Stock market is a promising financial investment that can generate great wealth. However, the volatile nature of the stock market makes it a very high risk investment. Thus, a lot of researchers have contributed their efforts to forecast the stock market pricing and average movement. Researchers have used various methods in computer science and economics in their quests to gain a piece of this volatile information and make great fortune out of the stock market investment. This paper investigates various techniques for the stock market prediction using artificial neural network (ANN). We evaluate GALLIFORD TRY HOLDINGS PLC prediction models with Modular Neural Network (Social Media Sentiment Analysis) and Spearman Correlation1,2,3,4 and conclude that the LON:GFRD 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 Hold LON:GFRD stock.

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

## Key Points

1. How useful are statistical predictions?
2. What is Markov decision process in reinforcement learning?
3. What is the use of Markov decision process? ## LON:GFRD Target Price Prediction Modeling Methodology

With the advent of machine learning, numerous approaches have been proposed to forecast stock prices. Various models have been developed to date such as Recurrent Neural Networks, Long Short-Term Memory, Convolutional Neural Network sliding window, etc., but were not accurate enough. Here, the aim is to predict the price of a stock and compare the results obtained using three major algorithms namely Kalman filters, XGBoost and ARIMA. We consider GALLIFORD TRY HOLDINGS PLC Stock Decision Process with Spearman Correlation where A is the set of discrete actions of LON:GFRD 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(Spearman Correlation)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+6 month) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

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

Sample Set: Neural Network
Stock/Index: LON:GFRD GALLIFORD TRY HOLDINGS PLC
Time series to forecast n: 25 Sep 2022 for (n+6 month)

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

GALLIFORD TRY HOLDINGS PLC assigned short-term Ba2 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) with Spearman Correlation1,2,3,4 and conclude that the LON:GFRD 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 Hold LON:GFRD stock.

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

Rating Short-Term Long-Term Senior
Outlook*Ba2B2
Operational Risk 8431
Market Risk7250
Technical Analysis8558
Fundamental Analysis3250
Risk Unsystematic6561

### Prediction Confidence Score

Trust metric by Neural Network: 90 out of 100 with 630 signals.

## References

1. Chernozhukov V, Newey W, Robins J. 2018c. Double/de-biased machine learning using regularized Riesz representers. arXiv:1802.08667 [stat.ML]
2. Bai J, Ng S. 2017. Principal components and regularized estimation of factor models. arXiv:1708.08137 [stat.ME]
3. Breiman L. 1996. Bagging predictors. Mach. Learn. 24:123–40
4. Meinshausen N. 2007. Relaxed lasso. Comput. Stat. Data Anal. 52:374–93
5. Hornik K, Stinchcombe M, White H. 1989. Multilayer feedforward networks are universal approximators. Neural Netw. 2:359–66
6. A. Y. Ng, D. Harada, and S. J. Russell. Policy invariance under reward transformations: Theory and application to reward shaping. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pages 278–287, 1999.
7. Barkan O. 2016. Bayesian neural word embedding. arXiv:1603.06571 [math.ST]
Frequently Asked QuestionsQ: What is the prediction methodology for LON:GFRD stock?
A: LON:GFRD stock prediction methodology: We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) and Spearman Correlation
Q: Is LON:GFRD stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:GFRD Stock.
Q: Is GALLIFORD TRY HOLDINGS PLC stock a good investment?
A: The consensus rating for GALLIFORD TRY HOLDINGS PLC is Hold and assigned short-term Ba2 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of LON:GFRD stock?
A: The consensus rating for LON:GFRD is Hold.
Q: What is the prediction period for LON:GFRD stock?
A: The prediction period for LON:GFRD is (n+6 month)