IPG Interpublic Group of Companies Inc. (The) Common Stock (Forecast)

Outlook: Interpublic Group of Companies Inc. (The) Common Stock assigned short-term Ba3 & long-term Ba3 forecasted stock rating.
Dominant Strategy : Sell
Time series to forecast n: 14 Dec 2022 for (n+16 weeks)
Methodology : Transfer Learning (ML)

Abstract

The study of financial markets has been addressed in many works during the last years. Different methods have been used in order to capture the non-linear behavior which is characteristic of these complex systems. The development of profitable strategies has been associated with the predictive character of the market movement, and special attention has been devoted to forecast the trends of financial markets. (Dase, R.K. and Pawar, D.D., 2010. Application of Artificial Neural Network for stock market predictions: A review of literature. International Journal of Machine Intelligence, 2(2), pp.14-17.) We evaluate Interpublic Group of Companies Inc. (The) Common Stock prediction models with Transfer Learning (ML) and Sign Test^1,2,3,4 and conclude that the IPG stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Sell

Key Points

1. Stock Rating
2. What are buy sell or hold recommendations?
3. Trading Interaction

IPG Target Price Prediction Modeling Methodology

We consider Interpublic Group of Companies Inc. (The) Common Stock Decision Process with Transfer Learning (ML) where A is the set of discrete actions of IPG 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(Sign 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(Transfer Learning (ML)) X S(n):→ (n+16 weeks) $\begin{array}{l}\int e^x\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of IPG 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?

IPG Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: IPG Interpublic Group of Companies Inc. (The) Common Stock
Time series to forecast n: 14 Dec 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Sell

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 Interpublic Group of Companies Inc. (The) Common Stock

1. When defining default for the purposes of determining the risk of a default occurring, an entity shall apply a default definition that is consistent with the definition used for internal credit risk management purposes for the relevant financial instrument and consider qualitative indicators (for example, financial covenants) when appropriate. However, there is a rebuttable presumption that default does not occur later than when a financial asset is 90 days past due unless an entity has reasonable and supportable information to demonstrate that a more lagging default criterion is more appropriate. The definition of default used for these purposes shall be applied consistently to all financial instruments unless information becomes available that demonstrates that another default definition is more appropriate for a particular financial instrument.
2. An entity's business model is determined at a level that reflects how groups of financial assets are managed together to achieve a particular business objective. The entity's business model does not depend on management's intentions for an individual instrument. Accordingly, this condition is not an instrument-by-instrument approach to classification and should be determined on a higher level of aggregation. However, a single entity may have more than one business model for managing its financial instruments. Consequently, classification need not be determined at the reporting entity level. For example, an entity may hold a portfolio of investments that it manages in order to collect contractual cash flows and another portfolio of investments that it manages in order to trade to realise fair value changes. Similarly, in some circumstances, it may be appropriate to separate a portfolio of financial assets into subportfolios in order to reflect the level at which an entity manages those financial assets. For example, that may be the case if an entity originates or purchases a portfolio of mortgage loans and manages some of the loans with an objective of collecting contractual cash flows and manages the other loans with an objective of selling them.
3. An entity applies IAS 21 to financial assets and financial liabilities that are monetary items in accordance with IAS 21 and denominated in a foreign currency. IAS 21 requires any foreign exchange gains and losses on monetary assets and monetary liabilities to be recognised in profit or loss. An exception is a monetary item that is designated as a hedging instrument in a cash flow hedge (see paragraph 6.5.11), a hedge of a net investment (see paragraph 6.5.13) or a fair value hedge of an equity instrument for which an entity has elected to present changes in fair value in other comprehensive income in accordance with paragraph 5.7.5 (see paragraph 6.5.8).
4. Such designation may be used whether paragraph 4.3.3 requires the embedded derivatives to be separated from the host contract or prohibits such separation. However, paragraph 4.3.5 would not justify designating the hybrid contract as at fair value through profit or loss in the cases set out in paragraph 4.3.5(a) and (b) because doing so would not reduce complexity or increase reliability.

*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

Interpublic Group of Companies Inc. (The) Common Stock assigned short-term Ba3 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Transfer Learning (ML) with Sign Test^1,2,3,4 and conclude that the IPG stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Sell

Financial State Forecast for IPG Interpublic Group of Companies Inc. (The) Common Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Ba3	Ba3
Operational Risk	66	79
Market Risk	61	46
Technical Analysis	69	63
Fundamental Analysis	88	89
Risk Unsystematic	33	51

Prediction Confidence Score

Trust metric by Neural Network: 80 out of 100 with 487 signals.

References

1. M. J. Hausknecht. Cooperation and Communication in Multiagent Deep Reinforcement Learning. PhD thesis, The University of Texas at Austin, 2016
2. J. Z. Leibo, V. Zambaldi, M. Lanctot, J. Marecki, and T. Graepel. Multi-agent Reinforcement Learning in Sequential Social Dilemmas. In Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2017), Sao Paulo, Brazil, 2017
3. Canova, F. B. E. Hansen (1995), "Are seasonal patterns constant over time? A test for seasonal stability," Journal of Business and Economic Statistics, 13, 237–252.
4. Armstrong, J. S. M. C. Grohman (1972), "A comparative study of methods for long-range market forecasting," Management Science, 19, 211–221.
5. N. B ̈auerle and A. Mundt. Dynamic mean-risk optimization in a binomial model. Mathematical Methods of Operations Research, 70(2):219–239, 2009.
6. J. Filar, L. Kallenberg, and H. Lee. Variance-penalized Markov decision processes. Mathematics of Opera- tions Research, 14(1):147–161, 1989
7. S. Devlin, L. Yliniemi, D. Kudenko, and K. Tumer. Potential-based difference rewards for multiagent reinforcement learning. In Proceedings of the Thirteenth International Joint Conference on Autonomous Agents and Multiagent Systems, May 2014

Frequently Asked Questions

Q: What is the prediction methodology for IPG stock?
A: IPG stock prediction methodology: We evaluate the prediction models Transfer Learning (ML) and Sign Test
Q: Is IPG stock a buy or sell?
A: The dominant strategy among neural network is to Sell IPG Stock.
Q: Is Interpublic Group of Companies Inc. (The) Common Stock stock a good investment?
A: The consensus rating for Interpublic Group of Companies Inc. (The) Common Stock is Sell and assigned short-term Ba3 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of IPG stock?
A: The consensus rating for IPG is Sell.
Q: What is the prediction period for IPG stock?
A: The prediction period for IPG is (n+16 weeks)

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