Balancing Risk and Reward: AI-Powered Portfolio Strategies

Author: Denis Avetisyan

A new study examines how deep reinforcement learning can be used to dynamically adjust investment portfolios, but finds that minimizing volatility doesn’t always maximize gains.

A deep reinforcement learning framework enables an agent to learn optimal behavior by observing environmental states and utilizing a deep neural network to compute a policy, $ \pi(s,a)$, which guides action selection, reward acquisition, and subsequent policy refinement for improved decision-making.

Research reveals that risk-aware deep reinforcement learning for portfolio optimization requires careful design and backtesting to avoid diminishing returns and a lower Sharpe ratio.

Balancing portfolio return with downside risk remains a central challenge in modern finance, often requiring complex trade-offs between exploration and exploitation. This is addressed in ‘Risk-Aware Deep Reinforcement Learning for Dynamic Portfolio Optimization’, which investigates a deep reinforcement learning framework for adaptive asset allocation under market uncertainty. Results demonstrate that while the proposed model successfully stabilizes portfolio volatility, achieving robust risk-adjusted returns proves difficult due to a tendency toward overly conservative policy convergence. Can improved reward shaping and hybrid strategies unlock the full potential of risk-aware deep reinforcement learning for practical portfolio management?

Beyond Static Models: Adapting to Real-World Financial Dynamics

Conventional portfolio construction, heavily influenced by Modern Portfolio Theory, frequently encounters limitations when navigating the intricacies of real-world financial markets. These strategies, while historically valuable, often operate under simplifying assumptions regarding asset correlations and return distributions, proving inadequate when confronted with non-stationary conditions and unexpected events. The inherent rigidity of these models struggles to capture the dynamic interplay of macroeconomic factors, investor sentiment, and evolving market structures. Consequently, portfolios optimized using these traditional methods can exhibit diminished performance during periods of high volatility or fail to capitalize on emerging opportunities, highlighting the need for more adaptive and responsive approaches to asset allocation.

Conventional portfolio construction often operates under the constraint of static assumptions regarding risk and return, a methodology increasingly challenged by real-world market behavior. This approach frequently overlooks the intricate relationships between diverse market factors – such as macroeconomic indicators, investor sentiment, and geopolitical events – which dynamically influence asset performance. The failure to account for these nuanced interactions limits the potential for optimization, as portfolios remain fixed despite shifting conditions. Consequently, static models struggle to adapt to evolving market regimes and may not fully capture opportunities for enhanced returns or effectively mitigate downside risk, hindering performance in environments characterized by volatility and complexity. A dynamic approach, capable of continuously reassessing and adjusting to these changing factors, is therefore crucial for building truly resilient and high-performing portfolios.

Traditional portfolio construction methods, while historically significant, can falter when confronted with the realities of turbulent markets and evolving opportunities. Early analyses revealed these portfolios initially achieved a Sharpe Ratio of 1.41-a measure of risk-adjusted return-but this performance proved susceptible to erosion during periods of increased volatility. The inherent limitations of static assumptions regarding risk and return prevent these strategies from dynamically adapting to changing market conditions, potentially leading to suboptimal outcomes and missed opportunities for enhanced gains. Consequently, investors employing these conventional approaches may experience underperformance compared to strategies capable of more agile and responsive portfolio management.

The Efficient Frontier demonstrates the best possible return for a given level of risk, while the Capital Market Line indicates optimal portfolio combinations incorporating a risk-free asset.

Embracing Intelligence: Deep Reinforcement Learning for Dynamic Allocation

Deep Reinforcement Learning (DRL) provides a method for dynamic portfolio allocation by training an agent to maximize cumulative rewards through interaction with historical and real-time market data. Unlike traditional portfolio optimization techniques that rely on predefined models and assumptions, DRL agents learn optimal trading policies directly from data, adapting to changing market conditions without explicit reprogramming. This is achieved through a trial-and-error process where the agent receives feedback in the form of rewards or penalties based on the outcome of its trading actions, iteratively refining its strategy to improve performance. The agent learns to select optimal asset allocations and trade execution timings based on observed market states, aiming to achieve a desired investment objective, such as maximizing returns or minimizing risk.

Defining portfolio management as a sequential decision-making process allows Deep Reinforcement Learning (DRL) agents to model investment strategies as a Markov Decision Process (MDP). In this framework, the agent observes a market state, takes an action – such as buying, selling, or holding assets – and receives a reward based on portfolio performance. This iterative process enables the agent to learn an optimal policy for maximizing cumulative rewards over time. Crucially, the sequential nature of this approach facilitates adaptation to changing market dynamics, as the agent continuously updates its policy based on new observations and outcomes, unlike static allocation strategies. This capability is particularly valuable in navigating complex market landscapes characterized by non-stationarity and uncertainty.

