Application of machine learning for stock prediction is attracting a lot of attention in recent years. A large amount of research has been conducted in this area and multiple existing results have shown that machine learning methods could be successfully used toward stock predicting using stocks' historical data. Most of these existing approaches have focused on short term prediction using stocks' historical price and technical indicators. We evaluate NetApp prediction models with Modular Neural Network (Market Direction Analysis) and Factor1,2,3,4 and conclude that the NTAP stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold NTAP stock.

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

## Key Points

1. What is a prediction confidence?
2. Is Target price a good indicator?
3. Why do we need predictive models?

## NTAP Target Price Prediction Modeling Methodology

Recently, numerous investigations for stock price prediction and portfolio management using machine learning have been trying to develop efficient mechanical trading systems. But these systems have a limitation in that they are mainly based on the supervised learning which is not so adequate for learning problems with long-term goals and delayed rewards. This paper proposes a method of applying reinforcement learning, suitable for modeling and learning various kinds of interactions in real situations, to the problem of stock price prediction. We consider NetApp Stock Decision Process with Factor where A is the set of discrete actions of NTAP 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(Factor)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(Modular Neural Network (Market Direction Analysis)) X S(n):→ (n+4 weeks) $\stackrel{\to }{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

p:Price signals of NTAP 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?

## NTAP Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: NTAP NetApp
Time series to forecast n: 15 Sep 2022 for (n+4 weeks)

According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold NTAP 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

NetApp assigned short-term B1 & long-term Caa1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market Direction Analysis) with Factor1,2,3,4 and conclude that the NTAP stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold NTAP stock.

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

Rating Short-Term Long-Term Senior
Outlook*B1Caa1
Operational Risk 7051
Market Risk4831
Technical Analysis6636
Fundamental Analysis6632
Risk Unsystematic4946

### Prediction Confidence Score

Trust metric by Neural Network: 80 out of 100 with 675 signals.

## References

1. Jiang N, Li L. 2016. Doubly robust off-policy value evaluation for reinforcement learning. In Proceedings of the 33rd International Conference on Machine Learning, pp. 652–61. La Jolla, CA: Int. Mach. Learn. Soc.
2. Imbens G, Wooldridge J. 2009. Recent developments in the econometrics of program evaluation. J. Econ. Lit. 47:5–86
3. D. Bertsekas. Dynamic programming and optimal control. Athena Scientific, 1995.
4. Meinshausen N. 2007. Relaxed lasso. Comput. Stat. Data Anal. 52:374–93
5. Athey S, Mobius MM, Pál J. 2017c. The impact of aggregators on internet news consumption. Unpublished manuscript, Grad. School Bus., Stanford Univ., Stanford, CA
6. Canova, F. B. E. Hansen (1995), "Are seasonal patterns constant over time? A test for seasonal stability," Journal of Business and Economic Statistics, 13, 237–252.
7. G. Theocharous and A. Hallak. Lifetime value marketing using reinforcement learning. RLDM 2013, page 19, 2013
Frequently Asked QuestionsQ: What is the prediction methodology for NTAP stock?
A: NTAP stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Direction Analysis) and Factor
Q: Is NTAP stock a buy or sell?
A: The dominant strategy among neural network is to Hold NTAP Stock.
Q: Is NetApp stock a good investment?
A: The consensus rating for NetApp is Hold and assigned short-term B1 & long-term Caa1 forecasted stock rating.
Q: What is the consensus rating of NTAP stock?
A: The consensus rating for NTAP is Hold.
Q: What is the prediction period for NTAP stock?
A: The prediction period for NTAP is (n+4 weeks)