Modelling A.I. in Economics

Buy, sell or hold: CAC 40 Index Stock Forecast

In the finance world stock trading is one of the most important activities. Stock market prediction is an act of trying to determine the future value of a stock other financial instrument traded on a financial exchange. This paper explains the prediction of a stock using Machine Learning. The technical and fundamental or the time series analysis is used by the most of the stockbrokers while making the stock predictions. We evaluate CAC 40 Index prediction models with Transductive Learning (ML) and Pearson Correlation1,2,3,4 and conclude that the CAC 40 Index 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 Buy CAC 40 Index stock.


Keywords: CAC 40 Index, CAC 40 Index, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

  1. Can machine learning predict?
  2. What is prediction in deep learning?
  3. What is a prediction confidence?

CAC 40 Index Target Price Prediction Modeling Methodology

This paper examines the theory and practice of regression techniques for prediction of stock price trend by using a transformed data set in ordinal data format. The original pretransformed data source contains data of heterogeneous data types used for handling of currency values and financial ratios. The data formats in currency values and financial ratios provide a process for computation of stock prices. The transformed data set contains only a standardized ordinal data type which provides a process to measure rankings of stock price trends. We consider CAC 40 Index Stock Decision Process with Pearson Correlation where A is the set of discrete actions of CAC 40 Index 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(Pearson Correlation)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) R = r 1 r 2 r 3

n:Time series to forecast

p:Price signals of CAC 40 Index 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?

CAC 40 Index Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: CAC 40 Index CAC 40 Index
Time series to forecast n: 04 Oct 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Buy CAC 40 Index 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

CAC 40 Index assigned short-term Caa2 & long-term B2 forecasted stock rating. We evaluate the prediction models Transductive Learning (ML) with Pearson Correlation1,2,3,4 and conclude that the CAC 40 Index 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 Buy CAC 40 Index stock.

Financial State Forecast for CAC 40 Index Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Caa2B2
Operational Risk 3875
Market Risk5868
Technical Analysis3146
Fundamental Analysis4049
Risk Unsystematic3733

Prediction Confidence Score

Trust metric by Neural Network: 87 out of 100 with 487 signals.

References

  1. Mnih A, Hinton GE. 2007. Three new graphical models for statistical language modelling. In International Conference on Machine Learning, pp. 641–48. La Jolla, CA: Int. Mach. Learn. Soc.
  2. P. Marbach. Simulated-Based Methods for Markov Decision Processes. PhD thesis, Massachusetts Institute of Technology, 1998
  3. Bai J, Ng S. 2002. Determining the number of factors in approximate factor models. Econometrica 70:191–221
  4. Rosenbaum PR, Rubin DB. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70:41–55
  5. Robins J, Rotnitzky A. 1995. Semiparametric efficiency in multivariate regression models with missing data. J. Am. Stat. Assoc. 90:122–29
  6. Li L, Chu W, Langford J, Moon T, Wang X. 2012. An unbiased offline evaluation of contextual bandit algo- rithms with generalized linear models. In Proceedings of 4th ACM International Conference on Web Search and Data Mining, pp. 297–306. New York: ACM
  7. Z. Wang, T. Schaul, M. Hessel, H. van Hasselt, M. Lanctot, and N. de Freitas. Dueling network architectures for deep reinforcement learning. In Proceedings of the International Conference on Machine Learning (ICML), pages 1995–2003, 2016.
Frequently Asked QuestionsQ: What is the prediction methodology for CAC 40 Index stock?
A: CAC 40 Index stock prediction methodology: We evaluate the prediction models Transductive Learning (ML) and Pearson Correlation
Q: Is CAC 40 Index stock a buy or sell?
A: The dominant strategy among neural network is to Buy CAC 40 Index Stock.
Q: Is CAC 40 Index stock a good investment?
A: The consensus rating for CAC 40 Index is Buy and assigned short-term Caa2 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of CAC 40 Index stock?
A: The consensus rating for CAC 40 Index is Buy.
Q: What is the prediction period for CAC 40 Index stock?
A: The prediction period for CAC 40 Index is (n+16 weeks)

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