Machine Learning stock prediction: CSE All-Share Index Stock Prediction

Stock market investment strategies are complex and rely on an evaluation of vast amounts of data. In recent years, machine learning techniques have increasingly been examined to assess whether they can improve market forecasting when compared with traditional approaches. The objective for this study is to identify directions for future machine learning stock market prediction research based upon a review of current literature. We evaluate CSE All-Share Index prediction models with Modular Neural Network (CNN Layer) and Chi-Square^1,2,3,4 and conclude that the CSE All-Share 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 CSE All-Share Index stock.

Keywords: CSE All-Share Index, CSE All-Share Index, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

1. Technical Analysis with Algorithmic Trading
2. Technical Analysis with Algorithmic Trading
3. Reaction Function

CSE All-Share Index Target Price Prediction Modeling Methodology

Social media comments have in the past had an instantaneous effect on stock markets. This paper investigates the sentiments expressed on the social media platform Twitter and their pr edictive impact on the Stock Market. We consider CSE All-Share Index Stock Decision Process with Chi-Square where A is the set of discrete actions of CSE All-Share 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(Chi-Square)^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 (CNN Layer)) X S(n):→ (n+1 year) $\overrightarrow{R}=\left(r_1,r_2,r_3\right)$

n:Time series to forecast

p:Price signals of CSE All-Share 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?

CSE All-Share Index Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: CSE All-Share Index CSE All-Share Index
Time series to forecast n: 14 Oct 2022 for (n+1 year)

According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold CSE All-Share 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

CSE All-Share Index assigned short-term Baa2 & long-term Ba2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (CNN Layer) with Chi-Square^1,2,3,4 and conclude that the CSE All-Share 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 CSE All-Share Index stock.

Financial State Forecast for CSE All-Share Index Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Baa2	Ba2
Operational Risk	88	55
Market Risk	89	66
Technical Analysis	89	75
Fundamental Analysis	63	64
Risk Unsystematic	40	83

Prediction Confidence Score

Trust metric by Neural Network: 72 out of 100 with 464 signals.

References

1. Friedberg R, Tibshirani J, Athey S, Wager S. 2018. Local linear forests. arXiv:1807.11408 [stat.ML]
2. Chipman HA, George EI, McCulloch RE. 2010. Bart: Bayesian additive regression trees. Ann. Appl. Stat. 4:266–98
3. Nie X, Wager S. 2019. Quasi-oracle estimation of heterogeneous treatment effects. arXiv:1712.04912 [stat.ML]
4. 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
5. Dudik M, Erhan D, Langford J, Li L. 2014. Doubly robust policy evaluation and optimization. Stat. Sci. 29:485–511
6. Holland PW. 1986. Statistics and causal inference. J. Am. Stat. Assoc. 81:945–60
7. Breusch, T. S. (1978), "Testing for autocorrelation in dynamic linear models," Australian Economic Papers, 17, 334–355.

Frequently Asked Questions

Q: What is the prediction methodology for CSE All-Share Index stock?
A: CSE All-Share Index stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Chi-Square
Q: Is CSE All-Share Index stock a buy or sell?
A: The dominant strategy among neural network is to Hold CSE All-Share Index Stock.
Q: Is CSE All-Share Index stock a good investment?
A: The consensus rating for CSE All-Share Index is Hold and assigned short-term Baa2 & long-term Ba2 forecasted stock rating.
Q: What is the consensus rating of CSE All-Share Index stock?
A: The consensus rating for CSE All-Share Index is Hold.
Q: What is the prediction period for CSE All-Share Index stock?
A: The prediction period for CSE All-Share Index is (n+1 year)

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