Conditional portfolio allocation: Does aggregate market liquidity matter?

This paper investigates how aggregate liquidity influences optimal portfolio allocations across various US characteristic portfolios. We consider short-term allocation problems, with single and multiple risky assets, and use the nonparametric approach of Brandt (1999) to directly express optimal portfolio weights as functions of aggregate liquidity shocks. We find, first, that the effect of aggregate liquidity is positive and decreasing with the investment horizon. Second, at daily and weekly horizons, this effect is weaker on allocations in large stocks and gets stronger as we move toward small stocks, regardless of the other stock characteristics, suggesting that liquidity is the main concern of very short-term investors. Third, conditional allocations in risky assets decrease and exhibit shifts toward more liquid assets as aggregate liquidity worsens. Overall, conditioning on aggregate liquidity yields empirical results that are consistent with the so-called flight-to-safety and flight-to-liquidity episodes. Finally, we propose a simple tactical investment strategy and show how aggregate liquidity information can be exploited to enhance the out-of-sample performance of long-term strategies.     

Moments of multivariate regime switching with application to risk-return trade-off

We use a Fourier transform to derive multivariate conditional and unconditional moments of multi-horizon returns under a regime-switching model. These moments are applied to examine the relevance of risk horizon and regimes for buy-and-hold investors. We analyze the impact of time-varying expected returns and risk (variance and covariance) on portfolio allocations' “term structure”—portfolio allocations as a function of the investment horizon. Using monthly observations on S&P composite index and 10-year Government Bond, we find that the term structure of the optimal allocations depends on market conditions measured by the probability of being in bull state. At short horizons and when this probability is low, buy-and-hold investors decrease their holdings of risky assets. We also find that the conditional optimal portfolio performs quite well at short and intermediate horizons and less at long horizons.     

Intra-day market activity

This paper presents a study of intra-day patterns of stock market activity and introduces duration based activity measures for single stocks and multiple assets. The proposed measures involve weighted durations, i.e. times necessary to sell (buy) a predetermined volume or value of stocks. As such, they capture dependencies between intra-trade durations, transaction volumes and prices, and can be interpreted as liquidity measures. This approach allows us to highlight the intra-day variations of liquidity, its costs and volatility, and to develop a liquidity based asset ordering. The extension to a multivariate analysis yields new insights into the dynamics of portfolio liquidity by revealing various aspects of asset substitution, including the effects of correlated trade intensities of portfolio components. Several examples are used to show that in practice, the proposed liquidity measures become efficient instruments for strategic block trading and optimal portfolio adjustments. The paper also contains an empirical study of asset activity on the Paris Bourse. We examine the liquidity dynamics throughout the day and reveal the existence of periodic patterns resulting from world-wide interactions of major stock markets. In the multivariate setup, we report evidence on common patterns and correlations of trade intensities of selected stocks.     

Dynamic portfolio optimization with liquidity cost and market impact: a simulation-and-regression approach

We present a simulation-and-regression method for solving dynamic portfolio optimization problems in the presence of general transaction costs, liquidity costs and market impact. This method extends the classical least squares Monte Carlo algorithm to incorporate switching costs, corresponding to transaction costs and transient liquidity costs, as well as multiple endogenous state variables, namely the portfolio value and the asset prices subject to permanent market impact. To handle endogenous state variables, we adapt a control randomization approach to portfolio optimization problems and further improve the numerical accuracy of this technique for the case of discrete controls. We validate our modified numerical method by solving a realistic cash-and-stock portfolio with a power-law liquidity model. We identify the certainty equivalent losses associated with ignoring liquidity effects, and illustrate how our dynamic optimization method protects the investor's capital under illiquid market conditions. Lastly, we analyze, under different liquidity conditions, the sensitivities of certainty equivalent returns and optimal allocations with respect to trading volume, stock price volatility, initial investment amount, risk aversion level and investment horizon.     

Does Idiosyncratic Risk Really Matter?

Goyal and Santa‐Clara (2003) find a significantly positive relation between the equal‐weighted average stock volatility and the value‐weighted portfolio returns on the NYSE/AMEX/Nasdaq stocks for the period of 1963:08 to 1999:12. We show that this result is driven by small stocks traded on the Nasdaq, and is in part due to a liquidity premium. In addition, their result does not hold for the extended sample of 1963:08 to 2001:12 and for the NYSE/AMEX and NYSE stocks. More importantly, we find no evidence of a significant link between the value‐weighted portfolio returns and the median and value‐weighted average stock volatility.     

Investment Horizon Effects

Abstract:   Boudry and Gray (2003) have documented that the optimal buy‐and‐hold demand for Australian stocks is not necessarily increasing in the investment horizon when returns are predictable. Such finding is in contrast with Barberis (2000) who shows that positive monotonic horizon effects predominate for US stocks. Using a closed‐form approximation to the asset allocation problem, this paper relates the return dynamics to the investor's portfolio choice for different investment horizons. In the special case of a single risky asset, it is shown that return predictability under stationarity may induce both positive and negative horizon effects in the optimal allocation to the risky asset. The paper extends previous empirical results by solving for the optimal portfolio when two risky assets with predictable returns are available for investment.     

