In this paper we investigate ways to use prior knowledge and neural networks to improve multivariate prediction ability. Daily stock prices are predicted as a complicated real-world problem, taking non-numerical factors such as political and international events are into account. We have studied types of prior knowledge which are difficult to insert into initial network structures or to represent in the form of error measurements. We evaluate LLOYDS BANKING GROUP PLC prediction models with Modular Neural Network (Market Volatility Analysis) and Wilcoxon Sign-Rank Test1,2,3,4 and conclude that the LON:LLOY stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Buy LON:LLOY stock.

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

## Key Points

1. Can machine learning predict?
2. Trust metric by Neural Network
3. Can neural networks predict stock market?

## LON:LLOY Target Price Prediction Modeling Methodology

Understanding the pattern of financial activities and predicting their development and changes are research hotspots in academic and financial circles. Because financial data contain complex, incomplete and fuzzy information, predicting their development trends is an extremely difficult challenge. Fluctuations in financial data depend on a myriad of correlated constantly changing factors. Therefore, predicting and analysing financial data are a nonlinear, time-dependent problem. Deep neural networks (DNNs) combine the advantages of deep learning (DL) and neural networks and can be used to solve nonlinear problems more satisfactorily compared to conventional machine learning algorithms. We consider LLOYDS BANKING GROUP PLC Stock Decision Process with Wilcoxon Sign-Rank Test where A is the set of discrete actions of LON:LLOY 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 (Market Volatility Analysis)) X S(n):→ (n+8 weeks) $∑ i = 1 n a i$

n:Time series to forecast

p:Price signals of LON:LLOY 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:LLOY Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: LON:LLOY LLOYDS BANKING GROUP PLC
Time series to forecast n: 17 Sep 2022 for (n+8 weeks)

According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Buy LON:LLOY 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

LLOYDS BANKING GROUP PLC assigned short-term B2 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) with Wilcoxon Sign-Rank Test1,2,3,4 and conclude that the LON:LLOY stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Buy LON:LLOY stock.

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

Rating Short-Term Long-Term Senior
Outlook*B2B1
Operational Risk 4432
Market Risk7777
Technical Analysis3846
Fundamental Analysis7586
Risk Unsystematic5160

### Prediction Confidence Score

Trust metric by Neural Network: 88 out of 100 with 777 signals.

## References

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2. V. Borkar. An actor-critic algorithm for constrained Markov decision processes. Systems & Control Letters, 54(3):207–213, 2005.
3. Bottou L. 1998. Online learning and stochastic approximations. In On-Line Learning in Neural Networks, ed. D Saad, pp. 9–42. New York: ACM
4. M. Petrik and D. Subramanian. An approximate solution method for large risk-averse Markov decision processes. In Proceedings of the 28th International Conference on Uncertainty in Artificial Intelligence, 2012.
5. R. Sutton, D. McAllester, S. Singh, and Y. Mansour. Policy gradient methods for reinforcement learning with function approximation. In Proceedings of Advances in Neural Information Processing Systems 12, pages 1057–1063, 2000
6. Schapire RE, Freund Y. 2012. Boosting: Foundations and Algorithms. Cambridge, MA: MIT Press
7. Li L, Chu W, Langford J, Moon T, Wang X. 2012. An unbiased offline evaluation of contextual bandit algo- rithms with generalized linear models. In Proceedings of 4th ACM International Conference on Web Search and Data Mining, pp. 297–306. New York: ACM
Frequently Asked QuestionsQ: What is the prediction methodology for LON:LLOY stock?
A: LON:LLOY stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and Wilcoxon Sign-Rank Test
Q: Is LON:LLOY stock a buy or sell?
A: The dominant strategy among neural network is to Buy LON:LLOY Stock.
Q: Is LLOYDS BANKING GROUP PLC stock a good investment?
A: The consensus rating for LLOYDS BANKING GROUP PLC is Buy and assigned short-term B2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:LLOY stock?
A: The consensus rating for LON:LLOY is Buy.
Q: What is the prediction period for LON:LLOY stock?
A: The prediction period for LON:LLOY is (n+8 weeks)