The aim of this study is to evaluate the effectiveness of using external indicators, such as commodity prices and currency exchange rates, in predicting movements. The performance of each technique is evaluated using different domain specific metrics. A comprehensive evaluation procedure is described, involving the use of trading simulations to assess the practical value of predictive models, and comparison with simple benchmarks that respond to underlying market growth. We evaluate PURPLEBRICKS GROUP PLC prediction models with Modular Neural Network (Social Media Sentiment Analysis) and Multiple Regression1,2,3,4 and conclude that the LON:PURP stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Sell LON:PURP stock.

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

## Key Points

1. What is prediction model?
2. Is it better to buy and sell or hold?
3. Trust metric by Neural Network

## LON:PURP Target Price Prediction Modeling Methodology

Stock price forecasting is a popular and important topic in financial and academic studies. Share market is an volatile place for predicting since there are no significant rules to estimate or predict the price of a share in the share market. Many methods like technical analysis, fundamental analysis, time series analysis and statistical analysis etc. are used to predict the price in tie share market but none of these methods are proved as a consistently acceptable prediction tool. In this paper, we implemented a Random Forest approach to predict stock market prices. We consider PURPLEBRICKS GROUP PLC Stock Decision Process with Multiple Regression where A is the set of discrete actions of LON:PURP 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(Multiple 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 (Social Media Sentiment Analysis)) X S(n):→ (n+3 month) $\stackrel{\to }{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of LON:PURP 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:PURP Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: LON:PURP PURPLEBRICKS GROUP PLC
Time series to forecast n: 15 Sep 2022 for (n+3 month)

According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Sell LON:PURP 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

PURPLEBRICKS GROUP PLC assigned short-term B2 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) with Multiple Regression1,2,3,4 and conclude that the LON:PURP stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Sell LON:PURP stock.

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

Rating Short-Term Long-Term Senior
Outlook*B2B1
Operational Risk 3278
Market Risk9040
Technical Analysis8956
Fundamental Analysis3269
Risk Unsystematic4253

### Prediction Confidence Score

Trust metric by Neural Network: 86 out of 100 with 877 signals.

## References

1. Bottou L. 1998. Online learning and stochastic approximations. In On-Line Learning in Neural Networks, ed. D Saad, pp. 9–42. New York: ACM
2. Byron, R. P. O. Ashenfelter (1995), "Predicting the quality of an unborn grange," Economic Record, 71, 40–53.
3. Friedberg R, Tibshirani J, Athey S, Wager S. 2018. Local linear forests. arXiv:1807.11408 [stat.ML]
4. Morris CN. 1983. Parametric empirical Bayes inference: theory and applications. J. Am. Stat. Assoc. 78:47–55
5. Kitagawa T, Tetenov A. 2015. Who should be treated? Empirical welfare maximization methods for treatment choice. Tech. Rep., Cent. Microdata Methods Pract., Inst. Fiscal Stud., London
6. Ashley, R. (1988), "On the relative worth of recent macroeconomic forecasts," International Journal of Forecasting, 4, 363–376.
7. G. Konidaris, S. Osentoski, and P. Thomas. Value function approximation in reinforcement learning using the Fourier basis. In AAAI, 2011
Frequently Asked QuestionsQ: What is the prediction methodology for LON:PURP stock?
A: LON:PURP stock prediction methodology: We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) and Multiple Regression
Q: Is LON:PURP stock a buy or sell?
A: The dominant strategy among neural network is to Sell LON:PURP Stock.
Q: Is PURPLEBRICKS GROUP PLC stock a good investment?
A: The consensus rating for PURPLEBRICKS GROUP PLC is Sell and assigned short-term B2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:PURP stock?
A: The consensus rating for LON:PURP is Sell.
Q: What is the prediction period for LON:PURP stock?
A: The prediction period for LON:PURP is (n+3 month)