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.

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曾探讨用于检测信息有效性的随机数生成器测试方法。将二元日收益率视为硬件随机数生成器的模拟，重叠排列测试表明这些时间序列存在特有的重复模式。与先前研究不同，我们将分析分为年度和公司两个层面，并研究纳斯达克上市公共公司在更大时间跨度内的长期有效性，以减弱交易噪音的影响，使市场能够实际消化新信息。我们的结果表明，信息有效性随年度变化，并反映了金融危机等大规模市场影响。我们还展示了与经过充分测试的伪随机数生成器结果的接近程度，讨论了理论与实用市场有效性之间的区别，并发现支持有效市场假说的个股分离收益的统计资质取决于扭曲市场评估的小型非有效性子集的驱动因素。