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Purpose: Corporate site visits increase labor investment efficiency. Design/methodology/approach: Our empirical model for the baseline analysis follows those of Jung et al. (2014) and Ghaly et al. (2020). Findings: We show that corporate site visits are associated with significantly higher labor investment efficiency; more specifically, site visits reduce both over-hiring and under-hiring of employees. The effect of site visits on labor investment efficiency is more pronounced for firms with higher labor adjustment costs, greater financial constraints, weaker corporate governance and lower financial reporting quality. We also find that site visits mitigate labor cost stickiness. Originality/value: First, while the literature has suggested how the presence of institutional investors and analysts may affect labor investment decisions, we focus on institutional investors and analysts' activities and interactions with firm executives. We provide direct evidence that institutional investors and analysts may use corporate site visits to improve labor investment efficiency. Second, our study adds to a line of recent studies on how corporate site visits reduce information asymmetry and agency conflicts. We show that corporate site visits allow institutional investors and analysts to influence labor investment efficiency. We also provide new evidence that corporate site visits reduce labor cost stickiness. [ABSTRACT FROM AUTHOR]

Fluctuations in the financial market are influenced by various driving forces and numerous factors. Traditional financial research aims to identify the factors influencing stock prices, and existing works construct a common neural network learning framework that learns temporal dependency using a fixed time window of historical information, such as RNN and LSTM models. However, these models only consider the short-term and point-to-point relationships within stock series. The financial market is a complex and dynamic system with many unobservable temporal patterns. Therefore, we propose an adaptive period-aggregation model called the Latent Period-Aggregated Stock Transformer (LPAST). The model integrates a variational autoencoder (VAE) with a period-to-period attention mechanism for multistep prediction in the financial time series. Additionally, we introduce a self-correlation learning method and routing mechanism to handle complex multi-period aggregations and information distribution. Main contributions include proposing a novel period-aggregation representation scheme, introducing a new attention mechanism, and validating the model's superiority in long-horizon prediction tasks. The LPAST model demonstrates its potential and effectiveness in financial market prediction, highlighting its relevance in financial research and predictive analytics. [ABSTRACT FROM AUTHOR]

We investigate the association between stock return synchronicity and insider trading profitability. Morck, Yeung and Yu (2000) suggest that greater stock return synchronicity (or R2) reflects less firm‐specific information in stock prices. Consistent with the view, we find significantly higher insider profitability in firms with greater return synchronicity. The results mainly reside in opportunistic trades rather than in routine trades, and are more pronounced for trades by key insiders such as officers and directors. Furthermore, our results are weaker for industry bellwether firms, and stronger for firms with more opaque earnings or lower institutional ownership. We also document significantly more insider purchasing activity in firms with greater return synchronicity. Overall, our results support the view that greater return synchronicity means less firm‐specific information in stock prices, and suggest that insiders take advantage of this by trading and profiting more from firms with greater return synchronicity. [ABSTRACT FROM AUTHOR]