We present an Artificial Neural Network (ANN) approach to predict stock market indices, particularly with respect to the forecast of their trend movements up or down. Exploiting different Neural Networks architectures, we provide numerical analysis of concrete financial time series. In particular, after a brief r ́esum ́e of the existing literature on the subject, we consider the Multi-layer Perceptron (MLP), the Convolutional Neural Net- works (CNN), and the Long Short-Term Memory (LSTM) recurrent neural networks techniques. We evaluate Edwards Lifesciences prediction models with Inductive Learning (ML) and Pearson Correlation1,2,3,4 and conclude that the EW 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 EW stock.

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

## Key Points

1. Can we predict stock market using machine learning?
2. Can stock prices be predicted?
3. Can machine learning predict?

## EW 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 Edwards Lifesciences Stock Decision Process with Pearson Correlation 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(Pearson Correlation)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Inductive Learning (ML)) X S(n):→ (n+16 weeks) $∑ i = 1 n s i$

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+16 weeks)

Sample Set: Neural Network
Stock/Index: EW Edwards Lifesciences
Time series to forecast n: 23 Sep 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) 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 Baa2 & long-term B3 forecasted stock rating. We evaluate the prediction models Inductive Learning (ML) with Pearson Correlation1,2,3,4 and conclude that the EW 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 EW stock.

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

Rating Short-Term Long-Term Senior
Outlook*Baa2B3
Operational Risk 8636
Market Risk8340
Technical Analysis7073
Fundamental Analysis8645
Risk Unsystematic6344

### Prediction Confidence Score

Trust metric by Neural Network: 80 out of 100 with 785 signals.

## References

1. Harris ZS. 1954. Distributional structure. Word 10:146–62
2. Pennington J, Socher R, Manning CD. 2014. GloVe: global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods on Natural Language Processing, pp. 1532–43. New York: Assoc. Comput. Linguist.
3. Imbens GW, Lemieux T. 2008. Regression discontinuity designs: a guide to practice. J. Econom. 142:615–35
4. Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, et al. 2016a. Double machine learning for treatment and causal parameters. Tech. Rep., Cent. Microdata Methods Pract., Inst. Fiscal Stud., London
5. Firth JR. 1957. A synopsis of linguistic theory 1930–1955. In Studies in Linguistic Analysis (Special Volume of the Philological Society), ed. JR Firth, pp. 1–32. Oxford, UK: Blackwell
6. N. B ̈auerle and J. Ott. Markov decision processes with average-value-at-risk criteria. Mathematical Methods of Operations Research, 74(3):361–379, 2011
7. Z. Wang, T. Schaul, M. Hessel, H. van Hasselt, M. Lanctot, and N. de Freitas. Dueling network architectures for deep reinforcement learning. In Proceedings of the International Conference on Machine Learning (ICML), pages 1995–2003, 2016.
Frequently Asked QuestionsQ: What is the prediction methodology for EW stock?
A: EW stock prediction methodology: We evaluate the prediction models Inductive Learning (ML) and Pearson Correlation
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 Baa2 & long-term B3 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+16 weeks)