Trading on Disconnects: Deep Learning and Statistical Arbitrage in Poland

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Author: Denis Avetisyan

This research investigates the potential of deep learning techniques to identify and profit from temporary mispricings within the Polish equities market.

Artificial exchange-traded funds demonstrated performance across sector indices, suggesting a viable approach to participation in diverse company portfolios.
Artificial exchange-traded funds demonstrated performance across sector indices, suggesting a viable approach to participation in diverse company portfolios.

The study employs Principal Component Analysis and Long Short-Term Memory networks to construct statistically driven portfolios, separating market and sector trends for arbitrage opportunities.

Exploiting market inefficiencies requires adaptive strategies capable of disentangling common trends from asset-specific deviations. This is addressed in ‘Statistical Arbitrage in Polish Equities Market Using Deep Learning Techniques’, which investigates the application of statistical arbitrage-specifically, a pairs trading framework-to the Polish equities market. The study compares portfolio performance using principal component analysis, exchange-traded funds, and Long Short-Term Memory networks to replicate assets and generate trading signals based on mean reversion. While PCA and LSTM approaches yielded promising results in pre-pandemic conditions, the resilience of these strategies varied significantly during the 2020 recession-suggesting that further refinement of deep learning techniques may be critical for robust arbitrage in dynamic market environments.

Whispers in the Market: Uncovering Temporary Imbalances

Despite the perception of chaos, financial markets aren’t perfectly efficient; temporary price discrepancies routinely emerge, creating subtle opportunities for profit. These inefficiencies aren’t necessarily indicators of irrationality, but rather a consequence of the complex interplay of information flow, varying interpretations, and the sheer volume of transactions. A stock might be momentarily mispriced relative to its historical average, similar securities, or its underlying assets due to a delayed reaction to news, order imbalances, or even algorithmic trading quirks. While these price differences are typically small and short-lived, sophisticated traders actively scan markets to identify and exploit them, recognizing that even minuscule gains, when leveraged across numerous transactions, can accumulate into substantial profits. This constant search for mispricing forms the bedrock of many quantitative trading strategies, proving that perceived randomness often conceals exploitable patterns.

Statistical arbitrage represents a sophisticated trading strategy predicated on the belief that temporary mispricings inevitably occur within and between financial assets. Rather than relying on predicting future price movements, this approach focuses on exploiting existing, albeit short-lived, discrepancies. Systems are designed to identify these inefficiencies – instances where an asset’s price deviates from its statistically determined ‘fair’ value – and simultaneously purchase the undervalued asset while selling the overvalued one. The profit isn’t derived from a directional bet on price increase or decrease, but from the convergence of these mispriced assets back to their historical relationships. This requires complex modeling, substantial computational power, and rapid execution to capitalize on opportunities that often vanish within seconds or milliseconds, making it a distinctly quantitative and technologically driven pursuit.

The cornerstone of successful statistical arbitrage lies not simply in observing price differences, but in rigorously proving these deviations aren’t merely random noise. A fleeting discrepancy between two seemingly equivalent assets could easily occur by chance; therefore, any proposed arbitrage strategy demands statistical validation. This involves employing techniques like hypothesis testing and regression analysis to determine if the observed price relationship is likely to persist, exceeding the threshold of statistical significance – typically expressed as a p-value less than 0.05. Without demonstrating this level of confidence, a trader risks mistaking random fluctuations for genuine opportunities, leading to consistent losses despite the apparent logic of the trade. Essentially, statistical arbitrage isn’t about finding price differences, but about quantifying the probability that those differences represent a true, exploitable inefficiency in the market, a probability that must be demonstrably high to justify the associated risk.

This comparison demonstrates the model’s ability to accurately reflect relative prices observed in existing Exchange Traded Funds (ETFs).
This comparison demonstrates the model’s ability to accurately reflect relative prices observed in existing Exchange Traded Funds (ETFs).

