Three networks are compared for low false alarm stock trend predictions. Short-term trends, particularly attractive for neural network analysis, can be used profitably in scenarios such as option trading, but only with significant risk. Therefore, we focus on limiting false alarms, which improves the risk/reward ratio by preventing losses. To predict stock trends, we exploit time delay, recurrent, and probabilistic neural networks (TDNN, RNN, and PNN, respectively), utilizing conjugate gradient and multistream extended Kalman filter training for TDNN and RNN.** We evaluate Karachi 100 Index prediction models with Statistical Inference (ML) and Wilcoxon Sign-Rank Test ^{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+6 month) period: The dominant strategy among neural network is to Hold Karachi 100 Index stock.**

**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.**

*Keywords:*## Key Points

- Trading Signals
- Can we predict stock market using machine learning?
- What are main components of Markov decision process?

## Karachi 100 Index Target Price Prediction Modeling Methodology

This study presents financial network indicators that can be applied to global stock market investment strategies. We propose to design both undirected and directed volatility networks of global stock market based on simple pair-wise correlation and system-wide connectedness of stock date using a vector auto-regressive model. We consider Karachi 100 Index Stock Decision Process with Wilcoxon Sign-Rank 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(Wilcoxon Sign-Rank Test)

^{5,6,7}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Statistical Inference (ML)) X S(n):→ (n+6 month) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of Karachi 100 Index stock

j:Nash equilibria

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+6 month)

**Sample Set:**Neural Network

**Stock/Index:**Karachi 100 Index Karachi 100 Index

**Time series to forecast n: 18 Sep 2022**for (n+6 month)

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold 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 B2 & long-term B2 forecasted stock rating.** We evaluate the prediction models Statistical Inference (ML) with Wilcoxon Sign-Rank Test ^{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+6 month) period: The dominant strategy among neural network is to Hold Karachi 100 Index stock.**

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

Rating | Short-Term | Long-Term Senior |
---|---|---|

Outlook* | B2 | B2 |

Operational Risk | 73 | 36 |

Market Risk | 58 | 62 |

Technical Analysis | 41 | 55 |

Fundamental Analysis | 58 | 41 |

Risk Unsystematic | 42 | 76 |

### Prediction Confidence Score

## References

- Bottomley, P. R. Fildes (1998), "The role of prices in models of innovation diffusion," Journal of Forecasting, 17, 539–555.
- M. J. Hausknecht and P. Stone. Deep recurrent Q-learning for partially observable MDPs. CoRR, abs/1507.06527, 2015
- Farrell MH, Liang T, Misra S. 2018. Deep neural networks for estimation and inference: application to causal effects and other semiparametric estimands. arXiv:1809.09953 [econ.EM]
- Thompson WR. 1933. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25:285–94
- Athey S, Imbens G. 2016. Recursive partitioning for heterogeneous causal effects. PNAS 113:7353–60
- Chen X. 2007. Large sample sieve estimation of semi-nonparametric models. In Handbook of Econometrics, Vol. 6B, ed. JJ Heckman, EE Learner, pp. 5549–632. Amsterdam: Elsevier
- Byron, R. P. O. Ashenfelter (1995), "Predicting the quality of an unborn grange," Economic Record, 71, 40–53.

## 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 Statistical Inference (ML) and Wilcoxon Sign-Rank Test

Q: Is Karachi 100 Index stock a buy or sell?

A: The dominant strategy among neural network is to Hold Karachi 100 Index Stock.

Q: Is Karachi 100 Index stock a good investment?

A: The consensus rating for Karachi 100 Index is Hold and assigned short-term B2 & long-term B2 forecasted stock rating.

Q: What is the consensus rating of Karachi 100 Index stock?

A: The consensus rating for Karachi 100 Index is Hold.

Q: What is the prediction period for Karachi 100 Index stock?

A: The prediction period for Karachi 100 Index is (n+6 month)