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What is the best time point in distribuiting questionaire? 


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The best time point for distributing questionnaires can be determined by considering various factors such as the need for precise synchronization, scalability, and the avoidance of anomalies in temporal representation. Utilizing a nanosecond-accuracy, scalable time dissemination system like the White Rabbit Precision Time Protocol (PTP) can ensure accurate timing across multiple points. Additionally, incorporating time points with zero real-world duration can enhance temporal representation and reasoning by eliminating anomalies that may arise with naive time interval assumptions . Moreover, considering distribution timing based on forecasts for moments up to the fourth one can yield significant economic value in decision-making processes . By integrating these insights, distributing questionnaires at a time point synchronized through advanced time dissemination protocols, while accounting for precise temporal representation and economic forecasting, can optimize the questionnaire distribution process.

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Arnaud Dufays, Jeroen V.K. Rombouts 
8 Citations
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The best time point for distributing questionnaires is when using time points with zero real-world duration to avoid anomalies and ensure effective temporal reasoning in event calculus applications.
Open accessProceedings ArticleDOI
07 Jul 2021
1 Citations
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Jonathan Tallant, David Ingram 
01 Apr 2012-Analysis
17 Citations
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What are the key considerations in constructing a well-balanced and diversified stock portfolio using deep reinforcement learning models?
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Constructing a well-balanced and diversified stock portfolio using deep reinforcement learning (DRL) models involves several key considerations. First, it's crucial to define the investor's objectives clearly, as these objectives directly influence the optimization process. For instance, investors may aim for excess alpha, reduced downside risks, or lower transaction costs, which can be achieved by formulating an objective function that considers these aspects. Additionally, incorporating technical indicators and the covariance of portfolio stocks can further improve returns by diversifying investment weight and enhancing the portfolio's risk-adjusted performance. The Markov decision process (MDP) model is fundamental in DRL for stock portfolio trading, enabling the system to make sequential decisions that maximize the portfolio's expected returns over time. Moreover, the application of DRL models, such as the deep deterministic strategy gradient (DDPG) algorithm, can optimize tactical asset allocation by reallocating assets to increase portfolio value, demonstrating superior cumulative returns compared to traditional strategies. In the context of China's financial market, deep learning neural networks have shown promising results in predicting stock prices and constructing efficient stock portfolios, outperforming major indexes in terms of annualized return and Sharpe ratio. The exploration and exploitation dilemma in DRL also plays a critical role in training models for intelligent trading agents, with significant improvements observed in portfolio performance when this dilemma is effectively managed. Furthermore, modeling the nonlinear correlation among assets using policy networks and the self-attention mechanism can address the challenge of asset correlation in portfolio selection, enhancing the DRL method's optimality. Experiments in various markets, including the Indonesian Stock Exchange, have shown that DRL portfolios can achieve better performance in terms of portfolio value and Sharpe Ratio, especially with a smaller number of stocks. These considerations highlight the importance of objective formulation, technical analysis, model selection, and the management of asset correlations in constructing well-balanced and diversified stock portfolios using DRL models.
How actor-critic play the role in reinforcement learning based portfolio management?
7 answers
Actor-critic methods play a pivotal role in reinforcement learning (RL) based portfolio management by leveraging the strengths of both value-based and policy-based approaches to optimize investment strategies. These methods consist of two main components: the actor, which proposes actions based on the current policy, and the critic, which evaluates the actions by estimating the value function. This dual mechanism enables a more nuanced and effective approach to learning optimal policies for asset allocation. In the context of portfolio management, actor-critic methods have been applied to address various challenges, including risk control, asset allocation, and optimization of investment returns. For instance, Jiang and Wang combined long- and short-term risk control with the Twin Delayed Deep Deterministic policy gradient (TD3) algorithm, an actor-critic method, to enhance portfolio management with risk management capabilities, demonstrating improved investment returns and risk control. Similarly, Jin's work on portfolio optimization utilized an actor-critic architecture to maximize an objective function that includes both the expectation and value at risk (VaR) of portfolio cumulative return, showcasing superior performance to benchmark strategies. The flexibility and efficiency of actor-critic methods have also been harnessed to learn from historical data without explicit predictions of market movements, as seen in the work by Hao, Zhang, and Zhang, where an ensemble of RL algorithms, including actor-critic methods, was used to outperform market indexes. Moreover, the incorporation of advanced techniques like graph convolutional networks in actor-critic frameworks, as proposed by Soleymani and Paquet, exploits time-varying interrelations between financial instruments to optimize returns, further illustrating the method's adaptability and effectiveness. Actor-critic methods have shown promise in various aspects of portfolio management, from enhancing risk-adjusted returns to adapting to market changes and optimizing asset allocations. Their ability to combine the strengths of both policy and value methods, along with their adaptability to incorporate additional features like risk control and market interrelations, positions them as a powerful tool in the arsenal of RL-based portfolio management strategies.
