The main objective of this research is to predict the market performance on day closing using different machine learning techniques. The prediction model uses different attributes as an input and predicts market as Positive & Negative. ** We evaluate Costco prediction models with Modular Neural Network (Social Media Sentiment Analysis) and ElasticNet Regression ^{1,2,3,4} and conclude that the COST stock is predictable in the short/long term. **

**According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold COST stock.**

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

*Keywords:*## Key Points

- Nash Equilibria
- What are main components of Markov decision process?
- Stock Forecast Based On a Predictive Algorithm

## COST Target Price Prediction Modeling Methodology

In this paper, we propose a hybrid machine learning system based on Genetic Algor ithm (GA) and Support Vector Machines (SVM) for stock market prediction. A variety of indicators from the technical analysis field of study are used as input features. We also make use of the correlation between stock prices of different companies to forecast the price of a stock, making use of technical indicators of highly correlated stocks, not only the stock to be predicted. The genetic algorithm is used to select the set of most informative input features from among all the technical indicators. We consider Costco Stock Decision Process with ElasticNet Regression where A is the set of discrete actions of COST 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(ElasticNet 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 (Social Media Sentiment Analysis)) X S(n):→ (n+1 year) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of COST stock

j:Nash equilibria

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?

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

**Sample Set:**Neural Network

**Stock/Index:**COST Costco

**Time series to forecast n: 19 Sep 2022**for (n+1 year)

**According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold COST 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

Costco assigned short-term Ba3 & long-term Ba2 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) with ElasticNet Regression ^{1,2,3,4} and conclude that the COST stock is predictable in the short/long term.**

**According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold COST stock.**

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

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

Outlook* | Ba3 | Ba2 |

Operational Risk | 89 | 87 |

Market Risk | 54 | 58 |

Technical Analysis | 58 | 81 |

Fundamental Analysis | 39 | 69 |

Risk Unsystematic | 87 | 50 |

### Prediction Confidence Score

## References

- M. L. Littman. Friend-or-foe q-learning in general-sum games. In Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, June 28 - July 1, 2001, pages 322–328, 2001
- Allen, P. G. (1994), "Economic forecasting in agriculture," International Journal of Forecasting, 10, 81–135.
- J. Ott. A Markov decision model for a surveillance application and risk-sensitive Markov decision processes. PhD thesis, Karlsruhe Institute of Technology, 2010.
- Belsley, D. A. (1988), "Modelling and forecast reliability," International Journal of Forecasting, 4, 427–447.
- Meinshausen N. 2007. Relaxed lasso. Comput. Stat. Data Anal. 52:374–93
- J. Spall. Multivariate stochastic approximation using a simultaneous perturbation gradient approximation. IEEE Transactions on Automatic Control, 37(3):332–341, 1992.
- Zou H, Hastie T. 2005. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67:301–20

## Frequently Asked Questions

Q: What is the prediction methodology for COST stock?A: COST stock prediction methodology: We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) and ElasticNet Regression

Q: Is COST stock a buy or sell?

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

Q: Is Costco stock a good investment?

A: The consensus rating for Costco is Hold and assigned short-term Ba3 & long-term Ba2 forecasted stock rating.

Q: What is the consensus rating of COST stock?

A: The consensus rating for COST is Hold.

Q: What is the prediction period for COST stock?

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

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