkow M, Kelly BT, Taddy M. 2017. Text as data. NBER Work. Pap. 23276

## 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 (Speculative Sentiment Analysis) and Linear Regression

Q: Is EXEL stock a buy or sell?

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

Q: Is Exelixis stock a good investment?

A: The consensus rating for Exelixis is Hold and assigned short-term B3 & long-term B1 forecasted stock rating.

Q: What is the consensus rating of EXEL stock?

A: The consensus rating for EXEL is Hold.

Q: What is the prediction period for EXEL stock?

A: The prediction period for EXEL is (n+16 weeks)

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