Modelling A.I. in Economics

How do you know when a stock will go up or down? (XOM Stock Forecast)

Predictions on stock market prices are a great challenge due to the fact that it is an immensely complex, chaotic and dynamic environment. There are many studies from various areas aiming to take on that challenge and Machine Learning approaches have been the focus of many of them. There are many examples of Machine Learning algorithms been able to reach satisfactory results when doing that type of prediction. This article studies the usage of LSTM networks on that scenario, to predict future trends of stock prices based on the price history, alongside with technical analysis indicators. We evaluate ExxonMobil prediction models with Transductive Learning (ML) and Factor1,2,3,4 and conclude that the XOM stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold XOM stock.


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

Key Points

  1. Can stock prices be predicted?
  2. Understanding Buy, Sell, and Hold Ratings
  3. Buy, Sell and Hold Signals

XOM Target Price Prediction Modeling Methodology

The stock market is very volatile and non-stationary and generates huge volumes of data in every second. In this article, the existing machine learning algorithms are analyzed for stock market forecasting and also a new pattern-finding algorithm for forecasting stock trend is developed. Three approaches can be used to solve the problem: fundamental analysis, technical analysis, and the machine learning. Experimental analysis done in this article shows that the machine learning could be useful for investors to make profitable decisions. We consider ExxonMobil Stock Decision Process with Factor where A is the set of discrete actions of XOM 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= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Transductive Learning (ML)) X S(n):→ (n+16 weeks) i = 1 n a i

n:Time series to forecast

p:Price signals of XOM 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?

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

Sample Set: Neural Network
Stock/Index: XOM ExxonMobil
Time series to forecast n: 09 Oct 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold XOM 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

ExxonMobil assigned short-term Ba2 & long-term B3 forecasted stock rating. We evaluate the prediction models Transductive Learning (ML) with Factor1,2,3,4 and conclude that the XOM stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold XOM stock.

Financial State Forecast for XOM Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Ba2B3
Operational Risk 4847
Market Risk8835
Technical Analysis6981
Fundamental Analysis6734
Risk Unsystematic7044

Prediction Confidence Score

Trust metric by Neural Network: 73 out of 100 with 753 signals.

References

  1. Thompson WR. 1933. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25:285–94
  2. J. Harb and D. Precup. Investigating recurrence and eligibility traces in deep Q-networks. In Deep Reinforcement Learning Workshop, NIPS 2016, Barcelona, Spain, 2016.
  3. D. Bertsekas. Nonlinear programming. Athena Scientific, 1999.
  4. Abadie A, Diamond A, Hainmueller J. 2010. Synthetic control methods for comparative case studies: estimat- ing the effect of California's tobacco control program. J. Am. Stat. Assoc. 105:493–505
  5. Challen, D. W. A. J. Hagger (1983), Macroeconomic Systems: Construction, Validation and Applications. New York: St. Martin's Press.
  6. Tibshirani R. 1996. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58:267–88
  7. Mikolov T, Yih W, Zweig G. 2013c. Linguistic regularities in continuous space word representations. In Pro- ceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 746–51. New York: Assoc. Comput. Linguist.
Frequently Asked QuestionsQ: What is the prediction methodology for XOM stock?
A: XOM stock prediction methodology: We evaluate the prediction models Transductive Learning (ML) and Factor
Q: Is XOM stock a buy or sell?
A: The dominant strategy among neural network is to Hold XOM Stock.
Q: Is ExxonMobil stock a good investment?
A: The consensus rating for ExxonMobil is Hold and assigned short-term Ba2 & long-term B3 forecasted stock rating.
Q: What is the consensus rating of XOM stock?
A: The consensus rating for XOM is Hold.
Q: What is the prediction period for XOM stock?
A: The prediction period for XOM is (n+16 weeks)

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