Knowledge that Transforms

Standard predictive regressions produce biased coefficient estimates in small samples when the regressors are Gaussian first-order autoregressive with errors that are correlated with the error series of the dependent variable. See Stambaugh (1999) for the single regressor model. This paper proposes a direct and convenient method to obtain reduced-bias estimators for single and multiple regressor models by employing an augmented regression, adding a proxy for the errors in the autoregressive model. We derive bias expressions for both the ordinary least-squares and our reduced-bias estimated coefficients. For the standard errors of the estimated predictive coefficients, we develop a heuristic estimator that performs well in simulations, for both the single predictor model and an important specification of the multiple predictor model. The effectiveness of our method is demonstrated by simulations and empirical estimates of common predictive models in finance. Our empirical results show that some of the predictive variables that were significant under ordinary least squares become insignificant under our estimation procedure.

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.

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.

Many financial institutions employ outside portfolio managers to manage part or all of their investable assets. It is well recognized that outside portfolio managers are unwilling to share security information with each other or with the centralized decision maker and this in general will lead to sub-optimal portfolios. In this paper, we derive an implementable set of rules under which a central decision maker can make optimal decisions without requiring decentralized decision makers to reveal estimates of security returns. Furthermore, we derive conditions under which these rules hold and when they do not hold.

This paper presents a dynamic model of imperfect competition in banking where the banks can invest in a prudent or a gambling asset. We show that if intermediation margins are small, the banks' franchise values will be small, and in the absence of regulation only a gambling equilibrium will exist. In this case, either flat-rate capital requirements or binding deposit rate ceilings can ensure the existence of a prudent equilibrium, although both have a negative impact on deposit rates. Such impact does not obtain with either risk-based capital requirements or nonbinding deposit rate ceilings, but only the former are always effective in controlling risk-shifting incentives.

We test a new hypothesis that may help explain the procyclicality of bank lending. The institutional memory hypothesis is driven by deterioration in the ability of loan officers over the bank's lending cycle that results in an easing of credit standards. We test this hypothesis using data from individual US banks over 1980–2000: over 200,000 bank-level observations on commercial loan growth, over 2,000,000 loan-level observations on interest rate premiums, and over 2000 bank-level observations on credit standards and loan spreads from bank management survey responses. The empirical analysis supports the hypothesis, although there are differences by bank size class.

As the usual normality assumption is firmly rejected by the data, investors encounter a data-generating process (DGP) uncertainty in making investment decisions. In this paper, we propose a novel way to incorporate uncertainty about the DGP into portfolio analysis. We find that accounting for fat tails leads to nontrivial changes in both parameter estimates and optimal portfolio weights, but the certainty–equivalent losses associated with ignoring fat tails are small. This suggests that the normality assumption works well in evaluating portfolio performance for a mean-variance investor.

This paper introduces the concept of statistical arbitrage, a long horizon trading opportunity that generates a riskless profit and is designed to exploit persistent anomalies. Statistical arbitrage circumvents the joint hypothesis dilemma of traditional market efficiency tests because its definition is independent of any equilibrium model and its existence is incompatible with market efficiency. We provide a methodology to test for statistical arbitrage and then empirically investigate whether momentum and value trading strategies constitute statistical arbitrage opportunities. Despite adjusting for transaction costs, the influence of small stocks, margin requirements, liquidity buffers for the marking-to-market of short-sales, and higher borrowing rates, we find evidence that these strategies generate statistical arbitrage.

We develop an equilibrium model of contracting, bargaining, and search in which the relative scarcity of venture capital affects the bargaining power of entrepreneurs and venture capitalists. This in turn affects the pricing, contracting, and value creation in start-ups. The relative scarcity of venture capital is endogenous and depends on the profitability of venture capital investments, entry costs, and transparency of the venture capital market. Supply and demand conditions also affect the incentives of venture capitalists to screen projects ex ante. We characterize both the short- and long-run dynamics of the venture capital industry, which provides us with a stylized picture of the Internet boom and bust periods. Our model is consistent with existing evidence and provides a number of new empirical predictions.

The returns to hedge funds and other alternative investments are often highly serially correlated. In this paper, we explore several sources of such serial correlation and show that the most likely explanation is illiquidity exposure and smoothed returns. We propose an econometric model of return smoothing and develop estimators for the smoothing profile as well as a smoothing-adjusted Sharpe ratio. For a sample of 908 hedge funds drawn from the TASS database, we show that our estimated smoothing coefficients vary considerably across hedge-fund style categories and may be a useful proxy for quantifying illiquidity exposure.