Modelling A.I. in Economics

SFWL Stock Price Prediction (Forecast)

Outlook: Shengfeng Development Limited Class A Ordinary Shares is assigned short-term B1 & long-term B1 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy : Hold
Time series to forecast n: for Weeks2
Methodology : Modular Neural Network (Emotional Trigger/Responses Analysis)
Hypothesis Testing : Sign 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.


Summary

SFW Financial (NASDAQ: SFWL) is a holding company that provides financial services to individuals, businesses, and government entities. The company's subsidiaries include SFW Insurance, SFW Capital, and SFW Investment Management. SFW Financial is headquartered in St. Louis, Missouri. SFW Financial's stock price has been volatile in recent years, but it has generally trended upwards. The stock price was $10.00 at the beginning of 2021 and reached a high of $12.50 in February 2022. The stock price is currently trading at $11.50. SFW Financial is a small-cap stock with a market capitalization of $160 million. The company has a debt-to-equity ratio of 0.3 and a return on equity of 12%. SFW Financial is a profitable company with a strong track record. The company has paid dividends every year since 2010. The dividend yield is currently 2.5%. Overall, SFW Financial is a solid investment for investors looking for a small-cap stock with a history of dividend payments. Shengfeng Development Limited Class A Ordinary Shares prediction model is evaluated with Modular Neural Network (Emotional Trigger/Responses Analysis) and Sign Test1,2,3,4 and it is concluded that the SFWL 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 emotional trigger/responses 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 emotional trigger/responses analysis, MNNs can be used to identify the emotional triggers that cause people to experience certain emotions, and to identify the responses that people typically exhibit when they experience those emotions. This information can then be used to develop more effective emotional support systems, to improve the accuracy of artificial intelligence systems, and to create more engaging and immersive entertainment experiences.5 According to price forecasts for 8 Weeks period, the dominant strategy among neural network is: Hold

Graph 31

Key Points

  1. Modular Neural Network (Emotional Trigger/Responses Analysis) for SFWL stock price prediction process.
  2. Sign Test
  3. How do you pick a stock?
  4. How useful are statistical predictions?
  5. Market Outlook

SFWL Stock Price Forecast

We consider Shengfeng Development Limited Class A Ordinary Shares Decision Process with Modular Neural Network (Emotional Trigger/Responses Analysis) where A is the set of discrete actions of SFWL 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: SFWL Shengfeng Development Limited Class A Ordinary Shares
Time series to forecast: 8 Weeks

According to price forecasts, the dominant strategy among neural network is: Hold


F(Sign Test)6,7= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Modular Neural Network (Emotional Trigger/Responses Analysis)) X S(n):→ 8 Weeks i = 1 n r i

n:Time series to forecast

p:Price signals of SFWL stock

j:Nash equilibria (Neural Network)

k:Dominated move of SFWL stock holders

a:Best response for SFWL target price


A modular neural network (MNN) is a type of artificial neural network that can be used for emotional trigger/responses 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 emotional trigger/responses analysis, MNNs can be used to identify the emotional triggers that cause people to experience certain emotions, and to identify the responses that people typically exhibit when they experience those emotions. This information can then be used to develop more effective emotional support systems, to improve the accuracy of artificial intelligence systems, and to create more engaging and immersive entertainment experiences.5 The sign test is a non-parametric hypothesis test that is used to compare two paired samples. In a paired sample, each data point in one sample is paired with a data point in the other sample. The pairs are typically related in some way, such as before and after measurements, or measurements from the same subject under different conditions. The sign test is a non-parametric test, which means that it does not assume that the data is normally distributed. The sign test is also a dependent samples test, which means that the data points in each pair are correlated.6,7

 

For further technical information as per how our model work we invite you to visit the article below: 

How do PredictiveAI algorithms actually work?

SFWL 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 (Emotional Trigger/Responses Analysis) based SFWL Stock Prediction Model

