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 CAPITAL LIMITED prediction models with Modular Neural Network (DNN Layer) and Chi-Square1,2,3,4 and conclude that the LON:CAPD stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold LON:CAPD stock.

Keywords: LON:CAPD, CAPITAL LIMITED, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. Investment Risk
2. What are the most successful trading algorithms?
3. Market Risk

## LON:CAPD Target Price Prediction Modeling Methodology

Stock index price prediction is prevalent in both academic and economic fields. The index price is hard to forecast due to its uncertain noise. With the development of computer science, neural networks are applied in kinds of industrial fields. In this paper, we introduce four different methods in machine learning including three typical machine learning models: Multilayer Perceptron (MLP), Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) and one attention-based neural network. We consider CAPITAL LIMITED Stock Decision Process with Chi-Square where A is the set of discrete actions of LON:CAPD 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(Chi-Square)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 (DNN Layer)) X S(n):→ (n+1 year) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of LON:CAPD 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:CAPD Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: LON:CAPD CAPITAL LIMITED
Time series to forecast n: 15 Oct 2022 for (n+1 year)

According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold LON:CAPD 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

CAPITAL LIMITED assigned short-term B3 & long-term Baa2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (DNN Layer) with Chi-Square1,2,3,4 and conclude that the LON:CAPD stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold LON:CAPD stock.

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

Rating Short-Term Long-Term Senior
Outlook*B3Baa2
Operational Risk 3371
Market Risk4489
Technical Analysis8863
Fundamental Analysis3982
Risk Unsystematic3168

### Prediction Confidence Score

Trust metric by Neural Network: 86 out of 100 with 737 signals.

## References

1. Bierens HJ. 1987. Kernel estimators of regression functions. In Advances in Econometrics: Fifth World Congress, Vol. 1, ed. TF Bewley, pp. 99–144. Cambridge, UK: Cambridge Univ. Press
2. Chipman HA, George EI, McCulloch RE. 2010. Bart: Bayesian additive regression trees. Ann. Appl. Stat. 4:266–98
3. Burkov A. 2019. The Hundred-Page Machine Learning Book. Quebec City, Can.: Andriy Burkov
4. R. Howard and J. Matheson. Risk sensitive Markov decision processes. Management Science, 18(7):356– 369, 1972
5. Mnih A, Teh YW. 2012. A fast and simple algorithm for training neural probabilistic language models. In Proceedings of the 29th International Conference on Machine Learning, pp. 419–26. La Jolla, CA: Int. Mach. Learn. Soc.
6. M. Ono, M. Pavone, Y. Kuwata, and J. Balaram. Chance-constrained dynamic programming with application to risk-aware robotic space exploration. Autonomous Robots, 39(4):555–571, 2015
7. Breiman L. 2001b. Statistical modeling: the two cultures (with comments and a rejoinder by the author). Stat. Sci. 16:199–231
Frequently Asked QuestionsQ: What is the prediction methodology for LON:CAPD stock?
A: LON:CAPD stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Chi-Square
Q: Is LON:CAPD stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:CAPD Stock.
Q: Is CAPITAL LIMITED stock a good investment?
A: The consensus rating for CAPITAL LIMITED is Hold and assigned short-term B3 & long-term Baa2 forecasted stock rating.
Q: What is the consensus rating of LON:CAPD stock?
A: The consensus rating for LON:CAPD is Hold.
Q: What is the prediction period for LON:CAPD stock?
A: The prediction period for LON:CAPD is (n+1 year)