Modelling A.I. in Economics

SISI Stock: A Cautionary Tale (Forecast)

Outlook: Shineco Inc. Common Stock is assigned short-term B3 & long-term B1 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy : Sell
Time series to forecast n: for Weeks2
Methodology : Modular Neural Network (Market Volatility Analysis)
Hypothesis Testing : Beta
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

Shineco Inc. Common Stock prediction model is evaluated with Modular Neural Network (Market Volatility Analysis) and Beta1,2,3,4 and it is concluded that the SISI stock is predictable in the short/long term. Modular neural networks (MNNs) are a type of artificial neural network that can be used for market volatility analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying patterns in data or predicting future price movements. The modules are then combined to form a single neural network that can perform multiple tasks.In the context of market volatility analysis, MNNs can be used to identify patterns in market data that suggest that the market is becoming more or less volatile. This information can then be used to make predictions about future price movements.5 According to price forecasts for 1 Year period, the dominant strategy among neural network is: Sell

Graph 45

Key Points

  1. Modular Neural Network (Market Volatility Analysis) for SISI stock price prediction process.
  2. Beta
  3. What are the most successful trading algorithms?
  4. What is neural prediction?
  5. What is prediction in deep learning?

SISI Stock Price Forecast

We consider Shineco Inc. Common Stock Decision Process with Modular Neural Network (Market Volatility Analysis) where A is the set of discrete actions of SISI 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: SISI Shineco Inc. Common Stock
Time series to forecast: 1 Year

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


F(Beta)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 (Market Volatility Analysis)) X S(n):→ 1 Year i = 1 n a i

n:Time series to forecast

p:Price signals of SISI stock

j:Nash equilibria (Neural Network)

k:Dominated move of SISI stock holders

a:Best response for SISI target price


Modular neural networks (MNNs) are a type of artificial neural network that can be used for market volatility analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying patterns in data or predicting future price movements. The modules are then combined to form a single neural network that can perform multiple tasks.In the context of market volatility analysis, MNNs can be used to identify patterns in market data that suggest that the market is becoming more or less volatile. This information can then be used to make predictions about future price movements.5 In statistics, beta (β) is a measure of the strength of the relationship between two variables. It is calculated as the slope of the line of best fit in a regression analysis. Beta can range from -1 to 1, with a value of 0 indicating no relationship between the two variables. A positive beta indicates that as one variable increases, the other variable also increases. A negative beta indicates that as one variable increases, the other variable decreases. For example, a study might find that there is a positive relationship between height and weight. This means that taller people tend to weigh more. The beta coefficient for this relationship would be positive.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?

SISI 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 (Market Volatility Analysis) based SISI Stock Prediction Model

  1. If the group of items does have offsetting risk positions (for example, a group of sales and expenses denominated in a foreign currency hedged together for foreign currency risk) then an entity shall present the hedging gains or losses in a separate line item in the statement of profit or loss and other comprehensive income. Consider, for example, a hedge of the foreign currency risk of a net position of foreign currency sales of FC100 and foreign currency expenses of FC80 using a forward exchange contract for FC20. The gain or loss on the forward exchange contract that is reclassified from the cash flow hedge reserve to profit or loss (when the net position affects profit or loss) shall be presented in a separate line item from the hedged sales and expenses. Moreover, if the sales occur in an earlier period than the expenses, the sales revenue is still measured at the spot exchange rate in accordance with IAS 21. The related hedging gain or loss is presented in a separate line item, so that profit or loss reflects the effect of hedging the net position, with a corresponding adjustment to the cash flow hedge reserve. When the hedged expenses affect profit or loss in a later period, the hedging gain or loss previously recognised in the cash flow hedge reserve on the sales is reclassified to profit or loss and presented as a separate line item from those that include the hedged expenses, which are measured at the spot exchange rate in accordance with IAS 21.
  2. If any instrument in the pool does not meet the conditions in either paragraph B4.1.23 or paragraph B4.1.24, the condition in paragraph B4.1.21(b) is not met. In performing this assessment, a detailed instrument-byinstrument analysis of the pool may not be necessary. However, an entity must use judgement and perform sufficient analysis to determine whether the instruments in the pool meet the conditions in paragraphs B4.1.23–B4.1.24. (See also paragraph B4.1.18 for guidance on contractual cash flow characteristics that have only a de minimis effect.)
  3. 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.
  4. If items are hedged together as a group in a cash flow hedge, they might affect different line items in the statement of profit or loss and other comprehensive income. The presentation of hedging gains or losses in that statement depends on the group of items

*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.

SISI Shineco Inc. Common Stock Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*B3B1
Income StatementCaa2Caa2
Balance SheetBa2Ba1
Leverage RatiosCB3
Cash FlowCBaa2
Rates of Return and ProfitabilityBa2B1

*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. C. Szepesvári. Algorithms for Reinforcement Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 2010
  2. Doudchenko N, Imbens GW. 2016. Balancing, regression, difference-in-differences and synthetic control methods: a synthesis. NBER Work. Pap. 22791
  3. Breusch, T. S. (1978), "Testing for autocorrelation in dynamic linear models," Australian Economic Papers, 17, 334–355.
  4. Athey S, Imbens GW. 2017a. The econometrics of randomized experiments. In Handbook of Economic Field Experiments, Vol. 1, ed. E Duflo, A Banerjee, pp. 73–140. Amsterdam: Elsevier
  5. Bai J, Ng S. 2002. Determining the number of factors in approximate factor models. Econometrica 70:191–221
  6. Wager S, Athey S. 2017. Estimation and inference of heterogeneous treatment effects using random forests. J. Am. Stat. Assoc. 113:1228–42
  7. A. Shapiro, W. Tekaya, J. da Costa, and M. Soares. Risk neutral and risk averse stochastic dual dynamic programming method. European journal of operational research, 224(2):375–391, 2013
Frequently Asked QuestionsQ: Is SISI stock expected to rise?
A: SISI stock prediction model is evaluated with Modular Neural Network (Market Volatility Analysis) and Beta and it is concluded that dominant strategy for SISI stock is Sell
Q: Is SISI stock a buy or sell?
A: The dominant strategy among neural network is to Sell SISI Stock.
Q: Is Shineco Inc. Common Stock stock a good investment?
A: The consensus rating for Shineco Inc. Common Stock is Sell and is assigned short-term B3 & long-term B1 estimated rating.
Q: What is the consensus rating of SISI stock?
A: The consensus rating for SISI is Sell.
Q: What is the forecast for SISI stock?
A: SISI target price forecast: Sell

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