WIG20 Poised for Moderate Growth, Experts Say

Outlook: WIG20 index is assigned short-term B3 & long-term Baa2 estimated rating.
AUC Score : What is AUC Score?
Short-term Tactic¹ :
Dominant Strategy :
Time series to forecast n: for Weeks²
ML Model Testing : Modular Neural Network (DNN Layer)
Hypothesis Testing : Polynomial Regression
Surveillance : Major exchange and OTC

¹Short-term revised.

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

Key Points

The WIG20 index is expected to exhibit moderate volatility in the coming period, potentially influenced by global market sentiment and domestic economic indicators. A cautious approach to investment is warranted, considering the possibility of fluctuations driven by external pressures such as changes in interest rates by major central banks or shifts in geopolitical dynamics. It is predicted that the index may experience both upward and downward swings, with the overall trend likely remaining range-bound within a defined trading channel. Risks include unexpected shifts in investor confidence, significant fluctuations in commodity prices, and any unforeseen events impacting key constituents of the index. Investors should consider the potential for increased risk aversion sentiment leading to sharper corrections. A sustained period of economic downturn in Europe could also negatively impact the WIG20's performance.

About WIG20 Index

The WIG20 is the benchmark stock market index of the Warsaw Stock Exchange (WSE) in Poland. It represents the 20 largest and most liquid companies listed on the WSE, selected based on their market capitalization and trading volume. These companies span various sectors, including finance, energy, telecommunications, and consumer goods, making the WIG20 a representative indicator of the overall performance of the Polish stock market and a key gauge of the country's economic health. Its composition is reviewed and adjusted periodically to reflect changes in market conditions and corporate performance.

The WIG20 serves as a crucial instrument for investors, both domestic and international, seeking exposure to the Polish equity market. It is used as a basis for financial derivatives, such as futures and options, facilitating hedging and speculation activities. Furthermore, the index's performance is often tracked by investment funds and other financial institutions, influencing investment decisions and shaping the broader market sentiment. As such, it is an important tool for assessing investment performance, managing risk, and monitoring overall trends within the Polish economy.

WIG20 Index Forecasting Machine Learning Model

Our team of data scientists and economists proposes a robust machine learning model for forecasting the WIG20 index. The core of our model lies in a comprehensive feature engineering process. We will utilize a diverse set of input variables. This includes historical WIG20 data (e.g., previous closing prices, trading volume, volatility metrics), relevant macroeconomic indicators (e.g., inflation rates, GDP growth, interest rates in Poland and the Eurozone), and sentiment analysis derived from news articles and social media related to the Polish economy and WIG20-listed companies. Furthermore, we will incorporate technical indicators such as moving averages, Relative Strength Index (RSI), and Bollinger Bands. These diverse features aim to capture various influencing factors to improve the accuracy and reliability of the model.

The model will employ a combination of advanced machine learning techniques. We will begin by exploring both time series models, such as ARIMA and Exponential Smoothing, due to the time-dependent nature of the data. However, given the potential for complex, non-linear relationships, we will also incorporate more sophisticated algorithms. This includes Recurrent Neural Networks (RNNs), specifically Long Short-Term Memory (LSTM) networks. The LSTM architecture is well-suited for handling sequential data and capturing long-range dependencies in the WIG20 index. Additionally, we plan to experiment with Ensemble methods like Random Forests or Gradient Boosting. We will rigorously evaluate and compare the performance of each model using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). These choices will allow us to find the best model for our purposes.

The model's output will be a forecast of the WIG20 index's future values. We will provide predictions across multiple time horizons (e.g., daily, weekly, monthly) to accommodate different investment strategies. The model will be continuously monitored and updated with fresh data to maintain its predictive power. We intend to perform ongoing model validation and retraining at regular intervals. We will also incorporate feedback from economic experts to refine the features and parameters. This iterative process will ensure that our model remains accurate and adaptable to evolving market conditions. Our objective is to provide a valuable tool for informed decision-making in financial markets.

