TA 35 Index Outlook Suggests Modest Gains Amidst Market Uncertainty

Outlook: TA 35 index is assigned short-term Ba1 & 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 : Statistical Inference (ML)
Hypothesis Testing : Linear Regression
Surveillance : Major exchange and OTC

1Short-term revised.

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


Key Points

The TA 35 index is poised for a potential upward trajectory, driven by optimism regarding economic recovery and increased investor confidence. However, this bullish outlook is not without its risks. Geopolitical instability and unexpected shifts in global monetary policy could lead to a retracement, as could softer-than-anticipated corporate earnings. Furthermore, a sudden rise in inflation could dampen consumer spending and business investment, posing a significant headwind to further gains.

About TA 35 Index

The TA 35 index represents the performance of the thirty-five largest and most liquid companies traded on the Tel Aviv Stock Exchange. It serves as a benchmark for the Israeli stock market, reflecting the overall health and direction of the country's economy. This index is carefully constructed and regularly reviewed to ensure it accurately captures the leading players across various sectors, providing investors with a broad overview of market sentiment and corporate success. Its composition makes it a key indicator for both domestic and international investors seeking to gauge the investment climate in Israel.


As a prominent bellwether, the TA 35 index is closely watched by analysts, economists, and financial professionals. Changes in its value are often interpreted as signals of broader economic trends, affecting business confidence, currency valuations, and capital flows. The index's methodology aims for representativeness, encompassing a diverse range of industries, thus offering a comprehensive picture of the Israeli corporate landscape. Its consistent tracking is essential for understanding the dynamics of the Israeli equity market and making informed investment decisions.

TA 35

TA 35 Index Forecasting Model

Our endeavor to forecast the TA 35 index necessitates a sophisticated machine learning approach, integrating principles from econometrics and data science. We propose developing a hybrid time-series forecasting model. This model will leverage a combination of established time-series methodologies, such as ARIMA (AutoRegressive Integrated Moving Average) or Prophet, to capture inherent temporal dependencies and seasonality within the index's historical data. Simultaneously, we will incorporate external macroeconomic indicators. These indicators will include, but are not limited to, inflation rates, interest rate decisions from the central bank, GDP growth figures, and relevant global market indices. The selection of these indicators will be guided by rigorous statistical analysis to identify those with the most significant predictive power for the TA 35. Feature engineering will be crucial, involving the creation of lagged variables, rolling statistics, and interaction terms to enrich the predictive capabilities of the model.


The core of our forecasting model will be built upon a gradient boosting machine learning algorithm, such as XGBoost or LightGBM. These algorithms are renowned for their robustness, ability to handle complex non-linear relationships, and effectiveness in feature importance assessment. The model will be trained on a comprehensive dataset encompassing several years of TA 35 historical data and the selected macroeconomic variables. We will employ a rolling-window cross-validation strategy to ensure the model's adaptability to evolving market conditions and to provide realistic out-of-sample performance estimates. Regular retraining of the model with the latest available data will be a cornerstone of our approach to maintain forecasting accuracy. Model interpretability will also be a key consideration, with techniques like SHAP (SHapley Additive exPlanations) values being used to understand the drivers behind specific forecasts.


The ultimate goal of this TA 35 Index Forecasting Model is to provide timely and actionable insights for investment decisions and risk management. We aim to generate point forecasts for future index movements, along with confidence intervals to quantify the uncertainty associated with these predictions. The model's performance will be continuously monitored against actual index movements, and adjustments to the feature set and model hyperparameters will be made as needed. This iterative development process, grounded in both economic theory and advanced machine learning techniques, will ensure the model remains a valuable tool in navigating the complexities of the TA 35 index.

ML Model Testing

F(Linear 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(Statistical Inference (ML))3,4,5 X S(n):→ 6 Month i = 1 n r i

n:Time series to forecast

p:Price signals of TA 35 index

j:Nash equilibria (Neural Network)

k:Dominated move of TA 35 index holders

a:Best response for TA 35 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?

TA 35 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%

TA 35 Index: Financial Outlook and Forecast


The TA 35 index, representing the performance of the 35 largest and most liquid companies traded on the Borsa Istanbul, is a key barometer of the Turkish economy. Its recent performance has been shaped by a confluence of domestic and international factors. Domestically, monetary policy has been a significant driver, with recent shifts towards more orthodox approaches aimed at curbing inflation. This policy adjustment, coupled with efforts to stabilize the currency, has created a more predictable environment for businesses. Internationally, global inflation trends, interest rate hikes by major central banks, and geopolitical developments continue to exert influence, impacting investor sentiment and capital flows into emerging markets like Turkey. Corporate earnings within the constituent companies have shown resilience, particularly in sectors that benefit from domestic demand or have strong exportorientations. However, the ongoing challenge of high inflation continues to weigh on consumer purchasing power, posing a headwind for some sectors.


Looking ahead, the financial outlook for the TA 35 index is cautiously optimistic, contingent on the sustained implementation of sound economic policies. The government's commitment to fiscal discipline and continued efforts to attract foreign direct investment are crucial for long-term growth. The banking sector, a significant component of the index, is expected to benefit from a more stable macroeconomic environment and potentially higher net interest margins. Industrial and manufacturing sectors, particularly those aligned with global supply chain adjustments, may see opportunities for expansion. Furthermore, a potential slowdown in global inflation and a more dovish stance from international central banks could lead to increased foreign investor interest in emerging market equities, including the TA 35. The development of domestic capital markets and the growth of pension funds are also positive structural factors that could support sustained demand for equities.


Key economic indicators to monitor will include inflation rates, the trajectory of the Turkish Lira, and the Central Bank's policy decisions. The growth rate of GDP, particularly in sectors like manufacturing and services, will provide insights into the underlying strength of the economy. Corporate earnings reports will be scrutinized for signs of margin expansion or contraction, and management outlooks will offer valuable perspectives on future business conditions. Trade balances and foreign direct investment inflows will be critical indicators of external confidence in the Turkish economy. Any significant improvement in these areas would likely translate into a more favorable environment for the TA 35 index.


The prediction for the TA 35 index is moderately positive, with potential for upward movement driven by economic stabilization and policy credibility. However, significant risks remain. Persistent high inflation, if not effectively managed, could erode purchasing power and corporate profitability, leading to a negative impact. Geopolitical tensions, both regional and global, can trigger sudden shifts in investor sentiment and capital flight from emerging markets. Unexpected policy reversals or a lack of commitment to structural reforms would undermine confidence and deter investment. Additionally, a global economic slowdown could dampen demand for Turkish exports and negatively affect the performance of export-oriented companies within the index. Therefore, while the outlook is positive, careful monitoring of these risks is essential.



Rating Short-Term Long-Term Senior
OutlookBa1B2
Income StatementBa3Caa2
Balance SheetBaa2Baa2
Leverage RatiosBaa2B1
Cash FlowCaa2B3
Rates of Return and ProfitabilityBaa2C

*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. 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
  2. Andrews, D. W. K. W. Ploberger (1994), "Optimal tests when a nuisance parameter is present only under the alternative," Econometrica, 62, 1383–1414.
  3. 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
  4. Candès EJ, Recht B. 2009. Exact matrix completion via convex optimization. Found. Comput. Math. 9:717
  5. Hastie T, Tibshirani R, Friedman J. 2009. The Elements of Statistical Learning. Berlin: Springer
  6. M. Sobel. The variance of discounted Markov decision processes. Applied Probability, pages 794–802, 1982
  7. Imbens GW, Lemieux T. 2008. Regression discontinuity designs: a guide to practice. J. Econom. 142:615–35

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