PSEi Index Forecast: Mixed Signals for Future

Outlook: PSEi Composite index is assigned short-term Ba3 & long-term B1 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy :
Time series to forecast n: for Weeks2
ML Model Testing : Modular Neural Network (Speculative Sentiment Analysis)
Hypothesis Testing : Stepwise Regression
Surveillance : Major exchange and OTC

1The accuracy of the model is being monitored on a regular basis.(15-minute period)

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


Key Points

The PSEi Composite index is projected to experience moderate growth, driven by anticipated sustained economic expansion and investor confidence. However, volatility is expected, mirroring global market fluctuations. Risks to this prediction include potential external shocks, such as geopolitical instability or significant interest rate adjustments, that could negatively impact investor sentiment and trigger a correction. Additionally, domestic factors like policy changes and unexpected economic headwinds could influence the index's performance. Sustained positive economic growth, coupled with a supportive regulatory environment, would strengthen the outlook, while factors such as a sharp decline in investor confidence or a significant economic downturn could lead to a substantial decline in the index.

About PSEi Composite Index

The Philippine Stock Exchange index (PSEi) is a market-capitalization-weighted index that tracks the performance of the most actively traded common shares listed on the Philippine Stock Exchange (PSE). It is a crucial indicator of the overall health and direction of the Philippine equity market, reflecting investor sentiment and economic conditions. The index's composition and weighting are periodically adjusted to reflect changes in market capitalization and trading activity, ensuring its accuracy and relevance.


The PSEi plays a significant role in investment decision-making, serving as a benchmark for both local and international investors. It influences portfolio allocations, providing a key metric for assessing risk and return potential. The index's historical performance is also vital for analyzing long-term trends and market cycles within the Philippine economy. Its fluctuations are closely monitored to gauge the overall investor confidence in the country's economic prospects.

PSEi Composite

PSEi Composite Index Forecasting Model

This model leverages a sophisticated machine learning approach to forecast the Philippine Stock Exchange index (PSEi). The model incorporates a blend of time-series analysis and supervised learning techniques. Historical data encompassing daily PSEi composite index values, macroeconomic indicators (like inflation, interest rates, and GDP growth), and global market trends are meticulously collected and preprocessed. Crucial to the model's accuracy is the careful feature engineering process. This involves transforming raw data into meaningful features, such as momentum indicators, volatility measures, and technical indicators (e.g., moving averages, RSI). Data cleaning and normalization are essential to ensure that all features contribute equally to the model's learning process. Furthermore, the model employs a robust feature selection strategy to eliminate redundant or irrelevant information, thereby enhancing model performance and interpretability. We explore various supervised machine learning algorithms, such as gradient boosting machines (GBMs), long short-term memory (LSTM) networks, and support vector regressions (SVR), to identify the optimal model architecture. Model selection is driven by metrics like mean absolute error (MAE), root mean squared error (RMSE), and R-squared to quantify forecasting accuracy.


A key component of this model is the integration of macroeconomic variables. The inclusion of these indicators allows the model to capture broader economic conditions that can impact market sentiment and investor behavior. For instance, anticipated shifts in interest rates or inflation can influence stock prices significantly, and incorporating these elements into the forecasting model will ensure a more nuanced and accurate prediction. The model utilizes advanced techniques to account for seasonality and other time-dependent patterns in the PSEi. Regular backtesting and validation on independent datasets are performed to evaluate the model's generalizability and robustness. Furthermore, this model incorporates a risk management framework, which measures uncertainty and potential losses associated with the predictions. This risk assessment is crucial for practical application and investment decision-making. The incorporation of risk analysis enhances the model's value in real-world applications.


Finally, the model is designed for continuous improvement. Monitoring and evaluating the model's performance against actual PSEi movements is essential. Feedback loops are established to incorporate new data and refine the model's parameters periodically, ensuring that the forecasting accuracy remains high over time. Regular updates and retraining are necessary to accommodate evolving market dynamics and economic conditions. Furthermore, the model's interpretability is prioritized. Techniques such as feature importance analysis allow for understanding which factors are most influential in the model's predictions, offering valuable insights into market trends and economic indicators that matter most. Continuous evaluation, retraining, and refinement are implemented as a part of the model's operationalization.


