DIS Stock Forecast

Outlook: DIS is assigned short-term Caa2 & 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 : Multi-Task Learning (ML)
Hypothesis Testing : Wilcoxon Rank-Sum Test
Surveillance : Major exchange and OTC

¹Short-term revised.

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

Key Points

This exclusive content is only available to premium users.

About DIS

The Walt Disney Company, commonly referred to as Disney, is a globally recognized entertainment conglomerate with a diversified portfolio spanning media networks, theme parks, and consumer products. Its media segment includes a vast array of television channels, studios, and streaming services, producing and distributing content across various genres for worldwide audiences. The company's iconic theme parks and resorts offer immersive experiences, drawing millions of visitors annually and contributing significantly to its revenue streams. Furthermore, Disney's extensive consumer products division licenses its intellectual property for a wide range of merchandise, further extending its brand reach.

Disney's strategic vision prioritizes the creation and leverage of compelling intellectual property, encompassing beloved characters, stories, and franchises. The company's commitment to innovation is evident in its ongoing investments in new technologies and entertainment formats. Through strategic acquisitions and organic growth, Disney has established itself as a dominant force in the entertainment industry, consistently adapting to evolving consumer preferences and market dynamics. Its enduring legacy is built upon storytelling and its ability to connect with audiences of all ages through a variety of media platforms.

This exclusive content is only available to premium users.

ML Model Testing

F(Wilcoxon Rank-Sum Test)^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(Multi-Task Learning (ML))^3,4,5 X S(n):→ 16 Weeks $\sum _{i=1}^n\left(r_i\right)$

n:Time series to forecast

p:Price signals of DIS stock

j:Nash equilibria (Neural Network)

k:Dominated move of DIS stock holders

a:Best response for DIS 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?

DIS 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%

This exclusive content is only available to premium users.

Rating	Short-Term	Long-Term Senior
Outlook	Caa2	B1
Income Statement	B2	Caa2
Balance Sheet	C	Caa2
Leverage Ratios	Caa2	Baa2
Cash Flow	B3	Baa2
Rates of Return and Profitability	C	B2

*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. Rumelhart DE, Hinton GE, Williams RJ. 1986. Learning representations by back-propagating errors. Nature 323:533–36
2. Candès E, Tao T. 2007. The Dantzig selector: statistical estimation when p is much larger than n. Ann. Stat. 35:2313–51
3. Wager S, Athey S. 2017. Estimation and inference of heterogeneous treatment effects using random forests. J. Am. Stat. Assoc. 113:1228–42
4. Dudik M, Erhan D, Langford J, Li L. 2014. Doubly robust policy evaluation and optimization. Stat. Sci. 29:485–511
5. Banerjee, A., J. J. Dolado, J. W. Galbraith, D. F. Hendry (1993), Co-integration, Error-correction, and the Econometric Analysis of Non-stationary Data. Oxford: Oxford University Press.
6. Armstrong, J. S. M. C. Grohman (1972), "A comparative study of methods for long-range market forecasting," Management Science, 19, 211–221.
7. S. Bhatnagar, R. Sutton, M. Ghavamzadeh, and M. Lee. Natural actor-critic algorithms. Automatica, 45(11): 2471–2482, 2009