This paper examines whether common institutional ownership is associated with CEO connectedness across firms. We document that higher common ownership between two same-industry firms predicts a greater likelihood that a newly appointed CEO has preexisting social ties to the incumbent CEO of the peer firm. To address endogeneity, we use mergers among institutional investors in a stacked difference-in-differences design. In a hiring-firm-peer panel that carries connection status forward from the most recent appointment, exposure to a merger-induced common blockholder approximately doubles the probability that the pair is observed in a connected-CEO state. In a broader firm-pair panel, it increases the probability of CEO connections by 48.7%. We further document that gaining CEO connections through another firm’s CEO appointment is associated with improvements in peer firms’ returns on assets and Tobin’s Q, in both OLS and IV specifications. Peer firms that gain such a connection also experience positive abnormal returns around other firms’ CEO hiring announcements, corresponding to an average increase of $112.5 million in shareholder value. These performance patterns suggest that CEO connections may be valuable from a portfolio-level perspective. Consistent with this interpretation, the association between common ownership and CEO connections is concentrated among product-similar and organizationally complex firms and strengthens after the 2008–2009 financial crisis, when connections appear more valuable. Our findings point to CEO connection as a potential governance channel through which common institutional ownership is linked to firm outcomes, complementing prior work on executive compensation, shareholder voting, and board interlocks.

We examine how institutional saving mechanisms influence retirement saving decisions under bounded rationality and income risk. Using a life-cycle experiment with habit formation and loss aversion, we test mandatory and voluntary binding savings under deterministic and stochastic income. Voluntary commitment improves saving performance only when income is predictable; under uncertainty, it fails to improve performance. Mandatory savings do not raise total saving, as participants reduce voluntary contributions. These results emphasize the role of income smoothing in enabling behavioral interventions to improve long-term financial outcomes.

Predicting IPO first-day returns is inherently challenging due to the wide range of contributing factors, each with distinct statistical properties. We assess the performance of several machine learning (ML) techniques and identify XGBoost as the most statistically effective model for forecasting first-day returns. Using a comprehensive set of 863 pre-IPO variables, our high-performing predictive model accurately estimates both the direction and magnitude of IPO first-day returns. The most influential predictors include underwriter agency measures, price revision, and the free-float fraction. Using a rolling-window predictive approach, the model demonstrates substantial practical value, generating approximately $300 billion in gains from IPOs with positive first-day returns and avoiding more than $22 billion in losses from those with negative returns over the 2000–2016 period.

This paper provides a comprehensive analysis of the forecastability of the real price of natural gas in the United States at the monthly frequency considering a universe of models that differ in their complexity and economic content. Our key finding is that considerable reductions in mean-squared prediction error relative to a random walk benchmark can be achieved in real time for forecast horizons of up to two years. A particularly promising model is a six-variable Bayesian vector autoregressive model that includes the fundamental determinants of the supply and demand for natural gas. To capture real-time data constraints of these and other predictor variables, we assemble a rich database of historical vintages from multiple sources. We also compare our model-based forecasts to readily available model-free forecasts provided by experts and futures markets. Given that no single forecasting method dominates all others, we explore the usefulness of pooling forecasts and find that combining forecasts from individual models selected in real time based on their most recent performance delivers the most accurate forecasts.

This paper investigates forecast aggregation via the random subspace regressions method (RSM) and explores the potential link between RSM and the Shapley value decomposition (SVD) using the US GDP growth rates. This technique combination enables handling high-dimensional data and reveals the relative importance of each individual forecast. First, it is possible to enhance forecasting performance in certain practical instances by randomly selecting smaller subsets of individual forecasts and obtaining a new set of predictions based on a regression-based weighting scheme. The optimal value of selected individual forecasts is also empirically studied. Then, a connection between RSM and SVD is proposed, enabling the examination of each individual forecast’s contribution to the final prediction, even when there is a large number of forecasts. This approach is model-agnostic (can be applied to any set of predictions) and facilitates understanding of how the aggregated prediction is obtained based on individual forecasts, which is crucial for decision-makers.