Modelling A.I. in Economics

SMFG Stock: Is This the End of the Bull Market? (Forecast)

Outlook: Sumitomo Mitsui Financial Group Inc Unsponsored American Depositary Shares (Japan) is assigned short-term B1 & long-term B2 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy : Hold
Time series to forecast n: for Weeks2
Methodology : Active 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.

Abstract

Sumitomo Mitsui Financial Group Inc Unsponsored American Depositary Shares (Japan) prediction model is evaluated with Active Learning (ML) and Multiple Regression1,2,3,4 and it is concluded that the SMFG stock is predictable in the short/long term. Active learning (AL) is a machine learning (ML) method in which the model actively queries the user for labels on data points. This allows the model to learn more efficiently, as it is only learning about the data points that are most informative. According to price forecasts for 16 Weeks period, the dominant strategy among neural network is: Hold

Graph 15

Key Points

  1. Probability Distribution
  2. Market Signals
  3. What are the most successful trading algorithms?

SMFG Target Price Prediction Modeling Methodology

We consider Sumitomo Mitsui Financial Group Inc Unsponsored American Depositary Shares (Japan) Decision Process with Active Learning (ML) where A is the set of discrete actions of SMFG 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(Multiple Regression)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(Active Learning (ML)) X S(n):→ 16 Weeks e x rx

n:Time series to forecast

p:Price signals of SMFG stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Active Learning (ML)

Active learning (AL) is a machine learning (ML) method in which the model actively queries the user for labels on data points. This allows the model to learn more efficiently, as it is only learning about the data points that are most informative.

Multiple Regression

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.

 

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?

SMFG Stock Forecast (Buy or Sell)

Sample Set: Neural Network
Stock/Index: SMFG Sumitomo Mitsui Financial Group Inc Unsponsored American Depositary Shares (Japan)
Time series to forecast: 16 Weeks

According to price forecasts, the dominant strategy among neural network is: Hold

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 Active Learning (ML) based SMFG Stock Prediction Model

  1. Expected credit losses shall be discounted to the reporting date, not to the expected default or some other date, using the effective interest rate determined at initial recognition or an approximation thereof. If a financial instrument has a variable interest rate, expected credit losses shall be discounted using the current effective interest rate determined in accordance with paragraph B5.4.5.
  2. A layer component that includes a prepayment option is not eligible to be designated as a hedged item in a fair value hedge if the prepayment option's fair value is affected by changes in the hedged risk, unless the designated layer includes the effect of the related prepayment option when determining the change in the fair value of the hedged item.
  3. Time value of money is the element of interest that provides consideration for only the passage of time. That is, the time value of money element does not provide consideration for other risks or costs associated with holding the financial asset. In order to assess whether the element provides consideration for only the passage of time, an entity applies judgement and considers relevant factors such as the currency in which the financial asset is denominated and the period for which the interest rate is set.
  4. Rebalancing is accounted for as a continuation of the hedging relationship in accordance with paragraphs B6.5.9–B6.5.21. On rebalancing, the hedge ineffectiveness of the hedging relationship is determined and recognised immediately before adjusting the hedging relationship.

*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.

SMFG Sumitomo Mitsui Financial Group Inc Unsponsored American Depositary Shares (Japan) Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*B1B2
Income StatementBaa2C
Balance SheetBaa2B1
Leverage RatiosCCaa2
Cash FlowB1Baa2
Rates of Return and ProfitabilityCC

*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?

Conclusions

Sumitomo Mitsui Financial Group Inc Unsponsored American Depositary Shares (Japan) is assigned short-term B1 & long-term B2 estimated rating. Sumitomo Mitsui Financial Group Inc Unsponsored American Depositary Shares (Japan) prediction model is evaluated with Active Learning (ML) and Multiple Regression1,2,3,4 and it is concluded that the SMFG stock is predictable in the short/long term. According to price forecasts for 16 Weeks period, the dominant strategy among neural network is: Hold

Prediction Confidence Score

Trust metric by Neural Network: 73 out of 100 with 468 signals.

References

  1. Jorgenson, D.W., Weitzman, M.L., ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. Can Neural Networks Predict Stock Market?. AC Investment Research Journal, 220(44).
  2. K. Tuyls and G. Weiss. Multiagent learning: Basics, challenges, and prospects. AI Magazine, 33(3): 41–52, 2012
  3. G. Theocharous and A. Hallak. Lifetime value marketing using reinforcement learning. RLDM 2013, page 19, 2013
  4. J. Baxter and P. Bartlett. Infinite-horizon policy-gradient estimation. Journal of Artificial Intelligence Re- search, 15:319–350, 2001.
  5. Abadir, K. M., K. Hadri E. Tzavalis (1999), "The influence of VAR dimensions on estimator biases," Econometrica, 67, 163–181.
  6. Chamberlain G. 2000. Econometrics and decision theory. J. Econom. 95:255–83
  7. A. Tamar and S. Mannor. Variance adjusted actor critic algorithms. arXiv preprint arXiv:1310.3697, 2013.
Frequently Asked QuestionsQ: What is the prediction methodology for SMFG stock?
A: SMFG stock prediction methodology: We evaluate the prediction models Active Learning (ML) and Multiple Regression
Q: Is SMFG stock a buy or sell?
A: The dominant strategy among neural network is to Hold SMFG Stock.
Q: Is Sumitomo Mitsui Financial Group Inc Unsponsored American Depositary Shares (Japan) stock a good investment?
A: The consensus rating for Sumitomo Mitsui Financial Group Inc Unsponsored American Depositary Shares (Japan) is Hold and is assigned short-term B1 & long-term B2 estimated rating.
Q: What is the consensus rating of SMFG stock?
A: The consensus rating for SMFG is Hold.
Q: What is the prediction period for SMFG stock?
A: The prediction period for SMFG is 16 Weeks

Premium

  • Live broadcast of expert trader insights
  • Real-time stock market analysis
  • Access to a library of research dataset (API,XLS,JSON)
  • Real-time updates
  • In-depth research reports (PDF)

Login
This project is licensed under the license; additional terms may apply.