Another look at trading costs and short-term reversal profits

Several studies report that abnormal returns associated with short-term reversal investment strategies diminish once trading costs are taken into account. We show that the impact of trading costs on the strategies’ profitability can largely be attributed to excessively trading in small cap stocks. Limiting the stock universe to large cap stocks significantly reduces trading costs. Applying a more sophisticated portfolio construction algorithm to lower turnover reduces trading costs even further. Our finding that reversal strategies generate 30-50 basis points per week net of trading costs poses a serious challenge to standard rational asset pricing models. Our findings also have important implications for the understanding and practical implementation of reversal strategies.     

THE ECONOMIC GAINS OF TRADING STOCKS AROUND HOLIDAYS

I assess the economic gains of strategies that account for the effect of holiday calendar effects on the daily returns and volatility of the 30 stocks in the Dow Jones Industrial Average index. The dynamic strategies use forecasts from stochastic volatility models that distinguish between regular trading days and different types of holidays. More important, I assess the economic value of conditioning on holiday effects and find that a risk-averse investor will pay a high performance fee to switch from a dynamic portfolio strategy that does not account for the effect of holidays on daily conditional expected returns and volatility to a strategy that does. This result is robust to reasonable transaction costs.     

Portfolio optimization in discrete time with proportional transaction costs under stochastic volatility

This paper is devoted to evaluating the optimal self-financing strategy and the optimal trading frequency for a portfolio with a risky asset and a risk-free asset. The objective is to maximize the expected future utility of the terminal wealth in a stochastic volatility setting, when transaction costs are incurred at each discrete trading time. A HARA utility function is used, allowing a simple approximation of the optimization problem, which is implementable forward in time. For each of various transaction cost rates, we find the optimal trading frequency, i.e. the one that attains the maximum of the expected utility at time zero. We study the relation between transaction cost rate and optimal trading frequency. The numerical method used is based on a stochastic volatility particle filtering algorithm, combined with a Monte-Carlo method. The filtering algorithm updates the estimate of the volatility distribution forward in time, as new stock observations arrive; these updates are used at each of these discrete times to compute the new portfolio allocation.     

The cross-section of industry equity returns and global tactical asset allocation across regions and industries

This study investigates which index characteristics predict returns in the cross-section of local industry indexes in six regions. The results show that geographical origin and market capitalization of indexes critically determine the predictive ability of characteristics. We find that industry indexes of any market capitalization with high earnings-to-price ratio yield higher expected returns in the US, Europe, and Asia-Pacific. Recent winner (loser) portfolios in Europe have a tendency to outperform (underperform) recent loser (winner) portfolios in the near future for all groups of market capitalization. Small portfolios with high idiosyncratic volatility in Asia-Pacific earn an idiosyncratic volatility premium. Dividend yield is positively related to future returns of small European portfolios. These results are robust to the inclusion of transaction costs and control variables and have implications for portfolio managers following a global tactical asset allocation policy.     

Do optimal diversification strategies outperform the 1/N strategy in U.K. stock returns?

This paper examines whether optimal diversification strategies outperform the 1/N strategy in U.K. stock returns. The study focuses on the performance of recent strategies developed by Tu and Zhou (2011) and Kirby and Ostdiek (2010). I find that a number of optimal asset allocation strategies can significantly outperform the 1/N strategy even after adjusting for trading costs. The strategies developed by Kirby and Ostdiek outperform the 1/N strategy, even at higher trading costs, due to the low turnover of these strategies. The strategies of Tu and Zhou have mixed performance after adjusting for trading costs due to the high turnover of these strategies. The results of the paper provide support for the use of optimal diversification strategies.     

Discount rate changes,stock market returns,volatility, and trading volume: Evidence from intraday data and implications for market efficiency

We examine the effect of discount rate changes on stock market returns, volatility, and trading volume using intraday data. Equity returns generally respond negatively and significantly to the unexpected announcements; however, the effect of expected changes on equity returns is insignificant. Furthermore, our results indicate that equity prices respond to announcements within the trading period/hour after the information release. An indication of a return reversal is too small to cover the full transaction costs. Unexpected discount rate changes also contribute to higher market volatility although the volatility is short-lived. Similarly, unexpected changes in discount rates induce larger trading volume while expected changes do not. Abnormal trading volume occurs only in period t. Our results also support the notion that unexpected changes in the discount rates impact market returns irrespective of the Federal Reserve operating procedures.     

