Informational Efficiency

Multiscale Causal Analysis of Market Efficiency via News Uncertainty Networks and the Financial Chaos Index ArXiv ID: 2505.01543 “View on arXiv” Authors: Masoud Ataei Abstract This study evaluates the scale-dependent informational efficiency of stock markets using the Financial Chaos Index, a tensor-eigenvalue-based measure of realized volatility. Incorporating Granger causality and network-theoretic analysis across a range of economic, policy, and news-based uncertainty indices, we assess whether public information is efficiently incorporated into asset price fluctuations. Based on a 34-year time period from 1990 to 2023, at the daily frequency, the semi-strong form of the Efficient Market Hypothesis is rejected at the 1% level of significance, indicating that asset price changes respond predictably to lagged news-based uncertainty. In contrast, at the monthly frequency, such predictive structure largely vanishes, supporting informational efficiency at coarser temporal resolutions. A structural analysis of the Granger causality network reveals that fiscal and monetary policy uncertainties act as core initiators of systemic volatility, while peripheral indices, such as those related to healthcare and consumer prices, serve as latent bridges that become activated under crisis conditions. These findings underscore the role of time-scale decomposition and structural asymmetries in diagnosing market inefficiencies and mapping the propagation of macro-financial uncertainty. ...

Market efficiency, informational asymmetry and pseudo-collusion of adaptively learning agents ArXiv ID: 2411.05032 “View on arXiv” Authors: Unknown Abstract We examine the dynamics of informational efficiency in a market with asymmetrically informed, boundedly rational traders who adaptively learn optimal strategies using simple multiarmed bandit (MAB) algorithms. The strategies available to the traders have two dimensions: on the one hand, the traders must endogenously choose whether to acquire a costly information signal, on the other, they must determine how aggressively they trade by choosing the share of their wealth to be invested in the risky asset. Our study contributes to two strands of literature: the literature comparing the effects of competitive and strategic behavior on asset price efficiency under costly information as well as the actively growing literature on algorithmic tacit collusion and pseudo-collusion in financial markets. We find that for certain market environments (with low information costs) our model reproduces the results of Kyle [“1989”] in that the ability of traders to trade strategically leads to worse price efficiency compared to the purely competitive case. For other environments (with high information costs), on the other hand, our results show that a market with strategically acting traders can be more efficient than a purely competitive one. Furthermore, we obtain novel results on the ability of independently learning traders to coordinate on a pseudo-collusive behavior, leading to non-competitive pricing. Contrary to some recent contributions (see e.g. [“Cartea et al. 2022”]), we find that the pseudo-collusive behavior in our model is robust to a large number of agents, demonstrating that even in the setting of financial markets with a large number of independently learning traders non-competitive pricing and pseudo-collusive behavior can frequently arise. ...

An Information Theory Approach to the Stock and Cryptocurrency Market: A Statistical Equilibrium Perspective ArXiv ID: 2310.04907 “View on arXiv” Authors: Unknown Abstract We study the stochastic structure of cryptocurrency rates of returns as compared to stock returns by focusing on the associated cross-sectional distributions. We build two datasets. The first comprises forty-six major cryptocurrencies, and the second includes all the companies listed in the S&P 500. We collect individual data from January 2017 until December 2022. We then apply the Quantal Response Statistical Equilibrium (QRSE) model to recover the cross-sectional frequency distribution of the daily returns of cryptocurrencies and S&P 500 companies. We study the stochastic structure of these two markets and the properties of investors’ behavior over bear and bull trends. Finally, we compare the degree of informational efficiency of these two markets. ...

On random number generators and practical market efficiency ArXiv ID: 2305.17419 “View on arXiv” Authors: Unknown Abstract Modern mainstream financial theory is underpinned by the efficient market hypothesis, which posits the rapid incorporation of relevant information into asset pricing. Limited prior studies in the operational research literature have investigated tests designed for random number generators to check for these informational efficiencies. Treating binary daily returns as a hardware random number generator analogue, tests of overlapping permutations have indicated that these time series feature idiosyncratic recurrent patterns. Contrary to prior studies, we split our analysis into two streams at the annual and company level, and investigate longer-term efficiency over a larger time frame for Nasdaq-listed public companies to diminish the effects of trading noise and allow the market to realistically digest new information. Our results demonstrate that information efficiency varies across years and reflects large-scale market impacts such as financial crises. We also show the proximity to results of a well-tested pseudo-random number generator, discuss the distinction between theoretical and practical market efficiency, and find that the statistical qualification of stock-separated returns in support of the efficient market hypothesis is dependent on the driving factor of small inefficient subsets that skew market assessments. ...

Markets are Efficient if and Only if P = NP ArXiv ID: ssrn-1773169 “View on arXiv” Authors: Unknown Abstract I prove that if markets are efficient, meaning current prices fully reflect all information available in past prices, then P = NP, meaning every computational p Keywords: Market Efficiency Hypothesis, Computational Complexity, Algorithmic Trading, P vs NP Problem, Informational Efficiency, Equities Complexity vs Empirical Score Math Complexity: 8.5/10 Empirical Rigor: 1.0/10 Quadrant: Lab Rats Why: The paper presents a formal theoretical proof linking market efficiency to computational complexity classes (P vs NP), requiring advanced mathematical reasoning and abstract computer science concepts. However, it contains no actual data, backtests, or implementation details; the empirical part is a brief illustrative example rather than rigorous analysis. flowchart TD A["Research Goal: Are Markets Efficient?"] B["Key Methodology: Complexity Theoretic Proof"] C["Input: Historical Price Data & Market Efficiency Assumption"] D["Computational Process: Reducing Market Arbitrage to NP-Hard Problem"] E["Key Finding: Market Efficiency Implies P = NP"] F["Implication: If P ≠ NP, Markets are Not Fully Efficient"] A --> B B --> C C --> D D --> E E --> F

The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective ArXiv ID: ssrn-602222 “View on arXiv” Authors: Unknown Abstract One of the most influential ideas in the past 30 years is the Efficient Markets Hypothesis, the idea that market prices incorporate all information rationally a Keywords: Efficient Markets Hypothesis, Market Efficiency, Asset Pricing, Informational Efficiency, Financial Theory, Equity Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper proposes a conceptual framework (Adaptive Markets Hypothesis) to reconcile EMH and behavioral finance using evolutionary principles, but it lacks mathematical derivations, empirical data, or backtesting details, focusing instead on theoretical exposition and implications for practice. flowchart TD A["Research Goal:<br>Challenge EMH with<br>Evolutionary Perspective"] --> B["Methodology:<br>Literature Review &<br>Theoretical Framework"] B --> C["Input Data:<br>Historical Market Anomalies<br>& Behavioral Studies"] C --> D["Process:<br>Adaptive Markets Hypothesis<br>Integration (Lo 2004)"] D --> E["Key Findings:<br>1. Markets are adaptive<br>2. Efficiency varies<br>3. Profit opportunities<br>fluctuate with evolution"]