Three networks are compared for low false alarm stock trend predictions. Short-term trends, particularly attractive for neural network analysis, can be used profitably in scenarios such as option trading, but only with significant risk. Therefore, we focus on limiting false alarms, which improves the risk/reward ratio by preventing losses. To predict stock trends, we exploit time delay, recurrent, and probabilistic neural networks (TDNN, RNN, and PNN, respectively), utilizing conjugate gradient and multistream extended Kalman filter training for TDNN and RNN.** We evaluate IDE GROUP HOLDINGS PLC prediction models with Modular Neural Network (Market Volatility Analysis) and Lasso Regression ^{1,2,3,4} and conclude that the LON:IDE 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 Hold LON:IDE stock.**

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

*Keywords:*## Key Points

- Stock Rating
- What are buy sell or hold recommendations?
- Understanding Buy, Sell, and Hold Ratings

## LON:IDE Target Price Prediction Modeling Methodology

Prediction of future movement of stock prices has been a subject matter of many research work. There is a gamut of literature of technical analysis of stock prices where the objective is to identify patterns in stock price movements and derive profit from it. Improving the prediction accuracy remains the single most challenge in this area of research. We propose a hybrid approach for stock price movement prediction using machine learning, deep learning, and natural language processing. We consider IDE GROUP HOLDINGS PLC Stock Decision Process with Lasso Regression where A is the set of discrete actions of LON:IDE 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(Lasso 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 (Market Volatility Analysis)) X S(n):→ (n+1 year) $\overrightarrow{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of LON:IDE 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:IDE Stock Forecast (Buy or Sell) for (n+1 year)

**Sample Set:**Neural Network

**Stock/Index:**LON:IDE IDE GROUP HOLDINGS PLC

**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 Hold LON:IDE 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

IDE GROUP HOLDINGS PLC assigned short-term Ba3 & long-term Ba3 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) with Lasso Regression ^{1,2,3,4} and conclude that the LON:IDE 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 Hold LON:IDE stock.**

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

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

Outlook* | Ba3 | Ba3 |

Operational Risk | 84 | 67 |

Market Risk | 55 | 86 |

Technical Analysis | 80 | 46 |

Fundamental Analysis | 55 | 83 |

Risk Unsystematic | 57 | 42 |

### Prediction Confidence Score

## References

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- Barrett, C. B. (1997), "Heteroscedastic price forecasting for food security management in developing countries," Oxford Development Studies, 25, 225–236.
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- Arjovsky M, Bottou L. 2017. Towards principled methods for training generative adversarial networks. arXiv:1701.04862 [stat.ML]
- V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. P. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu. Asynchronous methods for deep reinforcement learning. In Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016, pages 1928–1937, 2016
- Breiman L. 1996. Bagging predictors. Mach. Learn. 24:123–40
- Rumelhart DE, Hinton GE, Williams RJ. 1986. Learning representations by back-propagating errors. Nature 323:533–36

## Frequently Asked Questions

Q: What is the prediction methodology for LON:IDE stock?A: LON:IDE stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and Lasso Regression

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

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

Q: Is IDE GROUP HOLDINGS PLC stock a good investment?

A: The consensus rating for IDE GROUP HOLDINGS PLC is Hold and assigned short-term Ba3 & long-term Ba3 forecasted stock rating.

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

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

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

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