Prediction of future movement of stock prices has been a subject matter of many research work. There is a gamut of literature of technical analysis of stock prices where the objective is to identify patterns in stock price movements and derive profit from it. Improving the prediction accuracy remains the single most challenge in this area of research. We propose a hybrid approach for stock price movement prediction using machine learning, deep learning, and natural language processing. We evaluate CAPITAL LIMITED prediction models with Modular Neural Network (Speculative Sentiment Analysis) and Ridge Regression1,2,3,4 and conclude that the LON:CAPD 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 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. Game Theory
2. How do predictive algorithms actually work?
3. Prediction Modeling ## LON:CAPD Target Price Prediction Modeling Methodology

The nature of stock market movement has always been ambiguous for investors because of various influential factors. This study aims to significantly reduce the risk of trend prediction with machine learning and deep learning algorithms. We consider CAPITAL LIMITED Stock Decision Process with Ridge Regression 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(Ridge 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 (Speculative Sentiment Analysis)) X S(n):→ (n+3 month) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

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+3 month)

Sample Set: Neural Network
Stock/Index: LON:CAPD CAPITAL LIMITED
Time series to forecast n: 10 Oct 2022 for (n+3 month)

According to price forecasts for (n+3 month) 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 B2 & long-term B3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) with Ridge Regression1,2,3,4 and conclude that the LON:CAPD 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 Hold LON:CAPD stock.

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

Rating Short-Term Long-Term Senior
Outlook*B2B3
Operational Risk 6831
Market Risk5967
Technical Analysis3234
Fundamental Analysis4659
Risk Unsystematic8033

### Prediction Confidence Score

Trust metric by Neural Network: 91 out of 100 with 465 signals.

## References

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2. Thomas P, Brunskill E. 2016. Data-efficient off-policy policy evaluation for reinforcement learning. In Pro- ceedings of the International Conference on Machine Learning, pp. 2139–48. La Jolla, CA: Int. Mach. Learn. Soc.
3. Pennington J, Socher R, Manning CD. 2014. GloVe: global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods on Natural Language Processing, pp. 1532–43. New York: Assoc. Comput. Linguist.
4. Knox SW. 2018. Machine Learning: A Concise Introduction. Hoboken, NJ: Wiley
5. Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J. 2013b. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems, Vol. 26, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 3111–19. San Diego, CA: Neural Inf. Process. Syst. Found.
6. Efron B, Hastie T, Johnstone I, Tibshirani R. 2004. Least angle regression. Ann. Stat. 32:407–99
7. Bennett J, Lanning S. 2007. The Netflix prize. In Proceedings of KDD Cup and Workshop 2007, p. 35. New York: ACM
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 (Speculative Sentiment Analysis) and Ridge Regression
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 B2 & long-term B3 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+3 month)