AUC Score :
Short-Term Revised1 :
Dominant Strategy : Hold
Time series to forecast n:
Methodology : Modular Neural Network (Market News Sentiment Analysis)
Hypothesis Testing : Pearson Correlation
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.
Solitario Zinc Corp. prediction model is evaluated with Modular Neural Network (Market News Sentiment Analysis) and Pearson Correlation1,2,3,4 and it is concluded that the SLR:TSX 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 news feed 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 news feed sentiment analysis, MNNs can be used to identify the sentiment of news articles, social media posts, and other forms of online content. 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.5 According to price forecasts for 1 Year period, the dominant strategy among neural network is: Hold

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SLR:TSX Stock Price Forecast
We consider Solitario Zinc Corp. Decision Process with Modular Neural Network (Market News Sentiment Analysis) where A is the set of discrete actions of SLR:TSX 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: SLR:TSX Solitario Zinc Corp.
Time series to forecast: 1 Year
According to price forecasts, the dominant strategy among neural network is: Hold
n:Time series to forecast
p:Price signals of SLR:TSX stock
j:Nash equilibria (Neural Network)
k:Dominated move of SLR:TSX stock holders
a:Best response for SLR:TSX target price
A modular neural network (MNN) is a type of artificial neural network that can be used for news feed 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 news feed sentiment analysis, MNNs can be used to identify the sentiment of news articles, social media posts, and other forms of online content. 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.5 Pearson correlation, also known as Pearson's product-moment correlation, is a measure of the linear relationship between two variables. It is a statistical measure that assesses the strength and direction of a linear relationship between two variables. The sign of the correlation coefficient indicates the direction of the relationship, while the magnitude of the correlation coefficient indicates the strength of the relationship. A correlation coefficient of 0.9 indicates a strong positive correlation, while a correlation coefficient of 0.2 indicates a weak positive correlation.6,7
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?
SLR:TSX 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 Modular Neural Network (Market News Sentiment Analysis) based SLR:TSX Stock Prediction Model
- If a call option right retained by an entity prevents a transferred asset from being derecognised and the entity measures the transferred asset at fair value, the asset continues to be measured at its fair value. The associated liability is measured at (i) the option exercise price less the time value of the option if the option is in or at the money, or (ii) the fair value of the transferred asset less the time value of the option if the option is out of the money. The adjustment to the measurement of the associated liability ensures that the net carrying amount of the asset and the associated liability is the fair value of the call option right. For example, if the fair value of the underlying asset is CU80, the option exercise price is CU95 and the time value of the option is CU5, the carrying amount of the associated liability is CU75 (CU80 – CU5) and the carrying amount of the transferred asset is CU80 (ie its fair value)
- An entity shall assess whether contractual cash flows are solely payments of principal and interest on the principal amount outstanding for the currency in which the financial asset is denominated.
- To be eligible for designation as a hedged item, a risk component must be a separately identifiable component of the financial or the non-financial item, and the changes in the cash flows or the fair value of the item attributable to changes in that risk component must be reliably measurable.
- The risk of a default occurring on financial instruments that have comparable credit risk is higher the longer the expected life of the instrument; for example, the risk of a default occurring on an AAA-rated bond with an expected life of 10 years is higher than that on an AAA-rated bond with an expected life of five years.
*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.
SLR:TSX Solitario Zinc Corp. Financial Analysis*
Rating | Short-Term | Long-Term Senior |
---|---|---|
Outlook* | Ba3 | B2 |
Income Statement | Baa2 | B3 |
Balance Sheet | B2 | Ba3 |
Leverage Ratios | C | C |
Cash Flow | Baa2 | C |
Rates of Return and Profitability | Baa2 | Baa2 |
*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
- 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
- Imai K, Ratkovic M. 2013. Estimating treatment effect heterogeneity in randomized program evaluation. Ann. Appl. Stat. 7:443–70
- K. Boda and J. Filar. Time consistent dynamic risk measures. Mathematical Methods of Operations Research, 63(1):169–186, 2006
- Andrews, D. W. K. (1993), "Tests for parameter instability and structural change with unknown change point," Econometrica, 61, 821–856.
- Li L, Chu W, Langford J, Moon T, Wang X. 2012. An unbiased offline evaluation of contextual bandit algo- rithms with generalized linear models. In Proceedings of 4th ACM International Conference on Web Search and Data Mining, pp. 297–306. New York: ACM
- Burgess, D. F. (1975), "Duality theory and pitfalls in the specification of technologies," Journal of Econometrics, 3, 105–121.
- J. Baxter and P. Bartlett. Infinite-horizon policy-gradient estimation. Journal of Artificial Intelligence Re- search, 15:319–350, 2001.
Frequently Asked Questions
Q: What is the prediction methodology for SLR:TSX stock?A: SLR:TSX stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) and Pearson Correlation
Q: Is SLR:TSX stock a buy or sell?
A: The dominant strategy among neural network is to Hold SLR:TSX Stock.
Q: Is Solitario Zinc Corp. stock a good investment?
A: The consensus rating for Solitario Zinc Corp. is Hold and is assigned short-term Ba3 & long-term B2 estimated rating.
Q: What is the consensus rating of SLR:TSX stock?
A: The consensus rating for SLR:TSX is Hold.
Q: What is the prediction period for SLR:TSX stock?
A: The prediction period for SLR:TSX is 1 Year
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