Dominant Strategy : Speculative Trend
Time series to forecast n: 21 Jun 2023 for 16 Weeks
Methodology : Modular Neural Network (Financial Sentiment Analysis)
Summary
Laboratory Corporation of America Holdings Common Stock prediction model is evaluated with Modular Neural Network (Financial Sentiment Analysis) and Polynomial Regression1,2,3,4 and it is concluded that the LH stock is predictable in the short/long term. Modular neural networks (MNNs) are a type of artificial neural network that can be used for financial 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 financial sentiment analysis, MNNs can be used to identify the sentiment of financial news articles, social media posts, and other forms of online content. This information can then be used to make investment decisions, to identify trends in the market, and to target investors with relevant advertising. According to price forecasts for 16 Weeks period, the dominant strategy among neural network is: Speculative Trend
Key Points
- Game Theory
- Dominated Move
- How accurate is machine learning in stock market?
LH Target Price Prediction Modeling Methodology
We consider Laboratory Corporation of America Holdings Common Stock Decision Process with Modular Neural Network (Financial Sentiment Analysis) where A is the set of discrete actions of LH 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(Polynomial Regression)5,6,7= X R(Modular Neural Network (Financial Sentiment Analysis)) X S(n):→ 16 Weeks
n:Time series to forecast
p:Price signals of LH stock
j:Nash equilibria (Neural Network)
k:Dominated move
a:Best response for target price
Modular Neural Network (Financial Sentiment Analysis)
Modular neural networks (MNNs) are a type of artificial neural network that can be used for financial 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 financial sentiment analysis, MNNs can be used to identify the sentiment of financial news articles, social media posts, and other forms of online content. This information can then be used to make investment decisions, to identify trends in the market, and to target investors with relevant advertising.Polynomial Regression
Polynomial regression is a type of regression analysis that uses a polynomial function to model the relationship between a dependent variable and one or more independent variables. Polynomial functions are mathematical functions that have a polynomial term, which is a term that is raised to a power greater than 1. In polynomial regression, the dependent variable is modeled as a polynomial function of the independent variables. The degree of the polynomial function is determined by the researcher. The higher the degree of the polynomial function, the more complex the model will be.
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?
LH Stock Forecast (Buy or Sell) for 16 Weeks
Sample Set: Neural NetworkStock/Index: LH Laboratory Corporation of America Holdings Common Stock
Time series to forecast n: 21 Jun 2023 for 16 Weeks
According to price forecasts for 16 Weeks period, the dominant strategy among neural network is: Speculative Trend
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%
IFRS Reconciliation Adjustments for Laboratory Corporation of America Holdings Common Stock
- For a discontinued hedging relationship, when the interest rate benchmark on which the hedged future cash flows had been based is changed as required by interest rate benchmark reform, for the purpose of applying paragraph 6.5.12 in order to determine whether the hedged future cash flows are expected to occur, the amount accumulated in the cash flow hedge reserve for that hedging relationship shall be deemed to be based on the alternative benchmark rate on which the hedged future cash flows will be based.
- IFRS 17, issued in May 2017, amended paragraphs 2.1, B2.1, B2.4, B2.5 and B4.1.30, and added paragraph 3.3.5. Amendments to IFRS 17, issued in June 2020, further amended paragraph 2.1 and added paragraphs 7.2.36‒7.2.42. An entity shall apply those amendments when it applies IFRS 17.
- In some circumstances an entity does not have reasonable and supportable information that is available without undue cost or effort to measure lifetime expected credit losses on an individual instrument basis. In that case, lifetime expected credit losses shall be recognised on a collective basis that considers comprehensive credit risk information. This comprehensive credit risk information must incorporate not only past due information but also all relevant credit information, including forward-looking macroeconomic information, in order to approximate the result of recognising lifetime expected credit losses when there has been a significant increase in credit risk since initial recognition on an individual instrument level.
- When determining whether the recognition of lifetime expected credit losses is required, an entity shall consider reasonable and supportable information that is available without undue cost or effort and that may affect the credit risk on a financial instrument in accordance with paragraph 5.5.17(c). An entity need not undertake an exhaustive search for information when determining whether credit risk has increased significantly since initial recognition.
*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.
Conclusions
Laboratory Corporation of America Holdings Common Stock is assigned short-term B3 & long-term B1 estimated rating. Laboratory Corporation of America Holdings Common Stock prediction model is evaluated with Modular Neural Network (Financial Sentiment Analysis) and Polynomial Regression1,2,3,4 and it is concluded that the LH stock is predictable in the short/long term. According to price forecasts for 16 Weeks period, the dominant strategy among neural network is: Speculative Trend
LH Laboratory Corporation of America Holdings Common Stock Financial Analysis*
Rating | Short-Term | Long-Term Senior |
---|---|---|
Outlook* | B3 | B1 |
Income Statement | C | B3 |
Balance Sheet | Caa2 | Baa2 |
Leverage Ratios | B1 | C |
Cash Flow | Ba1 | Caa2 |
Rates of Return and Profitability | C | 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?
Prediction Confidence Score
References
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- Pennington J, Socher R, Manning CD. 2014. GloVe: global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods on Natural Language Processing, pp. 1532–43. New York: Assoc. Comput. Linguist.
- Pennington J, Socher R, Manning CD. 2014. GloVe: global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods on Natural Language Processing, pp. 1532–43. New York: Assoc. Comput. Linguist.
- M. Babes, E. M. de Cote, and M. L. Littman. Social reward shaping in the prisoner's dilemma. In 7th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2008), Estoril, Portugal, May 12-16, 2008, Volume 3, pages 1389–1392, 2008.
- Mnih A, Hinton GE. 2007. Three new graphical models for statistical language modelling. In International Conference on Machine Learning, pp. 641–48. La Jolla, CA: Int. Mach. Learn. Soc.
Frequently Asked Questions
Q: What is the prediction methodology for LH stock?A: LH stock prediction methodology: We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) and Polynomial Regression
Q: Is LH stock a buy or sell?
A: The dominant strategy among neural network is to Speculative Trend LH Stock.
Q: Is Laboratory Corporation of America Holdings Common Stock stock a good investment?
A: The consensus rating for Laboratory Corporation of America Holdings Common Stock is Speculative Trend and is assigned short-term B3 & long-term B1 estimated rating.
Q: What is the consensus rating of LH stock?
A: The consensus rating for LH is Speculative Trend.
Q: What is the prediction period for LH stock?
A: The prediction period for LH is 16 Weeks
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