Different machine learning algorithms are discussed in this literature review. These algorithms can be used for predicting the stock market. The prediction of the stock market is one of the challenging tasks that must have to be handled. In this paper, it is discussed how the machine learning algorithms can be used for predicting the stock value. We evaluate Karachi 100 Index prediction models with Modular Neural Network (Market Volatility Analysis) and Multiple Regression1,2,3,4 and conclude that the Karachi 100 Index stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Sell 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. Can neural networks predict stock market?
2. What is neural prediction?
3. What is prediction model? ## Karachi 100 Index Target Price Prediction Modeling Methodology

Financial markets are fascinating if you can predict them. Also, the traders acting on financial markets produce a vast amount of information to analyse the consequences of investing according to the current market trends. Stock Market prediction is the technique to determine whether stock value will go up or down as it plays an active role in the financial gain of nation's economic status. We consider Karachi 100 Index Stock Decision Process with Multiple Regression 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(Multiple Regression)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(Modular Neural Network (Market Volatility Analysis)) X S(n):→ (n+3 month) $\stackrel{\to }{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

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

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## Karachi 100 Index Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: Karachi 100 Index Karachi 100 Index
Time series to forecast n: 10 Sep 2022 for (n+3 month)

According to price forecasts for (n+3 month) 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%

## Conclusions

Karachi 100 Index assigned short-term B2 & long-term Ba2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) with Multiple Regression1,2,3,4 and conclude that the Karachi 100 Index stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Sell Karachi 100 Index stock.

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

Rating Short-Term Long-Term Senior
Outlook*B2Ba2
Operational Risk 7034
Market Risk4687
Technical Analysis5766
Fundamental Analysis3283
Risk Unsystematic6869

### Prediction Confidence Score

Trust metric by Neural Network: 90 out of 100 with 599 signals.

## References

1. A. Tamar, D. Di Castro, and S. Mannor. Policy gradients with variance related risk criteria. In Proceedings of the Twenty-Ninth International Conference on Machine Learning, pages 387–396, 2012.
2. Athey S, Imbens GW. 2017b. The state of applied econometrics: causality and policy evaluation. J. Econ. Perspect. 31:3–32
3. Chernozhukov V, Newey W, Robins J. 2018c. Double/de-biased machine learning using regularized Riesz representers. arXiv:1802.08667 [stat.ML]
4. Pennington J, Socher R, Manning CD. 2014. GloVe: global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods on Natural Language Processing, pp. 1532–43. New York: Assoc. Comput. Linguist.
5. V. Borkar. Stochastic approximation: a dynamical systems viewpoint. Cambridge University Press, 2008
6. V. Borkar. Q-learning for risk-sensitive control. Mathematics of Operations Research, 27:294–311, 2002.
7. Hastie T, Tibshirani R, Tibshirani RJ. 2017. Extended comparisons of best subset selection, forward stepwise selection, and the lasso. arXiv:1707.08692 [stat.ME]
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 Modular Neural Network (Market Volatility Analysis) and Multiple Regression
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 B2 & long-term Ba2 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+3 month)

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