What are the key considerations in constructing a well-balanced and diversified stock portfolio using deep reinforcement learning models?
10 answers
Constructing a well-balanced and diversified stock portfolio using deep reinforcement learning (DRL) models involves several key considerations, as highlighted by recent research. First, it's crucial to optimize tactical asset allocation to maximize portfolio risk-adjusted returns, which involves reallocating assets to increase portfolio value over time. Addressing the challenge of modeling the correlation among assets in the portfolio is also essential, as traditional methods struggle with the complex nonlinearity in these correlations. Utilizing policy networks that model nonlinear correlations through self-attention mechanisms can offer a more effective approach. The selection of stable stocks, as demonstrated by a study focusing on China's financial market, is another important factor. This involves analyzing stock performance and utilizing deep learning neural networks to predict stock prices, thereby informing the stock selection process for the portfolio. A hierarchical decision-making architecture can enhance portfolio optimization across multiple markets by selecting among low-level agents based on their recent performances. Incorporating market information data without the need for prediction allows for learning trading strategies that can outperform market indexes, emphasizing the importance of a data-driven approach. Integrating risk control with DRL models, such as combining long- and short-term risk control with the TD3 algorithm, is vital for managing investment risks effectively. The ability of DRL agents to construct an internal representation of the market for optimal fund allocation without relying on hand-picked features or market model assumptions further underscores the flexibility and potential of DRL in portfolio management. Comparative analysis of RL agents against baseline agents in real-world stock prices demonstrates the superiority of RL in asset allocation, with on-policy, actor-critic RL agents showing the most promise. The integration of data processing methods with DRL models, such as the CEEMDAN method with multi-head attention reinforcement learning, can improve the perception and decision-making abilities of investors in dynamic portfolio optimization. Lastly, combining modern portfolio theory with DRL approaches can bridge the gap between traditional financial theories and recent technological advancements, offering improved performance in terms of the Sharpe ratio, annualized return, and maximum drawdown.
How reinforcement learning plays the roles in automated stock portfolio management?
10 answers
Reinforcement Learning (RL) has significantly impacted automated stock portfolio management by enabling systems to make data-driven, automated decisions for asset allocation without relying on traditional market models or hand-picked features. Dhruval H. Joshi's research illustrates how RL agents can construct internal market models to optimally allocate funds across a finite universe of financial instruments, thereby serving as potential consulting tools for portfolio managers or for low-frequency algorithmic trading. Similarly, the application of Deep Reinforcement Learning (DRL) models for tactical asset allocation has shown to outperform traditional uniform weights portfolios by adjusting asset allocations to maximize portfolio value. The exploration of DRL in managing portfolios composed of stock market indices further demonstrates the method's efficacy in improving financial market strategies through a balance of exploration and exploitation phases during model training. Moreover, Liu's work on employing Models-free frameworks like DQN-S and DQN-R for portfolio management emphasizes overcoming the limitations of predicting future returns and variances through traditional methods, showcasing the superiority of RL-based strategies in optimizing Sharpe Ratios. Research by Yue Ma et al. extends the application of DRL to diverse portfolios, including those positively or negatively influenced by external factors like COVID-19, and highlights the superior performance of DRL agents over equal-weighted and single-stock investment strategies. The integration of DRL for profit generation in stock markets, as discussed by Zheng Hao et al., and Taylan Kabbani and Ekrem Duman, further validates the potential of RL in automating trading strategies that surpass traditional benchmarks without the need for explicit market prediction. In summary, RL plays a pivotal role in automated stock portfolio management by leveraging data-driven strategies to optimize asset allocation, enhance portfolio performance, and automate trading decisions, thereby offering a robust alternative to traditional investment strategies.