MOBQW Mobiquity Technologies Inc. Warrant

Outlook: Mobiquity Technologies Inc. Warrant assigned short-term B1 & long-term Ba3 forecasted stock rating.
Dominant Strategy : Buy
Time series to forecast n: 16 Dec 2022 for (n+4 weeks)
Methodology : Modular Neural Network (Speculative Sentiment Analysis)

Abstract

Short-term trading is a difficult task due to fluctuating demand and supply in the stock market. These demands and supply are reflected in stock prices. The stock prices may be predicted using technical indicators. Most of the existing literature considered the limited technical indicators to measure short-term prices. We have considered 82 different combinations of technical indicators to predict the stock prices. (Devadoss, A.V. and Ligori, T.A.A., 2013. Stock prediction using artificial neural networks. International Journal of Data Mining Techniques and Applications, 2(1), pp.283-291.) We evaluate Mobiquity Technologies Inc. Warrant prediction models with Modular Neural Network (Speculative Sentiment Analysis) and Multiple Regression^1,2,3,4 and conclude that the MOBQW stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Buy

Key Points

1. Game Theory
2. What is prediction model?
3. Reaction Function

MOBQW Target Price Prediction Modeling Methodology

We consider Mobiquity Technologies Inc. Warrant Decision Process with Modular Neural Network (Speculative Sentiment Analysis) where A is the set of discrete actions of MOBQW 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(Multiple Regression)^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 (Speculative Sentiment Analysis)) X S(n):→ (n+4 weeks) $\begin{array}{l}\int e^x\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of MOBQW stock

j:Nash equilibria (Neural Network)

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?

MOBQW Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: MOBQW Mobiquity Technologies Inc. Warrant
Time series to forecast n: 16 Dec 2022 for (n+4 weeks)

According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Buy

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 (Grey to Black): *Technical Analysis%

Adjusted IFRS* Prediction Methods for Mobiquity Technologies Inc. Warrant

1. The fact that a derivative is in or out of the money when it is designated as a hedging instrument does not in itself mean that a qualitative assessment is inappropriate. It depends on the circumstances whether hedge ineffectiveness arising from that fact could have a magnitude that a qualitative assessment would not adequately capture.
2. In some cases, the qualitative and non-statistical quantitative information available may be sufficient to determine that a financial instrument has met the criterion for the recognition of a loss allowance at an amount equal to lifetime expected credit losses. That is, the information does not need to flow through a statistical model or credit ratings process in order to determine whether there has been a significant increase in the credit risk of the financial instrument. In other cases, an entity may need to consider other information, including information from its statistical models or credit ratings processes.
3. IFRS 15, issued in May 2014, amended paragraphs 3.1.1, 4.2.1, 5.1.1, 5.2.1, 5.7.6, B3.2.13, B5.7.1, C5 and C42 and deleted paragraph C16 and its related heading. Paragraphs 5.1.3 and 5.7.1A, and a definition to Appendix A, were added. An entity shall apply those amendments when it applies IFRS 15.
4. The credit risk on a financial instrument is considered low for the purposes of paragraph 5.5.10, if the financial instrument has a low risk of default, the borrower has a strong capacity to meet its contractual cash flow obligations in the near term and adverse changes in economic and business conditions in the longer term may, but will not necessarily, reduce the ability of the borrower to fulfil its contractual cash flow obligations. Financial instruments are not considered to have low credit risk when they are regarded as having a low risk of loss simply because of the value of collateral and the financial instrument without that collateral would not be considered low credit risk. Financial instruments are also not considered to have low credit risk simply because they have a lower risk of default than the entity's other financial instruments or relative to the credit risk of the jurisdiction within which an entity operates.

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

Conclusions

Mobiquity Technologies Inc. Warrant assigned short-term B1 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) with Multiple Regression^1,2,3,4 and conclude that the MOBQW stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Buy

Financial State Forecast for MOBQW Mobiquity Technologies Inc. Warrant Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B1	Ba3
Operational Risk	49	75
Market Risk	65	77
Technical Analysis	58	39
Fundamental Analysis	79	67
Risk Unsystematic	47	67

Prediction Confidence Score

Trust metric by Neural Network: 92 out of 100 with 835 signals.

References

1. Athey S, Tibshirani J, Wager S. 2016b. Generalized random forests. arXiv:1610.01271 [stat.ME]
2. A. Tamar, Y. Glassner, and S. Mannor. Policy gradients beyond expectations: Conditional value-at-risk. In AAAI, 2015
3. J. Baxter and P. Bartlett. Infinite-horizon policy-gradient estimation. Journal of Artificial Intelligence Re- search, 15:319–350, 2001.
4. Hill JL. 2011. Bayesian nonparametric modeling for causal inference. J. Comput. Graph. Stat. 20:217–40
5. Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J. 2013b. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems, Vol. 26, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 3111–19. San Diego, CA: Neural Inf. Process. Syst. Found.
6. Mikolov T, Chen K, Corrado GS, Dean J. 2013a. Efficient estimation of word representations in vector space. arXiv:1301.3781 [cs.CL]
7. Ruiz FJ, Athey S, Blei DM. 2017. SHOPPER: a probabilistic model of consumer choice with substitutes and complements. arXiv:1711.03560 [stat.ML]

Frequently Asked Questions

Q: What is the prediction methodology for MOBQW stock?
A: MOBQW stock prediction methodology: We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) and Multiple Regression
Q: Is MOBQW stock a buy or sell?
A: The dominant strategy among neural network is to Buy MOBQW Stock.
Q: Is Mobiquity Technologies Inc. Warrant stock a good investment?
A: The consensus rating for Mobiquity Technologies Inc. Warrant is Buy and assigned short-term B1 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of MOBQW stock?
A: The consensus rating for MOBQW is Buy.
Q: What is the prediction period for MOBQW stock?
A: The prediction period for MOBQW is (n+4 weeks)

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