Practical Deep Reinforcement Learning Approach for Stock Trading

Stock trading strategy plays a crucial role in investment companies. However, it is challenging to obtain optimal strategy in the complex and dynamic stock market. We explore the potential of deep reinforcement learning to optimize stock trading strategy and thus maximize investment return. 30 stocks are selected as our trading stocks and their daily prices are used as the training and trading market environment. We train a deep reinforcement learning agent and obtain an adaptive trading strategy. The agent's performance is evaluated and compared with Dow Jones Industrial Average and the traditional min-variance portfolio allocation strategy. The proposed deep reinforcement learning approach is shown to outperform the two baselines in terms of both the Sharpe ratio and cumulative returns

Agent-Based Models and Human Subject Experiments

This paper considers the relationship between agent-based modeling and economic decision-making experiments with human subjects. Both approaches exploit controlled ``laboratory'' conditions as a means of isolating the sources of aggregate phenomena. Research findings from laboratory studies of human subject behavior have inspired studies using artificial agents in ``computational laboratories'' and vice versa. In certain cases, both methods have been used to examine the same phenomenon. The focus of this paper is on the empirical validity of agent-based modeling approaches in terms of explaining data from human subject experiments. We also point out synergies between the two methodologies that have been exploited as well as promising new possibilities.agent-based models, human subject experiments, zero- intelligence agents, learning, evolutionary algorithms

Consumption-Wealth Comovement of the Wrong Sign

Economic theory predicts that an unexpected wealth windfall should increase consumption shortly after the windfall is received. We test this prediction using administrative records on over 40,000 401(k) accounts. Contrary to theory, we estimate a negative short-run marginal propensity to consume out of idiosyncratic 401(k) capital gains shocks. These results cannot be interpreted as standard intertemporal substitution, since the idiosyncratic returns that we study do not predict future returns. Instead, our findings imply that many investors are influenced by a positive feedback effect, through which higher recent returns encourage higher short-run saving. Like any other animal, 401(k) participants appear to increase behaviors that have been associated with high rewards in the past.

Improving Wealth Management Strategies Through the Use of Reinforcement Learning Based Algorithms. A Study on the Romanian Stock Market

In the context of the growing pace of technological development and that of the transition to the knowledge-based economy, wealth management strategies have become subject to the application of new ideas. One of the fields of research that are increasing in influence in the scientific community is that of reinforcement learning-based algorithms. This trend is also manifesting in the domain of economics, where the algorithms have found a use in the field of stock trading. The use of algorithms has been tested by researchers in the last decade due to the fact that by applying these new concepts, fund managers could obtain an advantage when compared to using classic management techniques. The present paper will test the effects of applying these algorithms on the Romanian market, taking into account that it is a relatively new market, and compare it to the results obtained by applying classic optimization techniques based on passive wealth management concepts. We chose the Romanian stock market due to its recent evolution regarding the FTSE Russell ratings and the fact that the country is becoming an Eastern European hub of development in the IT sector, these facts could indicate that the Romanian stock market will become even more significant in the future at a local and maybe even at a regional level