Stock market prediction is a major exertion in the field of finance and establishing businesses. Stock market is totally uncertain as the prices of stocks keep fluctuating on a daily basis because of numerous factors that influence it. One of the traditional ways of predicting stock prices was by using only historical data. But with time it was observed that other factors such as peoples' sentiments and other news events occurring in and around the country affect the stock market, for e.g. national elections, natural calamity etc. We evaluate Electronic Arts prediction models with Multi-Instance Learning (ML) and Factor1,2,3,4 and conclude that the EA 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 Sell EA stock.

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

## Key Points

1. Investment Risk
2. Decision Making
3. Stock Forecast Based On a Predictive Algorithm ## EA Target Price Prediction Modeling Methodology

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. We consider Electronic Arts Stock Decision Process with Factor where A is the set of discrete actions of EA 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(Factor)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(Multi-Instance Learning (ML)) X S(n):→ (n+1 year) $∑ i = 1 n r i$

n:Time series to forecast

p:Price signals of EA 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?

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

Sample Set: Neural Network
Stock/Index: EA Electronic Arts
Time series to forecast n: 22 Oct 2022 for (n+1 year)

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

Electronic Arts assigned short-term Ba3 & long-term Ba2 forecasted stock rating. We evaluate the prediction models Multi-Instance Learning (ML) with Factor1,2,3,4 and conclude that the EA 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 Sell EA stock.

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

Rating Short-Term Long-Term Senior
Outlook*Ba3Ba2
Operational Risk 5772
Market Risk7261
Technical Analysis7665
Fundamental Analysis6766
Risk Unsystematic5072

### Prediction Confidence Score

Trust metric by Neural Network: 82 out of 100 with 596 signals.

## References

1. S. Bhatnagar, R. Sutton, M. Ghavamzadeh, and M. Lee. Natural actor-critic algorithms. Automatica, 45(11): 2471–2482, 2009
2. A. Tamar and S. Mannor. Variance adjusted actor critic algorithms. arXiv preprint arXiv:1310.3697, 2013.
3. Mullainathan S, Spiess J. 2017. Machine learning: an applied econometric approach. J. Econ. Perspect. 31:87–106
4. Bengio Y, Ducharme R, Vincent P, Janvin C. 2003. A neural probabilistic language model. J. Mach. Learn. Res. 3:1137–55
5. Ashley, R. (1983), "On the usefulness of macroeconomic forecasts as inputs to forecasting models," Journal of Forecasting, 2, 211–223.
6. H. Kushner and G. Yin. Stochastic approximation algorithms and applications. Springer, 1997.
7. Sutton RS, Barto AG. 1998. Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press
Frequently Asked QuestionsQ: What is the prediction methodology for EA stock?
A: EA stock prediction methodology: We evaluate the prediction models Multi-Instance Learning (ML) and Factor
Q: Is EA stock a buy or sell?
A: The dominant strategy among neural network is to Sell EA Stock.
Q: Is Electronic Arts stock a good investment?
A: The consensus rating for Electronic Arts is Sell and assigned short-term Ba3 & long-term Ba2 forecasted stock rating.
Q: What is the consensus rating of EA stock?
A: The consensus rating for EA is Sell.
Q: What is the prediction period for EA stock?
A: The prediction period for EA is (n+1 year)