Prediction of future movement of stock prices has always been a challenging task for the researchers. While the advocates of the efficient market hypothesis (EMH) believe that it is impossible to design any predictive framework that can accurately predict the movement of stock prices, there are seminal work in the literature that have clearly demonstrated that the seemingly random movement patterns in the time series of a stock price can be predicted with a high level of accuracy.** We evaluate Alexandria Real Estate Equities prediction models with Modular Neural Network (DNN Layer) and Multiple Regression ^{1,2,3,4} and conclude that the ARE 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 Sell ARE stock.**

**ARE, Alexandria Real Estate Equities, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Can neural networks predict stock market?
- Which neural network is best for prediction?
- What is the use of Markov decision process?

## ARE Target Price Prediction Modeling Methodology

This paper studies the possibilities of making prediction of stock market prices using historical data and machine learning algorithms. We consider Alexandria Real Estate Equities Stock Decision Process with Multiple Regression where A is the set of discrete actions of ARE 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(Multiple Regression)

^{5,6,7}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (DNN Layer)) X S(n):→ (n+1 year) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of ARE 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?

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

**Sample Set:**Neural Network

**Stock/Index:**ARE Alexandria Real Estate Equities

**Time series to forecast n: 24 Sep 2022**for (n+1 year)

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

Alexandria Real Estate Equities assigned short-term Ba3 & long-term Ba3 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (DNN Layer) with Multiple Regression ^{1,2,3,4} and conclude that the ARE 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 Sell ARE stock.**

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

Rating | Short-Term | Long-Term Senior |
---|---|---|

Outlook* | Ba3 | Ba3 |

Operational Risk | 85 | 43 |

Market Risk | 58 | 89 |

Technical Analysis | 71 | 78 |

Fundamental Analysis | 56 | 71 |

Risk Unsystematic | 58 | 35 |

### Prediction Confidence Score

## References

- Breiman L. 1993. Better subset selection using the non-negative garotte. Tech. Rep., Univ. Calif., Berkeley
- V. Mnih, K. Kavukcuoglu, D. Silver, A. Rusu, J. Veness, M. Bellemare, A. Graves, M. Riedmiller, A. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis. Human-level control through deep reinforcement learning. Nature, 518(7540):529–533, 02 2015.
- M. Benaim, J. Hofbauer, and S. Sorin. Stochastic approximations and differential inclusions, Part II: Appli- cations. Mathematics of Operations Research, 31(4):673–695, 2006
- Arora S, Li Y, Liang Y, Ma T. 2016. RAND-WALK: a latent variable model approach to word embeddings. Trans. Assoc. Comput. Linguist. 4:385–99
- Ashley, R. (1988), "On the relative worth of recent macroeconomic forecasts," International Journal of Forecasting, 4, 363–376.
- N. B ̈auerle and A. Mundt. Dynamic mean-risk optimization in a binomial model. Mathematical Methods of Operations Research, 70(2):219–239, 2009.
- J. Filar, D. Krass, and K. Ross. Percentile performance criteria for limiting average Markov decision pro- cesses. IEEE Transaction of Automatic Control, 40(1):2–10, 1995.

## Frequently Asked Questions

Q: What is the prediction methodology for ARE stock?A: ARE stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Multiple Regression

Q: Is ARE stock a buy or sell?

A: The dominant strategy among neural network is to Sell ARE Stock.

Q: Is Alexandria Real Estate Equities stock a good investment?

A: The consensus rating for Alexandria Real Estate Equities is Sell and assigned short-term Ba3 & long-term Ba3 forecasted stock rating.

Q: What is the consensus rating of ARE stock?

A: The consensus rating for ARE is Sell.

Q: What is the prediction period for ARE stock?

A: The prediction period for ARE is (n+1 year)