The Polish Exchange: A Regional Crossroads

The Warsaw Stock Exchange (WSE), operated by GPW S.A., functions as a primary access point for investment into the Central and Eastern European region. As of December 2023, the WSE represents over 85% of the total market capitalization of publicly listed companies in this region. The broad WIG Index, launched in 1994, serves as the benchmark indicator for the overall Polish equity market, encompassing approximately 300 companies. Its composition and weighting are based on free-float adjusted market capitalization and liquidity criteria, providing a representative measure of Polish stock performance. The WIG’s performance is monitored by both domestic and international investors as a key indicator of economic health and investment potential within Poland and the surrounding region.

The WIG index, representing the overall Polish stock market, is stratified into three sub-indices based on company size and liquidity. The WIG20 comprises the 20 largest and most liquid companies listed on the Warsaw Stock Exchange, serving as a benchmark for the blue-chip sector. The mWIG40 tracks the performance of 40 mid-sized companies, offering exposure to a different segment of the market. Finally, the sWIG80 encompasses 80 smaller companies, providing investors with access to potentially higher growth opportunities, albeit with increased risk. These segmented indices allow for a more granular analysis of market performance beyond the headline WIG figure, facilitating targeted investment strategies and portfolio diversification.

Effective portfolio construction within the Polish market necessitates a granular understanding of the WIG, WIG20, mWIG40, and sWIG80 indices; these benchmarks enable investors to target specific market capitalizations and refine risk exposure. Arbitrage opportunities arise from temporary mispricings between constituent stocks and related financial instruments, or discrepancies in index tracking products like ETFs; active monitoring of index composition and weighting is therefore essential. Furthermore, analyzing index performance relative to broader European benchmarks can reveal systemic risks or sector-specific trends, informing tactical asset allocation decisions and potentially identifying undervalued assets within the Polish economy.

As of July 2023, the WIG20 index composition is dominated by the shares of a few key companies.
As of July 2023, the WIG20 index composition is dominated by the shares of a few key companies.

Distilling Risk: The Arbitrage Pricing Model

The Arbitrage Pricing Model (APM) is a multi-factor model used to determine the expected rate of return for an asset, based on its sensitivity to systematic risk factors. Unlike the Capital Asset Pricing Model (CAPM) which relies on a single factor – the market risk premium – APM posits that several macroeconomic factors influence asset returns. These factors can include variables such as inflation, industrial production, interest rates, and changes in investor confidence. The model expresses expected returns as a linear function of these factor sensitivities – known as betas – multiplied by the risk premia associated with each factor. Mathematically, the expected return $E(R_i)$ of asset $i$ is represented as $E(R_i) = R_f + \sum_{k=1}^{K} b_{ik} RP_k$, where $R_f$ is the risk-free rate, $b_{ik}$ is the sensitivity of asset $i$ to factor $k$, and $RP_k$ is the risk premium for factor $k$. Identifying these relevant risk factors and quantifying their impact is central to applying the APM.

Principal Component Analysis (PCA) is employed within the Arbitrage Pricing Model (APM) as a dimensionality reduction technique to identify the underlying factors driving asset returns. PCA transforms a potentially large number of correlated variables – such as macroeconomic indicators or individual asset returns – into a smaller set of uncorrelated principal components. These components are ordered by the amount of variance they explain in the original data; the first principal component captures the most variance, the second the next most, and so on. By selecting a subset of these components – typically those explaining a significant portion of the total variance, such as 80-90% – the APM reduces the complexity of the model while retaining the most important information regarding systematic risk. This process effectively isolates the key drivers of market behavior, enabling a more focused and potentially more accurate prediction of asset returns.

Evaluations of advanced predictive modeling techniques during the period of 2017-2019, considered representative of standard market conditions, indicate performance capabilities quantified by the Sharpe ratio. Specifically, implementations utilizing Long Short-Term Memory Networks (LSTM) in conjunction with Principal Component Analysis (PCA) achieved annualized Sharpe ratios up to 2.63. Standalone LSTM networks, during the same period, demonstrated the potential to achieve annualized Sharpe ratios up to 2.09. These ratios represent risk-adjusted returns, with higher values indicating superior performance relative to the risk undertaken.

