A key puzzle in finance is why algorithmic traders with advanced neural models sometimes fail to beat simple traditional strategies, while in other cases they clearly outperform them. This study argues that such variation depends on how information is reflected in market prices. When markets are highly efficient, price dynamics are stable and structured. This environment is well suited for deep reinforcement learning, which can learn adaptive allocation patterns. In less efficient markets, price dynamics are noisier and more unstable, which makes it harder for data intensive models to perform consistently. To examine this idea, we analyse forty-five Nifty 50 stocks using a time varying Fuzzy Market Inefficiency Measure and group them into three efficiency levels. Within each cluster, four deep reinforcement learning models are compared with six traditional portfolio strategies under the same conditions. The results show that deep learning models perform best in highly efficient markets where signals are weak but consistent. In moderately and least efficient markets, traditional strategies often achieve similar or better returns. However, deep learning models still provide better control over downside risk in less efficient environments. Overall, the findings offer valuable insights for portfolio managers and investors by supporting efficiency-based portfolio allocation, enhanced risk management, and adaptive investment strategies across varying market conditions.
H. Sahu, Avishek Bhandari· Discover Artificial Intellig...· 0 citations
Many questions across the sciences take the same form: several coupled series are observed together, and the analyst wants to know not merely that they move together but which one moves first, and how strongly. This paper sets out a complete method built on one organising idea: the direction of a coupled system is exactly the part of its behaviour that changes when the record is played backwards. Tools built on contemporaneous covariance alone (correlation matrices, distance measures, spanning trees, undirected centralities, principal components) carry no information about direction: a reversible system and a circulating one can share identical covariance at every sampling of the same point-in-time record. Formally, direction is a circulation matrix carried by the lagged covariance. Its vanishing is exactly statistical time reversibility for linear systems, feature maps carry the characterisation to nonlinear ones, and under the Gaussian benchmark its magnitude is an entropy-production functional of the identified circulation, the quadratic component of the divergence per unit time between the forward and reversed records. Around this estimand we build a cross-fitted estimator removing first-order bias, delete-block jackknife standard errors, and a randomisation test exact under its stated block null, with a familywise correction and a nonlinear extension. A sampling theory says when the arrow is measurable at all, and a design layer separates transmission from the ordering of clocks. A laboratory of four systems with known answers compares the method with correlation networks, Granger causality, transfer entropy, and connectedness indices, reporting the failures of each when read as a measure of direction, including our own. Complete algorithms and worked examples in two languages make the paper the base reference for a series of applications.
Standard economics assumes the consumer as a flawless calculator who always buys the best basket it can afford. This paper models the shopper instead as a limited information channel: it compresses its world to the detail its attention affords, so its choice is a probability distribution, not a single basket. The textbook consumer returns exactly as the unlimited-attention limit, while at the zero-attention end the shopper falls back on pure habit. The central result is about how this shopper's demand responds to price changes. That pattern of responses is just a rescaling of how the shopper's own choices vary and move together, so it comes out symmetric. And provided the budget really binds, because the shopper wants more than it can afford, raising a good's own price lowers demand for it once buying power is held fixed. So the downward pull comes from the budget and from compression, not from rationality. The framework also covers an artificial agent running a limited-capacity policy. A worked two-good quadratic consumer carries every quantity in closed form.
Avishek Bhandari· 0 citations
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