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 evaluate Bristol Myers Squibb prediction models with Modular Neural Network (CNN Layer) and Stepwise Regression ^{1,2,3,4} and conclude that the BMY 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 BMY stock.**

**BMY, Bristol Myers Squibb, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- What are the most successful trading algorithms?
- Is Target price a good indicator?
- Market Risk

## BMY Target Price Prediction Modeling Methodology

The classical linear multi-factor stock selection model is widely used for long-term stock price trend prediction. However, the stock market is chaotic, complex, and dynamic, for which reasons the linear model assumption may be unreasonable, and it is more meaningful to construct a better-integrated stock selection model based on different feature selection and nonlinear stock price trend prediction methods. We consider Bristol Myers Squibb Stock Decision Process with Stepwise Regression where A is the set of discrete actions of BMY 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(Stepwise 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 (CNN Layer)) X S(n):→ (n+1 year) $\sum _{i=1}^{n}\left({r}_{i}\right)$

n:Time series to forecast

p:Price signals of BMY 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?

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

**Sample Set:**Neural Network

**Stock/Index:**BMY Bristol Myers Squibb

**Time series to forecast n: 20 Oct 2022**for (n+1 year)

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

Bristol Myers Squibb assigned short-term B2 & long-term Ba1 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (CNN Layer) with Stepwise Regression ^{1,2,3,4} and conclude that the BMY 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 BMY stock.**

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

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

Outlook* | B2 | Ba1 |

Operational Risk | 51 | 77 |

Market Risk | 54 | 59 |

Technical Analysis | 54 | 78 |

Fundamental Analysis | 48 | 80 |

Risk Unsystematic | 82 | 62 |

### Prediction Confidence Score

## References

- Breiman L. 1996. Bagging predictors. Mach. Learn. 24:123–40
- Greene WH. 2000. Econometric Analysis. Upper Saddle River, N J: Prentice Hall. 4th ed.
- Mikolov T, Chen K, Corrado GS, Dean J. 2013a. Efficient estimation of word representations in vector space. arXiv:1301.3781 [cs.CL]
- Chow, G. C. (1960), "Tests of equality between sets of coefficients in two linear regressions," Econometrica, 28, 591–605.
- Barkan O. 2016. Bayesian neural word embedding. arXiv:1603.06571 [math.ST]
- Burkov A. 2019. The Hundred-Page Machine Learning Book. Quebec City, Can.: Andriy Burkov
- Athey S, Bayati M, Doudchenko N, Imbens G, Khosravi K. 2017a. Matrix completion methods for causal panel data models. arXiv:1710.10251 [math.ST]

## Frequently Asked Questions

Q: What is the prediction methodology for BMY stock?A: BMY stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Stepwise Regression

Q: Is BMY stock a buy or sell?

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

Q: Is Bristol Myers Squibb stock a good investment?

A: The consensus rating for Bristol Myers Squibb is Hold and assigned short-term B2 & long-term Ba1 forecasted stock rating.

Q: What is the consensus rating of BMY stock?

A: The consensus rating for BMY is Hold.

Q: What is the prediction period for BMY stock?

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

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