Should You Buy, Sell, or Hold? (SET Index Stock Forecast)

The stock market is an interesting industry to study. There are various variations present in it. Many experts have been studying and researching on the various trends that the stock market goes through. One of the major studies has been the attempt to predict the stock prices of various companies based on historical data. Prediction of stock prices will greatly help people to understand where and how to invest so that the risk of losing money is minimized. We evaluate SET Index prediction models with Modular Neural Network (Market News Sentiment Analysis) and Linear Regression^1,2,3,4 and conclude that the SET 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 SellHold SET Index stock.

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

Key Points

1. Understanding Buy, Sell, and Hold Ratings
2. Is Target price a good indicator?
3. Game Theory

SET Index Target Price Prediction Modeling Methodology

Neural networks, as an intelligent data mining method, have been used in many different challenging pattern recognition problems such as stock market prediction. However, there is no formal method to determine the optimal neural network for prediction purpose in the literature. In this paper, two kinds of neural networks, a feed forward multi layer Perceptron (MLP) and an Elman recurrent network, are used to predict a company's stock value based on its stock share value history. We consider SET Index Stock Decision Process with Linear Regression where A is the set of discrete actions of SET 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(Linear Regression)^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 (Market News Sentiment Analysis)) X S(n):→ (n+1 year) $\begin{array}{l}\int e^x\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of SET 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?

SET Index Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: SET Index SET Index
Time series to forecast n: 05 Nov 2022 for (n+1 year)

According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to SellHold SET 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%

Adjusted IFRS* Prediction Methods for SET Index

1. Leverage is a contractual cash flow characteristic of some financial assets. Leverage increases the variability of the contractual cash flows with the result that they do not have the economic characteristics of interest. Stand-alone option, forward and swap contracts are examples of financial assets that include such leverage. Thus, such contracts do not meet the condition in paragraphs 4.1.2(b) and 4.1.2A(b) and cannot be subsequently measured at amortised cost or fair value through other comprehensive income.
2. The expected credit losses on a loan commitment shall be discounted using the effective interest rate, or an approximation thereof, that will be applied when recognising the financial asset resulting from the loan commitment. This is because for the purpose of applying the impairment requirements, a financial asset that is recognised following a draw down on a loan commitment shall be treated as a continuation of that commitment instead of as a new financial instrument. The expected credit losses on the financial asset shall therefore be measured considering the initial credit risk of the loan commitment from the date that the entity became a party to the irrevocable commitment.
3. There is a rebuttable presumption that unless inflation risk is contractually specified, it is not separately identifiable and reliably measurable and hence cannot be designated as a risk component of a financial instrument. However, in limited cases, it is possible to identify a risk component for inflation risk that is separately identifiable and reliably measurable because of the particular circumstances of the inflation environment and the relevant debt market
4. Expected credit losses reflect an entity's own expectations of credit losses. However, when considering all reasonable and supportable information that is available without undue cost or effort in estimating expected credit losses, an entity should also consider observable market information about the credit risk of the particular financial instrument or similar financial instruments.

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

Conclusions

SET Index assigned short-term B1 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) with Linear Regression^1,2,3,4 and conclude that the SET 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 SellHold SET Index stock.

Financial State Forecast for SET Index SET Index Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B1	B2
Operational Risk	90	37
Market Risk	39	47
Technical Analysis	69	50
Fundamental Analysis	63	77
Risk Unsystematic	35	63

Prediction Confidence Score

Trust metric by Neural Network: 86 out of 100 with 463 signals.

References

1. Bottou L. 1998. Online learning and stochastic approximations. In On-Line Learning in Neural Networks, ed. D Saad, pp. 9–42. New York: ACM
2. Chow, G. C. (1960), "Tests of equality between sets of coefficients in two linear regressions," Econometrica, 28, 591–605.
3. Harris ZS. 1954. Distributional structure. Word 10:146–62
4. Rumelhart DE, Hinton GE, Williams RJ. 1986. Learning representations by back-propagating errors. Nature 323:533–36
5. Mazumder R, Hastie T, Tibshirani R. 2010. Spectral regularization algorithms for learning large incomplete matrices. J. Mach. Learn. Res. 11:2287–322
6. Imai K, Ratkovic M. 2013. Estimating treatment effect heterogeneity in randomized program evaluation. Ann. Appl. Stat. 7:443–70
7. A. K. Agogino and K. Tumer. Analyzing and visualizing multiagent rewards in dynamic and stochastic environments. Journal of Autonomous Agents and Multi-Agent Systems, 17(2):320–338, 2008

Frequently Asked Questions

Q: What is the prediction methodology for SET Index stock?
A: SET Index stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) and Linear Regression
Q: Is SET Index stock a buy or sell?
A: The dominant strategy among neural network is to SellHold SET Index Stock.
Q: Is SET Index stock a good investment?
A: The consensus rating for SET Index is SellHold and assigned short-term B1 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of SET Index stock?
A: The consensus rating for SET Index is SellHold.
Q: What is the prediction period for SET Index stock?
A: The prediction period for SET Index is (n+1 year)

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