HCM HUTCHMED (China) Limited American Depositary Shares

Outlook: HUTCHMED (China) Limited American Depositary Shares assigned short-term Ba3 & long-term B2 forecasted stock rating.
Dominant Strategy : Sell
Time series to forecast n: 13 Dec 2022 for (n+16 weeks)
Methodology : Modular Neural Network (Speculative Sentiment Analysis)

Abstract

It has never been easy to invest in a set of assets, the abnormally of financial market does not allow simple models to predict future asset values with higher accuracy. Machine learning, which consist of making computers perform tasks that normally requiring human intelligence is currently the dominant trend in scientific research. This article aims to build a model using Recurrent Neural Networks (RNN) and especially Long-Short Term Memory model (LSTM) to predict future stock market values.(Lee, J.W., 2001, June. Stock price prediction using reinforcement learning. In ISIE 2001. 2001 IEEE International Symposium on Industrial Electronics Proceedings (Cat. No. 01TH8570) (Vol. 1, pp. 690-695). IEEE.) We evaluate HUTCHMED (China) Limited American Depositary Shares prediction models with Modular Neural Network (Speculative Sentiment Analysis) and Paired T-Test^1,2,3,4 and conclude that the HCM stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Sell

Key Points

1. Is Target price a good indicator?
2. Buy, Sell and Hold Signals
3. Operational Risk

HCM Target Price Prediction Modeling Methodology

We consider HUTCHMED (China) Limited American Depositary Shares Decision Process with Modular Neural Network (Speculative Sentiment Analysis) where A is the set of discrete actions of HCM 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(Paired T-Test)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Modular Neural Network (Speculative Sentiment Analysis)) X S(n):→ (n+16 weeks) $\overrightarrow{S}=\left(s_1,s_2,s_3\right)$

n:Time series to forecast

p:Price signals of HCM stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

 

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?

HCM Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: HCM HUTCHMED (China) Limited American Depositary Shares
Time series to forecast n: 13 Dec 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Sell

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%

Adjusted IFRS* Prediction Methods for HUTCHMED (China) Limited American Depositary Shares

1. When designating a hedging relationship and on an ongoing basis, an entity shall analyse the sources of hedge ineffectiveness that are expected to affect the hedging relationship during its term. This analysis (including any updates in accordance with paragraph B6.5.21 arising from rebalancing a hedging relationship) is the basis for the entity's assessment of meeting the hedge effectiveness requirements.
2. When designating a group of items as the hedged item, or a combination of financial instruments as the hedging instrument, an entity shall prospectively cease applying paragraphs 6.8.4–6.8.6 to an individual item or financial instrument in accordance with paragraphs 6.8.9, 6.8.10, or 6.8.11, as relevant, when the uncertainty arising from interest rate benchmark reform is no longer present with respect to the hedged risk and/or the timing and the amount of the interest rate benchmark-based cash flows of that item or financial instrument.
3. IFRS 15, issued in May 2014, amended paragraphs 3.1.1, 4.2.1, 5.1.1, 5.2.1, 5.7.6, B3.2.13, B5.7.1, C5 and C42 and deleted paragraph C16 and its related heading. Paragraphs 5.1.3 and 5.7.1A, and a definition to Appendix A, were added. An entity shall apply those amendments when it applies IFRS 15.
4. An entity shall apply Prepayment Features with Negative Compensation (Amendments to IFRS 9) retrospectively in accordance with IAS 8, except as specified in paragraphs 7.2.30–7.2.34

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

Conclusions

HUTCHMED (China) Limited American Depositary Shares assigned short-term Ba3 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) with Paired T-Test^1,2,3,4 and conclude that the HCM stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Sell

Financial State Forecast for HCM HUTCHMED (China) Limited American Depositary Shares Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Ba3	B2
Operational Risk	74	32
Market Risk	78	86
Technical Analysis	63	64
Fundamental Analysis	50	31
Risk Unsystematic	68	47

Prediction Confidence Score

Trust metric by Neural Network: 83 out of 100 with 516 signals.

References

1. Blei DM, Lafferty JD. 2009. Topic models. In Text Mining: Classification, Clustering, and Applications, ed. A Srivastava, M Sahami, pp. 101–24. Boca Raton, FL: CRC Press
2. Bai J. 2003. Inferential theory for factor models of large dimensions. Econometrica 71:135–71
3. Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, Newey W. 2017. Double/debiased/ Neyman machine learning of treatment effects. Am. Econ. Rev. 107:261–65
4. Vapnik V. 2013. The Nature of Statistical Learning Theory. Berlin: Springer
5. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., Can stock prices be predicted?(SMI Index Stock Forecast). AC Investment Research Journal, 101(3).
6. J. Baxter and P. Bartlett. Infinite-horizon policy-gradient estimation. Journal of Artificial Intelligence Re- search, 15:319–350, 2001.
7. G. J. Laurent, L. Matignon, and N. L. Fort-Piat. The world of independent learners is not Markovian. Int. J. Know.-Based Intell. Eng. Syst., 15(1):55–64, 2011

Frequently Asked Questions

Q: What is the prediction methodology for HCM stock?
A: HCM stock prediction methodology: We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) and Paired T-Test
Q: Is HCM stock a buy or sell?
A: The dominant strategy among neural network is to Sell HCM Stock.
Q: Is HUTCHMED (China) Limited American Depositary Shares stock a good investment?
A: The consensus rating for HUTCHMED (China) Limited American Depositary Shares is Sell and assigned short-term Ba3 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of HCM stock?
A: The consensus rating for HCM is Sell.
Q: What is the prediction period for HCM stock?
A: The prediction period for HCM is (n+16 weeks)

Premium

• Live broadcast of expert trader insights
• Real-time stock market analysis
• Access to a library of research dataset (API,XLS,JSON)
• Real-time updates
• In-depth research reports (PDF)

Login