It has never been easy to invest in a set of assets, the abnormally of financial market does not allow simple models to predict future asset values with higher accuracy. Machine learning, which consist of making computers perform tasks that normally requiring human intelligence is currently the dominant trend in scientific research. This article aims to build a model using Recurrent Neural Networks (RNN) and especially Long-Short Term Memory model (LSTM) to predict future stock market values. We evaluate IPG prediction models with Ensemble Learning (ML) and Lasso Regression1,2,3,4 and conclude that the IPG 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 IPG stock.

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

## Key Points

1. Market Signals
3. What is Markov decision process in reinforcement learning?

## IPG Target Price Prediction Modeling Methodology

Neural networks, as an intelligent data mining method, have been used in many different challenging pattern recognition problems such as stock market prediction. However, there is no formal method to determine the optimal neural network for prediction purpose in the literature. In this paper, two kinds of neural networks, a feed forward multi layer Perceptron (MLP) and an Elman recurrent network, are used to predict a company's stock value based on its stock share value history. We consider IPG Stock Decision Process with Lasso Regression 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(Lasso Regression)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Ensemble Learning (ML)) X S(n):→ (n+1 year) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of IPG 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?

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

Sample Set: Neural Network
Stock/Index: IPG IPG
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 IPG 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

IPG assigned short-term Ba1 & long-term B3 forecasted stock rating. We evaluate the prediction models Ensemble Learning (ML) with Lasso Regression1,2,3,4 and conclude that the IPG 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 IPG stock.

### Financial State Forecast for IPG Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Ba1B3
Operational Risk 4445
Market Risk7777
Technical Analysis7449
Fundamental Analysis8833
Risk Unsystematic7433

### Prediction Confidence Score

Trust metric by Neural Network: 89 out of 100 with 602 signals.

## References

1. Chernozhukov V, Newey W, Robins J. 2018c. Double/de-biased machine learning using regularized Riesz representers. arXiv:1802.08667 [stat.ML]
2. Breiman L. 2001b. Statistical modeling: the two cultures (with comments and a rejoinder by the author). Stat. Sci. 16:199–231
3. Ruiz FJ, Athey S, Blei DM. 2017. SHOPPER: a probabilistic model of consumer choice with substitutes and complements. arXiv:1711.03560 [stat.ML]
4. D. White. Mean, variance, and probabilistic criteria in finite Markov decision processes: A review. Journal of Optimization Theory and Applications, 56(1):1–29, 1988.
5. Dimakopoulou M, Zhou Z, Athey S, Imbens G. 2018. Balanced linear contextual bandits. arXiv:1812.06227 [cs.LG]
6. Rosenbaum PR, Rubin DB. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70:41–55
7. V. Konda and J. Tsitsiklis. Actor-Critic algorithms. In Proceedings of Advances in Neural Information Processing Systems 12, pages 1008–1014, 2000
Frequently Asked QuestionsQ: What is the prediction methodology for IPG stock?
A: IPG stock prediction methodology: We evaluate the prediction models Ensemble Learning (ML) and Lasso Regression
Q: Is IPG stock a buy or sell?
A: The dominant strategy among neural network is to Hold IPG Stock.
Q: Is IPG stock a good investment?
A: The consensus rating for IPG is Hold and assigned short-term Ba1 & long-term B3 forecasted stock rating.
Q: What is the consensus rating of IPG stock?
A: The consensus rating for IPG is Hold.
Q: What is the prediction period for IPG stock?
A: The prediction period for IPG is (n+1 year)