Modelling A.I. in Economics

How do you know when a stock will go up or down? (FTSE China A50 Index Stock Forecast) (Forecast)

The classical linear multi-factor stock selection model is widely used for long-term stock price trend prediction. However, the stock market is chaotic, complex, and dynamic, for which reasons the linear model assumption may be unreasonable, and it is more meaningful to construct a better-integrated stock selection model based on different feature selection and nonlinear stock price trend prediction methods. We evaluate FTSE China A50 Index prediction models with Ensemble Learning (ML) and Wilcoxon Sign-Rank Test1,2,3,4 and conclude that the FTSE China A50 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 FTSE China A50 Index stock.


Keywords: FTSE China A50 Index, FTSE China A50 Index, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

  1. What is Markov decision process in reinforcement learning?
  2. What are the most successful trading algorithms?
  3. Operational Risk

FTSE China A50 Index Target Price Prediction Modeling Methodology

The success of portfolio construction depends primarily on the future performance of stock markets. Recent developments in machine learning have brought significant opportunities to incorporate prediction theory into portfolio selection. However, many studies show that a single prediction model is insufficient to achieve very accurate predictions and affluent returns. In this paper, a novel portfolio construction approach is developed using a hybrid model based on machine learning for stock prediction. We consider FTSE China A50 Index Stock Decision Process with Wilcoxon Sign-Rank Test where A is the set of discrete actions of FTSE China A50 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(Wilcoxon Sign-Rank Test)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(Ensemble Learning (ML)) X S(n):→ (n+16 weeks) R = 1 0 0 0 1 0 0 0 1

n:Time series to forecast

p:Price signals of FTSE China A50 Index stock

j:Nash equilibria

k:Dominated move

a:Best response for target price

 

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How do AC Investment Research machine learning (predictive) algorithms actually work?

FTSE China A50 Index Stock Forecast (Buy or Sell) for (n+16 weeks)


Sample Set: Neural Network
Stock/Index: FTSE China A50 Index FTSE China A50 Index
Time series to forecast n: 02 Nov 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Buy FTSE China A50 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%

Adjusted IFRS* Prediction Methods for FTSE China A50 Index

  1. If an entity prepares interim financial reports in accordance with IAS 34 Interim Financial Reporting the entity need not apply the requirements in this Standard to interim periods prior to the date of initial application if it is impracticable (as defined in IAS 8).
  2. For the purposes of applying the requirement in paragraph 5.7.7(a), credit risk is different from asset-specific performance risk. Asset-specific performance risk is not related to the risk that an entity will fail to discharge a particular obligation but instead it is related to the risk that a single asset or a group of assets will perform poorly (or not at all).
  3. An entity need not undertake an exhaustive search for information but shall consider all reasonable and supportable information that is available without undue cost or effort and that is relevant to the estimate of expected credit losses, including the effect of expected prepayments. The information used shall include factors that are specific to the borrower, general economic conditions and an assessment of both the current as well as the forecast direction of conditions at the reporting date. An entity may use various sources of data, that may be both internal (entity-specific) and external. Possible data sources include internal historical credit loss experience, internal ratings, credit loss experience of other entities and external ratings, reports and statistics. Entities that have no, or insufficient, sources of entityspecific data may use peer group experience for the comparable financial instrument (or groups of financial instruments).
  4. Paragraph 5.7.5 permits an entity to make an irrevocable election to present in other comprehensive income subsequent changes in the fair value of particular investments in equity instruments. Such an investment is not a monetary item. Accordingly, the gain or loss that is presented in other comprehensive income in accordance with paragraph 5.7.5 includes any related foreign exchange component.

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

Conclusions

FTSE China A50 Index assigned short-term B2 & long-term Baa2 forecasted stock rating. We evaluate the prediction models Ensemble Learning (ML) with Wilcoxon Sign-Rank Test1,2,3,4 and conclude that the FTSE China A50 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 FTSE China A50 Index stock.

Financial State Forecast for FTSE China A50 Index FTSE China A50 Index Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B2Baa2
Operational Risk 8090
Market Risk4174
Technical Analysis8271
Fundamental Analysis4475
Risk Unsystematic3379

Prediction Confidence Score

Trust metric by Neural Network: 91 out of 100 with 463 signals.

References

  1. Bengio Y, Ducharme R, Vincent P, Janvin C. 2003. A neural probabilistic language model. J. Mach. Learn. Res. 3:1137–55
  2. J. Spall. Multivariate stochastic approximation using a simultaneous perturbation gradient approximation. IEEE Transactions on Automatic Control, 37(3):332–341, 1992.
  3. Greene WH. 2000. Econometric Analysis. Upper Saddle River, N J: Prentice Hall. 4th ed.
  4. Chen X. 2007. Large sample sieve estimation of semi-nonparametric models. In Handbook of Econometrics, Vol. 6B, ed. JJ Heckman, EE Learner, pp. 5549–632. Amsterdam: Elsevier
  5. Burkov A. 2019. The Hundred-Page Machine Learning Book. Quebec City, Can.: Andriy Burkov
  6. L. Panait and S. Luke. Cooperative multi-agent learning: The state of the art. Autonomous Agents and Multi-Agent Systems, 11(3):387–434, 2005.
  7. P. Milgrom and I. Segal. Envelope theorems for arbitrary choice sets. Econometrica, 70(2):583–601, 2002
Frequently Asked QuestionsQ: What is the prediction methodology for FTSE China A50 Index stock?
A: FTSE China A50 Index stock prediction methodology: We evaluate the prediction models Ensemble Learning (ML) and Wilcoxon Sign-Rank Test
Q: Is FTSE China A50 Index stock a buy or sell?
A: The dominant strategy among neural network is to Buy FTSE China A50 Index Stock.
Q: Is FTSE China A50 Index stock a good investment?
A: The consensus rating for FTSE China A50 Index is Buy and assigned short-term B2 & long-term Baa2 forecasted stock rating.
Q: What is the consensus rating of FTSE China A50 Index stock?
A: The consensus rating for FTSE China A50 Index is Buy.
Q: What is the prediction period for FTSE China A50 Index stock?
A: The prediction period for FTSE China A50 Index is (n+16 weeks)

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