Investors raise profit from stock market by maximising gains and minimising loses. The profit is difficult to raise because of the volatile nature of stock market prices. Predictive modelling allows investors to make informed decisions. We evaluate Zscaler prediction models with Modular Neural Network (News Feed Sentiment Analysis) and Ridge Regression1,2,3,4 and conclude that the ZS stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold ZS stock.

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

Key Points

1. Should I buy stocks now or wait amid such uncertainty?
2. Stock Rating
3. How can neural networks improve predictions?

ZS Target Price Prediction Modeling Methodology

Stock prediction with data mining techniques is one of the most important issues in finance being investigated by researchers across the globe. Data mining techniques can be used extensively in the financial markets to help investors make qualitative decision. One of the techniques is artificial neural network (ANN). However, in the application of ANN for predicting the financial market the use of technical analysis variables for stock prediction is predominant. In this paper, we present a hybridized approach which combines the use of the variables of technical and fundamental analysis of stock market indicators for prediction of future price of stock in order to improve on the existing approaches. We consider Zscaler Stock Decision Process with Ridge Regression where A is the set of discrete actions of ZS 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(Ridge Regression)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 (News Feed Sentiment Analysis)) X S(n):→ (n+3 month) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of ZS 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?

ZS Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: ZS Zscaler
Time series to forecast n: 16 Oct 2022 for (n+3 month)

According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold ZS 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

Zscaler assigned short-term B2 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) with Ridge Regression1,2,3,4 and conclude that the ZS stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold ZS stock.

Financial State Forecast for ZS Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B2B2
Operational Risk 3167
Market Risk8138
Technical Analysis5235
Fundamental Analysis5862
Risk Unsystematic6471

Prediction Confidence Score

Trust metric by Neural Network: 93 out of 100 with 735 signals.

References

1. G. Shani, R. Brafman, and D. Heckerman. An MDP-based recommender system. In Proceedings of the Eigh- teenth conference on Uncertainty in artificial intelligence, pages 453–460. Morgan Kaufmann Publishers Inc., 2002
2. K. Boda and J. Filar. Time consistent dynamic risk measures. Mathematical Methods of Operations Research, 63(1):169–186, 2006
3. Imbens G, Wooldridge J. 2009. Recent developments in the econometrics of program evaluation. J. Econ. Lit. 47:5–86
4. 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.
5. Breusch, T. S. (1978), "Testing for autocorrelation in dynamic linear models," Australian Economic Papers, 17, 334–355.
6. H. Kushner and G. Yin. Stochastic approximation algorithms and applications. Springer, 1997.
7. M. Benaim, J. Hofbauer, and S. Sorin. Stochastic approximations and differential inclusions, Part II: Appli- cations. Mathematics of Operations Research, 31(4):673–695, 2006
Frequently Asked QuestionsQ: What is the prediction methodology for ZS stock?
A: ZS stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and Ridge Regression
Q: Is ZS stock a buy or sell?
A: The dominant strategy among neural network is to Hold ZS Stock.
Q: Is Zscaler stock a good investment?
A: The consensus rating for Zscaler is Hold and assigned short-term B2 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of ZS stock?
A: The consensus rating for ZS is Hold.
Q: What is the prediction period for ZS stock?
A: The prediction period for ZS is (n+3 month)