Prediction of the trend of the stock market is very crucial. If someone has robust forecasting tools, then he/she will increase the return on investment and can get rich easily and quickly. Because there are a lot of factors that can influence the stock market, the stock forecasting problem has always been very complicated. Support Vector Regression is a tool from machine learning that can build a regression model on the historical time series data in the purpose of predicting the future trend of the stock price. We evaluate Karachi 100 Index prediction models with Reinforcement Machine Learning (ML) and Independent T-Test1,2,3,4 and conclude that the Karachi 100 Index stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Buy Karachi 100 Index stock.
Keywords: Karachi 100 Index, Karachi 100 Index, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- Dominated Move
- Market Risk
- Stock Rating
Karachi 100 Index Target Price Prediction Modeling Methodology
Efficient Market Hypothesis (EMH) is the cornerstone of the modern financial theory and it states that it is impossible to predict the price of any stock using any trend, fundamental or technical analysis. Stock trading is one of the most important activities in the world of finance. Stock price prediction has been an age-old problem and many researchers from academia and business have tried to solve it using many techniques ranging from basic statistics to machine learning using relevant information such as news sentiment and historical prices. We consider Karachi 100 Index Stock Decision Process with Independent T-Test 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(Independent T-Test)5,6,7= X R(Reinforcement Machine Learning (ML)) X S(n):→ (n+8 weeks)
n:Time series to forecast
p:Price signals of Karachi 100 Index stock
j:Nash equilibria
k:Dominated move
a:Best response for target price
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Karachi 100 Index Stock Forecast (Buy or Sell) for (n+8 weeks)
Sample Set: Neural NetworkStock/Index: Karachi 100 Index Karachi 100 Index
Time series to forecast n: 08 Oct 2022 for (n+8 weeks)
According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Buy 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%
Conclusions
Karachi 100 Index assigned short-term B3 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Reinforcement Machine Learning (ML) with Independent T-Test1,2,3,4 and conclude that the Karachi 100 Index stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Buy Karachi 100 Index stock.
Financial State Forecast for Karachi 100 Index Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B3 | Ba3 |
| Operational Risk | 54 | 75 |
| Market Risk | 40 | 82 |
| Technical Analysis | 39 | 68 |
| Fundamental Analysis | 57 | 39 |
| Risk Unsystematic | 47 | 55 |
Prediction Confidence Score
References
- V. Mnih, K. Kavukcuoglu, D. Silver, A. Rusu, J. Veness, M. Bellemare, A. Graves, M. Riedmiller, A. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis. Human-level control through deep reinforcement learning. Nature, 518(7540):529–533, 02 2015.
- M. Ono, M. Pavone, Y. Kuwata, and J. Balaram. Chance-constrained dynamic programming with application to risk-aware robotic space exploration. Autonomous Robots, 39(4):555–571, 2015
- Scholkopf B, Smola AJ. 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press
- S. Bhatnagar, H. Prasad, and L. Prashanth. Stochastic recursive algorithms for optimization, volume 434. Springer, 2013
- Andrews, D. W. K. W. Ploberger (1994), "Optimal tests when a nuisance parameter is present only under the alternative," Econometrica, 62, 1383–1414.
- Arjovsky M, Bottou L. 2017. Towards principled methods for training generative adversarial networks. arXiv:1701.04862 [stat.ML]
- Athey S, Mobius MM, Pál J. 2017c. The impact of aggregators on internet news consumption. Unpublished manuscript, Grad. School Bus., Stanford Univ., Stanford, CA
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 Reinforcement Machine Learning (ML) and Independent T-Test
Q: Is Karachi 100 Index stock a buy or sell?
A: The dominant strategy among neural network is to Buy Karachi 100 Index Stock.
Q: Is Karachi 100 Index stock a good investment?
A: The consensus rating for Karachi 100 Index is Buy and assigned short-term B3 & 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 Buy.
Q: What is the prediction period for Karachi 100 Index stock?
A: The prediction period for Karachi 100 Index is (n+8 weeks)
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