This paper examines long‐term block ownership by corporations and performance changes in firms with corporate block owners. We also examine potential reasons for corporate ownership including benefits in product market relationships, alleviation of financing constraints, and board monitoring by corporate owners. We find the largest significant increases in targets' stock prices, investment, and operating profitability when ownership is combined with alliances, joint ventures, and other product market relationships between purchasing and target firms, especially in industries with high research and development. Our findings are consistent with the conclusion that block ownership by corporations has significant benefits in product market relationships.

Recent papers show that predictability calibrated to U.S. data has a large effect on the rebalancing behavior of a multiperiod investor. We find that this continues to be true in the presence of realistic transaction costs. In particular, predictability causes the no‐trade region for the risky‐asset holding to become state dependent and, on average, wider and higher. Predictability also motivates the investor to spend considerably more on rebalancing and to rebalance more often. In other results, we find that introducing costly liquidation of the risky asset for consumption lowers the average allocation to the risky asset, though only marginally early in life. Our experiments also vary the nature of the return predictability and introduce return heteroskedasticity.

Banks can create liquidity precisely because deposits are fragile and prone to runs. Increased uncertainty makes deposits excessively fragile, creating a role for outside bank capital. Greater bank capital reduces the probability of financial distress but also reduces liquidity creation. The quantity of capital influences the amount that banks can induce borrowers to pay. Optimal bank capital structure trades off effects on liquidity creation, costs of bank distress, and the ability to force borrower repayment. The model explains the decline in bank capital over the last two centuries. It identifies overlooked consequences of having regulatory capital requirements and deposit insurance.

How will banks evolve as competition increases from other banks and from the capital market? Will banks become more like capital market underwriters and offer passive transaction loans or return to their roots as relationship lending experts? These are the questions we address. Our key result is that as interbank competition increases, banks make more relationship loans, but each has lower added value for borrowers. Capital market competition reduces relationship lending (and bank lending shrinks), but each relationship loan has greater added value for borrowers. In both cases, welfare increases for some borrowers but not necessarily for all.

This paper outlines and tests two agency models of dividends. According to the “outcome model,” dividends are paid because minority shareholders pressure corporate insiders to disgorge cash. According to the “substitute model,” insiders interested in issuing equity in the future pay dividends to establish a reputation for decent treatment of minority shareholders. The first model predicts that stronger minority shareholder rights should be associated with higher dividend payouts; the second model predicts the opposite. Tests on a cross section of 4,000 companies from 33 countries with different levels of minority shareholder rights support the outcome agency model of dividends.

Technical analysis, also known as “charting,” has been a part of financial practice for many decades, but this discipline has not received the same level of academic scrutiny and acceptance as more traditional approaches such as fundamental analysis. One of the main obstacles is the highly subjective nature of technical analysis—the presence of geometric shapes in historical price charts is often in the eyes of the beholder. In this paper, we propose a systematic and automatic approach to technical pattern recognition using nonparametric kernel regression, and we apply this method to a large number of U.S. stocks from 1962 to 1996 to evaluate the effectiveness of technical analysis. By comparing the unconditional empirical distribution of daily stock returns to the conditional distribution—conditioned on specific technical indicators such as head‐and‐shoulders or double bottoms—we find that over the 31‐year sample period, several technical indicators do provide incremental information and may have some practical value.