AUC Score :
Short-Term Revised1 :
Dominant Strategy : Sell
Time series to forecast n:
Methodology : Reinforcement Machine Learning (ML)
Hypothesis Testing : Multiple Regression
Surveillance : Major exchange and OTC
1The accuracy of the model is being monitored on a regular basis.(15-minute period)
2Time series is updated based on short-term trends.
Summary
JANISON EDUCATION GROUP LIMITED prediction model is evaluated with Reinforcement Machine Learning (ML) and Multiple Regression1,2,3,4 and it is concluded that the JAN stock is predictable in the short/long term. Reinforcement machine learning (RL) is a type of machine learning where an agent learns to take actions in an environment in order to maximize a reward. The agent does this by trial and error, and is able to learn from its mistakes. RL is a powerful tool that can be used for a variety of tasks, including game playing, robotics, and finance.5 According to price forecasts for 16 Weeks period, the dominant strategy among neural network is: Sell
Key Points
- Reinforcement Machine Learning (ML) for JAN stock price prediction process.
- Multiple Regression
- Is Target price a good indicator?
- Prediction Modeling
- Can stock prices be predicted?
JAN Stock Price Forecast
We consider JANISON EDUCATION GROUP LIMITED Decision Process with Reinforcement Machine Learning (ML) where A is the set of discrete actions of JAN 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
Sample Set: Neural Network
Stock/Index: JAN JANISON EDUCATION GROUP LIMITED
Time series to forecast: 16 Weeks
According to price forecasts, the dominant strategy among neural network is: Sell
n:Time series to forecast
p:Price signals of JAN stock
j:Nash equilibria (Neural Network)
k:Dominated move of JAN stock holders
a:Best response for JAN target price
Reinforcement machine learning (RL) is a type of machine learning where an agent learns to take actions in an environment in order to maximize a reward. The agent does this by trial and error, and is able to learn from its mistakes. RL is a powerful tool that can be used for a variety of tasks, including game playing, robotics, and finance.5 Multiple regression is a statistical method that analyzes the relationship between a dependent variable and multiple independent variables. The dependent variable is the variable that is being predicted, and the independent variables are the variables that are used to predict the dependent variable. Multiple regression is a more complex statistical method than simple linear regression, which only analyzes the relationship between a dependent variable and one independent variable. Multiple regression can be used to analyze more complex relationships between variables, and it can also be used to control for confounding variables. A confounding variable is a variable that is correlated with both the dependent variable and one or more of the independent variables. Confounding variables can distort the relationship between the dependent variable and the independent variables. Multiple regression can be used to control for confounding variables by including them in the model.6,7
For further technical information as per how our model work we invite you to visit the article below:
JAN Stock Forecast (Buy or Sell) Strategic Interaction Table
Strategic Interaction Table Legend:
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 (Grey to Black): *Technical Analysis%
Financial Data Adjustments for Reinforcement Machine Learning (ML) based JAN Stock Prediction Model
- Interest Rate Benchmark Reform—Phase 2, which amended IFRS 9, IAS 39, IFRS 7, IFRS 4 and IFRS 16, issued in August 2020, added paragraphs 5.4.5–5.4.9, 6.8.13, Section 6.9 and paragraphs 7.2.43–7.2.46. An entity shall apply these amendments for annual periods beginning on or after 1 January 2021. Earlier application is permitted. If an entity applies these amendments for an earlier period, it shall disclose that fact.
- An entity shall apply Prepayment Features with Negative Compensation (Amendments to IFRS 9) retrospectively in accordance with IAS 8, except as specified in paragraphs 7.2.30–7.2.34
- Interest Rate Benchmark Reform—Phase 2, which amended IFRS 9, IAS 39, IFRS 7, IFRS 4 and IFRS 16, issued in August 2020, added paragraphs 5.4.5–5.4.9, 6.8.13, Section 6.9 and paragraphs 7.2.43–7.2.46. An entity shall apply these amendments for annual periods beginning on or after 1 January 2021. Earlier application is permitted. If an entity applies these amendments for an earlier period, it shall disclose that fact.
- Amounts presented in other comprehensive income shall not be subsequently transferred to profit or loss. However, the entity may transfer the cumulative gain or loss within equity.
*International Financial Reporting Standards (IFRS) adjustment process involves reviewing the company's financial statements and identifying any differences between the company's current accounting practices and the requirements of the IFRS. If there are any such differences, neural network makes adjustments to financial statements to bring them into compliance with the IFRS.
JAN JANISON EDUCATION GROUP LIMITED Financial Analysis*
Rating | Short-Term | Long-Term Senior |
---|---|---|
Outlook* | Ba3 | Ba3 |
Income Statement | Caa2 | Ba2 |
Balance Sheet | Caa2 | Ba1 |
Leverage Ratios | Baa2 | Caa2 |
Cash Flow | Baa2 | B2 |
Rates of Return and Profitability | Baa2 | Ba3 |
*Financial analysis is the process of evaluating a company's financial performance and position by neural network. It involves reviewing the company's financial statements, including the balance sheet, income statement, and cash flow statement, as well as other financial reports and documents.
How does neural network examine financial reports and understand financial state of the company?
References
- Andrews, D. W. K. W. Ploberger (1994), "Optimal tests when a nuisance parameter is present only under the alternative," Econometrica, 62, 1383–1414.
- Wan M, Wang D, Goldman M, Taddy M, Rao J, et al. 2017. Modeling consumer preferences and price sensitiv- ities from large-scale grocery shopping transaction logs. In Proceedings of the 26th International Conference on the World Wide Web, pp. 1103–12. New York: ACM
- Chernozhukov V, Demirer M, Duflo E, Fernandez-Val I. 2018b. Generic machine learning inference on heteroge- nous treatment effects in randomized experiments. NBER Work. Pap. 24678
- Brailsford, T.J. R.W. Faff (1996), "An evaluation of volatility forecasting techniques," Journal of Banking Finance, 20, 419–438.
- Bai J, Ng S. 2017. Principal components and regularized estimation of factor models. arXiv:1708.08137 [stat.ME]
- Jorgenson, D.W., Weitzman, M.L., ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. Apple's Stock Price: How News Affects Volatility. AC Investment Research Journal, 220(44).
- V. Borkar and R. Jain. Risk-constrained Markov decision processes. IEEE Transaction on Automatic Control, 2014
Frequently Asked Questions
Q: Is JAN stock expected to rise?A: JAN stock prediction model is evaluated with Reinforcement Machine Learning (ML) and Multiple Regression and it is concluded that dominant strategy for JAN stock is Sell
Q: Is JAN stock a buy or sell?
A: The dominant strategy among neural network is to Sell JAN Stock.
Q: Is JANISON EDUCATION GROUP LIMITED stock a good investment?
A: The consensus rating for JANISON EDUCATION GROUP LIMITED is Sell and is assigned short-term Ba3 & long-term Ba3 estimated rating.
Q: What is the consensus rating of JAN stock?
A: The consensus rating for JAN is Sell.
Q: What is the forecast for JAN stock?
A: JAN target price forecast: Sell
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