As stock data is characterized by highly noisy and non-stationary, stock price prediction is regarded as a knotty problem. In this paper, we propose new two-stage ensemble models by combining empirical mode decomposition (EMD) (or variational mode decomposition (VMD)), extreme learning machine (ELM) and improved harmony search (IHS) algorithm for stock price prediction, which are respectively named EMD–ELM–IHS and VMD–ELM–IHS.** We evaluate FTSE 100 Index prediction models with Deductive Inference (ML) and Linear Regression ^{1,2,3,4} and conclude that the FTSE 100 Index stock is predictable in the short/long term. **

**According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Sell FTSE 100 Index stock.**

**FTSE 100 Index, FTSE 100 Index, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Probability Distribution
- Short/Long Term Stocks
- Can stock prices be predicted?

## FTSE 100 Index Target Price Prediction Modeling Methodology

Accurate prediction of stock market returns is a very challenging task due to volatile and non-linear nature of the financial stock markets. With the introduction of artificial intelligence and increased computational capabilities, programmed methods of prediction have proved to be more efficient in predicting stock prices. We consider FTSE 100 Index Stock Decision Process with Linear Regression where A is the set of discrete actions of FTSE 100 Index 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(Linear 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+16 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 FTSE 100 Index 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?

## FTSE 100 Index Stock Forecast (Buy or Sell) for (n+16 weeks)

**Sample Set:**Neural Network

**Stock/Index:**FTSE 100 Index FTSE 100 Index

**Time series to forecast n: 13 Sep 2022**for (n+16 weeks)

**According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Sell FTSE 100 Index 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

FTSE 100 Index assigned short-term B2 & long-term B1 forecasted stock rating.** We evaluate the prediction models Deductive Inference (ML) with Linear Regression ^{1,2,3,4} and conclude that the FTSE 100 Index stock is predictable in the short/long term.**

**According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Sell FTSE 100 Index stock.**

### Financial State Forecast for FTSE 100 Index Stock Options & Futures

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

Outlook* | B2 | B1 |

Operational Risk | 52 | 86 |

Market Risk | 44 | 52 |

Technical Analysis | 87 | 62 |

Fundamental Analysis | 30 | 50 |

Risk Unsystematic | 49 | 42 |

### Prediction Confidence Score

## References

- Imai K, Ratkovic M. 2013. Estimating treatment effect heterogeneity in randomized program evaluation. Ann. Appl. Stat. 7:443–70
- Efron B, Hastie T. 2016. Computer Age Statistical Inference, Vol. 5. Cambridge, UK: Cambridge Univ. Press
- Athey S, Blei D, Donnelly R, Ruiz F. 2017b. Counterfactual inference for consumer choice across many prod- uct categories. AEA Pap. Proc. 108:64–67
- Swaminathan A, Joachims T. 2015. Batch learning from logged bandit feedback through counterfactual risk minimization. J. Mach. Learn. Res. 16:1731–55
- Bottou L. 1998. Online learning and stochastic approximations. In On-Line Learning in Neural Networks, ed. D Saad, pp. 9–42. New York: ACM
- J. N. Foerster, Y. M. Assael, N. de Freitas, and S. Whiteson. Learning to communicate with deep multi-agent reinforcement learning. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, pages 2137–2145, 2016.
- Efron B, Hastie T. 2016. Computer Age Statistical Inference, Vol. 5. Cambridge, UK: Cambridge Univ. Press

## Frequently Asked Questions

Q: What is the prediction methodology for FTSE 100 Index stock?A: FTSE 100 Index stock prediction methodology: We evaluate the prediction models Deductive Inference (ML) and Linear Regression

Q: Is FTSE 100 Index stock a buy or sell?

A: The dominant strategy among neural network is to Sell FTSE 100 Index Stock.

Q: Is FTSE 100 Index stock a good investment?

A: The consensus rating for FTSE 100 Index is Sell and assigned short-term B2 & long-term B1 forecasted stock rating.

Q: What is the consensus rating of FTSE 100 Index stock?

A: The consensus rating for FTSE 100 Index is Sell.

Q: What is the prediction period for FTSE 100 Index stock?

A: The prediction period for FTSE 100 Index is (n+16 weeks)

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