Prediction of future movement of stock prices has been a subject matter of many research work. There is a gamut of literature of technical analysis of stock prices where the objective is to identify patterns in stock price movements and derive profit from it. Improving the prediction accuracy remains the single most challenge in this area of research. We propose a hybrid approach for stock price movement prediction using machine learning, deep learning, and natural language processing. ** We evaluate Edwards Lifesciences prediction models with Transductive Learning (ML) and Logistic Regression ^{1,2,3,4} and conclude that the EW stock is predictable in the short/long term. **

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold EW stock.**

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

*Keywords:*## Key Points

- Investment Risk
- How can neural networks improve predictions?
- Probability Distribution

## EW Target Price Prediction Modeling Methodology

As stock data is characterized by highly noisy and non-stationary, stock price prediction is regarded as a knotty problem. In this paper, we propose new two-stage ensemble models by combining empirical mode decomposition (EMD) (or variational mode decomposition (VMD)), extreme learning machine (ELM) and improved harmony search (IHS) algorithm for stock price prediction, which are respectively named EMD–ELM–IHS and VMD–ELM–IHS. We consider Edwards Lifesciences Stock Decision Process with Logistic Regression where A is the set of discrete actions of EW 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(Logistic 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(Transductive Learning (ML)) X S(n):→ (n+6 month) $\sum _{i=1}^{n}\left({a}_{i}\right)$

n:Time series to forecast

p:Price signals of EW 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?

## EW Stock Forecast (Buy or Sell) for (n+6 month)

**Sample Set:**Neural Network

**Stock/Index:**EW Edwards Lifesciences

**Time series to forecast n: 16 Oct 2022**for (n+6 month)

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold EW 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

Edwards Lifesciences assigned short-term B1 & long-term B1 forecasted stock rating.** We evaluate the prediction models Transductive Learning (ML) with Logistic Regression ^{1,2,3,4} and conclude that the EW stock is predictable in the short/long term.**

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold EW stock.**

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

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

Outlook* | B1 | B1 |

Operational Risk | 45 | 55 |

Market Risk | 66 | 88 |

Technical Analysis | 71 | 30 |

Fundamental Analysis | 89 | 61 |

Risk Unsystematic | 41 | 52 |

### Prediction Confidence Score

## References

- Hastie T, Tibshirani R, Wainwright M. 2015. Statistical Learning with Sparsity: The Lasso and Generalizations. New York: CRC Press
- Bastani H, Bayati M. 2015. Online decision-making with high-dimensional covariates. Work. Pap., Univ. Penn./ Stanford Grad. School Bus., Philadelphia/Stanford, CA
- Vapnik V. 2013. The Nature of Statistical Learning Theory. Berlin: Springer
- A. Tamar, Y. Glassner, and S. Mannor. Policy gradients beyond expectations: Conditional value-at-risk. In AAAI, 2015
- Y. Chow and M. Ghavamzadeh. Algorithms for CVaR optimization in MDPs. In Advances in Neural Infor- mation Processing Systems, pages 3509–3517, 2014.
- Alexander, J. C. Jr. (1995), "Refining the degree of earnings surprise: A comparison of statistical and analysts' forecasts," Financial Review, 30, 469–506.
- D. S. Bernstein, S. Zilberstein, and N. Immerman. The complexity of decentralized control of Markov Decision Processes. In UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30 - July 3, 2000, pages 32–37, 2000.

## Frequently Asked Questions

Q: What is the prediction methodology for EW stock?A: EW stock prediction methodology: We evaluate the prediction models Transductive Learning (ML) and Logistic Regression

Q: Is EW stock a buy or sell?

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

Q: Is Edwards Lifesciences stock a good investment?

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

Q: What is the consensus rating of EW stock?

A: The consensus rating for EW is Hold.

Q: What is the prediction period for EW stock?

A: The prediction period for EW is (n+6 month)