When to Sell and When to Hold ORCL Stock

Stock prediction is a very hot topic in our life. However, in the early time, because of some reasons and the limitation of the device, only a few people had the access to the study. Thanks to the rapid development of science and technology, in recent years more and more people are devoted to the study of the prediction and it becomes easier and easier for us to make stock prediction by using different ways now, including machine learning, deep learning and so on. We evaluate Oracle prediction models with Modular Neural Network (DNN Layer) and Chi-Square^1,2,3,4 and conclude that the ORCL stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold ORCL stock.

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

Key Points

1. Buy, Sell and Hold Signals
2. Can machine learning predict?
3. Probability Distribution

ORCL Target Price Prediction Modeling Methodology

Market systems are so complex that they overwhelm the ability of any individual to predict. But it is crucial for the investors to predict stock market price to generate notable profit. We have taken into factors such as Commodity Prices (crude oil, gold, silver), Market History, and Foreign exchange rate (FEX) that influence the stock trend. We consider Oracle Stock Decision Process with Chi-Square where A is the set of discrete actions of ORCL 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(Chi-Square)^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(Modular Neural Network (DNN Layer)) X S(n):→ (n+8 weeks) $\sum _{i=1}^n\left(s_i\right)$

n:Time series to forecast

p:Price signals of ORCL 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?

ORCL Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: ORCL Oracle
Time series to forecast n: 04 Oct 2022 for (n+8 weeks)

According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold ORCL 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

Oracle assigned short-term B1 & long-term Baa2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (DNN Layer) with Chi-Square^1,2,3,4 and conclude that the ORCL stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold ORCL stock.

Financial State Forecast for ORCL Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B1	Baa2
Operational Risk	63	89
Market Risk	78	68
Technical Analysis	37	82
Fundamental Analysis	68	51
Risk Unsystematic	55	82

Prediction Confidence Score

Trust metric by Neural Network: 74 out of 100 with 833 signals.

References

1. Armstrong, J. S. M. C. Grohman (1972), "A comparative study of methods for long-range market forecasting," Management Science, 19, 211–221.
2. Christou, C., P. A. V. B. Swamy G. S. Tavlas (1996), "Modelling optimal strategies for the allocation of wealth in multicurrency investments," International Journal of Forecasting, 12, 483–493.
3. P. Marbach. Simulated-Based Methods for Markov Decision Processes. PhD thesis, Massachusetts Institute of Technology, 1998
4. R. Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Ma- chine learning, 8(3-4):229–256, 1992
5. Gentzkow M, Kelly BT, Taddy M. 2017. Text as data. NBER Work. Pap. 23276
6. Ashley, R. (1988), "On the relative worth of recent macroeconomic forecasts," International Journal of Forecasting, 4, 363–376.
7. Bai J. 2003. Inferential theory for factor models of large dimensions. Econometrica 71:135–71

Frequently Asked Questions

Q: What is the prediction methodology for ORCL stock?
A: ORCL stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Chi-Square
Q: Is ORCL stock a buy or sell?
A: The dominant strategy among neural network is to Hold ORCL Stock.
Q: Is Oracle stock a good investment?
A: The consensus rating for Oracle is Hold and assigned short-term B1 & long-term Baa2 forecasted stock rating.
Q: What is the consensus rating of ORCL stock?
A: The consensus rating for ORCL is Hold.
Q: What is the prediction period for ORCL stock?
A: The prediction period for ORCL is (n+8 weeks)

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