CYBR CyberArk Software Ltd. Ordinary Shares

Outlook: CyberArk Software Ltd. Ordinary Shares assigned short-term Caa2 & long-term B3 forecasted stock rating.
Dominant Strategy : Buy
Time series to forecast n: 15 Dec 2022 for (n+3 month)
Methodology : Active Learning (ML)

Abstract

Nowadays, people show more and more enthusiasm for applying machine learning methods to finance domain. Many scholars and investors are trying to discover the mystery behind the stock market by applying deep learning. This thesis compares four machine learning methods: long short-term memory (LSTM), gated recurrent units (GRU), support vector machine (SVM), and eXtreme gradient boosting (XGBoost) to test which one performs the best in predicting the stock trend.(Kompella, S. and Chakravarthy Chilukuri, K.C.C., 2020. Stock market prediction using machine learning methods. International Journal of Computer Engineering and Technology, 10(3), p.2019.) We evaluate CyberArk Software Ltd. Ordinary Shares prediction models with Active Learning (ML) and Pearson Correlation^1,2,3,4 and conclude that the CYBR stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Buy

Key Points

1. How accurate is machine learning in stock market?
2. Which neural network is best for prediction?
3. Is now good time to invest?

CYBR Target Price Prediction Modeling Methodology

We consider CyberArk Software Ltd. Ordinary Shares Decision Process with Active Learning (ML) where A is the set of discrete actions of CYBR 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= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Active Learning (ML)) 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 CYBR stock

j:Nash equilibria (Neural Network)

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?

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

Sample Set: Neural Network
Stock/Index: CYBR CyberArk Software Ltd. Ordinary Shares
Time series to forecast n: 15 Dec 2022 for (n+3 month)

According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Buy

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 (Grey to Black): *Technical Analysis%

Adjusted IFRS* Prediction Methods for CyberArk Software Ltd. Ordinary Shares

1. When an entity separates the foreign currency basis spread from a financial instrument and excludes it from the designation of that financial instrument as the hedging instrument (see paragraph 6.2.4(b)), the application guidance in paragraphs B6.5.34–B6.5.38 applies to the foreign currency basis spread in the same manner as it is applied to the forward element of a forward contract.
2. An entity shall apply Annual Improvements to IFRS Standards 2018–2020 to financial liabilities that are modified or exchanged on or after the beginning of the annual reporting period in which the entity first applies the amendment.
3. Because the hedge accounting model is based on a general notion of offset between gains and losses on the hedging instrument and the hedged item, hedge effectiveness is determined not only by the economic relationship between those items (ie the changes in their underlyings) but also by the effect of credit risk on the value of both the hedging instrument and the hedged item. The effect of credit risk means that even if there is an economic relationship between the hedging instrument and the hedged item, the level of offset might become erratic. This can result from a change in the credit risk of either the hedging instrument or the hedged item that is of such a magnitude that the credit risk dominates the value changes that result from the economic relationship (ie the effect of the changes in the underlyings). A level of magnitude that gives rise to dominance is one that would result in the loss (or gain) from credit risk frustrating the effect of changes in the underlyings on the value of the hedging instrument or the hedged item, even if those changes were significant.
4. Subject to the conditions in paragraphs 4.1.5 and 4.2.2, this Standard allows an entity to designate a financial asset, a financial liability, or a group of financial instruments (financial assets, financial liabilities or both) as at fair value through profit or loss provided that doing so results in more relevant information.

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

Conclusions

CyberArk Software Ltd. Ordinary Shares assigned short-term Caa2 & long-term B3 forecasted stock rating. We evaluate the prediction models Active Learning (ML) with Pearson Correlation^1,2,3,4 and conclude that the CYBR stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Buy

Financial State Forecast for CYBR CyberArk Software Ltd. Ordinary Shares Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Caa2	B3
Operational Risk	38	32
Market Risk	31	39
Technical Analysis	35	63
Fundamental Analysis	66	54
Risk Unsystematic	31	36

Prediction Confidence Score

Trust metric by Neural Network: 92 out of 100 with 653 signals.

References

1. Bottou L. 2012. Stochastic gradient descent tricks. In Neural Networks: Tricks of the Trade, ed. G Montavon, G Orr, K-R Müller, pp. 421–36. Berlin: Springer
2. Candès E, Tao T. 2007. The Dantzig selector: statistical estimation when p is much larger than n. Ann. Stat. 35:2313–51
3. Hastie T, Tibshirani R, Wainwright M. 2015. Statistical Learning with Sparsity: The Lasso and Generalizations. New York: CRC Press
4. Mikolov T, Chen K, Corrado GS, Dean J. 2013a. Efficient estimation of word representations in vector space. arXiv:1301.3781 [cs.CL]
5. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., Trading Signals (WTS Stock Forecast). AC Investment Research Journal, 101(3).
6. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., Tempur Sealy Stock Forecast & Analysis. AC Investment Research Journal, 101(3).
7. Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, Newey W. 2017. Double/debiased/ Neyman machine learning of treatment effects. Am. Econ. Rev. 107:261–65

Frequently Asked Questions

Q: What is the prediction methodology for CYBR stock?
A: CYBR stock prediction methodology: We evaluate the prediction models Active Learning (ML) and Pearson Correlation
Q: Is CYBR stock a buy or sell?
A: The dominant strategy among neural network is to Buy CYBR Stock.
Q: Is CyberArk Software Ltd. Ordinary Shares stock a good investment?
A: The consensus rating for CyberArk Software Ltd. Ordinary Shares is Buy and assigned short-term Caa2 & long-term B3 forecasted stock rating.
Q: What is the consensus rating of CYBR stock?
A: The consensus rating for CYBR is Buy.
Q: What is the prediction period for CYBR stock?
A: The prediction period for CYBR is (n+3 month)

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