Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on a financial exchange. The successful prediction of a stock's future price will maximize investor's gains. This paper proposes a machine learning model to predict stock market price. We evaluate GRESHAM HOUSE ENERGY STORAGE FUND PLC prediction models with Modular Neural Network (Emotional Trigger/Responses Analysis) and Wilcoxon Sign-Rank Test1,2,3,4 and conclude that the LON:GRID 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 Hold LON:GRID stock.

Keywords: LON:GRID, GRESHAM HOUSE ENERGY STORAGE FUND PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. How do predictive algorithms actually work?
2. Probability Distribution
3. What is neural prediction? ## LON:GRID Target Price Prediction Modeling Methodology

In recent years there has been a significant growth of interest in the incorporation of historical series of variables related to stock prediction into mathematical models or computational algorithms in order to generate predictions or indications about expected price movements. We consider GRESHAM HOUSE ENERGY STORAGE FUND PLC Stock Decision Process with Wilcoxon Sign-Rank Test where A is the set of discrete actions of LON:GRID 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(Wilcoxon Sign-Rank Test)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 (Emotional Trigger/Responses Analysis)) X S(n):→ (n+4 weeks) $\stackrel{\to }{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

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

Sample Set: Neural Network
Stock/Index: LON:GRID GRESHAM HOUSE ENERGY STORAGE FUND PLC
Time series to forecast n: 13 Oct 2022 for (n+4 weeks)

According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold LON:GRID 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

GRESHAM HOUSE ENERGY STORAGE FUND PLC assigned short-term B3 & long-term B3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) with Wilcoxon Sign-Rank Test1,2,3,4 and conclude that the LON:GRID 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 Hold LON:GRID stock.

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

Rating Short-Term Long-Term Senior
Outlook*B3B3
Operational Risk 3636
Market Risk8447
Technical Analysis6252
Fundamental Analysis3744
Risk Unsystematic3531

### Prediction Confidence Score

Trust metric by Neural Network: 90 out of 100 with 850 signals.

## References

1. White H. 1992. Artificial Neural Networks: Approximation and Learning Theory. Oxford, UK: Blackwell
2. Imbens GW, Rubin DB. 2015. Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge, UK: Cambridge Univ. Press
3. K. Boda and J. Filar. Time consistent dynamic risk measures. Mathematical Methods of Operations Research, 63(1):169–186, 2006
4. D. Bertsekas. Min common/max crossing duality: A geometric view of conjugacy in convex optimization. Lab. for Information and Decision Systems, MIT, Tech. Rep. Report LIDS-P-2796, 2009
5. Dudik M, Langford J, Li L. 2011. Doubly robust policy evaluation and learning. In Proceedings of the 28th International Conference on Machine Learning, pp. 1097–104. La Jolla, CA: Int. Mach. Learn. Soc.
6. Cheung, Y. M.D. Chinn (1997), "Further investigation of the uncertain unit root in GNP," Journal of Business and Economic Statistics, 15, 68–73.
7. Bessler, D. A. S. W. Fuller (1993), "Cointegration between U.S. wheat markets," Journal of Regional Science, 33, 481–501.
Frequently Asked QuestionsQ: What is the prediction methodology for LON:GRID stock?
A: LON:GRID stock prediction methodology: We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) and Wilcoxon Sign-Rank Test
Q: Is LON:GRID stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:GRID Stock.
Q: Is GRESHAM HOUSE ENERGY STORAGE FUND PLC stock a good investment?
A: The consensus rating for GRESHAM HOUSE ENERGY STORAGE FUND PLC is Hold and assigned short-term B3 & long-term B3 forecasted stock rating.
Q: What is the consensus rating of LON:GRID stock?
A: The consensus rating for LON:GRID is Hold.
Q: What is the prediction period for LON:GRID stock?
A: The prediction period for LON:GRID is (n+4 weeks)