AUC Score :
Short-Term Revised1 :
Dominant Strategy : Sell
Time series to forecast n:
Methodology : Statistical Inference (ML)
Hypothesis Testing : Wilcoxon Rank-Sum Test
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
Grupo Financiero Galicia S.A. American Depositary Shares prediction model is evaluated with Statistical Inference (ML) and Wilcoxon Rank-Sum Test1,2,3,4 and it is concluded that the GGAL stock is predictable in the short/long term. Statistical inference is a process of drawing conclusions about a population based on data from a sample of that population. In machine learning (ML), statistical inference is used to make predictions about new data based on data that has already been seen. According to price forecasts for 16 Weeks period, the dominant strategy among neural network is: Sell
Key Points
- What is neural prediction?
- Market Risk
- How useful are statistical predictions?
GGAL Target Price Prediction Modeling Methodology
We consider Grupo Financiero Galicia S.A. American Depositary Shares Decision Process with Statistical Inference (ML) where A is the set of discrete actions of GGAL 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= X R(Statistical Inference (ML)) X S(n):→ 16 Weeks
n:Time series to forecast
p:Price signals of GGAL stock
j:Nash equilibria (Neural Network)
k:Dominated move
a:Best response for target price
Statistical Inference (ML)
Statistical inference is a process of drawing conclusions about a population based on data from a sample of that population. In machine learning (ML), statistical inference is used to make predictions about new data based on data that has already been seen.Wilcoxon Rank-Sum Test
The Wilcoxon rank-sum test, also known as the Mann-Whitney U test, is a non-parametric test that is used to compare the medians of two independent samples. It is a rank-based test, which means that it does not assume that the data is normally distributed. The Wilcoxon rank-sum test is calculated by first ranking the data from both samples, and then finding the sum of the ranks for one of the samples. The Wilcoxon rank-sum test statistic is then calculated by subtracting the sum of the ranks for one sample from the sum of the ranks for the other sample. The p-value for the Wilcoxon rank-sum test is calculated using a table of critical values. The p-value is the probability of obtaining a test statistic at least as extreme as the one observed, assuming that the null hypothesis is true.
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?
GGAL Stock Forecast (Buy or Sell)
Sample Set: Neural NetworkStock/Index: GGAL Grupo Financiero Galicia S.A. American Depositary Shares
Time series to forecast: 16 Weeks
According to price forecasts, the dominant strategy among neural network is: Sell
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 Statistical Inference (ML) based GGAL Stock Prediction Model
- To make that determination, an entity must assess whether it expects that the effects of changes in the liability's credit risk will be offset in profit or loss by a change in the fair value of another financial instrument measured at fair value through profit or loss. Such an expectation must be based on an economic relationship between the characteristics of the liability and the characteristics of the other financial instrument.
- When using historical credit loss experience in estimating expected credit losses, it is important that information about historical credit loss rates is applied to groups that are defined in a manner that is consistent with the groups for which the historical credit loss rates were observed. Consequently, the method used shall enable each group of financial assets to be associated with information about past credit loss experience in groups of financial assets with similar risk characteristics and with relevant observable data that reflects current conditions.
- The following example describes a situation in which an accounting mismatch would be created in profit or loss if the effects of changes in the credit risk of the liability were presented in other comprehensive income. A mortgage bank provides loans to customers and funds those loans by selling bonds with matching characteristics (eg amount outstanding, repayment profile, term and currency) in the market. The contractual terms of the loan permit the mortgage customer to prepay its loan (ie satisfy its obligation to the bank) by buying the corresponding bond at fair value in the market and delivering that bond to the mortgage bank. As a result of that contractual prepayment right, if the credit quality of the bond worsens (and, thus, the fair value of the mortgage bank's liability decreases), the fair value of the mortgage bank's loan asset also decreases. The change in the fair value of the asset reflects the mortgage customer's contractual right to prepay the mortgage loan by buying the underlying bond at fair value (which, in this example, has decreased) and delivering the bond to the mortgage bank. Consequently, the effects of changes in the credit risk of the liability (the bond) will be offset in profit or loss by a corresponding change in the fair value of a financial asset (the loan). If the effects of changes in the liability's credit risk were presented in other comprehensive income there would be an accounting mismatch in profit or loss. Consequently, the mortgage bank is required to present all changes in fair value of the liability (including the effects of changes in the liability's credit risk) in profit or loss.
- If there are changes in circumstances that affect hedge effectiveness, an entity may have to change the method for assessing whether a hedging relationship meets the hedge effectiveness requirements in order to ensure that the relevant characteristics of the hedging relationship, including the sources of hedge ineffectiveness, are still captured.
*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.
GGAL Grupo Financiero Galicia S.A. American Depositary Shares Financial Analysis*
Rating | Short-Term | Long-Term Senior |
---|---|---|
Outlook* | B3 | Ba3 |
Income Statement | C | Ba3 |
Balance Sheet | B2 | Caa2 |
Leverage Ratios | C | Baa2 |
Cash Flow | B2 | Baa2 |
Rates of Return and Profitability | Ba1 | C |
*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
Grupo Financiero Galicia S.A. American Depositary Shares is assigned short-term B3 & long-term Ba3 estimated rating. Grupo Financiero Galicia S.A. American Depositary Shares prediction model is evaluated with Statistical Inference (ML) and Wilcoxon Rank-Sum Test1,2,3,4 and it is concluded that the GGAL stock is predictable in the short/long term. According to price forecasts for 16 Weeks period, the dominant strategy among neural network is: Sell
Prediction Confidence Score
References
- J. N. Foerster, Y. M. Assael, N. de Freitas, and S. Whiteson. Learning to communicate with deep multi-agent reinforcement learning. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, pages 2137–2145, 2016.
- Athey S, Wager S. 2017. Efficient policy learning. arXiv:1702.02896 [math.ST]
- Z. Wang, T. Schaul, M. Hessel, H. van Hasselt, M. Lanctot, and N. de Freitas. Dueling network architectures for deep reinforcement learning. In Proceedings of the International Conference on Machine Learning (ICML), pages 1995–2003, 2016.
- Belsley, D. A. (1988), "Modelling and forecast reliability," International Journal of Forecasting, 4, 427–447.
- M. Puterman. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York, 1994.
- 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
- P. Milgrom and I. Segal. Envelope theorems for arbitrary choice sets. Econometrica, 70(2):583–601, 2002
Frequently Asked Questions
Q: What is the prediction methodology for GGAL stock?A: GGAL stock prediction methodology: We evaluate the prediction models Statistical Inference (ML) and Wilcoxon Rank-Sum Test
Q: Is GGAL stock a buy or sell?
A: The dominant strategy among neural network is to Sell GGAL Stock.
Q: Is Grupo Financiero Galicia S.A. American Depositary Shares stock a good investment?
A: The consensus rating for Grupo Financiero Galicia S.A. American Depositary Shares is Sell and is assigned short-term B3 & long-term Ba3 estimated rating.
Q: What is the consensus rating of GGAL stock?
A: The consensus rating for GGAL is Sell.
Q: What is the prediction period for GGAL stock?
A: The prediction period for GGAL is 16 Weeks
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