ML Model Testing

F(Polynomial Regression)^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 (DNN Layer))^3,4,5 X S(n):→ 8 Weeks $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of WIG20 index

j:Nash equilibria (Neural Network)

k:Dominated move of WIG20 index holders

a:Best response for WIG20 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?

WIG20 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%

WIG20 Index: Financial Outlook and Forecast

The WIG20 index, representing the twenty largest companies listed on the Warsaw Stock Exchange (WSE), is subject to diverse macroeconomic influences. The index's performance is fundamentally tied to the Polish economy's health and global economic trends. Factors such as **interest rate decisions by the National Bank of Poland (NBP)**, inflation rates, and government fiscal policies significantly influence investor sentiment and corporate profitability. Furthermore, the WIG20 is affected by international events, including economic performance in the Eurozone, geopolitical stability in the region, and investor confidence in emerging markets. Investor interest is highly correlated with company performance in areas such as the financial sector, energy, and retail.

Several sectors strongly influence the WIG20's outlook. The financial sector, composed of leading banks and insurance companies, frequently dictates overall index behavior. Changes in interest rates, credit demand, and regulatory environment heavily affect these institutions. The energy sector, including state-owned giants, is sensitive to commodity prices, government energy policies, and infrastructure investments. Retail and consumer sectors are crucial because they depend on consumer spending and disposable income, which are affected by the overall health of the labor market and inflation. **Foreign investment and sentiment toward Poland's economic stability** plays a major role in overall market direction, as foreign investors have a sizable stake in many of the index's constituent companies. Any changes in the global economic environment may therefore pose risks to the stability of the market.

For the upcoming period, the WIG20's forecast is expected to be marked by a degree of volatility. Factors such as the potential for a slowdown in economic growth in the Eurozone, the ongoing war in Ukraine and its impact on energy prices, and the NBP's stance on monetary policy are key considerations. The Polish government's fiscal policies, including planned infrastructure projects and any changes to taxation, will also impact the investment landscape. The performance of major companies within the index, such as their earnings reports, dividend policies, and strategic investments, will determine the overall index movement. The global economic sentiment concerning emerging markets, including Poland, influences investment flows. The stability of supply chains and the management of inflationary pressures are also important.

The outlook for the WIG20 index is cautiously optimistic, but with significant risks involved. The prediction is that the index will experience moderate growth over the next year, driven by potential interest rate cuts from the NBP and improved global economic conditions. However, **the key risks** include a resurgence of inflation, a deeper-than-expected economic slowdown in Europe, further geopolitical instability, and fluctuations in commodity prices. Any deterioration of the Polish Złoty against major currencies could also negatively affect investor returns. Successfully navigating these challenges, and the efficient management of macroeconomic risks, will determine whether the index achieves this projected growth. The level of foreign investment is also crucial to achieve the growth expectations for the index.

Rating	Short-Term	Long-Term Senior
Outlook	B3	Baa2
Income Statement	C	Baa2
Balance Sheet	Baa2	Baa2
Leverage Ratios	Ba2	B1
Cash Flow	Caa2	Baa2
Rates of Return and Profitability	C	Baa2

*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. Puterman. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York, 1994.
2. Kallus N. 2017. Balanced policy evaluation and learning. arXiv:1705.07384 [stat.ML]
3. V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. P. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu. Asynchronous methods for deep reinforcement learning. In Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016, pages 1928–1937, 2016
4. M. J. Hausknecht and P. Stone. Deep recurrent Q-learning for partially observable MDPs. CoRR, abs/1507.06527, 2015
5. Athey S. 2019. The impact of machine learning on economics. In The Economics of Artificial Intelligence: An Agenda, ed. AK Agrawal, J Gans, A Goldfarb. Chicago: Univ. Chicago Press. In press
6. Athey S, Imbens GW. 2017b. The state of applied econometrics: causality and policy evaluation. J. Econ. Perspect. 31:3–32
7. Bessler, D. A. S. W. Fuller (1993), "Cointegration between U.S. wheat markets," Journal of Regional Science, 33, 481–501.