TUYA Stock Forecast

Outlook: TUYA is assigned short-term B3 & long-term B1 estimated rating.
AUC Score : What is AUC Score?
Short-term Tactic¹ :
Dominant Strategy :
Time series to forecast n: for Weeks²
ML Model Testing : Modular Neural Network (Speculative Sentiment Analysis)
Hypothesis Testing : Beta
Surveillance : Major exchange and OTC

¹Short-term revised.

²Time series is updated based on short-term trends.

Key Points

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About TUYA

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ML Model Testing

F(Beta)^6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Modular Neural Network (Speculative Sentiment Analysis))^3,4,5 X S(n):→ 3 Month $\sum _{i=1}^n\left(a_i\right)$

n:Time series to forecast

p:Price signals of TUYA stock

j:Nash equilibria (Neural Network)

k:Dominated move of TUYA stock holders

a:Best response for TUYA target price

 

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

How do KappaSignal algorithms actually work?

TUYA 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%

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Rating	Short-Term	Long-Term Senior
Outlook	B3	B1
Income Statement	B1	Baa2
Balance Sheet	C	B1
Leverage Ratios	C	C
Cash Flow	Caa2	B1
Rates of Return and Profitability	Baa2	B1

*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

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5. Breiman L. 1996. Bagging predictors. Mach. Learn. 24:123–40
6. M. J. Hausknecht. Cooperation and Communication in Multiagent Deep Reinforcement Learning. PhD thesis, The University of Texas at Austin, 2016
7. R. Sutton and A. Barto. Reinforcement Learning. The MIT Press, 1998