Neural networks (NNs), as artificial intelligence (AI) methods, have become very important in making stock market predictions. Much research on the applications of NNs for solving business problems have proven their advantages over statistical and other methods that do not include AI, although there is no optimal methodology for a certain problem. We evaluate American Campus Communities prediction models with Modular Neural Network (CNN Layer) and Linear Regression1,2,3,4 and conclude that the ACC stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold ACC stock.

Keywords: ACC, American Campus Communities, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

2. What is prediction model?
3. What are main components of Markov decision process? ## ACC 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 American Campus Communities Stock Decision Process with Linear Regression where A is the set of discrete actions of ACC 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(Linear 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 (CNN Layer)) X S(n):→ (n+6 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 ACC 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?

## ACC Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: ACC American Campus Communities
Time series to forecast n: 23 Sep 2022 for (n+6 month)

According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold ACC 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

American Campus Communities assigned short-term B1 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (CNN Layer) with Linear Regression1,2,3,4 and conclude that the ACC stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold ACC stock.

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

Rating Short-Term Long-Term Senior
Outlook*B1B1
Operational Risk 7539
Market Risk3583
Technical Analysis5476
Fundamental Analysis6844
Risk Unsystematic7940

### Prediction Confidence Score

Trust metric by Neural Network: 93 out of 100 with 717 signals.

## References

1. Krizhevsky A, Sutskever I, Hinton GE. 2012. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, Vol. 25, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 1097–105. San Diego, CA: Neural Inf. Process. Syst. Found.
2. E. Altman. Constrained Markov decision processes, volume 7. CRC Press, 1999
3. Cortes C, Vapnik V. 1995. Support-vector networks. Mach. Learn. 20:273–97
4. P. Marbach. Simulated-Based Methods for Markov Decision Processes. PhD thesis, Massachusetts Institute of Technology, 1998
5. Bierens HJ. 1987. Kernel estimators of regression functions. In Advances in Econometrics: Fifth World Congress, Vol. 1, ed. TF Bewley, pp. 99–144. Cambridge, UK: Cambridge Univ. Press
6. Bessler, D. A. S. W. Fuller (1993), "Cointegration between U.S. wheat markets," Journal of Regional Science, 33, 481–501.
7. Swaminathan A, Joachims T. 2015. Batch learning from logged bandit feedback through counterfactual risk minimization. J. Mach. Learn. Res. 16:1731–55
Frequently Asked QuestionsQ: What is the prediction methodology for ACC stock?
A: ACC stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Linear Regression
Q: Is ACC stock a buy or sell?
A: The dominant strategy among neural network is to Hold ACC Stock.
Q: Is American Campus Communities stock a good investment?
A: The consensus rating for American Campus Communities is Hold and assigned short-term B1 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of ACC stock?
A: The consensus rating for ACC is Hold.
Q: What is the prediction period for ACC stock?
A: The prediction period for ACC is (n+6 month)

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