Buy, Sell, or Hold? (PSEi Composite Index Stock Forecast)

The prediction of a stock market direction may serve as an early recommendation system for short-term investors and as an early financial distress warning system for long-term shareholders. We evaluate PSEi Composite Index prediction models with Modular Neural Network (Social Media Sentiment Analysis) and Paired T-Test^1,2,3,4 and conclude that the PSEi Composite Index stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold PSEi Composite Index stock.

Keywords: PSEi Composite Index, PSEi Composite 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. What are main components of Markov decision process?
3. Why do we need predictive models?

PSEi Composite Index Target Price Prediction Modeling Methodology

Prediction of future movement of stock prices has been a subject matter of many research work. In this work, we propose a hybrid approach for stock price prediction using machine learning and deep learning-based methods. We consider PSEi Composite Index Stock Decision Process with Paired T-Test where A is the set of discrete actions of PSEi Composite 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= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Modular Neural Network (Social Media Sentiment Analysis)) X S(n):→ (n+16 weeks) $\begin{array}{l}\int e^x\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of PSEi Composite 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?

PSEi Composite Index Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: PSEi Composite Index PSEi Composite Index
Time series to forecast n: 17 Oct 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold PSEi Composite 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

PSEi Composite Index assigned short-term B3 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) with Paired T-Test^1,2,3,4 and conclude that the PSEi Composite Index stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold PSEi Composite Index stock.

Financial State Forecast for PSEi Composite Index Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B3	Ba3
Operational Risk	59	83
Market Risk	33	65
Technical Analysis	37	82
Fundamental Analysis	37	45
Risk Unsystematic	85	32

Prediction Confidence Score

Trust metric by Neural Network: 93 out of 100 with 646 signals.

References

1. K. Boda and J. Filar. Time consistent dynamic risk measures. Mathematical Methods of Operations Research, 63(1):169–186, 2006
2. Cheung, Y. M.D. Chinn (1997), "Further investigation of the uncertain unit root in GNP," Journal of Business and Economic Statistics, 15, 68–73.
3. Y. Le Tallec. Robust, risk-sensitive, and data-driven control of Markov decision processes. PhD thesis, Massachusetts Institute of Technology, 2007.
4. A. Tamar, Y. Glassner, and S. Mannor. Policy gradients beyond expectations: Conditional value-at-risk. In AAAI, 2015
5. Kitagawa T, Tetenov A. 2015. Who should be treated? Empirical welfare maximization methods for treatment choice. Tech. Rep., Cent. Microdata Methods Pract., Inst. Fiscal Stud., London
6. K. Tumer and D. Wolpert. A survey of collectives. In K. Tumer and D. Wolpert, editors, Collectives and the Design of Complex Systems, pages 1–42. Springer, 2004.
7. V. Borkar. An actor-critic algorithm for constrained Markov decision processes. Systems & Control Letters, 54(3):207–213, 2005.

Frequently Asked Questions

Q: What is the prediction methodology for PSEi Composite Index stock?
A: PSEi Composite Index stock prediction methodology: We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) and Paired T-Test
Q: Is PSEi Composite Index stock a buy or sell?
A: The dominant strategy among neural network is to Hold PSEi Composite Index Stock.
Q: Is PSEi Composite Index stock a good investment?
A: The consensus rating for PSEi Composite Index is Hold and assigned short-term B3 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of PSEi Composite Index stock?
A: The consensus rating for PSEi Composite Index is Hold.
Q: What is the prediction period for PSEi Composite Index stock?
A: The prediction period for PSEi Composite Index is (n+16 weeks)

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