In October 2000, the Securities and Exchange Commission (SEC) passed Regulation Fair Disclosure (FD) in an effort to reduce selective disclosure of material information by firms to analysts and other investment professionals. We find that the information asymmetry reflected in trading costs at earnings announcements has declined after Regulation FD, with the decrease more pronounced for smaller and less liquid stocks. Return volatility around mandatory announcements is also lower but overall information flow is unchanged when mandatory and voluntary announcements are combined. Thus, the SEC appears to have diminished the advantage of informed investors, without increasing volatility.

The informational opacity of small businesses makes them an interesting area for the study of banks' lending practices and procedures. We use data from a survey of small businesses to analyze the micro level differences in the loan approval processes of large and small banks. We provide evidence that large banks ($1 billion or more in assets) employ standard criteria obtained from financial statements in the loan decision process, whereas small banks rely to a greater extent on information about the character of the borrower. These cookie-cutter and character approaches are compatible with the incentives and environments facing large and small banks.

This paper introduces a Monte Carlo simulation method for pricing multidimensional American options based on the computation of the optimal exercise frontier. We consider Bermudan options that can be exercised at a finite number of times and compute the optimal exercise frontier recursively. We show that for every date of possible exercise, any single point of the optimal exercise frontier is a fixed point of a simple algorithm. Once the frontier is computed, we use plain vanilla Monte Carlo simulation to price the option and obtain a low-biased estimator. We illustrate the method with applications to several types of options.

In 1997, the Securities and Exchange Commission enacted significant reforms in U.S. markets. Several studies document that the new order handling rules increased competition for Nasdaq stocks, but the reforms were designed with an additional goal in mind—to increase quote competition for the trading of NYSE-listed securities on Nasdaq (i.e., third market trading). An evaluation of the reforms in the third market indicates that they did not achieve this objective. Instead, both quote quality and quoting frequency were diminished, due primarily to elimination of the excess spread rule. This suggests that more significant changes are needed to increase inter-exchange competition.

Motivated by economic models of sequential trade, empirical analyses of market dynamics frequently estimate liquidity as the coefficient of signed order flow in a price change regression. This paper implements such an analysis for futures transaction data from pit trading. To deal with the absence of timely bid and ask quotes (which are used to sign trades in most equity market studies), this paper proposes new techniques based on Markov chain Monte Carlo estimation. The model is estimated for four representative Chicago Mercantile Exchange contracts. The highest liquidity (lowest order flow coefficient) is found for the S&P 500 index. Liquidity for the Euro and U.K. £ contracts is somewhat lower. The pork belly contract exhibits the least liquidity.

Data from the Taiwan Stock Exchange identify the originator of each submitted order, and there are no designated dealers or specialists. We study marketable order imbalances, i.e., the net order flow resulting from trades that demand immediacy. We distinguish imbalances by trader type (individuals, domestic institutions, foreign institutions) and by the usual size of each trader's order. Day-to-day persistence in order imbalance is strongest for small foreign institutions and weakest for large individual traders. Such persistence emanates both from splitting orders over time and from herding, and there is little evidence that aggregate price pressures from such persistence last beyond a trading day, indicating that de facto market making is quite effective. We attempt to discern which types of traders are de facto liquidity providers, which are likely to be informed, and which trade for liquidity reasons. The evidence indicates that all trader classes are successful market makers, large domestic institutions conduct the most informed trades, and large individuals are noise or liquidity traders.

We document by several methods that trading in Nasdaq stocks is localized, but find little evidence that cloudy weather in the city in which a company is based affects its returns. The first evidence of localized trading is that the time zone of a company's headquarters affects intraday trading patterns in its stock. Second, firms in blizzard-struck cities see a dramatic trading volume drop compared to firms in other cities. Third, the Yom Kippur holiday dampens trading volume in companies located in cities with high Jewish populations. Despite the strong evidence of localized trading, cloudy conditions near the firm's headquarters do not provide profitable trading opportunities.

This paper examines the price dynamics in the S&P 500 and Nasdaq-100 index futures contracts. By utilizing transactions data with attached trader type identification codes, we are able to analyze price dynamics for trades initiated by exchange locals and off-exchange customers. The empirical results show that price discovery appears to be initiated in the E-mini index futures contracts and that trades initiated by exchange locals seem to be more informative than those initiated by off-exchange traders. Furthermore, results show that exchange locals appear to make informed trades on the E-mini contracts around large trades that occur on the open outcry floor. We maintain that the exchange locals' ability to observe pit dynamics may contribute toward explaining the price leadership of the Emini contracts. Overall, the results are consistent with the notion that exchange locals are informed traders who derive their informational advantage from the proximity to order flow.

We use a database of recent high tech IPOs to estimate opportunity cost of capital for venture capital investors and entrepreneurs. Entrepreneurs face the risk-return tradeoff of the CAPM as the opportunity cost of holding a portfolio that necessarily is underdiversified. For early stage firms, we estimate the effects of underdiversification, industry, and financial maturity on opportunity cost. Assuming a one-year holding period, the entrepreneur's opportunity cost generally is two to four times as high as that of a well-diversified investor. With a 4.0% risk-free rate and 6.0% market risk premium, for the sample average, we estimate the cost of capital of a well-diversified investor to be 11.4%, which equates to 16.7% before the management fees and carried interest of a typical venture capital fund. For an entrepreneur with 25% of total wealth invested in the venture, our corresponding estimate of cost of capital is 40.0%.

In this paper, we analyze the economic value of predicting stock index returns as well as volatility. On the basis of simple linear models, estimated recursively, we produce out-of-sample forecasts for the return on the S&P 500 index and its volatility. Using monthly data, we examine the economic value of a number of alternative trading strategies over the period 1970–2001. It appears easier to forecast returns at times when volatility is high. For a mean-variance investor, this predictability is economically profitable, even if short sales are not allowed and transaction costs are quite large. The economic value of trading strategies that employ market timing in returns and volatility exceeds that of strategies that only employ timing in returns. Most of the profitability of the dynamic strategies, however, is located in the first half of our sample period.