Is MLM a Buy?

Prediction of future movement of stock prices has been a subject matter of many research work. In this work, we propose a hybrid approach for stock price prediction using machine learning and deep learning-based methods. We evaluate Martin Marietta Materials prediction models with Active Learning (ML) and Wilcoxon Sign-Rank Test^1,2,3,4 and conclude that the MLM 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 MLM stock.

Keywords: MLM, Martin Marietta Materials, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

1. What is the best way to predict stock prices?
2. Operational Risk
3. Can stock prices be predicted?

MLM Target Price Prediction Modeling Methodology

Machine Learning refers to a concept in which a machine has been programmed to learn specific patterns from historical data using powerful algorithms and make predictions in future based on the patterns it learnt. Machine learning is a branch of Artificial Intelligence (AI), the term proposed in 1959 by Arthur Samuel who defined it as the ability of computers or machines to learn new rules and concepts from data without being explicitly programmed. We consider Martin Marietta Materials Stock Decision Process with Wilcoxon Sign-Rank Test where A is the set of discrete actions of MLM 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= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Active Learning (ML)) X S(n):→ (n+1 year) $\overrightarrow{S}=\left(s_1,s_2,s_3\right)$

n:Time series to forecast

p:Price signals of MLM 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?

MLM Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: MLM Martin Marietta Materials
Time series to forecast n: 23 Oct 2022 for (n+1 year)

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

Martin Marietta Materials assigned short-term B2 & long-term B1 forecasted stock rating. We evaluate the prediction models Active Learning (ML) with Wilcoxon Sign-Rank Test^1,2,3,4 and conclude that the MLM 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 MLM stock.

Financial State Forecast for MLM Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B2	B1
Operational Risk	36	88
Market Risk	60	30
Technical Analysis	53	43
Fundamental Analysis	78	86
Risk Unsystematic	34	34

Prediction Confidence Score

Trust metric by Neural Network: 73 out of 100 with 763 signals.

References

1. Jiang N, Li L. 2016. Doubly robust off-policy value evaluation for reinforcement learning. In Proceedings of the 33rd International Conference on Machine Learning, pp. 652–61. La Jolla, CA: Int. Mach. Learn. Soc.
2. Mikolov T, Yih W, Zweig G. 2013c. Linguistic regularities in continuous space word representations. In Pro- ceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 746–51. New York: Assoc. Comput. Linguist.
3. V. Borkar and R. Jain. Risk-constrained Markov decision processes. IEEE Transaction on Automatic Control, 2014
4. Z. Wang, T. Schaul, M. Hessel, H. van Hasselt, M. Lanctot, and N. de Freitas. Dueling network architectures for deep reinforcement learning. In Proceedings of the International Conference on Machine Learning (ICML), pages 1995–2003, 2016.
5. J. Spall. Multivariate stochastic approximation using a simultaneous perturbation gradient approximation. IEEE Transactions on Automatic Control, 37(3):332–341, 1992.
6. S. Bhatnagar and K. Lakshmanan. An online actor-critic algorithm with function approximation for con- strained Markov decision processes. Journal of Optimization Theory and Applications, 153(3):688–708, 2012.
7. Dudik M, Langford J, Li L. 2011. Doubly robust policy evaluation and learning. In Proceedings of the 28th International Conference on Machine Learning, pp. 1097–104. La Jolla, CA: Int. Mach. Learn. Soc.

Frequently Asked Questions

Q: What is the prediction methodology for MLM stock?
A: MLM stock prediction methodology: We evaluate the prediction models Active Learning (ML) and Wilcoxon Sign-Rank Test
Q: Is MLM stock a buy or sell?
A: The dominant strategy among neural network is to Hold MLM Stock.
Q: Is Martin Marietta Materials stock a good investment?
A: The consensus rating for Martin Marietta Materials is Hold and assigned short-term B2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of MLM stock?
A: The consensus rating for MLM is Hold.
Q: What is the prediction period for MLM stock?
A: The prediction period for MLM is (n+1 year)

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