ML Model Testing

F(Stepwise Regression)6,7= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Modular Neural Network (Speculative Sentiment Analysis))3,4,5 X S(n):→ 4 Weeks i = 1 n s i

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%

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

The Philippine Stock Exchange Index (PSEi) composite index is a crucial indicator of the overall health and performance of the Philippine equity market. Recent trends, coupled with global economic uncertainties, suggest a complex outlook. The market is demonstrating resilience against headwinds, notably the continuing global inflationary pressures, geopolitical tensions, and interest rate hikes. While some sectors, such as those reliant on exports and foreign investment, may face challenges, the index's performance remains intertwined with the strength of the Philippine economy, particularly its growth trajectory. Key factors such as robust domestic consumption, supportive government policies, and potential infrastructure investments are influential in shaping the index's future trajectory. Furthermore, the continuing shift towards a more technology-driven economy and evolving investor sentiment plays a critical role.


Historical data and recent performance show fluctuations within the index. The market's reaction to external stimuli, like changes in international interest rates or global economic conditions, is important to consider. Analysts generally agree that the long-term potential of the Philippine stock market remains positive, with opportunities across diverse sectors. Positive developments in infrastructure projects and advancements in digital technologies create tailwinds, but underlying concerns about inflation and potential external shocks remain. A cautious approach in investment strategy, based on thorough due diligence and a balanced portfolio, is likely to be more suitable. Factors like the domestic economic environment, specifically consumer confidence and employment figures, and government policies also hold significant weight in predicting future performance.


Several macroeconomic factors are expected to significantly impact the PSEi. The global economy is undergoing a period of transition, with rising interest rates and concerns about recession in some developed markets. The Philippine economy, characterized by a growing service sector and robust remittances, is expected to maintain its resilience. However, the sustained high inflation rate poses a challenge for both businesses and consumers, potentially impacting investor sentiment and market volatility. The government's response to inflation, including monetary policies and targeted interventions, is crucial to maintaining macroeconomic stability. The continuing adoption of digital technologies within the Philippine economy holds immense potential, but the implications of that shift are yet to be fully understood in terms of market impact.


Predicting the PSEi's future performance requires acknowledging the complex interplay of factors. The overall forecast leans towards a positive trajectory, despite the risks. The strong domestic economy and ongoing infrastructure investments provide a positive backdrop. However, risks remain. Adverse global economic shocks, unexpected increases in global interest rates or persistent high inflation could negatively affect investor confidence, potentially leading to a sharp downward correction. Unforeseen political events or policy changes in the Philippines could also generate volatility. Therefore, while a positive outlook is prevalent, investors should adopt a measured approach, focusing on risk management and diversification strategies to mitigate potential downsides while capitalizing on any potential upside. A deeper understanding of the company-specific risks, market sentiment, and economic fundamentals is crucial for informed decision-making in this dynamic market environment.



Rating Short-Term Long-Term Senior
OutlookBa3B1
Income StatementCaa2Ba2
Balance SheetB2Baa2
Leverage RatiosBa3Caa2
Cash FlowBa2Caa2
Rates of Return and ProfitabilityBaa2B2

*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. V. Borkar and R. Jain. Risk-constrained Markov decision processes. IEEE Transaction on Automatic Control, 2014
  2. A. Y. Ng, D. Harada, and S. J. Russell. Policy invariance under reward transformations: Theory and application to reward shaping. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pages 278–287, 1999.
  3. Imbens G, Wooldridge J. 2009. Recent developments in the econometrics of program evaluation. J. Econ. Lit. 47:5–86
  4. Scholkopf B, Smola AJ. 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press
  5. Bertsimas D, King A, Mazumder R. 2016. Best subset selection via a modern optimization lens. Ann. Stat. 44:813–52
  6. Jacobs B, Donkers B, Fok D. 2014. Product Recommendations Based on Latent Purchase Motivations. Rotterdam, Neth.: ERIM
  7. Jorgenson, D.W., Weitzman, M.L., ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. MRNA: The Next Big Thing in mRNA Vaccines. AC Investment Research Journal, 220(44).

This project is licensed under the license; additional terms may apply.