Stock prediction with data mining techniques is one of the most important issues in finance being investigated by researchers across the globe. Data mining techniques can be used extensively in the financial markets to help investors make qualitative decision. One of the techniques is artificial neural network (ANN). However, in the application of ANN for predicting the financial market the use of technical analysis variables for stock prediction is predominant. In this paper, we present a hybridized approach which combines the use of the variables of technical and fundamental analysis of stock market indicators for prediction of future price of stock in order to improve on the existing approaches. We evaluate EMIS GROUP PLC prediction models with Supervised Machine Learning (ML) and Logistic Regression1,2,3,4 and conclude that the LON:EMIS 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:EMIS stock.

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

Key Points

1. Which neural network is best for prediction?
2. How do you decide buy or sell a stock?
3. Buy, Sell and Hold Signals

LON:EMIS Target Price Prediction Modeling Methodology

The success of portfolio construction depends primarily on the future performance of stock markets. Recent developments in machine learning have brought significant opportunities to incorporate prediction theory into portfolio selection. However, many studies show that a single prediction model is insufficient to achieve very accurate predictions and affluent returns. In this paper, a novel portfolio construction approach is developed using a hybrid model based on machine learning for stock prediction. We consider EMIS GROUP PLC Stock Decision Process with Logistic Regression where A is the set of discrete actions of LON:EMIS 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(Logistic 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(Supervised Machine Learning (ML)) X S(n):→ (n+1 year) $\stackrel{\to }{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

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

Sample Set: Neural Network
Stock/Index: LON:EMIS EMIS GROUP PLC
Time series to forecast n: 12 Sep 2022 for (n+1 year)

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

EMIS GROUP PLC assigned short-term Ba3 & long-term B1 forecasted stock rating. We evaluate the prediction models Supervised Machine Learning (ML) with Logistic Regression1,2,3,4 and conclude that the LON:EMIS 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:EMIS stock.

Financial State Forecast for LON:EMIS Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Ba3B1
Operational Risk 3773
Market Risk9065
Technical Analysis7749
Fundamental Analysis6745
Risk Unsystematic6656

Prediction Confidence Score

Trust metric by Neural Network: 75 out of 100 with 645 signals.

References

1. Kallus N. 2017. Balanced policy evaluation and learning. arXiv:1705.07384 [stat.ML]
2. L. Panait and S. Luke. Cooperative multi-agent learning: The state of the art. Autonomous Agents and Multi-Agent Systems, 11(3):387–434, 2005.
3. A. Shapiro, W. Tekaya, J. da Costa, and M. Soares. Risk neutral and risk averse stochastic dual dynamic programming method. European journal of operational research, 224(2):375–391, 2013
4. Scholkopf B, Smola AJ. 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press
5. Farrell MH, Liang T, Misra S. 2018. Deep neural networks for estimation and inference: application to causal effects and other semiparametric estimands. arXiv:1809.09953 [econ.EM]
6. LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521:436–44
7. Mullainathan S, Spiess J. 2017. Machine learning: an applied econometric approach. J. Econ. Perspect. 31:87–106
Frequently Asked QuestionsQ: What is the prediction methodology for LON:EMIS stock?
A: LON:EMIS stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Logistic Regression
Q: Is LON:EMIS stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:EMIS Stock.
Q: Is EMIS GROUP PLC stock a good investment?
A: The consensus rating for EMIS GROUP PLC is Hold and assigned short-term Ba3 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:EMIS stock?
A: The consensus rating for LON:EMIS is Hold.
Q: What is the prediction period for LON:EMIS stock?
A: The prediction period for LON:EMIS is (n+1 year)