Hedging Strategies of Financial Intermediaries: Pricing Options with a Bid-Ask Spread

This paper uses a model similar to the Boyle-Vorst and Ritchken-Kuo arbitrage-free models for the valuation of options with transactions costs to determine the maximum price to be charged by the financial intermediary writing an option in a non-auction market. Earlier models are extended by recognizing that, in the presence of transactions costs, the price-taking intermediary devising a hedging portfolio faces a tradeoff: to choose a short trading interval with small hedging errors and high transactions costs, or a long trading interval with large hedging errors and low transactions costs. The model presented here also recognizes that when transactions costs induce less frequent portfolio adjustments, investors are faced with a multinomial distribution of asset returns rather than a binomial one. The price upper bound is determined by selecting the trading frequency that will equalize the marginal gain from decreasing hedging errors and the marginal cost of transactions.     

TIME-VARYING FACTORS AND CROSS-AUTOCORRELATIONS IN SHORT-HORIZON STOCK RETURNS

In this paper I show that the lead-lag pattern between large and small market value portfolio returns is consistent with differential variations in their expected return components. I find that the larger predictability of returns on the portfolio of small stocks may be due to a higher exposure of these firms to persistent (time-varying) latent factors. Additional evidence suggests that the asymmetric predictability cannot be fully explained by lagged price adjustments to common factor shocks: (i) lagged returns on large stocks do not have a strong causal effect on returns on small stocks; (ii) trading volume is positively related to own- and cross-autocorrelations in weekly portfolio returns; and (iii) significant cross-autocorrelation exists between current returns on large stocks and lagged returns on small stocks when trading volume is high.     

Dynamic Volatility Trading Strategies in the Currency Option Market

The conditional volatility of foreign exchange rates can be predicted using GARCH models or implied volatility extracted from currency options. This paper investigates whether these predictions are economically meaningful in trading strategies that are designed only to trade volatility risk. First, this article provides new evidence on the issue of information content of implied volatility and GARCH volatility in forecasting future variance. In an artificial world without transaction costs both delta-neutral and straddle trading stratgies lead to significant positive profits, regardless of which volatility prediction method is used. Specifically, the agent using the Implied Stochastic Volatility Regression method (ISVR) earns larger profits than the agent using the GARCH method. Second, it suggests that the currency options market is informationally efficient. After accounting for transaction costs, which are assumed to equal one percent of option prices, observed profits are not significantly differentfrom zero in most trading strategies. Finally, these strategies offered returns have higher Sharpe ratio and lower correlation with several major asset classes. Consequently, hedge funds and institutional investors who are seeking alternative “marketneutral” investment methods can use volatility trading to improvethe risk-return profile of their portfolio through diversification. This revised version was published online in November 2006 with corrections to the Cover Date.     

Options Trading Based on the Forecasting of Volatility Direction with the Incorporation of Investor Sentiment

Using options price data on the Taiwanese stock market, we propose an options trading strategy based on the forecasting of volatility direction. The forecasting models are constructed with the incorporation of absolute returns, heterogeneous autoregressive-realized volatility (HAR-RV), and proxy of investor sentiment. After we take into consideration the margin-based transaction costs, the results of our simulated trading indicate that a straddle trading strategy that considers the forecasting of volatility direction with the incorporation of market turnover achieves the best Sharpe ratios. Our trading algorithm bridges the gap between options trading, market volatility, and the information content of investor overreaction.     

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.     

Disagreement and asset prices

How do differences of opinion affect asset prices? Do investors earn a risk premium when disagreement arises in the market? Despite their fundamental importance, these questions are among the most controversial issues in finance. In this paper, we use a novel data set that allows us to directly measure the level of disagreement among Wall Street mortgage dealers about prepayment speeds. We examine how disagreement evolves over time and study its effects on expected returns, return volatility, and trading volume in the mortgage-backed security market. We find that increased disagreement is associated with higher expected returns, higher return volatility, and larger trading volume. These results imply that there is a positive risk premium for disagreement in asset prices. We also show that volatility in and of itself does not lead to higher trading volume. Instead, only when disagreement arises in the market is higher uncertainty associated with more trading. Finally, we are able to distinguish empirically between two competing hypotheses regarding how information in markets gets incorporated into asset prices. We find that sophisticated investors appear to update their beliefs through a rational expectations mechanism when disagreement arises.     

The plausibility of risk estimates and implied costs to international equity investments

We reconsider the costs to international equity investments implied by standard portfolio theory (Cooper and Kaplanis, 1994; Sercu and Vanpée, 2008). Estimated costs are mostly driven by risk estimates, not by asset holdings. For OECD markets, risks are fairly stable and relatively easy to estimate, but for emerging markets this is not the case. Many required expected returns implied by unconditional risk estimates defy credibility, both a priori and empirically. More sophisticated volatility estimates based on a dynamic risk model a la Bekaert and Harvey (1997) lead to implicit costs that are far more credible, but the results remain fragile.     

