Stock price prediction has always been a challenging task for the researchers in financial domain. While the Efficient Market Hypothesis claims that it is impossible to predict stock prices accurately, there are work in the literature that have demonstrated that stock price movements can be forecasted with a reasonable degree of accuracy, if appropriate variables are chosen and suitable predictive models are built using those variables. In this work, we present a robust and accurate framework of stock price prediction using statistical, machine learning and deep learning methods We evaluate PSEi Composite Index prediction models with Modular Neural Network (Emotional Trigger/Responses Analysis) and Ridge Regression1,2,3,4 and conclude that the PSEi Composite 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 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
- Understanding Buy, Sell, and Hold Ratings
- Is now good time to invest?
- Game Theory
PSEi Composite Index Target Price Prediction Modeling Methodology
Stock market is considered chaotic, complex, volatile and dynamic. Undoubtedly, its prediction is one of the most challenging tasks in time series forecasting. Moreover existing Artificial Neural Network (ANN) approaches fail to provide encouraging results. Meanwhile advances in machine learning have presented favourable results for speech recognition, image classification and language processing. We consider PSEi Composite Index Stock Decision Process with Ridge Regression 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(Ridge Regression)5,6,7= X R(Modular Neural Network (Emotional Trigger/Responses Analysis)) X S(n):→ (n+3 month)
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+3 month)
Sample Set: Neural NetworkStock/Index: PSEi Composite Index PSEi Composite Index
Time series to forecast n: 15 Oct 2022 for (n+3 month)
According to price forecasts for (n+3 month) 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 B2 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) with Ridge Regression1,2,3,4 and conclude that the PSEi Composite 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 Hold PSEi Composite Index stock.
Financial State Forecast for PSEi Composite Index Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B2 | B2 |
| Operational Risk | 52 | 74 |
| Market Risk | 58 | 46 |
| Technical Analysis | 58 | 72 |
| Fundamental Analysis | 76 | 35 |
| Risk Unsystematic | 43 | 39 |
Prediction Confidence Score
References
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- Zubizarreta JR. 2015. Stable weights that balance covariates for estimation with incomplete outcome data. J. Am. Stat. Assoc. 110:910–22
- M. Benaim, J. Hofbauer, and S. Sorin. Stochastic approximations and differential inclusions, Part II: Appli- cations. Mathematics of Operations Research, 31(4):673–695, 2006
- M. L. Littman. Markov games as a framework for multi-agent reinforcement learning. In Ma- chine Learning, Proceedings of the Eleventh International Conference, Rutgers University, New Brunswick, NJ, USA, July 10-13, 1994, pages 157–163, 1994
- Morris CN. 1983. Parametric empirical Bayes inference: theory and applications. J. Am. Stat. Assoc. 78:47–55
- Chen, C. L. Liu (1993), "Joint estimation of model parameters and outlier effects in time series," Journal of the American Statistical Association, 88, 284–297.
- Mazumder R, Hastie T, Tibshirani R. 2010. Spectral regularization algorithms for learning large incomplete matrices. J. Mach. Learn. Res. 11:2287–322
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 (Emotional Trigger/Responses Analysis) and Ridge Regression
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 B2 & long-term B2 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+3 month)
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