Deep Neural Networks (DNNs) function as non-linear function approximators within the DRL framework, addressing the curse of dimensionality inherent in high-dimensional state spaces typical of financial markets. Traditional methods struggle to model the complex relationships between numerous assets and market indicators; DNNs, with their multiple layers and adjustable weights, can learn these intricate dependencies directly from data. Specifically, DNNs approximate the optimal Q-function or policy, mapping states to actions or action values, respectively. This capability allows the agent to generalize from observed data to unseen market conditions and effectively navigate the vast state space without requiring explicit feature engineering or pre-defined rules. The network’s parameters are adjusted during the training process via algorithms like backpropagation, optimizing the agent’s ability to predict optimal actions and maximize cumulative rewards.

A deep reinforcement learning portfolio strategy dynamically rebalances asset weights to maximize cumulative value, as demonstrated by its differing weight allocation compared to a static equal-weights portfolio.

Mitigating Risk: Strategies for Optimization and Control

Risk-Aware Optimization in Deep Reinforcement Learning (DRL) involves modifying the standard reward function to directly account for undesirable risk metrics. Specifically, measures like Conditional Value-at-Risk (CVaR), which quantifies expected loss beyond a certain percentile, are incorporated as penalties or adjustments to the reward. This encourages the DRL agent to not simply maximize cumulative returns, but to actively minimize potential downside risk. The CVaR is calculated as the average loss exceeding the Value-at-Risk (VaR) threshold, providing a more sensitive measure of tail risk than standard deviation. By explicitly including these risk measures, the agent learns to prioritize strategies with lower probability of significant losses, resulting in more robust and reliable performance in volatile environments. The mathematical formulation typically involves subtracting a weighted multiple of the CVaR from the standard reward signal, effectively penalizing high-risk actions.

Integrating downside risk mitigation into Deep Reinforcement Learning (DRL) agent training involves explicitly penalizing outcomes that fall below acceptable thresholds. This is achieved by modifying the reward function to account for factors beyond simple profit maximization; the agent is incentivized to avoid substantial losses. Drawdown, defined as the peak-to-trough decline during a specific period, is a key metric used to quantify this risk. By directly controlling drawdown during training, the DRL agent learns to make trading decisions that prioritize capital preservation alongside return generation, resulting in a more robust and stable trading strategy, particularly in volatile market conditions. This approach moves beyond solely maximizing cumulative reward and towards optimizing for a more risk-adjusted return profile.

Incorporating transaction costs into the training of Deep Reinforcement Learning (DRL) agents for trading applications improves the agent’s ability to develop realistic and deployable strategies. Traditional DRL training often neglects these costs, leading to over-optimistic policies that would be unprofitable in a live trading environment due to fees such as brokerage commissions, slippage, and bid-ask spreads. By explicitly modeling transaction costs as a component of the reward function, the agent learns to internalize these expenses when making trading decisions, resulting in strategies that more accurately reflect real-world profitability and are therefore more practical for implementation. This leads to a more conservative and sustainable trading approach, reducing the likelihood of generating high-frequency trades that erode profits through cumulative costs.

Validating Intelligence: Refinement and Robustness Testing

Backtesting, the process of applying a trading strategy to historical data, serves as the initial evaluation phase for the DRL agent. This methodology allows developers to observe the agent’s behavior across a defined period, quantifying metrics such as profitability, trade frequency, and drawdown. The resulting performance data provides preliminary insights into the viability of the implemented trading strategy and identifies potential areas for refinement. However, it is crucial to acknowledge that backtesting results are susceptible to overfitting, where the strategy performs well on the historical data but fails to generalize to future, unseen market conditions. Therefore, backtesting is best utilized as a first step, followed by more robust validation techniques like cross-validation to ensure the agent’s true performance capabilities are accurately assessed.

Cross-validation is a resampling technique used to assess the generalization ability of a machine learning model, such as a Deep Reinforcement Learning (DRL) agent, by partitioning the available data into multiple subsets. Typically, the data is divided into k folds; the model is trained on $k-1$ folds and validated on the remaining fold. This process is repeated k times, with each fold serving as the validation set once, and the performance metrics are averaged to provide a more robust estimate of the agent’s expected performance on unseen data. Utilizing cross-validation mitigates the risk of overfitting to the specific characteristics of a single historical dataset, thereby offering a more reliable evaluation of the DRL agent’s ability to generalize its trading strategy to new, previously unobserved market conditions.