Loss-aversion and household portfolio choice

In this paper we empirically test if loss-aversion affects household participation in equity markets, household allocations to equity, and household allocations between mutual funds and individual stocks. Using household survey data, we obtain direct measures of each surveyed household's loss-aversion coefficient from questions involving hypothetical payoffs. We find that higher loss-aversion is associated with a lower probability of participation. We also find that higher loss-aversion reduces the probability of direct stockholding by significantly more than the probability of owning mutual funds. After controlling for sample selection we do not find a relationship between loss-aversion and portfolio allocations to equity.     

MUTUAL FUND TRADES: ASYMMETRIC LIQUIDITY PREFERENCES AND FUND PERFORMANCE

We investigate the role of the liquidity of stocks traded by mutual funds on the performance of funds experiencing substantial and sustained redemptions (outflows) or inflows. Accordingly, we identify 770 redeeming fund‐periods and 1,757 inflow fund‐periods and find a statistically significant relation between the liquidity of the stocks they trade and the quantity of the stock traded. Notably, when funds experience redemptions, those with low portfolio liquidity have an elevated preference for selling more‐liquid stocks. In the following period, such funds statistically and economically underperform funds that sell less‐liquid stocks. This is consistent with redemptions detrimentally affecting shareholders that remain in a fund.     

Can margin traders predict future stock returns in Japan?

A growing body of literature suggests that investor sentiment affects stock prices both at the firm level and at the market level. This study examines the relationship between investor behavior and stock returns focusing on Japanese margin transactions using weekly data from 1994 to 2003. Margin trading is dominated by individual investors in Japan. In analysis at the firm level, we find a significant cross-sectional relationship between margin buying and stock returns. Both market-level and firm-level analyses show that margin buying traders follow herding behavior. They seem to follow positive feedback trading behavior for small-firm stocks and negative feedback trading behavior for large firm stocks. Our results show that information about margin buying helps predict future stock returns, especially for small-firm stocks at short horizons. The predictive power does not diminish even after controlling for firm size and liquidity.     

The market liquidity of DIAMONDS,Q's,and their underlying stocks

We investigate the market liquidity effects of the introduction of index-tracking stocks for the Dow Jones Industrial Average (DIAMONDS) and the NASDAQ 100 index (Q's). Our main finding is liquidity of the underlying DJIA 30 index stocks improves after the introduction of the exchange-traded fund, largely because of a decline in the cost of informed trading. Further, we find the DIAMONDS has significantly lower liquidity costs over the first 50 days of trading as compared to the portfolio of its component stocks, again primarily because of lower adverse selection costs. Finally, we find weaker but qualitatively similar results for the Q's.     

Money and liquidity in financial markets

We argue that there is a connection between the interbank market for liquidity and the broader financial markets, which has its basis in demand for liquidity by banks. Tightness in the market for liquidity leads banks to engage in what we term “liquidity pull-back,” which involves selling financial assets either by banks directly or by levered investors. Empirical tests on the stock market are supportive. Tighter interbank markets are associated with relatively more volume in more liquid stocks; selling pressure, especially in more liquid stocks; and transitory negative returns. We control for market-wide uncertainty and in the process also contribute to the literature on portfolio rebalancing. Our general point is that money matters in financial markets.     

Comment on “Optimal portfolio selection in a value-at-risk framework”

This comment discusses some errors in [Journal of Banking and Finance 25 (2001) 1789]. Given the portfolio rate of return is normally distributed, the following can be inferred. First, taking expected portfolio return rate as the benchmark of value-at-risk (VaR), the risk–return ratio collapses to a multiple of the Sharpe index. However, using risk-free rate as the benchmark, then above inference does not hold. Second, whether the benchmark of VaR is expected portfolio return rate or the risk-free rate, the optimal asset allocations for maximizing the risk–return ratio and Sharpe index are identical.     

Investment Horizon and Composition of Optimal Portofolio: International Evidence

Little attention has been paid in the literature to the impact of different investment horizons on the portfolio compositiondespite its importance to portfolio managers. One exception isthe study by Gunthorpe and Levy (1994) on the U.S. stock market.Our paper extends the same study to the stock markets of Japan,Hong Kong and Korea. Using 40 individual stocks in each market,our results support those of Gunthorpe and Levy (1994) in thatthe composition of an optimal portfolio depends heavily on theinvestment horizon. When the investment horizon lengthens, theproportion of defensive stocks becomes larger while that ofaggressive stocks becomes smaller.     

