Jakarta Stock Exchange Composite Index Stock Forecast & Analysis

The categorization of high dimensional data present a fascinating challenge to machine learning models as frequent number of highly correlated dimensions or attributes can affect the accuracy of classification model. In this paper, the problem of high dimensionality of stock exchange is investigated to predict the market trends by applying the principal component analysis (PCA) with linear regression. PCA can help to improve the predictive performance of machine learning methods while reducing the redundancy among the data. We evaluate Jakarta Stock Exchange Composite Index prediction models with Modular Neural Network (Financial Sentiment Analysis) and Wilcoxon Rank-Sum Test^1,2,3,4 and conclude that the Jakarta Stock Exchange Composite Index stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold Jakarta Stock Exchange Composite Index stock.

Keywords: Jakarta Stock Exchange Composite Index, Jakarta Stock Exchange Composite Index, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

1. Can statistics predict the future?
2. Game Theory
3. Should I buy stocks now or wait amid such uncertainty?

Jakarta Stock Exchange Composite Index Target Price Prediction Modeling Methodology

Stock markets are affected by many uncertainties and interrelated economic and political factors at both local and global levels. The key to successful stock market forecasting is achieving best results with minimum required input data. To determine the set of relevant factors for making accurate predictions is a complicated task and so regular stock market analysis is very essential. More specifically, the stock market's movements are analyzed and predicted in order to retrieve knowledge that could guide investors on when to buy and sell. We consider Jakarta Stock Exchange Composite Index Stock Decision Process with Wilcoxon Rank-Sum Test where A is the set of discrete actions of Jakarta Stock Exchange Composite 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 Rank-Sum Test)^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(Modular Neural Network (Financial Sentiment Analysis)) X S(n):→ (n+4 weeks) $\sum _{i=1}^n\left(s_i\right)$

n:Time series to forecast

p:Price signals of Jakarta Stock Exchange Composite 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?

Jakarta Stock Exchange Composite Index Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: Jakarta Stock Exchange Composite Index Jakarta Stock Exchange Composite Index
Time series to forecast n: 09 Nov 2022 for (n+4 weeks)

According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold Jakarta Stock Exchange Composite 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 Jakarta Stock Exchange Composite Index

1. Hedge effectiveness is the extent to which changes in the fair value or the cash flows of the hedging instrument offset changes in the fair value or the cash flows of the hedged item (for example, when the hedged item is a risk component, the relevant change in fair value or cash flows of an item is the one that is attributable to the hedged risk). Hedge ineffectiveness is the extent to which the changes in the fair value or the cash flows of the hedging instrument are greater or less than those on the hedged item.
2. An entity's estimate of expected credit losses on loan commitments shall be consistent with its expectations of drawdowns on that loan commitment, ie it shall consider the expected portion of the loan commitment that will be drawn down within 12 months of the reporting date when estimating 12-month expected credit losses, and the expected portion of the loan commitment that will be drawn down over the expected life of the loan commitment when estimating lifetime expected credit losses.
3. Paragraph 4.1.1(a) requires an entity to classify financial assets on the basis of the entity's business model for managing the financial assets, unless paragraph 4.1.5 applies. An entity assesses whether its financial assets meet the condition in paragraph 4.1.2(a) or the condition in paragraph 4.1.2A(a) on the basis of the business model as determined by the entity's key management personnel (as defined in IAS 24 Related Party Disclosures).
4. An entity shall apply this Standard for annual periods beginning on or after 1 January 2018. Earlier application is permitted. If an entity elects to apply this Standard early, it must disclose that fact and apply all of the requirements in this Standard at the same time (but see also paragraphs 7.1.2, 7.2.21 and 7.3.2). It shall also, at the same time, apply the amendments in Appendix C.

*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

Jakarta Stock Exchange Composite Index assigned short-term B3 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) with Wilcoxon Rank-Sum Test^1,2,3,4 and conclude that the Jakarta Stock Exchange Composite Index stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold Jakarta Stock Exchange Composite Index stock.

Financial State Forecast for Jakarta Stock Exchange Composite Index Jakarta Stock Exchange Composite Index Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B3	B1
Operational Risk	38	83
Market Risk	38	68
Technical Analysis	78	31
Fundamental Analysis	42	72
Risk Unsystematic	58	36

Prediction Confidence Score

Trust metric by Neural Network: 78 out of 100 with 717 signals.

References

1. D. Bertsekas. Nonlinear programming. Athena Scientific, 1999.
2. Bennett J, Lanning S. 2007. The Netflix prize. In Proceedings of KDD Cup and Workshop 2007, p. 35. New York: ACM
3. Athey S, Imbens G, Wager S. 2016a. Efficient inference of average treatment effects in high dimensions via approximate residual balancing. arXiv:1604.07125 [math.ST]
4. Tibshirani R, Hastie T. 1987. Local likelihood estimation. J. Am. Stat. Assoc. 82:559–67
5. P. Marbach. Simulated-Based Methods for Markov Decision Processes. PhD thesis, Massachusetts Institute of Technology, 1998
6. Belloni A, Chernozhukov V, Hansen C. 2014. High-dimensional methods and inference on structural and treatment effects. J. Econ. Perspect. 28:29–50
7. Andrews, D. W. K. W. Ploberger (1994), "Optimal tests when a nuisance parameter is present only under the alternative," Econometrica, 62, 1383–1414.

Frequently Asked Questions

Q: What is the prediction methodology for Jakarta Stock Exchange Composite Index stock?
A: Jakarta Stock Exchange Composite Index stock prediction methodology: We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) and Wilcoxon Rank-Sum Test
Q: Is Jakarta Stock Exchange Composite Index stock a buy or sell?
A: The dominant strategy among neural network is to Hold Jakarta Stock Exchange Composite Index Stock.
Q: Is Jakarta Stock Exchange Composite Index stock a good investment?
A: The consensus rating for Jakarta Stock Exchange Composite Index is Hold and assigned short-term B3 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of Jakarta Stock Exchange Composite Index stock?
A: The consensus rating for Jakarta Stock Exchange Composite Index is Hold.
Q: What is the prediction period for Jakarta Stock Exchange Composite Index stock?
A: The prediction period for Jakarta Stock Exchange Composite Index is (n+4 weeks)

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