In this paper, we propose a hybrid machine learning system based on Genetic Algor ithm (GA) and Support Vector Machines (SVM) for stock market prediction. A variety of indicators from the technical analysis field of study are used as input features. We also make use of the correlation between stock prices of different companies to forecast the price of a stock, making use of technical indicators of highly correlated stocks, not only the stock to be predicted. The genetic algorithm is used to select the set of most informative input features from among all the technical indicators.** We evaluate Jakarta Stock Exchange Composite Index prediction models with Modular Neural Network (Speculative Sentiment Analysis) and Chi-Square ^{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+16 weeks) period: The dominant strategy among neural network is to Hold Jakarta Stock Exchange Composite Index stock.**

**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.**

*Keywords:*## Key Points

- Decision Making
- Decision Making
- Trust metric by Neural Network

## Jakarta Stock Exchange Composite Index Target Price Prediction Modeling Methodology

Recently, a lot of interesting work has been done in the area of applying Machine Learning Algorithms for analyzing price patterns and predicting stock prices and index changes. Most stock traders nowadays depend on Intelligent Trading Systems which help them in predicting prices based on various situations and conditions, thereby helping them in making instantaneous investment decisions. We consider Jakarta Stock Exchange Composite Index Stock Decision Process with Chi-Square 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 Î³ ∈ [0, 1] is a move factor for expectation.^{1,2,3,4}

F(Chi-Square)

^{5,6,7}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (Speculative Sentiment Analysis)) X S(n):→ (n+16 weeks) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

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+16 weeks)

**Sample Set:**Neural Network

**Stock/Index:**Jakarta Stock Exchange Composite Index Jakarta Stock Exchange Composite Index

**Time series to forecast n: 14 Oct 2022**for (n+16 weeks)

**According to price forecasts for (n+16 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%**

## Conclusions

Jakarta Stock Exchange Composite Index assigned short-term B2 & long-term B2 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) with Chi-Square ^{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+16 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 Stock Options & Futures

Rating | Short-Term | Long-Term Senior |
---|---|---|

Outlook* | B2 | B2 |

Operational Risk | 40 | 83 |

Market Risk | 61 | 40 |

Technical Analysis | 31 | 61 |

Fundamental Analysis | 89 | 54 |

Risk Unsystematic | 43 | 30 |

### Prediction Confidence Score

## References

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- Chernozhukov V, Newey W, Robins J. 2018c. Double/de-biased machine learning using regularized Riesz representers. arXiv:1802.08667 [stat.ML]
- Dudik M, Langford J, Li L. 2011. Doubly robust policy evaluation and learning. In Proceedings of the 28th International Conference on Machine Learning, pp. 1097–104. La Jolla, CA: Int. Mach. Learn. Soc.
- M. Sobel. The variance of discounted Markov decision processes. Applied Probability, pages 794–802, 1982
- M. Sobel. The variance of discounted Markov decision processes. Applied Probability, pages 794–802, 1982
- Sutton RS, Barto AG. 1998. Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press

## 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 (Speculative Sentiment Analysis) and Chi-Square

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 B2 & long-term B2 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+16 weeks)