Running From a Bear: How Poor Stock Market Performance Affects the Determinants of Mutual Fund Flows

Abstract:  Using a proprietary data set to study how past performance affects the determinants of mutual fund flows for a sample of load fund investors, I provide evidence that the determinants of fund flow depend on market conditions for both redemptions and purchases. Specifically, I show that, for redemptions, relative performance and risk adjusted performance are important determinants during a period of record flows into mutual funds. Conversely, during a period of poor performance, absolute performance becomes much more important and relative performance and risk adjusted performance become less important. For purchases, absolute performance, risk adjusted performance, and most relative performance measures become more important during the bear market.     

AN ARBITRAGE PRICING APPROACH TO EVALUATING MUTUAL FUND PERFORMANCE

A test procedure, derived from arbitrage pricing theory, that permits the measurement of the security selection performance of professional portfolio managers is developed and applied to a sample of mutual funds over the period of the 1970's. The evidence indicates that more than one factor was present in the market during that interval as a systematic influence on the profile of securities returns. Consistent with prior studies, the evidence also suggests that mutual fund portfolios did not outperform a passive buy-and-hold investment strategy. A comparative analysis of the same data, using performance measures based on the single-factor market model, produces similar but less powerful results, both in the aggregate and at the individual fund level.     

Forecasting with an econometric model: Some recent results using the national institute model

It is widely believed that the large econometric models cannot be used for forecasting without considerable intervention on the part of the forecaster. In this paper we challenge this view by reproducing a number of recent forecasts published by the National Institute but without the ad hoc interventions used at the time. We show that in no case would the forecast, produced by the model used mechanically, have been radically different from that actually published. Further, in an ex-post comparison against actual out-turns, the mechanical model forecast is not obviously dominated by the published version.     

‘Best Practices’ in Regional Trading Agreements: An Application to Asia

Regionalism in Asia, particularly in the form of free‐trade areas (FTAs), is a recent trend that is becoming increasingly important. This has been disturbing to many, given the significance of trade and investment in Asian economic growth and development and the region's key role in global commerce. In light of this trend, the goal of this paper is to develop a blueprint, or a set of 10 ‘best practices’, that can be used as a guide to FTAs in order to ensure that they approximate first‐best outcomes to the greatest extent possible. Next, the paper applies this framework to the existing FTAs between Asian countries and their regional and extra‐regional partners. The results suggest that the more advanced regional accords generally receive high grades, with the notable exception of rules of origin, which tend to be even more problematic in the context of accords in which an OECD country is a party.     

MODELING LIQUIDITY EFFECTS IN DISCRETE TIME

We study optimal portfolio choices for an agent with the aim of maximizing utility from terminal wealth within a market with liquidity costs. Under some mild conditions, we show the existence of optimal portfolios and that the marginal utility of the optimal terminal wealth serves as a change of measure to turn the marginal price process of the optimal strategy into a martingale. Finally, we illustrate our results numerically in a Cox–Ross–Rubinstein binomial model with liquidity costs and find the reservation ask prices for simple European put options.     

Robust min–max portfolio strategies for rival forecast and risk scenarios

We consider an extension of the Markowitz mean–variance optimization framework to multiple return and risk scenarios. It is well known that asset return forecasts and risk estimates are inherently inaccurate. The method proposed provides a means for considering rival representations of the future. The optimal portfolio is computed, simultaneously with the worst case, to take account of all rival scenarios. This is a min-max strategy which is essentially equivalent to a robust pooling of the scenarios. Robustness is ensured by the noninferiority of min–max. For example, a basic worst-case optimal return is guaranteed in view of multiple return scenarios. If robustness happens to have too high a cost, guided by the min–max pooling, it is also possible to explore other pooling alternatives. A min–max algorithm is used to solve the problem and illustrate the robust character of min–max with return and risk scenarios. We study the properties of the min–max risk–return frontier and compare with the potentially suboptimal worst-case where the investment strategy and the worst case are computed separately.