Is now good time to invest? (COP Stock Forecast)

Social media comments have in the past had an instantaneous effect on stock markets. This paper investigates the sentiments expressed on the social media platform Twitter and their pr edictive impact on the Stock Market. We evaluate ConocoPhillips prediction models with Transfer Learning (ML) and Logistic Regression^1,2,3,4 and conclude that the COP 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 Buy COP stock.

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

Key Points

1. Is it better to buy and sell or hold?
2. What are buy sell or hold recommendations?
3. What is prediction in deep learning?

COP Target Price Prediction Modeling Methodology

Efficient Market Hypothesis (EMH) is the cornerstone of the modern financial theory and it states that it is impossible to predict the price of any stock using any trend, fundamental or technical analysis. Stock trading is one of the most important activities in the world of finance. Stock price prediction has been an age-old problem and many researchers from academia and business have tried to solve it using many techniques ranging from basic statistics to machine learning using relevant information such as news sentiment and historical prices. We consider ConocoPhillips Stock Decision Process with Logistic Regression where A is the set of discrete actions of COP 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(Logistic Regression)^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(Transfer Learning (ML)) X S(n):→ (n+3 month) $\sum _{i=1}^n\left(s_i\right)$

n:Time series to forecast

p:Price signals of COP 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?

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

Sample Set: Neural Network
Stock/Index: COP ConocoPhillips
Time series to forecast n: 09 Sep 2022 for (n+3 month)

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

ConocoPhillips assigned short-term Ba3 & long-term Ba1 forecasted stock rating. We evaluate the prediction models Transfer Learning (ML) with Logistic Regression^1,2,3,4 and conclude that the COP 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 Buy COP stock.

Financial State Forecast for COP Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Ba3	Ba1
Operational Risk	40	63
Market Risk	55	64
Technical Analysis	81	85
Fundamental Analysis	84	79
Risk Unsystematic	66	60

Prediction Confidence Score

Trust metric by Neural Network: 93 out of 100 with 644 signals.

References

1. Bai J, Ng S. 2017. Principal components and regularized estimation of factor models. arXiv:1708.08137 [stat.ME]
2. LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521:436–44
3. S. Bhatnagar and K. Lakshmanan. An online actor-critic algorithm with function approximation for con- strained Markov decision processes. Journal of Optimization Theory and Applications, 153(3):688–708, 2012.
4. Challen, D. W. A. J. Hagger (1983), Macroeconomic Systems: Construction, Validation and Applications. New York: St. Martin's Press.
5. Breusch, T. S. (1978), "Testing for autocorrelation in dynamic linear models," Australian Economic Papers, 17, 334–355.
6. G. Konidaris, S. Osentoski, and P. Thomas. Value function approximation in reinforcement learning using the Fourier basis. In AAAI, 2011
7. Breiman L. 1993. Better subset selection using the non-negative garotte. Tech. Rep., Univ. Calif., Berkeley

Frequently Asked Questions

Q: What is the prediction methodology for COP stock?
A: COP stock prediction methodology: We evaluate the prediction models Transfer Learning (ML) and Logistic Regression
Q: Is COP stock a buy or sell?
A: The dominant strategy among neural network is to Buy COP Stock.
Q: Is ConocoPhillips stock a good investment?
A: The consensus rating for ConocoPhillips is Buy and assigned short-term Ba3 & long-term Ba1 forecasted stock rating.
Q: What is the consensus rating of COP stock?
A: The consensus rating for COP is Buy.
Q: What is the prediction period for COP stock?
A: The prediction period for COP is (n+3 month)

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