Forecasting liquidity-adjusted intraday Value-at-Risk with vine copulas

We propose to model the joint distribution of bid-ask spreads and log returns of a stock portfolio by using Autoregressive Conditional Double Poisson and GARCH processes for the marginals and vine copulas for the dependence structure. By estimating the joint multivariate distribution of both returns and bid-ask spreads from intraday data, we incorporate the measurement of commonalities in liquidity and comovements of stocks and bid-ask spreads into the forecasting of three types of liquidity-adjusted intraday Value-at-Risk (L-IVaR). In a preliminary analysis, we document strong extreme comovements in liquidity and strong tail dependence between bid-ask spreads and log returns across the firms in our sample thus motivating our use of a vine copula model. Furthermore, the backtesting results for the L-IVaR of a portfolio consisting of five stocks listed on the NASDAQ show that the proposed models perform well in forecasting liquidity-adjusted intraday portfolio profits and losses.     

Liquidity Commonality in the London Stock Exchange

Abstract:  A number of events such as the international market crash of October 1987 and the 1997 East Asian crisis show that individual firm liquidity is affected by market-wide factors. However, research in systematic liquidity is still at an embryonic stage and given the gap in the literature, the paper offers first time evidence (to the best of our knowledge) on the presence of systematic liquidity in the UK using FTSE100 and FTSE250 stocks. The unique setting of the London Stock Exchange as regards changes in trading regimes, allows an original answer as to whether changes in the nature of market making from obligatory to non-obligatory, affect commonality in liquidity. Results indicate that commonality is quite strong for FTSE100 stocks at individual and portfolio level, while for the FTSE250 it is strong only at portfolio level. Overall commonality is on average similar across trading regimes, irrespective of the nature of the provision of liquidity.     

Are Stocks Really Less Volatile in the Long Run?

According to conventional wisdom, annualized volatility of stock returns is lower over long horizons than over short horizons, due to mean reversion induced by return predictability. In contrast, we find that stocks are substantially more volatile over long horizons from an investor's perspective. This perspective recognizes that parameters are uncertain, even with two centuries of data, and that observable predictors imperfectly deliver the conditional expected return. Mean reversion contributes strongly to reducing long‐horizon variance but is more than offset by various uncertainties faced by the investor. The same uncertainties reduce desired stock allocations of long‐horizon investors contemplating target‐date funds.     

Cleaning house: Stock reassignments on the NYSE

A frequently occurring, yet unexplored, phenomenon of the New York Stock Exchange specialist system is that of reassignments of stocks by specialist firms on the floor of the Exchange. These events change the portfolios at the individual specialist level by reassigning one or more stocks from one individual specialist portfolio to another. We find that reassigned stocks have unusually wide spreads before reassignments and experience a decline in spreads to levels comparable to matched stocks after the reassignment. This improvement in liquidity is associated with a reduced cost of capital for the reassigned firms. We find that portfolio size, and industry and size concentration of the individual specialist portfolios are associated with the decision of specialist firms to reassign stocks.     

Cash demand,liquidation costs and capital market equilibrium under uncertainty

In this paper, the portfolio and the liquidity planning problems are unified and analyzed in one model. Stochastic cash demands have a significant impact on both the composition of an individual's optimal portfolio and the pricing of capital assets in market equilibrium. The derived capital asset pricing model with cash demands and liquidation costs shows that both the market price of risk and the systematic risk of an asset are affected by the aggregate cash demands and liquidity risk. The modified model does not require that all investors hold an identical risky portfolio as implied by the Sharpe-Lintner-Mossin model. Furthermore, it provides a possible explanation for the noted discrepancies between the empirical evidence and the prediction of the traditional capital asset pricing model.     

Investing for the Long Run when Returns Are Predictable

We examine how the evidence of predictability in asset returns affects optimal portfolio choice for investors with long horizons. Particular attention is paid to estimation risk, or uncertainty about the true values of model parameters. We find that even after incorporating parameter uncertainty, there is enough predictability in returns to make investors allocate substantially more to stocks, the longer their horizon. Moreover, the weak statistical significance of the evidence for predictability makes it important to take estimation risk into account; a long-horizon investor who ignores it may overallocate to stocks by a sizeable amount.     

Conflict of interest and certification in the U.S. IPO market

We examine the long-term return performance of U.S. IPOs underwritten by relationship banks. We show that, over one- to three-year horizons, IPOs managed by relationship banks experience buy-and-hold benchmark-adjusted returns that are similar to those observed for a matching sample of stocks managed by non-relationship underwriters. This result holds even when the returns' skewness and cross-sectional correlation is accounted for. Further, we examine the calendar-time returns on a portfolio that is long the stocks underwritten by relationship banks and short ex-ante similar stocks taken public by non-relationship institutions. Again, we conclude that the two groups of IPOs yield similar long-run returns. These findings support the certification role of relationship banks and suggest that, in this respect, the effect of the 1999 repeal of Sections 20 and 32 of the Glass–Steagall Act has not been negative.