Finance is one of the pioneering industries that started using Machine Learning (ML), a subset of Artificial Intelligence (AI) in the early 80s for market prediction. Since then, major firms and hedge funds have adopted machine learning for stock prediction, portfolio optimization, credit lending, stock betting, etc. In this paper, we survey all the different approaches of machine learning that can be incorporated in applied finance. We evaluate Crown Castle prediction models with Modular Neural Network (Social Media Sentiment Analysis) and Polynomial Regression1,2,3,4 and conclude that the CCI stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Buy CCI stock.

Keywords: CCI, Crown Castle, 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. Technical Analysis with Algorithmic Trading
3. What is a prediction confidence?

## CCI Target Price Prediction Modeling Methodology

Prediction of stock prices has been an important area of research for a long time. While supporters of the efficient market hypothesis believe that it is impossible to predict stock prices accurately, there are formal propositions demonstrating that accurate modeling and designing of appropriate variables may lead to models using which stock prices and stock price movement patterns can be very accurately predicted. We consider Crown Castle Stock Decision Process with Polynomial Regression where A is the set of discrete actions of CCI 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(Polynomial 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 (Social Media Sentiment Analysis)) X S(n):→ (n+1 year) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of CCI stock

j:Nash equilibria

k:Dominated move

a:Best response for target price

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## CCI Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: CCI Crown Castle
Time series to forecast n: 22 Sep 2022 for (n+1 year)

According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Buy CCI 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

Crown Castle assigned short-term Ba3 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) with Polynomial Regression1,2,3,4 and conclude that the CCI stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Buy CCI stock.

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

Rating Short-Term Long-Term Senior
Outlook*Ba3B2
Operational Risk 6570
Market Risk8840
Technical Analysis6246
Fundamental Analysis5160
Risk Unsystematic4836

### Prediction Confidence Score

Trust metric by Neural Network: 84 out of 100 with 674 signals.

## References

1. R. Sutton and A. Barto. Reinforcement Learning. The MIT Press, 1998
2. Morris CN. 1983. Parametric empirical Bayes inference: theory and applications. J. Am. Stat. Assoc. 78:47–55
3. K. Boda and J. Filar. Time consistent dynamic risk measures. Mathematical Methods of Operations Research, 63(1):169–186, 2006
4. Y. Chow and M. Ghavamzadeh. Algorithms for CVaR optimization in MDPs. In Advances in Neural Infor- mation Processing Systems, pages 3509–3517, 2014.
5. T. Shardlow and A. Stuart. A perturbation theory for ergodic Markov chains and application to numerical approximations. SIAM journal on numerical analysis, 37(4):1120–1137, 2000
6. T. Morimura, M. Sugiyama, M. Kashima, H. Hachiya, and T. Tanaka. Nonparametric return distribution ap- proximation for reinforcement learning. In Proceedings of the 27th International Conference on Machine Learning, pages 799–806, 2010
7. T. Shardlow and A. Stuart. A perturbation theory for ergodic Markov chains and application to numerical approximations. SIAM journal on numerical analysis, 37(4):1120–1137, 2000
Frequently Asked QuestionsQ: What is the prediction methodology for CCI stock?
A: CCI stock prediction methodology: We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) and Polynomial Regression
Q: Is CCI stock a buy or sell?
A: The dominant strategy among neural network is to Buy CCI Stock.
Q: Is Crown Castle stock a good investment?
A: The consensus rating for Crown Castle is Buy and assigned short-term Ba3 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of CCI stock?
A: The consensus rating for CCI is Buy.
Q: What is the prediction period for CCI stock?
A: The prediction period for CCI is (n+1 year)

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