LON:ACIC Target Price Forecast

ABRDN CHINA INVESTMENT COMPANY LIMITED Research Report

Summary

Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on a financial exchange. The successful prediction of a stock's future price will maximize investor's gains. This paper proposes a machine learning model to predict stock market price. We evaluate ABRDN CHINA INVESTMENT COMPANY LIMITED prediction models with Multi-Task Learning (ML) and Logistic Regression1,2,3,4 and conclude that the LON:ACIC stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold LON:ACIC stock.

Key Points

  1. Fundemental Analysis with Algorithmic Trading
  2. What is the best way to predict stock prices?
  3. Trust metric by Neural Network

LON:ACIC Target Price Prediction Modeling Methodology

We consider ABRDN CHINA INVESTMENT COMPANY LIMITED Stock Decision Process with Multi-Task Learning (ML) where A is the set of discrete actions of LON:ACIC 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(Logistic Regression)5,6,7= 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(Multi-Task Learning (ML)) X S(n):→ (n+1 year) r s rs

n:Time series to forecast

p:Price signals of LON:ACIC 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?

LON:ACIC Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: LON:ACIC ABRDN CHINA INVESTMENT COMPANY LIMITED
Time series to forecast n: 21 Nov 2022 for (n+1 year)

According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold LON:ACIC stock.

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 (Yellow to Green): *Technical Analysis%

Adjusted IFRS* Prediction Methods for ABRDN CHINA INVESTMENT COMPANY LIMITED

  1. When rebalancing a hedging relationship, an entity shall update its analysis of the sources of hedge ineffectiveness that are expected to affect the hedging relationship during its (remaining) term (see paragraph B6.4.2). The documentation of the hedging relationship shall be updated accordingly.
  2. In addition to those hedging relationships specified in paragraph 6.9.1, an entity shall apply the requirements in paragraphs 6.9.11 and 6.9.12 to new hedging relationships in which an alternative benchmark rate is designated as a non-contractually specified risk component (see paragraphs 6.3.7(a) and B6.3.8) when, because of interest rate benchmark reform, that risk component is not separately identifiable at the date it is designated.
  3. The fair value of a financial instrument at initial recognition is normally the transaction price (ie the fair value of the consideration given or received, see also paragraph B5.1.2A and IFRS 13). However, if part of the consideration given or received is for something other than the financial instrument, an entity shall measure the fair value of the financial instrument. For example, the fair value of a long-term loan or receivable that carries no interest can be measured as the present value of all future cash receipts discounted using the prevailing market rate(s) of interest for a similar instrument (similar as to currency, term, type of interest rate and other factors) with a similar credit rating. Any additional amount lent is an expense or a reduction of income unless it qualifies for recognition as some other type of asset.
  4. Conversely, if the critical terms of the hedging instrument and the hedged item are not closely aligned, there is an increased level of uncertainty about the extent of offset. Consequently, the hedge effectiveness during the term of the hedging relationship is more difficult to predict. In such a situation it might only be possible for an entity to conclude on the basis of a quantitative assessment that an economic relationship exists between the hedged item and the hedging instrument (see paragraphs B6.4.4–B6.4.6). In some situations a quantitative assessment might also be needed to assess whether the hedge ratio used for designating the hedging relationship meets the hedge effectiveness requirements (see paragraphs B6.4.9–B6.4.11). An entity can use the same or different methods for those two different purposes.

*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

ABRDN CHINA INVESTMENT COMPANY LIMITED assigned short-term Ba1 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Multi-Task Learning (ML) with Logistic Regression1,2,3,4 and conclude that the LON:ACIC stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold LON:ACIC stock.

Financial State Forecast for LON:ACIC ABRDN CHINA INVESTMENT COMPANY LIMITED Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba3
Operational Risk 5656
Market Risk8871
Technical Analysis5186
Fundamental Analysis8656
Risk Unsystematic7448

Prediction Confidence Score

Trust metric by Neural Network: 88 out of 100 with 729 signals.

References

  1. Matzkin RL. 2007. Nonparametric identification. In Handbook of Econometrics, Vol. 6B, ed. J Heckman, E Learner, pp. 5307–68. Amsterdam: Elsevier
  2. D. Bertsekas. Nonlinear programming. Athena Scientific, 1999.
  3. Miller A. 2002. Subset Selection in Regression. New York: CRC Press
  4. D. S. Bernstein, S. Zilberstein, and N. Immerman. The complexity of decentralized control of Markov Decision Processes. In UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30 - July 3, 2000, pages 32–37, 2000.
  5. S. Devlin, L. Yliniemi, D. Kudenko, and K. Tumer. Potential-based difference rewards for multiagent reinforcement learning. In Proceedings of the Thirteenth International Joint Conference on Autonomous Agents and Multiagent Systems, May 2014
  6. 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
  7. Bottomley, P. R. Fildes (1998), "The role of prices in models of innovation diffusion," Journal of Forecasting, 17, 539–555.
Frequently Asked QuestionsQ: What is the prediction methodology for LON:ACIC stock?
A: LON:ACIC stock prediction methodology: We evaluate the prediction models Multi-Task Learning (ML) and Logistic Regression
Q: Is LON:ACIC stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:ACIC Stock.
Q: Is ABRDN CHINA INVESTMENT COMPANY LIMITED stock a good investment?
A: The consensus rating for ABRDN CHINA INVESTMENT COMPANY LIMITED is Hold and assigned short-term Ba1 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of LON:ACIC stock?
A: The consensus rating for LON:ACIC is Hold.
Q: What is the prediction period for LON:ACIC stock?
A: The prediction period for LON:ACIC is (n+1 year)

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