MTG Target Price Prediction

This study presents financial network indicators that can be applied to global stock market investment strategies. We propose to design both undirected and directed volatility networks of global stock market based on simple pair-wise correlation and system-wide connectedness of stock date using a vector auto-regressive model. We evaluate MGIC prediction models with Modular Neural Network (News Feed Sentiment Analysis) and Wilcoxon Rank-Sum Test^1,2,3,4 and conclude that the MTG stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold MTG stock.

Keywords: MTG, MGIC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

1. Can we predict stock market using machine learning?
2. Understanding Buy, Sell, and Hold Ratings
3. Which neural network is best for prediction?

MTG Target Price Prediction Modeling Methodology

Stock market is considered chaotic, complex, volatile and dynamic. Undoubtedly, its prediction is one of the most challenging tasks in time series forecasting. Moreover existing Artificial Neural Network (ANN) approaches fail to provide encouraging results. Meanwhile advances in machine learning have presented favourable results for speech recognition, image classification and language processing. We consider MGIC Stock Decision Process with Wilcoxon Rank-Sum Test where A is the set of discrete actions of MTG 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= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Modular Neural Network (News Feed Sentiment Analysis)) X S(n):→ (n+6 month) $\begin{array}{l}\int r^s\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of MTG 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?

MTG Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: MTG MGIC
Time series to forecast n: 22 Sep 2022 for (n+6 month)

According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold MTG 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

MGIC assigned short-term B3 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) with Wilcoxon Rank-Sum Test^1,2,3,4 and conclude that the MTG stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold MTG stock.

Financial State Forecast for MTG Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B3	B2
Operational Risk	53	45
Market Risk	82	72
Technical Analysis	43	50
Fundamental Analysis	31	43
Risk Unsystematic	39	36

Prediction Confidence Score

Trust metric by Neural Network: 79 out of 100 with 593 signals.

References

1. Van der Vaart AW. 2000. Asymptotic Statistics. Cambridge, UK: Cambridge Univ. Press
2. H. Khalil and J. Grizzle. Nonlinear systems, volume 3. Prentice hall Upper Saddle River, 2002.
3. Vapnik V. 2013. The Nature of Statistical Learning Theory. Berlin: Springer
4. V. Konda and J. Tsitsiklis. Actor-Critic algorithms. In Proceedings of Advances in Neural Information Processing Systems 12, pages 1008–1014, 2000
5. E. Altman. Constrained Markov decision processes, volume 7. CRC Press, 1999
6. S. Bhatnagar, H. Prasad, and L. Prashanth. Stochastic recursive algorithms for optimization, volume 434. Springer, 2013
7. Andrews, D. W. K. W. Ploberger (1994), "Optimal tests when a nuisance parameter is present only under the alternative," Econometrica, 62, 1383–1414.

Frequently Asked Questions

Q: What is the prediction methodology for MTG stock?
A: MTG stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and Wilcoxon Rank-Sum Test
Q: Is MTG stock a buy or sell?
A: The dominant strategy among neural network is to Hold MTG Stock.
Q: Is MGIC stock a good investment?
A: The consensus rating for MGIC is Hold and assigned short-term B3 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of MTG stock?
A: The consensus rating for MTG is Hold.
Q: What is the prediction period for MTG stock?
A: The prediction period for MTG is (n+6 month)

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