Predictions on stock market prices are a great challenge due to the fact that it is an immensely complex, chaotic and dynamic environment. There are many studies from various areas aiming to take on that challenge and Machine Learning approaches have been the focus of many of them. There are many examples of Machine Learning algorithms been able to reach satisfactory results when doing that type of prediction. This article studies the usage of LSTM networks on that scenario, to predict future trends of stock prices based on the price history, alongside with technical analysis indicators. We evaluate Compass Group prediction models with Modular Neural Network (Market Direction Analysis) and ElasticNet Regression1,2,3,4 and conclude that the CPG 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 CPG stock.
Keywords: CPG, Compass Group, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- Can statistics predict the future?
- Buy, Sell and Hold Signals
- How useful are statistical predictions?
CPG Target Price Prediction Modeling Methodology
Stock index price prediction is prevalent in both academic and economic fields. The index price is hard to forecast due to its uncertain noise. With the development of computer science, neural networks are applied in kinds of industrial fields. In this paper, we introduce four different methods in machine learning including three typical machine learning models: Multilayer Perceptron (MLP), Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) and one attention-based neural network. We consider Compass Group Stock Decision Process with ElasticNet Regression where A is the set of discrete actions of CPG 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(ElasticNet Regression)5,6,7= X R(Modular Neural Network (Market Direction Analysis)) X S(n):→ (n+16 weeks)
n:Time series to forecast
p:Price signals of CPG 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?
CPG Stock Forecast (Buy or Sell) for (n+16 weeks)
Sample Set: Neural NetworkStock/Index: CPG Compass Group
Time series to forecast n: 21 Oct 2022 for (n+16 weeks)
According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Buy CPG 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
Compass Group assigned short-term B3 & long-term B3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market Direction Analysis) with ElasticNet Regression1,2,3,4 and conclude that the CPG 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 CPG stock.
Financial State Forecast for CPG Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B3 | B3 |
| Operational Risk | 37 | 34 |
| Market Risk | 61 | 67 |
| Technical Analysis | 35 | 35 |
| Fundamental Analysis | 60 | 33 |
| Risk Unsystematic | 47 | 45 |
Prediction Confidence Score
References
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- Bertsimas D, King A, Mazumder R. 2016. Best subset selection via a modern optimization lens. Ann. Stat. 44:813–52
- Harris ZS. 1954. Distributional structure. Word 10:146–62
- 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.
- 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.
- Athey S, Bayati M, Imbens G, Zhaonan Q. 2019. Ensemble methods for causal effects in panel data settings. NBER Work. Pap. 25675
- uyer, S. Whiteson, B. Bakker, and N. A. Vlassis. Multiagent reinforcement learning for urban traffic control using coordination graphs. In Machine Learning and Knowledge Discovery in Databases, European Conference, ECML/PKDD 2008, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part I, pages 656–671, 2008.
Frequently Asked Questions
Q: What is the prediction methodology for CPG stock?A: CPG stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Direction Analysis) and ElasticNet Regression
Q: Is CPG stock a buy or sell?
A: The dominant strategy among neural network is to Buy CPG Stock.
Q: Is Compass Group stock a good investment?
A: The consensus rating for Compass Group is Buy and assigned short-term B3 & long-term B3 forecasted stock rating.
Q: What is the consensus rating of CPG stock?
A: The consensus rating for CPG is Buy.
Q: What is the prediction period for CPG stock?
A: The prediction period for CPG is (n+16 weeks)
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