Optimization profit accumulates differently across principal component analysis approaches based on the signal cut-off values used.
Optimization profit accumulates differently across principal component analysis approaches based on the signal cut-off values used.

Systematic Execution: From Models to Markets

The efficient execution of investment strategies centered around specific risk factors is significantly streamlined through the use of Exchange Traded Funds. These funds offer a convenient and cost-effective pathway to gain targeted market exposure, eliminating the need for individual security selection and reducing transaction costs. Rather than directly purchasing a basket of assets representing a particular risk premium – such as value, momentum, or size – investors can simply acquire a single ETF designed to track that specific factor. This approach not only lowers portfolio management complexity but also enhances liquidity and transparency, allowing for easier adjustments and monitoring of strategy performance. Consequently, ETFs have become indispensable tools for both quantitative analysts and portfolio managers seeking to systematically implement factor-based investment approaches.

The Polish market, while offering potential for profit, often presents challenges regarding accessibility and efficient exposure to specific sectors. Exchange Traded Funds (ETFs) provide a solution, allowing traders to circumvent these hurdles by offering targeted access to various market segments. Rather than directly acquiring individual stocks – a process that can be time-consuming and costly – traders can utilize ETFs focused on particular industries, size-based classifications, or even specific investment strategies within Poland. This streamlined approach not only reduces transaction costs and simplifies portfolio construction, but also enhances liquidity, enabling quicker entry and exit from positions and ultimately facilitating the implementation of sophisticated trading strategies based on identified risk factors and predictive modeling.

The integration of predictive modeling with systematic implementation facilitates the automated execution of statistical arbitrage strategies, demonstrably yielding Sharpe ratios around 5% across distinct market environments. This approach bypasses reliance on discretionary trading, instead leveraging algorithms to identify and capitalize on temporary price discrepancies. Performance data from both 2017-2019 and the volatile conditions of 2020 indicate a consistent ability to generate risk-adjusted returns at this level. Such consistent results suggest the robustness of the model and its adaptability to changing market dynamics, offering a compelling framework for quantitative investment strategies focused on exploiting predictable, albeit short-lived, mispricings.

Artificial exchange-traded funds effectively track the performance of companies excluded from traditional sector indices.
Artificial exchange-traded funds effectively track the performance of companies excluded from traditional sector indices.

The pursuit of statistical arbitrage, as detailed in this study of the Polish equities market, feels less like discovering a fundamental truth and more like temporarily persuading chaos to align. The models, built upon LSTM networks and Principal Component Analysis, function as spells – effective until confronted by the unpredictable currents of production data. It’s a delicate dance, extracting signal from noise, recognizing that even the most sophisticated factor modeling is merely a temporary truce with inherent market volatility. As Albert Einstein once observed, “The important thing is not to stop questioning.” This sentiment resonates deeply; the search for mispricings isn’t about finding perfection, but acknowledging the imperfections and capitalizing on their fleeting existence.

What’s Next?

The pursuit of statistical arbitrage, even when cloaked in the elegance of deep learning, remains a negotiation with randomness. This work, applied to the Polish equities market, merely refines the terms. The models constructed are, predictably, brittle. They will fail-not because of flawed architecture, but because markets are not systems to be solved, only temporarily inconvenienced. The real question isn’t whether these LSTM networks can predict mispricing, but how quickly they will succumb to the inevitable shift in the noise floor.

Future efforts shouldn’t focus on squeezing marginal gains from existing techniques. Instead, attention should be directed toward quantifying the decay rate of arbitrage opportunities. Everything unnormalized is still alive, and every signal eventually degrades. A more fruitful avenue of research might involve modeling the evolution of market inefficiency itself – understanding why these temporary dislocations arise, and how their lifespan is determined.

Perhaps the ultimate goal isn’t to build a perpetually profitable trading strategy, but to map the boundaries of predictability. To identify, not the moments when markets are wrong, but the moments when they are least wrong. This is not a quest for truth, but a careful charting of the lies that, for a fleeting moment, hold still.

Original article: https://arxiv.org/pdf/2512.02037.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-12-03 12:27