Time series forecasting has been widely used to determine the future prices of stock, and the analysis and modeling of finance time series importantly guide investors' decisions and trades. In addition, in a dynamic environment such as the stock market, the nonlinearity of the time series is pronounced, immediately affecting the efficacy of stock price forecasts. Thus, this paper proposes an intelligent time series prediction system that uses sliding-window metaheuristic optimization for the purpose of predicting the stock prices.** We evaluate TAYLOR WIMPEY PLC prediction models with Modular Neural Network (Market Direction Analysis) and Chi-Square ^{1,2,3,4} and conclude that the LON:TW. stock is predictable in the short/long term. **

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Sell LON:TW. stock.**

**LON:TW., TAYLOR WIMPEY 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
- Decision Making
- Probability Distribution

## LON:TW. Target Price Prediction Modeling Methodology

How to predict stock price movements based on quantitative market data modeling is an attractive topic. In front of the market news and stock prices that are commonly believed as two important market data sources, how to extract and exploit the hidden information within the raw data and make both accurate and fast predictions simultaneously becomes a challenging problem. In this paper, we present the design and architecture of our trading signal mining platform that employs extreme learning machine (ELM) to make stock price prediction based on those two data sources concurrently. We consider TAYLOR WIMPEY PLC Stock Decision Process with Chi-Square where A is the set of discrete actions of LON:TW. 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(Chi-Square)

^{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 Direction Analysis)) X S(n):→ (n+4 weeks) $\sum _{i=1}^{n}\left({a}_{i}\right)$

n:Time series to forecast

p:Price signals of LON:TW. 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:TW. Stock Forecast (Buy or Sell) for (n+4 weeks)

**Sample Set:**Neural Network

**Stock/Index:**LON:TW. TAYLOR WIMPEY PLC

**Time series to forecast n: 17 Sep 2022**for (n+4 weeks)

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Sell LON:TW. 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

TAYLOR WIMPEY PLC assigned short-term Ba3 & long-term Ba3 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Market Direction Analysis) with Chi-Square ^{1,2,3,4} and conclude that the LON:TW. stock is predictable in the short/long term.**

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Sell LON:TW. stock.**

### Financial State Forecast for LON:TW. Stock Options & Futures

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

Outlook* | Ba3 | Ba3 |

Operational Risk | 55 | 36 |

Market Risk | 75 | 82 |

Technical Analysis | 61 | 74 |

Fundamental Analysis | 43 | 39 |

Risk Unsystematic | 81 | 83 |

### Prediction Confidence Score

## References

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- Bai J, Ng S. 2002. Determining the number of factors in approximate factor models. Econometrica 70:191–221
- S. Bhatnagar, H. Prasad, and L. Prashanth. Stochastic recursive algorithms for optimization, volume 434. Springer, 2013
- A. Eck, L. Soh, S. Devlin, and D. Kudenko. Potential-based reward shaping for finite horizon online POMDP planning. Autonomous Agents and Multi-Agent Systems, 30(3):403–445, 2016
- V. Borkar. Q-learning for risk-sensitive control. Mathematics of Operations Research, 27:294–311, 2002.
- Bengio Y, Schwenk H, Senécal JS, Morin F, Gauvain JL. 2006. Neural probabilistic language models. In Innovations in Machine Learning: Theory and Applications, ed. DE Holmes, pp. 137–86. Berlin: Springer
- M. Ono, M. Pavone, Y. Kuwata, and J. Balaram. Chance-constrained dynamic programming with application to risk-aware robotic space exploration. Autonomous Robots, 39(4):555–571, 2015

## Frequently Asked Questions

Q: What is the prediction methodology for LON:TW. stock?A: LON:TW. stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Direction Analysis) and Chi-Square

Q: Is LON:TW. stock a buy or sell?

A: The dominant strategy among neural network is to Sell LON:TW. Stock.

Q: Is TAYLOR WIMPEY PLC stock a good investment?

A: The consensus rating for TAYLOR WIMPEY PLC is Sell and assigned short-term Ba3 & long-term Ba3 forecasted stock rating.

Q: What is the consensus rating of LON:TW. stock?

A: The consensus rating for LON:TW. is Sell.

Q: What is the prediction period for LON:TW. stock?

A: The prediction period for LON:TW. is (n+4 weeks)

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