Malcolm P. Baker

Investor Sentiment in the Stock Market

The history of the stock market is full of events striking enough to earn their own names: the Great Crash of 1929, the ’Tronics Boom of the early 1960s, the Go-Go Years of the late 1960s, the Nifty Fifty bubble of the early 1970s, the Black Monday crash of October 1987, and the Internet or Dot.com bubble of the 1990s. Each of these events refers to a dramatic level or change in stock prices that seems to defy explanation. The standard finance model, in which unemotional investors always force capital market prices to equal the rational present value of expected future cash flows, has considerable difficulty fitting these patterns. Researchers in behavioral finance have therefore been working to augment the standard model with an alternative model built on two basic assumptions.

Benchmarks as Limits to Arbitrage: Understanding the Low Volatility Anomaly

Contrary to basic finance principles, high-beta and high-volatility stocks have long underperformed low-beta and low-volatility stocks. This anomaly may be partly explained by the fact that the typical institutional investor’s mandate to beat a fixed benchmark discourages arbitrage activity in both high-alpha, low-beta stocks and low-alpha, high-beta stocks. Although there are many candidates for the greatest anomaly in finance, a particularly compelling one is the long-term success of low-volatility and low-beta stock portfolios. Over 1968–2008, low-volatility and low-beta portfolios have offered an enviable combination of high average returns and small drawdowns. This runs counter to the fundamental principle that risk is compensated with higher expected return. We applied principles of behavioral finance to shed light on the drivers of this anomalous performance and to assess the likelihood that it will persist. To recap the anomaly, whether risk is defined as volatility or beta and whether we consider all stocks or only large caps, low risk consistently outperformed high risk over this period. A dollar invested in the lowest-volatility portfolio in January 1968 would have increased to

Can Mutual Fund Managers Pick Stocks? Evidence from Their Trades Prior to Earnings Announcements

59.55 by the end of 2008. Over this period, inflation eroded the real value of a dollar to about

Corporate financing decisions when investors take the path of least resistance

0.17, meaning that the low-risk portfolio produced a

Catering Through Nominal Share Prices

10.12 gain in real terms. In contrast, a dollar invested in the highest-volatility portfolio would have been worth 58 cents at the end of December 2008, assuming no transaction costs. Given the declining value of the dollar, the real value of the high-volatility portfolio declined to less than 10 cents—a 90 percent decline in real terms! The anomaly with respect to beta risk is similar. A dollar invested in the lowest-beta portfolio in January 1968 would have grown to

The Effect of Dividends on Consumption

60.46 (

The Low Risk Anomaly: A Decomposition into Micro and Macro Effects

10.28 in real terms), and a dollar invested in the highest-beta portfolio would have grown to

A Reference Point Theory of Mergers and Acquisitions

3.77 (64 cents in real terms). Like the high-volatility investor, the high-beta investor also failed to recover his dollar in real terms and underperformed his “conservative” beta neighbor by 964 percent. Behavioral models of security prices, such as ours, combine two ingredients. The first is that some market participants are irrational in some particular way. In the context of the low-risk anomaly, we believe that an important subset of investors have a preference for risky stocks. This preference derives from the biases that afflict the individual investor. We believe individuals’ preferences for lotteries and well-established biases of representativeness and overconfidence lead to demand for risk that is not warranted by fundamentals. This irrational demand causes such high-risk stocks to be overpriced, which, all else equal, leads to lower future returns. The second ingredient is limits to arbitrage—an explanation for why the “smart money” does not step in and offset the price impact of any irrational demand. With respect to the low-risk anomaly, we believe that the underappreciated limit on arbitrage is benchmarking. Many institutional investors who are in a position to offset the irrational demand for risk have fixed-benchmark mandates, typically capitalization weighted, which, by their nature, discourage investments in low-beta and low-volatility stocks. We showed that traditional fixed-benchmark mandates (with a leverage constraint, an assumption that we discuss) cause institutional investors to pass up the superior risk–return trade-off of low-beta and low-volatility portfolios. Rather than being a stabilizing force on prices, the typical institutional contract for delegated portfolio management can induce the manager to hold higher-beta stocks, even those with negative alpha. In this article, we review in more detail the long-term performance of low-risk portfolios, present our behavioral explanation and some associated evidence, and discuss the practical implications for investors and investment managers. Perhaps the most important practical implication is that unless individual investors’ preference for volatile stocks and the use of benchmarks are somehow reversed, the low-risk anomaly is likely to persist.

Would Stricter Capital Requirements Raise the Cost of Capital? Bank Capital Regulation and the Low Risk Anomaly

Recent research finds that the stocks that mutual fund managers buy outperform the stocks that they sell (e.g., Chen, Jegadeesh, and Wermers (2000)). We study the nature of this stock-picking ability. We construct measures of trading skill based on how the stocks held and traded by fund managers perform at subsequent corporate earnings announcements. This approach increases the power to detect skilled trading and sheds light on its source. We find that the average fund’s recent buys significantly outperform its recent sells around the next earnings announcement, and that this accounts for a disproportionate fraction of the total abnormal returns to fund trades estimated in prior work. We find that mutual fund trades also forecast earnings surprises. We conclude that mutual fund managers are able to trade profitably in part because they are able to forecast earnings-related fundamentals.

Chapter 5 - Behavioral Corporate Finance: An Updated Survey*

Abstract We argue that inertial behavior on the part of investors can have significant consequences for corporate financial policy. One implication of investor inertia is that it improves the terms for the acquiring firm in a stock-for-stock merger, because acquirer shares are placed in the hands of investors, who, independent of their beliefs, do not resell these shares on the open market. In the presence of a downward-sloping demand curve, this leads to a reduction in price pressure and, hence, to cheaper equity financing. We develop a simple model to illustrate this idea and present supporting empirical evidence.