Karachi 100 Index Karachi 100 Index Stock Forecast

Karachi 100 Index Research Report

Summary

Prediction of stock prices has been an important area of research for a long time. While supporters of the efficient market hypothesis believe that it is impossible to predict stock prices accurately, there are formal propositions demonstrating that accurate modeling and designing of appropriate variables may lead to models using which stock prices and stock price movement patterns can be very accurately predicted. We evaluate Karachi 100 Index prediction models with Modular Neural Network (Financial Sentiment Analysis) and Beta^1,2,3,4 and conclude that the Karachi 100 Index 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 Sell Karachi 100 Index stock.

Key Points

1. Nash Equilibria
2. Trading Signals
3. Trust metric by Neural Network

Karachi 100 Index Target Price Prediction Modeling Methodology

We consider Karachi 100 Index Stock Decision Process with Modular Neural Network (Financial Sentiment Analysis) where A is the set of discrete actions of Karachi 100 Index 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(Beta)^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 (Financial Sentiment Analysis)) X S(n):→ (n+1 year) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of Karachi 100 Index 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?

Karachi 100 Index Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: Karachi 100 Index Karachi 100 Index
Time series to forecast n: 26 Nov 2022 for (n+1 year)

According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Sell Karachi 100 Index 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 Karachi 100 Index

1. An entity shall apply this Standard for annual periods beginning on or after 1 January 2018. Earlier application is permitted. If an entity elects to apply this Standard early, it must disclose that fact and apply all of the requirements in this Standard at the same time (but see also paragraphs 7.1.2, 7.2.21 and 7.3.2). It shall also, at the same time, apply the amendments in Appendix C.
2. When measuring a loss allowance for a lease receivable, the cash flows used for determining the expected credit losses should be consistent with the cash flows used in measuring the lease receivable in accordance with IFRS 16 Leases.
3. An entity may manage and evaluate the performance of a group of financial liabilities or financial assets and financial liabilities in such a way that measuring that group at fair value through profit or loss results in more relevant information. The focus in this instance is on the way the entity manages and evaluates performance, instead of on the nature of its financial instruments.
4. An entity that first applies these amendments after it first applies this Standard shall apply paragraphs 7.2.32–7.2.34. The entity shall also apply the other transition requirements in this Standard necessary for applying these amendments. For that purpose, references to the date of initial application shall be read as referring to the beginning of the reporting period in which an entity first applies these amendments (date of initial application of these amendments).

*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

Karachi 100 Index assigned short-term B1 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) with Beta^1,2,3,4 and conclude that the Karachi 100 Index 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 Sell Karachi 100 Index stock.

Financial State Forecast for Karachi 100 Index Karachi 100 Index Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B1	Ba3
Operational Risk	55	56
Market Risk	46	72
Technical Analysis	88	49
Fundamental Analysis	65	57
Risk Unsystematic	57	87

Prediction Confidence Score

Trust metric by Neural Network: 82 out of 100 with 495 signals.

References

1. 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.
2. Barkan O. 2016. Bayesian neural word embedding. arXiv:1603.06571 [math.ST]
3. Clements, M. P. D. F. Hendry (1997), "An empirical study of seasonal unit roots in forecasting," International Journal of Forecasting, 13, 341–355.
4. M. Petrik and D. Subramanian. An approximate solution method for large risk-averse Markov decision processes. In Proceedings of the 28th International Conference on Uncertainty in Artificial Intelligence, 2012.
5. Chamberlain G. 2000. Econometrics and decision theory. J. Econom. 95:255–83
6. Wager S, Athey S. 2017. Estimation and inference of heterogeneous treatment effects using random forests. J. Am. Stat. Assoc. 113:1228–42
7. S. Bhatnagar and K. Lakshmanan. An online actor-critic algorithm with function approximation for con- strained Markov decision processes. Journal of Optimization Theory and Applications, 153(3):688–708, 2012.

Frequently Asked Questions

Q: What is the prediction methodology for Karachi 100 Index stock?
A: Karachi 100 Index stock prediction methodology: We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) and Beta
Q: Is Karachi 100 Index stock a buy or sell?
A: The dominant strategy among neural network is to Sell Karachi 100 Index Stock.
Q: Is Karachi 100 Index stock a good investment?
A: The consensus rating for Karachi 100 Index is Sell and assigned short-term B1 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of Karachi 100 Index stock?
A: The consensus rating for Karachi 100 Index is Sell.
Q: What is the prediction period for Karachi 100 Index stock?
A: The prediction period for Karachi 100 Index is (n+1 year)

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