In this paper a Bayesian regularized artificial neural network is proposed as a novel method to forecast financial market behavior. Daily market prices and financial technical indicators are utilized as inputs to predict the one day future closing price of individual stocks. The prediction of stock price movement is generally considered to be a challenging and important task for financial time series analysis. ** We evaluate GREAT PORTLAND ESTATES PLC prediction models with Statistical Inference (ML) and Polynomial Regression ^{1,2,3,4} and conclude that the LON:GPE 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 Hold LON:GPE stock.**

**LON:GPE, GREAT PORTLAND ESTATES PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Can machine learning predict?
- Short/Long Term Stocks
- Which neural network is best for prediction?

## LON:GPE Target Price Prediction Modeling Methodology

The main perfect of this composition is to discover the stylish version to prognosticate the cost of the inventory request. During the procedure of analyzing the colorful ways and variables to remember, we plant that approaches similar as Random woodland, machine help Vector were not absolutely exploited. We consider GREAT PORTLAND ESTATES PLC Stock Decision Process with Polynomial Regression where A is the set of discrete actions of LON:GPE 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}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Statistical Inference (ML)) X S(n):→ (n+3 month) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of LON:GPE 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:GPE Stock Forecast (Buy or Sell) for (n+3 month)

**Sample Set:**Neural Network

**Stock/Index:**LON:GPE GREAT PORTLAND ESTATES PLC

**Time series to forecast n: 12 Oct 2022**for (n+3 month)

**According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold LON:GPE 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

GREAT PORTLAND ESTATES PLC assigned short-term B2 & long-term B2 forecasted stock rating.** We evaluate the prediction models Statistical Inference (ML) with Polynomial Regression ^{1,2,3,4} and conclude that the LON:GPE 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 Hold LON:GPE stock.**

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

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

Outlook* | B2 | B2 |

Operational Risk | 50 | 81 |

Market Risk | 50 | 44 |

Technical Analysis | 76 | 47 |

Fundamental Analysis | 46 | 35 |

Risk Unsystematic | 62 | 65 |

### Prediction Confidence Score

## References

- Canova, F. B. E. Hansen (1995), "Are seasonal patterns constant over time? A test for seasonal stability," Journal of Business and Economic Statistics, 13, 237–252.
- J. Harb and D. Precup. Investigating recurrence and eligibility traces in deep Q-networks. In Deep Reinforcement Learning Workshop, NIPS 2016, Barcelona, Spain, 2016.
- Bottou L. 2012. Stochastic gradient descent tricks. In Neural Networks: Tricks of the Trade, ed. G Montavon, G Orr, K-R Müller, pp. 421–36. Berlin: Springer
- Rumelhart DE, Hinton GE, Williams RJ. 1986. Learning representations by back-propagating errors. Nature 323:533–36
- Alpaydin E. 2009. Introduction to Machine Learning. Cambridge, MA: MIT Press
- Hastie T, Tibshirani R, Tibshirani RJ. 2017. Extended comparisons of best subset selection, forward stepwise selection, and the lasso. arXiv:1707.08692 [stat.ME]
- Zubizarreta JR. 2015. Stable weights that balance covariates for estimation with incomplete outcome data. J. Am. Stat. Assoc. 110:910–22

## Frequently Asked Questions

Q: What is the prediction methodology for LON:GPE stock?A: LON:GPE stock prediction methodology: We evaluate the prediction models Statistical Inference (ML) and Polynomial Regression

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

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

Q: Is GREAT PORTLAND ESTATES PLC stock a good investment?

A: The consensus rating for GREAT PORTLAND ESTATES PLC is Hold and assigned short-term B2 & long-term B2 forecasted stock rating.

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

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

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

A: The prediction period for LON:GPE is (n+3 month)