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 EUROPA METALS LTD prediction models with Modular Neural Network (News Feed Sentiment Analysis) and Wilcoxon Rank-Sum Test1,2,3,4 and conclude that the LON:EUZ 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:EUZ stock.
Keywords: LON:EUZ, EUROPA METALS LTD, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- Market Signals
- What is neural prediction?
- Understanding Buy, Sell, and Hold Ratings
LON:EUZ Target Price Prediction Modeling Methodology
The stock market is one of the key sectors of a country's economy. It provides investors with an opportunity to invest and gain returns on their investment. Predicting the stock market is a very challenging task and has attracted serious interest from researchers from many fields such as statistics, artificial intelligence, economics, and finance. An accurate prediction of the stock market reduces investment risk in the market. Different approaches have been used to predict the stock market. The performances of Machine learning (ML) models are typically superior to those of statistical and econometric models. We consider EUROPA METALS LTD Stock Decision Process with Wilcoxon Rank-Sum Test where A is the set of discrete actions of LON:EUZ 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 Rank-Sum Test)5,6,7= X R(Modular Neural Network (News Feed Sentiment Analysis)) X S(n):→ (n+3 month)
n:Time series to forecast
p:Price signals of LON:EUZ stock
j:Nash equilibria
k:Dominated move
a:Best response for target price
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LON:EUZ Stock Forecast (Buy or Sell) for (n+3 month)
Sample Set: Neural NetworkStock/Index: LON:EUZ EUROPA METALS LTD
Time series to forecast n: 25 Sep 2022 for (n+3 month)
According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold LON:EUZ 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
EUROPA METALS LTD assigned short-term Ba2 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) with Wilcoxon Rank-Sum Test1,2,3,4 and conclude that the LON:EUZ 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:EUZ stock.
Financial State Forecast for LON:EUZ Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | Ba2 | B2 |
| Operational Risk | 33 | 52 |
| Market Risk | 88 | 83 |
| Technical Analysis | 60 | 32 |
| Fundamental Analysis | 77 | 38 |
| Risk Unsystematic | 83 | 41 |
Prediction Confidence Score
References
- Knox SW. 2018. Machine Learning: A Concise Introduction. Hoboken, NJ: Wiley
- Bessler, D. A. T. Covey (1991), "Cointegration: Some results on U.S. cattle prices," Journal of Futures Markets, 11, 461–474.
- V. Borkar. Stochastic approximation: a dynamical systems viewpoint. Cambridge University Press, 2008
- D. S. Bernstein, S. Zilberstein, and N. Immerman. The complexity of decentralized control of Markov Decision Processes. In UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30 - July 3, 2000, pages 32–37, 2000.
- Candès E, Tao T. 2007. The Dantzig selector: statistical estimation when p is much larger than n. Ann. Stat. 35:2313–51
- Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, et al. 2018a. Double/debiased machine learning for treatment and structural parameters. Econom. J. 21:C1–68
- M. Benaim, J. Hofbauer, and S. Sorin. Stochastic approximations and differential inclusions, Part II: Appli- cations. Mathematics of Operations Research, 31(4):673–695, 2006
Frequently Asked Questions
Q: What is the prediction methodology for LON:EUZ stock?A: LON:EUZ stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and Wilcoxon Rank-Sum Test
Q: Is LON:EUZ stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:EUZ Stock.
Q: Is EUROPA METALS LTD stock a good investment?
A: The consensus rating for EUROPA METALS LTD is Hold and assigned short-term Ba2 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of LON:EUZ stock?
A: The consensus rating for LON:EUZ is Hold.
Q: What is the prediction period for LON:EUZ stock?
A: The prediction period for LON:EUZ is (n+3 month)
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