## Abstract

We evaluate Karachi 100 Index prediction models with Active Learning (ML) and Paired 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+1 year) period: The dominant strategy among neural network is to Hold 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

1. Reaction Function
2. How can neural networks improve predictions?
3. Is it better to buy and sell or hold?

## Karachi 100 Index Target Price Prediction Modeling Methodology

We consider Karachi 100 Index Stock Decision Process with Paired 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(Paired T-Test)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Active Learning (ML)) X S(n):→ (n+1 year) $\stackrel{\to }{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

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+1 year)

Sample Set: Neural Network
Stock/Index: Karachi 100 Index Karachi 100 Index
Time series to forecast n: 07 Sep 2022 for (n+1 year)

According to price forecasts for (n+1 year) 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 B1 & long-term B1 forecasted stock rating. We evaluate the prediction models Active Learning (ML) with Paired 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+1 year) 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*B1B1
Operational Risk 5933
Market Risk6064
Technical Analysis4086
Fundamental Analysis6758
Risk Unsystematic8454

### Prediction Confidence Score

Trust metric by Neural Network: 90 out of 100 with 523 signals.

## References

1. 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]
2. Abadie A, Diamond A, Hainmueller J. 2010. Synthetic control methods for comparative case studies: estimat- ing the effect of California's tobacco control program. J. Am. Stat. Assoc. 105:493–505
3. Athey S, Imbens GW. 2017a. The econometrics of randomized experiments. In Handbook of Economic Field Experiments, Vol. 1, ed. E Duflo, A Banerjee, pp. 73–140. Amsterdam: Elsevier
4. Matzkin RL. 1994. Restrictions of economic theory in nonparametric methods. In Handbook of Econometrics, Vol. 4, ed. R Engle, D McFadden, pp. 2523–58. Amsterdam: Elsevier
5. Morris CN. 1983. Parametric empirical Bayes inference: theory and applications. J. Am. Stat. Assoc. 78:47–55
6. M. Puterman. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York, 1994.
7. 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.
Frequently Asked QuestionsQ: What is the prediction methodology for Karachi 100 Index stock?
A: Karachi 100 Index stock prediction methodology: We evaluate the prediction models Active Learning (ML) and Paired T-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 B1 & long-term B1 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+1 year)