PSEi Composite Eyes Further Gains Amidst Positive Sentiment

Outlook: PSEi Composite index is assigned short-term Ba3 & long-term B2 estimated rating.
AUC Score : What is AUC Score?
Short-term Tactic¹ :
Dominant Strategy :
Time series to forecast n: for Weeks²
ML Model Testing : Multi-Task Learning (ML)
Hypothesis Testing : Chi-Square
Surveillance : Major exchange and OTC

¹Short-term revised.

²Time series is updated based on short-term trends.

Key Points

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About PSEi Composite Index

The PSEi Composite, or Philippine Stock Exchange Index, serves as the primary benchmark for the Philippine stock market's performance. It is a capitalization-weighted index, meaning the market value of each listed company influences its impact on the index's overall movement. The index reflects the combined performance of a select group of publicly listed companies deemed representative of the broader Philippine economy. Its value fluctuates continuously during trading hours, providing investors and analysts with an immediate gauge of market sentiment and economic health.

Understanding the PSEi is crucial for assessing investment opportunities within the Philippines. It provides a snapshot of overall market trends, indicating whether the majority of stocks are increasing or decreasing in value. Monitoring the index helps investors evaluate the potential risks and rewards associated with investing in the Philippine stock market. Furthermore, the PSEi is used as a reference for various financial products, including exchange-traded funds (ETFs) and other investment strategies, facilitating diversified portfolio management and exposure to the broader Philippine economy.

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ML Model Testing

F(Chi-Square)^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(Multi-Task Learning (ML))^3,4,5 X S(n):→ 16 Weeks $\overrightarrow{S}=\left(s_1,s_2,s_3\right)$

n:Time series to forecast

p:Price signals of PSEi Composite index

j:Nash equilibria (Neural Network)

k:Dominated move of PSEi Composite index holders

a:Best response for PSEi Composite target price

 

For further technical information as per how our model work we invite you to visit the article below: 

How do KappaSignal algorithms actually work?

PSEi Composite Index Forecast Strategic Interaction Table

Strategic Interaction Table Legend:

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 (Grey to Black): *Technical Analysis%

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Philippine Stock Exchange Index (PSEi) Composite: Financial Outlook and Forecast

The Philippine Stock Exchange Composite Index (PSEi) outlook for the near to mid-term is cautiously optimistic, contingent upon several critical factors. The Philippines, as an emerging market, remains susceptible to global economic shifts, particularly those originating from major economies like the United States and China. The nation's strong macroeconomic fundamentals, characterized by a stable inflation environment, a healthy level of foreign reserves, and robust domestic consumption, provide a solid foundation. Furthermore, government infrastructure spending initiatives, such as the "Build, Build, Build" program, are projected to fuel economic growth, benefiting construction, real estate, and related sectors. The ongoing digital transformation and rise of the business process outsourcing (BPO) industry are also key drivers, offering opportunities for companies involved in technology, telecommunications, and financial services. However, the speed and magnitude of these positive developments are intertwined with external headwinds that require careful monitoring and strategic adaptation.

The forecast for the PSEi incorporates the analysis of both domestic and global economic influences. The projected growth of the Philippine economy, estimated by various institutions, indicates a positive trend, albeit with varying degrees of optimism. This growth is expected to be driven by domestic demand, investment, and exports. However, geopolitical uncertainties, rising interest rates, and potential inflationary pressures worldwide could temper the bullish sentiment. The performance of specific sectors will likely diverge; for instance, sectors tied to infrastructure, consumer staples, and technology are anticipated to exhibit more resilience. Conversely, sectors heavily reliant on global demand, such as manufacturing and tourism, may encounter more pronounced volatility. Market sentiment, influenced by investor confidence and risk appetite, will also play a significant role in determining the trajectory of the index. Investor behavior will respond dynamically to changes in the regulatory environment, corporate earnings reports, and monetary policy adjustments.

Several key indicators will be closely scrutinized to assess the health of the PSEi. The inflation rate, a critical factor, is anticipated to influence monetary policy decisions made by the central bank. Interest rate movements will, in turn, affect investment flows and borrowing costs for companies listed on the PSE. Furthermore, the performance of key companies within the index, particularly those with substantial market capitalization, will have a significant impact on overall index performance. Foreign portfolio investments are a crucial component, as these capital inflows can both boost market liquidity and indicate international investor confidence. Corporate earnings reports will provide valuable insights into the financial health and future prospects of listed companies. The government's fiscal policies, including tax reforms and infrastructure projects, will also influence the outlook. Therefore, a holistic approach, considering economic, political, and social factors, will be essential in interpreting the movements of the PSEi.

The overall prediction is that the PSEi will experience moderate growth over the next 12-18 months, with potential for higher returns driven by favorable domestic factors. However, the realization of this positive forecast carries several inherent risks. Heightened global economic uncertainty, driven by inflation, interest rate hikes, and potential recession in major economies, represents a significant threat. Supply chain disruptions, geopolitical conflicts, and shifts in investor sentiment could also weigh on market performance. Domestic challenges, such as unexpected policy changes or delays in infrastructure projects, might also impede growth. Therefore, while the outlook remains optimistic, investors must carefully assess and mitigate the risks. Diversification and a long-term investment perspective are essential strategies for navigating the potential volatility and maximizing potential gains within the dynamic landscape of the Philippine stock market.

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Rating	Short-Term	Long-Term Senior
Outlook	Ba3	B2
Income Statement	Baa2	Caa2
Balance Sheet	Caa2	B3
Leverage Ratios	C	B1
Cash Flow	Baa2	B3
Rates of Return and Profitability	Baa2	B1

*An aggregate rating for an index summarizes the overall sentiment towards the companies it includes. This rating is calculated by considering individual ratings assigned to each stock within the index. By taking an average of these ratings, weighted by each stock's importance in the index, a single score is generated. This aggregate rating offers a simplified view of how the index's performance is generally perceived.
How does neural network examine financial reports and understand financial state of the company?

References

1. Doudchenko N, Imbens GW. 2016. Balancing, regression, difference-in-differences and synthetic control methods: a synthesis. NBER Work. Pap. 22791
2. Athey S, Imbens G, Wager S. 2016a. Efficient inference of average treatment effects in high dimensions via approximate residual balancing. arXiv:1604.07125 [math.ST]
3. Krizhevsky A, Sutskever I, Hinton GE. 2012. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, Vol. 25, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 1097–105. San Diego, CA: Neural Inf. Process. Syst. Found.
4. E. Altman. Constrained Markov decision processes, volume 7. CRC Press, 1999
5. M. Colby, T. Duchow-Pressley, J. J. Chung, and K. Tumer. Local approximation of difference evaluation functions. In Proceedings of the Fifteenth International Joint Conference on Autonomous Agents and Multiagent Systems, Singapore, May 2016
6. T. Morimura, M. Sugiyama, M. Kashima, H. Hachiya, and T. Tanaka. Nonparametric return distribution ap- proximation for reinforcement learning. In Proceedings of the 27th International Conference on Machine Learning, pages 799–806, 2010
7. Kallus N. 2017. Balanced policy evaluation and learning. arXiv:1705.07384 [stat.ML]