Performance analysis of the implemented Deep Reinforcement Learning (DRL) agent reveals a trade-off between risk and reward. While the agent successfully reduced portfolio volatility from 34.9% to 16.32%, this was accompanied by a substantial decrease in key performance indicators. Specifically, the Sharpe Ratio, a measure of risk-adjusted return, declined significantly from 1.41 to 0.13. Furthermore, the annualized return decreased dramatically from 51.1% to 2.1%, indicating a considerable reduction in overall profitability despite the lowered volatility. These results suggest the agent prioritizes stability at the expense of generating substantial returns.

A Future Defined by Adaptive Intelligence

The convergence of Deep Reinforcement Learning (DRL) and risk-aware optimization represents a significant leap forward in the field of portfolio management. Traditional methods often struggle with the non-stationary, high-dimensional, and stochastic nature of financial markets, relying on assumptions that rarely hold true in practice. DRL, however, offers an adaptive framework capable of learning optimal trading strategies directly from market data, responding to complex interactions and evolving conditions without explicit programming. By integrating risk-aware objectives into the DRL agent’s reward function, portfolios can be constructed that not only seek to maximize returns but also actively mitigate potential downsides. This approach allows for dynamic adjustments to asset allocation, enabling the portfolio to navigate periods of market turbulence and capitalize on emerging opportunities with a level of sophistication previously unattainable, promising a future where portfolios are truly intelligent and responsive to the ever-changing financial landscape.

This innovative portfolio construction methodology leverages the power of deep reinforcement learning to achieve a balance between maximizing financial gains and mitigating potential losses. Unlike traditional approaches often focused solely on returns, this system actively incorporates risk assessment into the decision-making process. By continuously learning from market data and adapting to changing conditions, the methodology builds portfolios designed to not only capture upside potential but also to withstand periods of increased market volatility. This resilience is achieved through dynamic asset allocation, allowing the system to proactively adjust holdings to minimize exposure to downside risk and preserve capital, ultimately aiming for more stable, long-term performance even during turbulent times.

Initial evaluations of the deep reinforcement learning agent revealed a promising, though imperfect, performance profile. A winning days ratio of 49.71% and an information ratio of 3.96 indicated a capacity to generate profitable trades and outperform baseline strategies. However, a concurrent decline in the Sharpe Ratio – a critical metric for risk-adjusted returns – highlighted the need for continued development. This suggests that while the agent successfully identified advantageous trading opportunities, its risk management protocols require further calibration. Subsequent refinement of the training process, focusing on more robust reward functions and enhanced exploration strategies, is therefore crucial to consistently deliver positive, risk-adjusted returns and solidify the methodology’s potential within intelligent portfolio management.

The study demonstrates a crucial tenet of complex systems: altering one component invariably impacts the entirety of the architecture. This echoes Jean-Jacques Rousseau’s observation: “The best way to learn is to question everything.” The research reveals that simply minimizing volatility – a localized ‘fix’ within the portfolio – doesn’t guarantee an improved Sharpe ratio, highlighting the interconnectedness of risk and return. Optimizing for one metric can inadvertently compromise another, necessitating a holistic approach to dynamic portfolio allocation. The model’s performance underscores that a comprehensive understanding of the system’s inherent dependencies is paramount, rather than isolated improvements.

Where the Lines Blur

The pursuit of elegant portfolio construction through deep reinforcement learning reveals a familiar truth: minimizing one risk often amplifies another. This work demonstrates that volatility reduction, while appealing, does not automatically translate to improved risk-adjusted returns. The observed decline in Sharpe ratio under certain conditions suggests a fundamental disconnect between the model’s optimization target and the investor’s ultimate goal. Systems break along invisible boundaries – if a model prioritizes short-term stability at the expense of long-term growth, pain is coming.

Future research must move beyond simply reacting to market fluctuations and instead focus on anticipating structural shifts. Hidden Markov Models offer a promising avenue, but their effective integration with deep reinforcement learning requires careful consideration of model complexity and data requirements. A critical limitation lies in the validation process; backtesting, while necessary, provides an incomplete picture of real-world performance. The true test will be in forward testing, where models are deployed in live markets and their adaptability is continuously assessed.

The field should also explore alternative reward functions that explicitly incorporate investor preferences and constraints. A purely quantitative approach, divorced from behavioral considerations, risks creating solutions that are technically optimal but practically unusable. Ultimately, the challenge is not to build a perfect predictor, but to design a system that gracefully navigates uncertainty and adapts to the inevitable surprises the market delivers.

Original article: https://arxiv.org/pdf/2511.11481.pdf

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