AUC Score :
Short-Term Revised1 :
Dominant Strategy : Sell
Time series to forecast n:
Methodology : Modular Neural Network (News Feed 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.
Summary
Lincoln National Corporation Depositary Shares Each Representing a 1/1000th Interest in a Share of 9.000% Non-Cumulative Preferred Stock Series D prediction model is evaluated with Modular Neural Network (News Feed Sentiment Analysis) and Factor1,2,3,4 and it is concluded that the LNC^D 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 8 Weeks period, the dominant strategy among neural network is: Sell
Key Points
- Modular Neural Network (News Feed Sentiment Analysis) for LNC^D stock price prediction process.
- Factor
- Technical Analysis with Algorithmic Trading
- What are the most successful trading algorithms?
- How do you decide buy or sell a stock?
LNC^D Stock Price Forecast
We consider Lincoln National Corporation Depositary Shares Each Representing a 1/1000th Interest in a Share of 9.000% Non-Cumulative Preferred Stock Series D Decision Process with Modular Neural Network (News Feed Sentiment Analysis) where A is the set of discrete actions of LNC^D 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: LNC^D Lincoln National Corporation Depositary Shares Each Representing a 1/1000th Interest in a Share of 9.000% Non-Cumulative Preferred Stock Series D
Time series to forecast: 8 Weeks
According to price forecasts, the dominant strategy among neural network is: Sell
n:Time series to forecast
p:Price signals of LNC^D stock
j:Nash equilibria (Neural Network)
k:Dominated move of LNC^D stock holders
a:Best response for LNC^D 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 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.6,7
For further technical information as per how our model work we invite you to visit the article below:
LNC^D 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 (News Feed Sentiment Analysis) based LNC^D Stock Prediction Model
- If a financial instrument that was previously recognised as a financial asset is measured at fair value through profit or loss and its fair value decreases below zero, it is a financial liability measured in accordance with paragraph 4.2.1. However, hybrid contracts with hosts that are assets within the scope of this Standard are always measured in accordance with paragraph 4.3.2.
- If a put option written by an entity prevents a transferred asset from being derecognised and the entity measures the transferred asset at fair value, the associated liability is measured at the option exercise price plus the time value of the option. The measurement of the asset at fair value is limited to the lower of the fair value and the option exercise price because the entity has no right to increases in the fair value of the transferred asset above the exercise price of the option. This ensures that the net carrying amount of the asset and the associated liability is the fair value of the put option obligation. For example, if the fair value of the underlying asset is CU120, the option exercise price is CU100 and the time value of the option is CU5, the carrying amount of the associated liability is CU105 (CU100 + CU5) and the carrying amount of the asset is CU100 (in this case the option exercise price).
- Amounts presented in other comprehensive income shall not be subsequently transferred to profit or loss. However, the entity may transfer the cumulative gain or loss within equity.
- The fact that a derivative is in or out of the money when it is designated as a hedging instrument does not in itself mean that a qualitative assessment is inappropriate. It depends on the circumstances whether hedge ineffectiveness arising from that fact could have a magnitude that a qualitative assessment would not adequately capture.
*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.
LNC^D Lincoln National Corporation Depositary Shares Each Representing a 1/1000th Interest in a Share of 9.000% Non-Cumulative Preferred Stock Series D Financial Analysis*
Rating | Short-Term | Long-Term Senior |
---|---|---|
Outlook* | B2 | Ba3 |
Income Statement | Caa2 | B3 |
Balance Sheet | C | Baa2 |
Leverage Ratios | Baa2 | B1 |
Cash Flow | B1 | Baa2 |
Rates of Return and Profitability | Caa2 | Caa2 |
*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
- Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85
- Athey S, Bayati M, Imbens G, Zhaonan Q. 2019. Ensemble methods for causal effects in panel data settings. NBER Work. Pap. 25675
- Ruiz FJ, Athey S, Blei DM. 2017. SHOPPER: a probabilistic model of consumer choice with substitutes and complements. arXiv:1711.03560 [stat.ML]
- D. Bertsekas. Dynamic programming and optimal control. Athena Scientific, 1995.
- Bottou L. 2012. Stochastic gradient descent tricks. In Neural Networks: Tricks of the Trade, ed. G Montavon, G Orr, K-R Müller, pp. 421–36. Berlin: Springer
- M. Colby, T. Duchow-Pressley, J. J. Chung, and K. Tumer. Local approximation of difference evaluation functions. In Proceedings of the Fifteenth International Joint Conference on Autonomous Agents and Multiagent Systems, Singapore, May 2016
- Mikolov T, Yih W, Zweig G. 2013c. Linguistic regularities in continuous space word representations. In Pro- ceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 746–51. New York: Assoc. Comput. Linguist.
Frequently Asked Questions
Q: Is LNC^D stock expected to rise?A: LNC^D stock prediction model is evaluated with Modular Neural Network (News Feed Sentiment Analysis) and Factor and it is concluded that dominant strategy for LNC^D stock is Sell
Q: Is LNC^D stock a buy or sell?
A: The dominant strategy among neural network is to Sell LNC^D Stock.
Q: Is Lincoln National Corporation Depositary Shares Each Representing a 1/1000th Interest in a Share of 9.000% Non-Cumulative Preferred Stock Series D stock a good investment?
A: The consensus rating for Lincoln National Corporation Depositary Shares Each Representing a 1/1000th Interest in a Share of 9.000% Non-Cumulative Preferred Stock Series D is Sell and is assigned short-term B2 & long-term Ba3 estimated rating.
Q: What is the consensus rating of LNC^D stock?
A: The consensus rating for LNC^D is Sell.
Q: What is the forecast for LNC^D stock?
A: LNC^D target price forecast: Sell