AUC Score :
Short-Term Revised1 :
Dominant Strategy : Sell
Time series to forecast n:
Methodology : Modular Neural Network (Speculative Sentiment Analysis)
Hypothesis Testing : Independent T-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
Canterbury Park Holding Corporation 'New' Common Stock prediction model is evaluated with Modular Neural Network (Speculative Sentiment Analysis) and Independent T-Test1,2,3,4 and it is concluded that the CPHC 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 speculative 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 speculative sentiment analysis, MNNs can be used to identify the sentiment of people who are speculating about the future value of an asset, such as a stock or a cryptocurrency. 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 8 Weeks period, the dominant strategy among neural network is: Sell
Key Points
- What are main components of Markov decision process?
- Stock Forecast Based On a Predictive Algorithm
- What is the use of Markov decision process?
CPHC Target Price Prediction Modeling Methodology
We consider Canterbury Park Holding Corporation 'New' Common Stock Decision Process with Modular Neural Network (Speculative Sentiment Analysis) where A is the set of discrete actions of CPHC 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(Independent T-Test)5,6,7= X R(Modular Neural Network (Speculative Sentiment Analysis)) X S(n):→ 8 Weeks
n:Time series to forecast
p:Price signals of CPHC stock
j:Nash equilibria (Neural Network)
k:Dominated move
a:Best response for target price
Modular Neural Network (Speculative Sentiment Analysis)
A modular neural network (MNN) is a type of artificial neural network that can be used for speculative 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 speculative sentiment analysis, MNNs can be used to identify the sentiment of people who are speculating about the future value of an asset, such as a stock or a cryptocurrency. This information can then be used to make investment decisions, to identify trends in the market, and to target investors with relevant advertising.Independent T-Test
An independent t-test is a statistical test that compares the means of two independent samples. In an independent t-test, the data points in each sample are not related to each other. The independent t-test is a parametric test, which means that it assumes that the data is normally distributed. The independent t-test is also a two-sample test, which means that it compares the means of two independent samples.
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?
CPHC Stock Forecast (Buy or Sell)
Sample Set: Neural NetworkStock/Index: CPHC Canterbury Park Holding Corporation 'New' Common Stock
Time series to forecast: 8 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 Modular Neural Network (Speculative Sentiment Analysis) based CPHC Stock Prediction Model
- 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.
- If an entity has applied paragraph 7.2.6 then at the date of initial application the entity shall recognise any difference between the fair value of the entire hybrid contract at the date of initial application and the sum of the fair values of the components of the hybrid contract at the date of initial application in the opening retained earnings (or other component of equity, as appropriate) of the reporting period that includes the date of initial application.
- Annual Improvements to IFRS Standards 2018–2020, issued in May 2020, added paragraphs 7.2.35 and B3.3.6A and amended paragraph B3.3.6. An entity shall apply that amendment for annual reporting periods beginning on or after 1 January 2022. Earlier application is permitted. If an entity applies the amendment for an earlier period, it shall disclose that fact.
- When designating risk components as hedged items, an entity considers whether the risk components are explicitly specified in a contract (contractually specified risk components) or whether they are implicit in the fair value or the cash flows of an item of which they are a part (noncontractually specified risk components). Non-contractually specified risk components can relate to items that are not a contract (for example, forecast transactions) or contracts that do not explicitly specify the component (for example, a firm commitment that includes only one single price instead of a pricing formula that references different underlyings)
*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.
CPHC Canterbury Park Holding Corporation 'New' Common Stock Financial Analysis*
Rating | Short-Term | Long-Term Senior |
---|---|---|
Outlook* | B1 | Ba2 |
Income Statement | Caa2 | Ba2 |
Balance Sheet | Baa2 | B2 |
Leverage Ratios | B1 | Baa2 |
Cash Flow | Baa2 | Baa2 |
Rates of Return and Profitability | B3 | Ba3 |
*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
Canterbury Park Holding Corporation 'New' Common Stock is assigned short-term B1 & long-term Ba2 estimated rating. Canterbury Park Holding Corporation 'New' Common Stock prediction model is evaluated with Modular Neural Network (Speculative Sentiment Analysis) and Independent T-Test1,2,3,4 and it is concluded that the CPHC stock is predictable in the short/long term. According to price forecasts for 8 Weeks period, the dominant strategy among neural network is: Sell
Prediction Confidence Score
References
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- A. Tamar, D. Di Castro, and S. Mannor. Policy gradients with variance related risk criteria. In Proceedings of the Twenty-Ninth International Conference on Machine Learning, pages 387–396, 2012.
- Jorgenson, D.W., Weitzman, M.L., ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. S&P 500: Is the Bull Market Ready to Run Out of Steam?. AC Investment Research Journal, 220(44).
- E. Altman. Constrained Markov decision processes, volume 7. CRC Press, 1999
- S. J. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, Englewood Cliffs, NJ, 3nd edition, 2010
- uyer, S. Whiteson, B. Bakker, and N. A. Vlassis. Multiagent reinforcement learning for urban traffic control using coordination graphs. In Machine Learning and Knowledge Discovery in Databases, European Conference, ECML/PKDD 2008, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part I, pages 656–671, 2008.
- Cortes C, Vapnik V. 1995. Support-vector networks. Mach. Learn. 20:273–97
Frequently Asked Questions
Q: What is the prediction methodology for CPHC stock?A: CPHC stock prediction methodology: We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) and Independent T-Test
Q: Is CPHC stock a buy or sell?
A: The dominant strategy among neural network is to Sell CPHC Stock.
Q: Is Canterbury Park Holding Corporation 'New' Common Stock stock a good investment?
A: The consensus rating for Canterbury Park Holding Corporation 'New' Common Stock is Sell and is assigned short-term B1 & long-term Ba2 estimated rating.
Q: What is the consensus rating of CPHC stock?
A: The consensus rating for CPHC is Sell.
Q: What is the prediction period for CPHC stock?
A: The prediction period for CPHC is 8 Weeks
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