In this paper, we propose a robust and novel hybrid model for prediction of stock returns. The proposed model is constituted of two linear models: autoregressive moving average model, exponential smoothing model and a non-linear model: recurrent neural network. Training data for recurrent neural network is generated by a new regression model. Recurrent neural network produces satisfactory predictions as compared to linear models. With the goal to further improve the accuracy of predictions, the proposed hybrid prediction model merges predictions obtained from these three prediction based models. ** We evaluate KEYSTONE LAW GROUP PLC prediction models with Modular Neural Network (Market Direction Analysis) and Stepwise Regression ^{1,2,3,4} and conclude that the LON:KEYS stock is predictable in the short/long term. **

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Buy LON:KEYS stock.**

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

*Keywords:*## Key Points

- Buy, Sell and Hold Signals
- Can statistics predict the future?
- How useful are statistical predictions?

## LON:KEYS Target Price Prediction Modeling Methodology

Stock market is basically nonlinear in nature and the research on stock market is one of the most important issues in recent years. People invest in stock market based on some prediction. For predict, the stock market prices people search such methods and tools which will increase their profits, while minimize their risks. Prediction plays a very important role in stock market business which is very complicated and challenging process. We consider KEYSTONE LAW GROUP PLC Stock Decision Process with Stepwise Regression where A is the set of discrete actions of LON:KEYS 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(Stepwise 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 Direction Analysis)) X S(n):→ (n+6 month) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of LON:KEYS 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:KEYS Stock Forecast (Buy or Sell) for (n+6 month)

**Sample Set:**Neural Network

**Stock/Index:**LON:KEYS KEYSTONE LAW GROUP PLC

**Time series to forecast n: 17 Sep 2022**for (n+6 month)

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Buy LON:KEYS 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

KEYSTONE LAW GROUP PLC assigned short-term B2 & long-term B1 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Market Direction Analysis) with Stepwise Regression ^{1,2,3,4} and conclude that the LON:KEYS stock is predictable in the short/long term.**

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Buy LON:KEYS stock.**

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

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

Outlook* | B2 | B1 |

Operational Risk | 50 | 57 |

Market Risk | 53 | 54 |

Technical Analysis | 65 | 83 |

Fundamental Analysis | 37 | 60 |

Risk Unsystematic | 73 | 48 |

### Prediction Confidence Score

## References

- Arora S, Li Y, Liang Y, Ma T. 2016. RAND-WALK: a latent variable model approach to word embeddings. Trans. Assoc. Comput. Linguist. 4:385–99
- Imai K, Ratkovic M. 2013. Estimating treatment effect heterogeneity in randomized program evaluation. Ann. Appl. Stat. 7:443–70
- Breiman L, Friedman J, Stone CJ, Olshen RA. 1984. Classification and Regression Trees. Boca Raton, FL: CRC Press
- S. Bhatnagar, R. Sutton, M. Ghavamzadeh, and M. Lee. Natural actor-critic algorithms. Automatica, 45(11): 2471–2482, 2009
- Bertsimas D, King A, Mazumder R. 2016. Best subset selection via a modern optimization lens. Ann. Stat. 44:813–52
- Imbens GW, Lemieux T. 2008. Regression discontinuity designs: a guide to practice. J. Econom. 142:615–35
- Breiman L. 1996. Bagging predictors. Mach. Learn. 24:123–40

## Frequently Asked Questions

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

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

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

Q: Is KEYSTONE LAW GROUP PLC stock a good investment?

A: The consensus rating for KEYSTONE LAW GROUP PLC is Buy and assigned short-term B2 & long-term B1 forecasted stock rating.

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

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

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

A: The prediction period for LON:KEYS is (n+6 month)