In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index; hence, there is a great deal of research being conducted regarding the prediction of the direction of stock price index movement. Many factors such as political events, general economic conditions, and traders' expectations may have an influence on the stock market index. There are numerous research studies that use similar indicators to forecast the direction of the stock market index. We evaluate HOMESERVE PLC prediction models with Modular Neural Network (Market Volatility Analysis) and Linear Regression1,2,3,4 and conclude that the LON:HSV 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 Hold LON:HSV stock.

Keywords: LON:HSV, HOMESERVE PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. Reaction Function
2. Trust metric by Neural Network
3. Can neural networks predict stock market?

## LON:HSV Target Price Prediction Modeling Methodology

Predictions on stock market prices are a great challenge due to the fact that it is an immensely complex, chaotic and dynamic environment. There are many studies from various areas aiming to take on that challenge and Machine Learning approaches have been the focus of many of them. There are many examples of Machine Learning algorithms been able to reach satisfactory results when doing that type of prediction. This article studies the usage of LSTM networks on that scenario, to predict future trends of stock prices based on the price history, alongside with technical analysis indicators. We consider HOMESERVE PLC Stock Decision Process with Linear Regression where A is the set of discrete actions of LON:HSV 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= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (Market Volatility Analysis)) X S(n):→ (n+16 weeks) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of LON:HSV 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:HSV Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: LON:HSV HOMESERVE PLC
Time series to forecast n: 21 Sep 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold LON:HSV 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

HOMESERVE PLC assigned short-term B2 & long-term B3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) with Linear Regression1,2,3,4 and conclude that the LON:HSV 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 Hold LON:HSV stock.

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

Rating Short-Term Long-Term Senior
Outlook*B2B3
Operational Risk 3736
Market Risk4553
Technical Analysis7030
Fundamental Analysis6134
Risk Unsystematic6578

### Prediction Confidence Score

Trust metric by Neural Network: 73 out of 100 with 521 signals.

## References

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2. J. Ott. A Markov decision model for a surveillance application and risk-sensitive Markov decision processes. PhD thesis, Karlsruhe Institute of Technology, 2010.
3. Armstrong, J. S. M. C. Grohman (1972), "A comparative study of methods for long-range market forecasting," Management Science, 19, 211–221.
4. Mikolov T, Chen K, Corrado GS, Dean J. 2013a. Efficient estimation of word representations in vector space. arXiv:1301.3781 [cs.CL]
5. Banerjee, A., J. J. Dolado, J. W. Galbraith, D. F. Hendry (1993), Co-integration, Error-correction, and the Econometric Analysis of Non-stationary Data. Oxford: Oxford University Press.
6. M. Colby, T. Duchow-Pressley, J. J. Chung, and K. Tumer. Local approximation of difference evaluation functions. In Proceedings of the Fifteenth International Joint Conference on Autonomous Agents and Multiagent Systems, Singapore, May 2016
7. Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, et al. 2016a. Double machine learning for treatment and causal parameters. Tech. Rep., Cent. Microdata Methods Pract., Inst. Fiscal Stud., London
Frequently Asked QuestionsQ: What is the prediction methodology for LON:HSV stock?
A: LON:HSV stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and Linear Regression
Q: Is LON:HSV stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:HSV Stock.
Q: Is HOMESERVE PLC stock a good investment?
A: The consensus rating for HOMESERVE PLC is Hold and assigned short-term B2 & long-term B3 forecasted stock rating.
Q: What is the consensus rating of LON:HSV stock?
A: The consensus rating for LON:HSV is Hold.
Q: What is the prediction period for LON:HSV stock?
A: The prediction period for LON:HSV is (n+16 weeks)