AUC Score :
Short-Term Revised1 :
Dominant Strategy : Sell
Time series to forecast n:
Methodology : Inductive Learning (ML)
Hypothesis Testing : Wilcoxon Rank-Sum 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
Chicago Atlantic Real Estate Finance Inc. Common Stock prediction model is evaluated with Inductive Learning (ML) and Wilcoxon Rank-Sum Test1,2,3,4 and it is concluded that the REFI stock is predictable in the short/long term. Inductive learning is a type of machine learning in which the model learns from a set of labeled data and makes predictions about new, unlabeled data. The model is trained on the labeled data and then used to make predictions on new data. Inductive learning is a supervised learning algorithm, which means that it requires labeled data to train. The labeled data is used to train the model to make predictions about new data. There are many different types of inductive learning algorithms, including decision trees, support vector machines, and neural networks. Each type of algorithm has its own strengths and weaknesses. According to price forecasts for 8 Weeks period, the dominant strategy among neural network is: Sell
Key Points
- Investment Risk
- What is the best way to predict stock prices?
- How do you pick a stock?
REFI Target Price Prediction Modeling Methodology
We consider Chicago Atlantic Real Estate Finance Inc. Common Stock Decision Process with Inductive Learning (ML) where A is the set of discrete actions of REFI 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(Wilcoxon Rank-Sum Test)5,6,7= X R(Inductive Learning (ML)) X S(n):→ 8 Weeks
n:Time series to forecast
p:Price signals of REFI stock
j:Nash equilibria (Neural Network)
k:Dominated move
a:Best response for target price
Inductive Learning (ML)
Inductive learning is a type of machine learning in which the model learns from a set of labeled data and makes predictions about new, unlabeled data. The model is trained on the labeled data and then used to make predictions on new data. Inductive learning is a supervised learning algorithm, which means that it requires labeled data to train. The labeled data is used to train the model to make predictions about new data. There are many different types of inductive learning algorithms, including decision trees, support vector machines, and neural networks. Each type of algorithm has its own strengths and weaknesses.Wilcoxon Rank-Sum Test
The Wilcoxon rank-sum test, also known as the Mann-Whitney U test, is a non-parametric test that is used to compare the medians of two independent samples. It is a rank-based test, which means that it does not assume that the data is normally distributed. The Wilcoxon rank-sum test is calculated by first ranking the data from both samples, and then finding the sum of the ranks for one of the samples. The Wilcoxon rank-sum test statistic is then calculated by subtracting the sum of the ranks for one sample from the sum of the ranks for the other sample. The p-value for the Wilcoxon rank-sum test is calculated using a table of critical values. The p-value is the probability of obtaining a test statistic at least as extreme as the one observed, assuming that the null hypothesis is true.
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?
REFI Stock Forecast (Buy or Sell)
Sample Set: Neural NetworkStock/Index: REFI Chicago Atlantic Real Estate Finance Inc. 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 Inductive Learning (ML) based REFI Stock Prediction Model
- The requirements in paragraphs 6.8.4–6.8.8 may cease to apply at different times. Therefore, in applying paragraph 6.9.1, an entity may be required to amend the formal designation of its hedging relationships at different times, or may be required to amend the formal designation of a hedging relationship more than once. When, and only when, such a change is made to the hedge designation, an entity shall apply paragraphs 6.9.7–6.9.12 as applicable. An entity also shall apply paragraph 6.5.8 (for a fair value hedge) or paragraph 6.5.11 (for a cash flow hedge) to account for any changes in the fair value of the hedged item or the hedging instrument.
- When a group of items that constitute a net position is designated as a hedged item, an entity shall designate the overall group of items that includes the items that can make up the net position. An entity is not permitted to designate a non-specific abstract amount of a net position. For example, an entity has a group of firm sale commitments in nine months' time for FC100 and a group of firm purchase commitments in 18 months' time for FC120. The entity cannot designate an abstract amount of a net position up to FC20. Instead, it must designate a gross amount of purchases and a gross amount of sales that together give rise to the hedged net position. An entity shall designate gross positions that give rise to the net position so that the entity is able to comply with the requirements for the accounting for qualifying hedging relationships.
- A regular way purchase or sale gives rise to a fixed price commitment between trade date and settlement date that meets the definition of a derivative. However, because of the short duration of the commitment it is not recognised as a derivative financial instrument. Instead, this Standard provides for special accounting for such regular way contracts (see paragraphs 3.1.2 and B3.1.3–B3.1.6).
- The purpose of estimating expected credit losses is neither to estimate a worstcase scenario nor to estimate the best-case scenario. Instead, an estimate of expected credit losses shall always reflect the possibility that a credit loss occurs and the possibility that no credit loss occurs even if the most likely outcome is no credit loss.
*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.
REFI Chicago Atlantic Real Estate Finance Inc. Common Stock Financial Analysis*
Rating | Short-Term | Long-Term Senior |
---|---|---|
Outlook* | B2 | B2 |
Income Statement | Ba2 | B2 |
Balance Sheet | C | B1 |
Leverage Ratios | Caa2 | Caa2 |
Cash Flow | Baa2 | Caa2 |
Rates of Return and Profitability | B2 | C |
*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
Chicago Atlantic Real Estate Finance Inc. Common Stock is assigned short-term B2 & long-term B2 estimated rating. Chicago Atlantic Real Estate Finance Inc. Common Stock prediction model is evaluated with Inductive Learning (ML) and Wilcoxon Rank-Sum Test1,2,3,4 and it is concluded that the REFI 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
- Efron B, Hastie T. 2016. Computer Age Statistical Inference, Vol. 5. Cambridge, UK: Cambridge Univ. Press
- Jorgenson, D.W., Weitzman, M.L., ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. Google's Stock Price Set to Soar in the Next 3 Months. AC Investment Research Journal, 220(44).
- G. Konidaris, S. Osentoski, and P. Thomas. Value function approximation in reinforcement learning using the Fourier basis. In AAAI, 2011
- R. Sutton, D. McAllester, S. Singh, and Y. Mansour. Policy gradient methods for reinforcement learning with function approximation. In Proceedings of Advances in Neural Information Processing Systems 12, pages 1057–1063, 2000
- Barrett, C. B. (1997), "Heteroscedastic price forecasting for food security management in developing countries," Oxford Development Studies, 25, 225–236.
- Jorgenson, D.W., Weitzman, M.L., ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. Tesla Stock: Hold for Now, But Watch for Opportunities. AC Investment Research Journal, 220(44).
- Greene WH. 2000. Econometric Analysis. Upper Saddle River, N J: Prentice Hall. 4th ed.
Frequently Asked Questions
Q: What is the prediction methodology for REFI stock?A: REFI stock prediction methodology: We evaluate the prediction models Inductive Learning (ML) and Wilcoxon Rank-Sum Test
Q: Is REFI stock a buy or sell?
A: The dominant strategy among neural network is to Sell REFI Stock.
Q: Is Chicago Atlantic Real Estate Finance Inc. Common Stock stock a good investment?
A: The consensus rating for Chicago Atlantic Real Estate Finance Inc. Common Stock is Sell and is assigned short-term B2 & long-term B2 estimated rating.
Q: What is the consensus rating of REFI stock?
A: The consensus rating for REFI is Sell.
Q: What is the prediction period for REFI stock?
A: The prediction period for REFI is 8 Weeks
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