Short-term trading is a difficult task due to fluctuating demand and supply in the stock market. These demands and supply are reflected in stock prices. The stock prices may be predicted using technical indicators. Most of the existing literature considered the limited technical indicators to measure short-term prices. We have considered 82 different combinations of technical indicators to predict the stock prices. ** We evaluate CUSTODIAN REIT PLC prediction models with Modular Neural Network (CNN Layer) and Factor ^{1,2,3,4} and conclude that the LON:CREI 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 LON:CREI stock.**

**LON:CREI, CUSTODIAN REIT PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Trading Signals
- What is Markov decision process in reinforcement learning?
- Should I buy stocks now or wait amid such uncertainty?

## LON:CREI 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 CUSTODIAN REIT PLC Stock Decision Process with Factor where A is the set of discrete actions of LON:CREI 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(Factor)

^{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 (CNN Layer)) X S(n):→ (n+8 weeks) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of LON:CREI 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?

## LON:CREI Stock Forecast (Buy or Sell) for (n+8 weeks)

**Sample Set:**Neural Network

**Stock/Index:**LON:CREI CUSTODIAN REIT PLC

**Time series to forecast n: 03 Oct 2022**for (n+8 weeks)

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

CUSTODIAN REIT PLC assigned short-term B2 & long-term Ba2 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (CNN Layer) with Factor ^{1,2,3,4} and conclude that the LON:CREI 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 LON:CREI stock.**

### Financial State Forecast for LON:CREI Stock Options & Futures

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

Outlook* | B2 | Ba2 |

Operational Risk | 33 | 65 |

Market Risk | 79 | 84 |

Technical Analysis | 32 | 51 |

Fundamental Analysis | 51 | 81 |

Risk Unsystematic | 83 | 54 |

### Prediction Confidence Score

## References

- Artis, M. J. W. Zhang (1990), "BVAR forecasts for the G-7," International Journal of Forecasting, 6, 349–362.
- Bai J, Ng S. 2017. Principal components and regularized estimation of factor models. arXiv:1708.08137 [stat.ME]
- Greene WH. 2000. Econometric Analysis. Upper Saddle River, N J: Prentice Hall. 4th ed.
- G. Konidaris, S. Osentoski, and P. Thomas. Value function approximation in reinforcement learning using the Fourier basis. In AAAI, 2011
- Rosenbaum PR, Rubin DB. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70:41–55
- V. Borkar. Q-learning for risk-sensitive control. Mathematics of Operations Research, 27:294–311, 2002.
- V. Borkar and R. Jain. Risk-constrained Markov decision processes. IEEE Transaction on Automatic Control, 2014

## Frequently Asked Questions

Q: What is the prediction methodology for LON:CREI stock?A: LON:CREI stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Factor

Q: Is LON:CREI stock a buy or sell?

A: The dominant strategy among neural network is to Hold LON:CREI Stock.

Q: Is CUSTODIAN REIT PLC stock a good investment?

A: The consensus rating for CUSTODIAN REIT PLC is Hold and assigned short-term B2 & long-term Ba2 forecasted stock rating.

Q: What is the consensus rating of LON:CREI stock?

A: The consensus rating for LON:CREI is Hold.

Q: What is the prediction period for LON:CREI stock?

A: The prediction period for LON:CREI is (n+8 weeks)