In this paper, we propose a hybrid machine learning system based on Genetic Algor ithm (GA) and Support Vector Machines (SVM) for stock market prediction. A variety of indicators from the technical analysis field of study are used as input features. We also make use of the correlation between stock prices of different companies to forecast the price of a stock, making use of technical indicators of highly correlated stocks, not only the stock to be predicted. The genetic algorithm is used to select the set of most informative input features from among all the technical indicators. We evaluate EUROPEAN METALS HOLDINGS LIMITED prediction models with Ensemble Learning (ML) and Polynomial Regression1,2,3,4 and conclude that the LON:EMH stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Buy LON:EMH stock.

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

## Key Points

1. What are main components of Markov decision process?
2. What is Markov decision process in reinforcement learning?
3. What is the best way to predict stock prices?

## LON:EMH Target Price Prediction Modeling Methodology

Stock market investment strategies are complex and rely on an evaluation of vast amounts of data. In recent years, machine learning techniques have increasingly been examined to assess whether they can improve market forecasting when compared with traditional approaches. The objective for this study is to identify directions for future machine learning stock market prediction research based upon a review of current literature. We consider EUROPEAN METALS HOLDINGS LIMITED Stock Decision Process with Polynomial Regression where A is the set of discrete actions of LON:EMH 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(Polynomial 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(Ensemble Learning (ML)) X S(n):→ (n+16 weeks) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of LON:EMH 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:EMH Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: LON:EMH EUROPEAN METALS HOLDINGS LIMITED
Time series to forecast n: 14 Sep 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Buy LON:EMH 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

EUROPEAN METALS HOLDINGS LIMITED assigned short-term B1 & long-term Ba1 forecasted stock rating. We evaluate the prediction models Ensemble Learning (ML) with Polynomial Regression1,2,3,4 and conclude that the LON:EMH stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Buy LON:EMH stock.

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

Rating Short-Term Long-Term Senior
Outlook*B1Ba1
Operational Risk 4188
Market Risk8171
Technical Analysis7474
Fundamental Analysis7873
Risk Unsystematic3941

### Prediction Confidence Score

Trust metric by Neural Network: 88 out of 100 with 554 signals.

## References

1. White H. 1992. Artificial Neural Networks: Approximation and Learning Theory. Oxford, UK: Blackwell
2. Chernozhukov V, Escanciano JC, Ichimura H, Newey WK. 2016b. Locally robust semiparametric estimation. arXiv:1608.00033 [math.ST]
3. Andrews, D. W. K. W. Ploberger (1994), "Optimal tests when a nuisance parameter is present only under the alternative," Econometrica, 62, 1383–1414.
4. Belsley, D. A. (1988), "Modelling and forecast reliability," International Journal of Forecasting, 4, 427–447.
5. S. Devlin, L. Yliniemi, D. Kudenko, and K. Tumer. Potential-based difference rewards for multiagent reinforcement learning. In Proceedings of the Thirteenth International Joint Conference on Autonomous Agents and Multiagent Systems, May 2014
6. Mullainathan S, Spiess J. 2017. Machine learning: an applied econometric approach. J. Econ. Perspect. 31:87–106
7. Ashley, R. (1988), "On the relative worth of recent macroeconomic forecasts," International Journal of Forecasting, 4, 363–376.
Frequently Asked QuestionsQ: What is the prediction methodology for LON:EMH stock?
A: LON:EMH stock prediction methodology: We evaluate the prediction models Ensemble Learning (ML) and Polynomial Regression
Q: Is LON:EMH stock a buy or sell?
A: The dominant strategy among neural network is to Buy LON:EMH Stock.
Q: Is EUROPEAN METALS HOLDINGS LIMITED stock a good investment?
A: The consensus rating for EUROPEAN METALS HOLDINGS LIMITED is Buy and assigned short-term B1 & long-term Ba1 forecasted stock rating.
Q: What is the consensus rating of LON:EMH stock?
A: The consensus rating for LON:EMH is Buy.
Q: What is the prediction period for LON:EMH stock?
A: The prediction period for LON:EMH is (n+16 weeks)