Stock market volatility and equity returns: Evidence from a two-state Markov-switching model with regressors

This paper proposes a two-state Markov-switching model for stock market returns in which the state-dependent expected returns, their variance and associated regime-switching dynamics are allowed to respond to market information. More specifically, we apply this model to examine the explanatory and predictive power of price range and trading volume for return volatility. Our findings indicate that a negative relation between equity market returns and volatility prevails even after having controlled for the time-varying determinants of conditional volatility within each regime. We also find an asymmetry in the effect of price range on intra- and inter-regime return volatility. While price range has a stronger effect in the high volatility state, it appears to significantly affect only the transition probabilities when the stock market is in the low volatility state but not in the high volatility state. Finally, we provide evidence consistent with the ‘rebound’ model of asset returns proposed by Samuelson (1991), suggesting that long-horizon investors are expected to invest more in risky assets than short-horizon investors.     

The economic value of range-based covariance between stock and bond returns with dynamic copulas

The covariance between stock and bond returns plays important roles in the setting up of asset allocation strategies and portfolio diversification. In the present study, we propose a multivariate range-based volatility model incorporating dynamic copulas into a range-based volatility model to describe the volatility and dependence structures of stock and bond returns. We then go on to assess the economic value of the covariance forecasts based on our proposed model under a mean-variance framework. The out-of-sample forecasting performance reveals that investors would be willing to pay between 39 and 2081 basis points per year to switch from a dynamic trading strategy under the return-based volatility model to a dynamic trading strategy under the range-based volatility model, with more risk-averse investors being willing to pay even higher switching fees. Furthermore, additional economic gains of between 33 and 1471 annualized basis points are achieved when taking the leverage effect into consideration.     

Aggregate volatility risk: Explaining the small growth anomaly and the new issues puzzle

The paper shows that new issues earn low expected returns because they are a hedge against increases in expected aggregate volatility. Consistent with that, the ICAPM with the aggregate volatility risk factor can explain the new issues puzzle, as well as the small growth anomaly and the cumulative issuance puzzle. The key mechanism is that, all else equal, growth options become less sensitive to the underlying asset value and more valuable as idiosyncratic volatility goes up. Idiosyncratic volatility usually increases together with aggregate volatility, that is, in recessions.     

Does futures trading increase stock market volatility? The case of the Nikkei stock index futures markets

We propose new tests to examine whether stock index futures affect stock market volatility. These tests decompose spot portfolio volatility into the cross-sectional dispersion and the average volatility of returns on the portfolio's constituent securities. Our tests show that for Nikkei stocks spot portfolio volatility increased and cross-sectional dispersion decreased compared with average volatility when Nikkei futures began trading on the Osaka Securities Exchange, but not on the Singapore International Monetary Exchange. For non-Nikkei stocks, no shift occurred when futures trading began on either exchange. These findings are consistent with the hypotheses that futures trading increases spot portfolio volatility but that there is no volatility “spillover” to stocks against which futures are not traded. However, the increase in volatility attributable to futures trading is small compared with volatility shifts induced by changes in broad economic factors.     

The efficiency of international information flow: Evidence from the ETF and CEF prices

While similar in their trading and organization, closed-end funds (CEFs) and exchange-traded funds (ETFs) differ in their liquidity and ease of arbitrage. We compare their price transmission dynamics using a sample of funds that invest in foreign securities and are most likely to show the deficiencies in the manner in which they process information. Our analysis shows that ETF returns are more closely related to their portfolio returns than are CEF returns. However, both fund types underreact to portfolio returns but overreact to domestic stock market returns. A simple trading strategy using these results is profitable with roundtrip trading costs less than 1.38% for CEFs and 0.71% for ETFs.     

Recent trends in trading activity and market quality

We explore the sharp uptrend in recent trading activity and accompanying changes in market efficiency. Higher turnover has been associated with more frequent smaller trades, which have progressively formed a larger fraction of trading volume over time. Evidence indicates that secular decreases in trading costs have influenced the turnover trend. Turnover has increased the most for stocks with the greatest level of institutional holdings, suggesting professional investing as a key contributor to the turnover trend. Variance ratio tests suggest that more institutional trading has increased information-based trading. Intraday volatility has decreased and prices conform more closely to random walk in recent years. The sensitivity of turnover to past returns has increased and cross-sectional predictability of returns has decreased significantly, revealing a more widespread use of quantitative trading strategies that allow for more efficient securities prices.     

Pricing Currency Risks

The currency market features a small cross-section, and conditional expected returns can be characterized by few signals: interest differential, trend, and mean reversion. We exploit these properties to construct the ex ante mean-variance efficient portfolio of individual currencies. The portfolio is updated in real time and prices all prominent currency trading strategies, conditionally and unconditionally. The fraction of risk in these assets that does not affect their risk premiums is at least 85%. Extant explanations of carry strategies based on intermediary capital or global volatility are related to these unpriced components, while consumption growth is related to the priced component of returns.