Can neural networks predict stock market? (EA Stock Forecast)

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 Electronic Arts prediction models with Active Learning (ML) and Multiple Regression^1,2,3,4 and conclude that the EA stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) 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. Can we predict stock market using machine learning?
2. Why do we need predictive models?
3. Decision Making

EA Target Price Prediction Modeling Methodology

Stocks are possibly the most popular financial instrument invented for building wealth and are the centerpiece of any investment portfolio. The advances in trading technology has opened up the markets so that nowadays nearly anybody can own stocks. From last few decades, there seen explosive increase in the average person's interest for stock market. In a financially explosive market, as the stock market, it is important to have a very accurate prediction of a future trend. Because of the financial crisis and recording profits, it is compulsory to have a secure prediction of the values of the stocks. Predicting a non-linear signal requires progressive algorithms of machine learning with help of Artificial Intelligence (AI). We consider Electronic Arts Stock Decision Process with Multiple Regression 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(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(Active Learning (ML)) X S(n):→ (n+8 weeks) $\begin{array}{l}\int r^s\mathrm{rs}\end{array}$

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+8 weeks)

Sample Set: Neural Network
Stock/Index: EA Electronic Arts
Time series to forecast n: 24 Sep 2022 for (n+8 weeks)

According to price forecasts for (n+8 weeks) 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 Caa2 & long-term Baa2 forecasted stock rating. We evaluate the prediction models Active Learning (ML) with Multiple Regression^1,2,3,4 and conclude that the EA stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) 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*	Caa2	Baa2
Operational Risk	57	90
Market Risk	39	82
Technical Analysis	49	85
Fundamental Analysis	32	69
Risk Unsystematic	39	38

Prediction Confidence Score

Trust metric by Neural Network: 89 out of 100 with 609 signals.

References

1. Morris CN. 1983. Parametric empirical Bayes inference: theory and applications. J. Am. Stat. Assoc. 78:47–55
2. Lai TL, Robbins H. 1985. Asymptotically efficient adaptive allocation rules. Adv. Appl. Math. 6:4–22
3. M. Puterman. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York, 1994.
4. Hartigan JA, Wong MA. 1979. Algorithm as 136: a k-means clustering algorithm. J. R. Stat. Soc. Ser. C 28:100–8
5. Athey S, Imbens GW. 2017a. The econometrics of randomized experiments. In Handbook of Economic Field Experiments, Vol. 1, ed. E Duflo, A Banerjee, pp. 73–140. Amsterdam: Elsevier
6. LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521:436–44
7. Tibshirani R, Hastie T. 1987. Local likelihood estimation. J. Am. Stat. Assoc. 82:559–67

Frequently Asked Questions

Q: What is the prediction methodology for EA stock?
A: EA stock prediction methodology: We evaluate the prediction models Active Learning (ML) and Multiple Regression
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 Caa2 & long-term Baa2 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+8 weeks)

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