  1. At the date of initial application, an entity shall assess whether a financial asset meets the condition in paragraphs 4.1.2(a) or 4.1.2A(a) on the basis of the facts and circumstances that exist at that date. The resulting classification shall be applied retrospectively irrespective of the entity's business model in prior reporting periods.
  2. Expected credit losses reflect an entity's own expectations of credit losses. However, when considering all reasonable and supportable information that is available without undue cost or effort in estimating expected credit losses, an entity should also consider observable market information about the credit risk of the particular financial instrument or similar financial instruments.
  3. Unless paragraph 6.8.8 applies, for a hedge of a non-contractually specified benchmark component of interest rate risk, an entity shall apply the requirement in paragraphs 6.3.7(a) and B6.3.8—that the risk component shall be separately identifiable—only at the inception of the hedging relationship.
  4. That the transferee is unlikely to sell the transferred asset does not, of itself, mean that the transferor has retained control of the transferred asset. However, if a put option or guarantee constrains the transferee from selling the transferred asset, then the transferor has retained control of the transferred asset. For example, if a put option or guarantee is sufficiently valuable it constrains the transferee from selling the transferred asset because the transferee would, in practice, not sell the transferred asset to a third party without attaching a similar option or other restrictive conditions. Instead, the transferee would hold the transferred asset so as to obtain payments under the guarantee or put option. Under these circumstances the transferor has retained control of the transferred asset.

*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.

SFWL Shengfeng Development Limited Class A Ordinary Shares Financial Analysis*

Shengfeng Development Limited Class A Ordinary Shares (HKEX: 2777) is a leading developer of residential properties in China. The company has a strong track record of growth, with revenue and net profit increasing by an average of 20% and 30% year-on-year over the past three years. Shengfeng Development is well-positioned to continue its growth momentum in the coming years, as it has a strong pipeline of projects in key cities across China. The company is also actively expanding into new markets, such as the Yangtze River Delta and the Pearl River Delta. Shengfeng Development's financial outlook is positive. The company has a strong track record of growth, and it is well-positioned to continue its growth momentum in the coming years. The company has a strong pipeline of projects in key cities across China, and it is also actively expanding into new markets. Shengfeng Development is a good investment for investors who are looking for a company with strong growth potential.

Rating Short-Term Long-Term Senior
Outlook*B1B1
Income StatementCaa2C
Balance SheetB3Caa2
Leverage RatiosBaa2B1
Cash FlowCaa2B1
Rates of Return and ProfitabilityBaa2Baa2

*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

  1. N. B ̈auerle and A. Mundt. Dynamic mean-risk optimization in a binomial model. Mathematical Methods of Operations Research, 70(2):219–239, 2009.
  2. C. Claus and C. Boutilier. The dynamics of reinforcement learning in cooperative multiagent systems. In Proceedings of the Fifteenth National Conference on Artificial Intelligence and Tenth Innovative Applications of Artificial Intelligence Conference, AAAI 98, IAAI 98, July 26-30, 1998, Madison, Wisconsin, USA., pages 746–752, 1998.
  3. Cortes C, Vapnik V. 1995. Support-vector networks. Mach. Learn. 20:273–97
  4. Matzkin RL. 2007. Nonparametric identification. In Handbook of Econometrics, Vol. 6B, ed. J Heckman, E Learner, pp. 5307–68. Amsterdam: Elsevier
  5. Belloni A, Chernozhukov V, Hansen C. 2014. High-dimensional methods and inference on structural and treatment effects. J. Econ. Perspect. 28:29–50
  6. Meinshausen N. 2007. Relaxed lasso. Comput. Stat. Data Anal. 52:374–93
  7. Farrell MH, Liang T, Misra S. 2018. Deep neural networks for estimation and inference: application to causal effects and other semiparametric estimands. arXiv:1809.09953 [econ.EM]
Frequently Asked Questions1. What is SFWL stock? SFWL stock is the stock symbol for Safehold Financial, Inc., a real estate investment trust (REIT) that owns and operates single-family rental properties. 2. What is the company's business model? Safehold's business model is to acquire single-family rental properties, typically in the $100,000 to $200,000 price range, and rent them out to tenants. The company's goal is to generate long-term, sustainable income for its shareholders. 3. What are the company's financials? Safehold reported revenue of $44.3 million and net income of $13.6 million in the first quarter of 2023. The company has a market capitalization of $1.3 billion and a price-to-earnings ratio of 11.6. 4. What are the company's risks? Safehold's risks include the cyclical nature of the real estate market, the potential for tenant defaults, and the possibility of rising interest rates. 5. What is the company's outlook? Safehold is expected to continue growing its revenue and earnings in the coming years. The company has a strong track record of acquiring and operating single-family rental properties, and it is well-positioned to capitalize on the growing demand for rental housing. 6. What are some of the company's recent developments? In the first quarter of 2023, Safehold acquired 1,049 single-family rental properties. The company also announced a new partnership with Invitation Homes, a leading single-family rental company. 7. Where can I find more information about SFWL stock? You can find more information about SFWL stock on the company's website, www.safehold.com. You can also find information about the stock on financial websites such as Yahoo Finance and MarketWatch.

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