AUC Score :
Short-Term Revised1 :
Dominant Strategy : Hold
Time series to forecast n:
Methodology : Modular Neural Network (Social Media Sentiment Analysis)
Hypothesis Testing : Factor
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
MACARTHUR MINERALS LIMITED prediction model is evaluated with Modular Neural Network (Social Media Sentiment Analysis) and Factor1,2,3,4 and it is concluded that the MIO stock is predictable in the short/long term. A modular neural network (MNN) is a type of artificial neural network that can be used for social media sentiment analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying sentiment in text or identifying patterns in data. The modules are then combined to form a single neural network that can perform multiple tasks. In the context of social media sentiment analysis, MNNs can be used to identify the sentiment of social media posts, such as tweets, Facebook posts, and Instagram stories. This information can then be used to filter out irrelevant or unwanted content, to identify trends in public opinion, and to target users with relevant advertising. According to price forecasts for 3 Month period, the dominant strategy among neural network is: Hold
Key Points
- Stock Rating
- How do you pick a stock?
- Is Target price a good indicator?
MIO Target Price Prediction Modeling Methodology
We consider MACARTHUR MINERALS LIMITED Decision Process with Modular Neural Network (Social Media Sentiment Analysis) where A is the set of discrete actions of MIO 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(Factor)5,6,7= X R(Modular Neural Network (Social Media Sentiment Analysis)) X S(n):→ 3 Month
n:Time series to forecast
p:Price signals of MIO stock
j:Nash equilibria (Neural Network)
k:Dominated move
a:Best response for target price
Modular Neural Network (Social Media Sentiment Analysis)
A modular neural network (MNN) is a type of artificial neural network that can be used for social media sentiment analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying sentiment in text or identifying patterns in data. The modules are then combined to form a single neural network that can perform multiple tasks. In the context of social media sentiment analysis, MNNs can be used to identify the sentiment of social media posts, such as tweets, Facebook posts, and Instagram stories. This information can then be used to filter out irrelevant or unwanted content, to identify trends in public opinion, and to target users with relevant advertising.Factor
In statistics, a factor is a variable that can influence the value of another variable. Factors can be categorical or continuous. Categorical factors have a limited number of possible values, such as gender (male or female) or blood type (A, B, AB, or O). Continuous factors can have an infinite number of possible values, such as height or weight. Factors can be used to explain the variation in a dependent variable. For example, a study might find that there is a relationship between gender and height. In this case, gender would be the independent variable, height would be the dependent variable, and the factor would be gender.
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How do AC Investment Research machine learning (predictive) algorithms actually work?
MIO Stock Forecast (Buy or Sell)
Sample Set: Neural NetworkStock/Index: MIO MACARTHUR MINERALS LIMITED
Time series to forecast: 3 Month
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 Modular Neural Network (Social Media Sentiment Analysis) based MIO 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.
- When measuring a loss allowance for a lease receivable, the cash flows used for determining the expected credit losses should be consistent with the cash flows used in measuring the lease receivable in accordance with IFRS 16 Leases.
- Adjusting the hedge ratio by decreasing the volume of the hedging instrument does not affect how the changes in the value of the hedged item are measured. The measurement of the changes in the fair value of the hedging instrument related to the volume that continues to be designated also remains unaffected. However, from the date of rebalancing, the volume by which the hedging instrument was decreased is no longer part of the hedging relationship. For example, if an entity originally hedged the price risk of a commodity using a derivative volume of 100 tonnes as the hedging instrument and reduces that volume by 10 tonnes on rebalancing, a nominal amount of 90 tonnes of the hedging instrument volume would remain (see paragraph B6.5.16 for the consequences for the derivative volume (ie the 10 tonnes) that is no longer a part of the hedging relationship).
- This Standard does not specify a method for assessing whether a hedging relationship meets the hedge effectiveness requirements. However, an entity shall use a method that captures the relevant characteristics of the hedging relationship including the sources of hedge ineffectiveness. Depending on those factors, the method can be a qualitative or a quantitative assessment.
*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.
MIO MACARTHUR MINERALS LIMITED Financial Analysis*
Rating | Short-Term | Long-Term Senior |
---|---|---|
Outlook* | B1 | Ba3 |
Income Statement | B1 | Baa2 |
Balance Sheet | Baa2 | Baa2 |
Leverage Ratios | Caa2 | C |
Cash Flow | Baa2 | Baa2 |
Rates of Return and Profitability | B3 | B1 |
*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
MACARTHUR MINERALS LIMITED is assigned short-term B1 & long-term Ba3 estimated rating. MACARTHUR MINERALS LIMITED prediction model is evaluated with Modular Neural Network (Social Media Sentiment Analysis) and Factor1,2,3,4 and it is concluded that the MIO stock is predictable in the short/long term. According to price forecasts for 3 Month period, the dominant strategy among neural network is: Hold
Prediction Confidence Score
References
- Bickel P, Klaassen C, Ritov Y, Wellner J. 1998. Efficient and Adaptive Estimation for Semiparametric Models. Berlin: Springer
- Hoerl AE, Kennard RW. 1970. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12:55–67
- Holland PW. 1986. Statistics and causal inference. J. Am. Stat. Assoc. 81:945–60
- E. Collins. Using Markov decision processes to optimize a nonlinear functional of the final distribution, with manufacturing applications. In Stochastic Modelling in Innovative Manufacturing, pages 30–45. Springer, 1997
- Tibshirani R. 1996. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58:267–88
- C. Wu and Y. Lin. Minimizing risk models in Markov decision processes with policies depending on target values. Journal of Mathematical Analysis and Applications, 231(1):47–67, 1999
- J. Hu and M. P. Wellman. Nash q-learning for general-sum stochastic games. Journal of Machine Learning Research, 4:1039–1069, 2003.
Frequently Asked Questions
Q: What is the prediction methodology for MIO stock?A: MIO stock prediction methodology: We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) and Factor
Q: Is MIO stock a buy or sell?
A: The dominant strategy among neural network is to Hold MIO Stock.
Q: Is MACARTHUR MINERALS LIMITED stock a good investment?
A: The consensus rating for MACARTHUR MINERALS LIMITED is Hold and is assigned short-term B1 & long-term Ba3 estimated rating.
Q: What is the consensus rating of MIO stock?
A: The consensus rating for MIO is Hold.
Q: What is the prediction period for MIO stock?
A: The prediction period for MIO is 3 Month