The search for models to predict the prices of financial markets is still a highly researched topic, despite major related challenges. The prices of financial assets are non-linear, dynamic, and chaotic; thus, they are financial time series that are difficult to predict. Among the latest techniques, machine learning models are some of the most researched, given their capabilities for recognizing complex patterns in various applications.** We evaluate ACTUAL EXPERIENCE PLC prediction models with Deductive Inference (ML) and Polynomial Regression ^{1,2,3,4} and conclude that the LON:ACT 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 Buy LON:ACT stock.**

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

*Keywords:*## Key Points

- What are the most successful trading algorithms?
- How do you decide buy or sell a stock?
- Investment Risk

## LON:ACT Target Price Prediction Modeling Methodology

Stock market is a promising financial investment that can generate great wealth. However, the volatile nature of the stock market makes it a very high risk investment. Thus, a lot of researchers have contributed their efforts to forecast the stock market pricing and average movement. Researchers have used various methods in computer science and economics in their quests to gain a piece of this volatile information and make great fortune out of the stock market investment. This paper investigates various techniques for the stock market prediction using artificial neural network (ANN). We consider ACTUAL EXPERIENCE PLC Stock Decision Process with Polynomial Regression where A is the set of discrete actions of LON:ACT 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(Deductive Inference (ML)) 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:ACT 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:ACT Stock Forecast (Buy or Sell) for (n+8 weeks)

**Sample Set:**Neural Network

**Stock/Index:**LON:ACT ACTUAL EXPERIENCE PLC

**Time series to forecast n: 21 Sep 2022**for (n+8 weeks)

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

ACTUAL EXPERIENCE PLC assigned short-term Ba1 & long-term B3 forecasted stock rating.** We evaluate the prediction models Deductive Inference (ML) with Polynomial Regression ^{1,2,3,4} and conclude that the LON:ACT 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 Buy LON:ACT stock.**

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

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

Outlook* | Ba1 | B3 |

Operational Risk | 79 | 36 |

Market Risk | 63 | 43 |

Technical Analysis | 59 | 32 |

Fundamental Analysis | 78 | 72 |

Risk Unsystematic | 76 | 31 |

### Prediction Confidence Score

## References

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- M. J. Hausknecht and P. Stone. Deep recurrent Q-learning for partially observable MDPs. CoRR, abs/1507.06527, 2015
- K. Tuyls and G. Weiss. Multiagent learning: Basics, challenges, and prospects. AI Magazine, 33(3): 41–52, 2012
- 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
- Rumelhart DE, Hinton GE, Williams RJ. 1986. Learning representations by back-propagating errors. Nature 323:533–36
- M. J. Hausknecht. Cooperation and Communication in Multiagent Deep Reinforcement Learning. PhD thesis, The University of Texas at Austin, 2016

## Frequently Asked Questions

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

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

A: The dominant strategy among neural network is to Buy LON:ACT Stock.

Q: Is ACTUAL EXPERIENCE PLC stock a good investment?

A: The consensus rating for ACTUAL EXPERIENCE PLC is Buy and assigned short-term Ba1 & long-term B3 forecasted stock rating.

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

A: The consensus rating for LON:ACT is Buy.

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

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