This study documents the timeliness of 10‐K filings on EDGAR between 1997 and 2018. Consistent with prior studies, we show that firms tend to file around the statutory filing deadlines. We find that 10‐K filings become timelier over time, especially after the statutory filing deadline changes. Prior to deadline changes, we find early filers tend to be dormant state companies, shell companies, development stage companies, or financial entities, such as trusts, asset‐backed securities, mortgage certificates, and funds. Most of these early filers have little or zero assets, liabilities, revenues, and expenses. After deadline changes, however, we find firms with large market capitalization and better financial position and performance also report 10‐Ks early, indicating that early filers are not a random sample in recent years. Finally, we show that the changes of filing deadlines increase the number of early filers, but do not affect the proportion of late filers.

BackgroundCurrent electrocardiogram (ECG) criteria of left ventricular hypertrophy (LVH) have low sensitivity. Deep learning (DL) techniques have been widely used to detect cardiac diseases due to its ability of automatic feature extraction of ECG. However, DL was rarely applied in LVH diagnosis. Our study aimed to construct a DL model for rapid and effective detection of LVH using 12-lead ECG.MethodsWe built a DL model based on convolutional neural network-long short-term memory (CNN-LSTM) to detect LVH using 12-lead ECG. The echocardiogram and ECG of 1,863 patients obtained within 1 week after hospital admission were analyzed. Patients were evenly allocated into 3 sets at 3:1:1 ratio: the training set (n = 1,120), the validation set (n = 371) and the test set 1 (n = 372). In addition, we recruited 453 hospitalized patients into the internal test set 2. Different DL model of each subgroup was developed according to gender and relative wall thickness (RWT).ResultsThe LVH was predicted by the CNN-LSTM model with an area under the curve (AUC) of 0.62 (sensitivity 68%, specificity 57%) in the test set 1, which outperformed Cornell voltage criteria (AUC: 0.57, sensitivity 48%, specificity 72%) and Sokolow-Lyon voltage (AUC: 0.51, sensitivity 14%, specificity 96%). In the internal test set 2, the CNN-LSTM model had a stable performance in predicting LVH with an AUC of 0.59 (sensitivity 65%, specificity 57%). In the subgroup analysis, the CNN-LSTM model predicted LVH by 12-lead ECG with an AUC of 0.66 (sensitivity 72%, specificity 60%) for male patients, which performed better than that for female patients (AUC: 0.59, sensitivity 50%, specificity 71%).ConclusionOur study established a CNN-LSTM model to diagnose LVH by 12-lead ECG with higher sensitivity than current ECG diagnostic criteria. This CNN-LSTM model may be a simple and effective screening tool of LVH.

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This study investigates some of the most important avenues that mangers use to manipulate the value of stock option grants. It also compares the use of these avenues in firms that issue scheduled options and in firms that issue irregular options. We document that before the Sarbanes‐Oxley Act (SOX), cumulative abnormal returns were significantly negative in the 30‐day window before an option grant, but cumulative abnormal returns turned significantly positive after the option grant. This pattern is more pronounced for irregular options, and the evidence supports the hypothesis that opportunistic manipulation of strike prices by CEOs maximized the value of the option grants. We find the disclosure requirement of option grants included in SOX successfully curtails opportunistic behavior in firms that issue scheduled options, but has a lesser effect stopping opportunistic behavior in firms that issue irregular options. Firms granting irregular options take larger negative discretionary accruals in advance of the grant than firms that grant scheduled options, and the degree of downward earnings management increases with the size of the subsequent grant. We further show that firms are more likely to issue irregular options when they offer larger option grants, have a less independent board, receive less analyst coverage, have a new CEO, exhibit poor prior performance, have higher stock return volatility and are smaller in size.

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