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 the use of 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 different years and reflects large-scale market impacts such as financial crises. We also show the proximity to results of a logistic map comparison, 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.

翻译：现代主流金融理论以有效市场假说为基础，该假说认为相关信息会迅速融入资产定价中。运筹学文献中有限的先前研究已探讨过使用为随机数生成器设计的检验方法来检查这些信息效率。将二元日收益率视为硬件随机数生成器的模拟，重叠排列检验表明这些时间序列具有独特的周期性模式。与先前研究不同，我们将分析分为年度和公司两个层面，并在更长时间跨度内研究纳斯达克上市公司的长期效率，以减弱交易噪声的影响，并使市场能够现实地消化新信息。我们的结果表明，信息效率在不同年份间存在差异，并反映了金融危机等大规模市场冲击。我们还展示了与逻辑斯蒂映射比较结果的接近性，讨论了理论市场效率与实用市场效率之间的区别，并发现支持有效市场假说的个股分离收益的统计资格取决于导致市场评估偏差的小型非效率子集的驱动因素。