Momentum and Foreign Investors: Evidence from the Korean Stock Market

Abstract:

We examine whether the price impact of foreign investors on the Korean stock market from December 2000 to February 2007 generated a momentum phenomenon. In our empirical results, foreigners seem to have exerted a significantly positive impact on prices in “up” markets (periods of positive stock returns), but have had little impact on prices in “down” markets (periods of negative returns). We document that the impact of foreigners’ trades is concentrated in large companies. Most importantly, when the market is in the up state, the returns of stocks of large companies that were positively affected by foreign investors in the previous six-month period continue to increase in the subsequent six-month period. As a result, the subsequent six-month return on a past “winner” stock portfolio is significantly higher than that on a past “loser” stock portfolio. This brings to mind a momentum phenomenon that has been reported not to exist in the Korean stock market.     

Consumption, Aggregate Wealth, and Expected Stock Returns

This paper studies the role of fluctuations in the aggregate consumption–wealth ratio for predicting stock returns. Using U.S. quarterly stock market data, we find that these fluctuations in the consumption–wealth ratio are strong predictors of both real stock returns and excess returns over a Treasury bill rate. We also find that this variable is a better forecaster of future returns at short and intermediate horizons than is the dividend yield, the dividend payout ratio, and several other popular forecasting variables. Why should the consumption–wealth ratio forecast asset returns? We show that a wide class of optimal models of consumer behavior imply that the log consumption–aggregate wealth (human capital plus asset holdings) ratio summarizes expected returns on aggregate wealth, or the market portfolio. Although this ratio is not observable, we provide assumptions under which its important predictive components for future asset returns may be expressed in terms of observable variables, namely in terms of consumption, asset holdings and labor income. The framework implies that these variables are cointegrated, and that deviations from this shared trend summarize agents' expectations of future returns on the market portfolio.     

Focusing on the worst state for robust investing

Despite its shortcomings, the Markowitz model remains the norm for asset allocation and portfolio construction. A major issue involves sensitivity of the model's solution to its input parameters. The prevailing approach employed by practitioners to overcome this problem is to use worst-case optimization. Generally, these methods have been adopted without incorporating equity market behavior and we believe that an analysis is necessary. Therefore, in this paper, we present the importance of market information during the worst state for achieving robust performance. We focus on the equity market and find that the optimal portfolio in a market with multiple states is the portfolio with robust returns and observe that focusing on the worst market state provides robust returns. Furthermore, we propose alternative robust approaches that emphasize returns during market downside periods without solving worst-case optimization problems. Through our analyses, we demonstrate the value of focusing on the worst market state and as a result find support for the value of worst-case optimization for achieving portfolio robustness.     

Which past returns affect trading volume?

Anecdotal evidence suggests and recent theoretical models argue that past stock returns affect subsequent stock trading volume. We study 3,000 individual investors over a 51 month period to test this apparent link between past returns and volume using several different panel regression models (linear panel regressions, negative binomial panel regressions, Tobit panel regressions). We find that both past market returns as well as past portfolio returns affect trading activity of individual investors (as measured by stock portfolio turnover, the number of stock transactions, and the propensity to trade stocks in a given month). After high portfolio returns, investors buy high risk stocks and reduce the number of stocks in their portfolio. High past market returns do not lead to higher risk taking or underdiversification. We argue that the only explanations for our findings are overconfidence theories based on biased self-attribution and differences of opinion explanations for high levels of trading activity.     

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.     

Portfolio Returns,Market Volatility,and Seasonality

This paper examines three important issues related to the relationship between stock returns and volatility. First, are Duffee's (1995) findings of the relationship between individual stock returns and volatility valid at the portfolio level? Second, is there a seasonality of the market return volatility? Lastly, do size portfolio returns react symmetrically to the market volatility during business cycles? We find that the market volatility exhibits strong autocorrelation and small size portfolio returns exhibit seasonality. However, this phenomenon is not present in large size portfolios. For the entire sample period of 1962–1995, the highest average monthly volatility occurred in October, followed by November, and then January. Examining the two sub-sample periods, we find that the average market volatility increases by 15.4% in the second sample period of 1980–1995 compared to the first sample period of 1962–1979. During the contraction period, the average market volatility is 60.9% higher than that during the expansion period. Using a binary regression model, we find that size portfolio returns react asymmetrically with the market volatility during business cycles. This paper documents a strongly negative contemporaneous relationship between the size portfolio returns and the market volatility that is consistent with the previous findings at the aggregate level, but is inconsistent with the findings at the individual firm level. In contrast with the previous findings, however, we find an ambiguous relationship between the percentage change in the market volatility and the contemporaneous stock portfolio returns. This ambiguity is attributed to strongly negative contemporaneous and one-month ahead relationships between the market volatility and portfolio returns.

     

Post‐Retirement Financial Strategies: Forecasts and Valuation

This paper uses a discrete‐time, discrete‐state Monte Carlo simulation model to evaluate representative strategies for investing and spending a fixed sum designed to fund consumption during the period after retirement. Two assets are considered – one providing a riskless real return, the other a market portfolio of bonds and stocks. A stochastic process for the returns from the market portfolio is proposed. Then a set of Arrow‐Debreu state prices is obtained on the assumption that the market portfolio is an efficient investment strategy. The model is used to forecast ranges of consumption and ranges of the ratios of year‐to‐year consumption, and also to estimate the values of components of future consumption.     

The cross-section of stock returns in an early stock market

Using a new dataset which contains monthly data on 1015 stocks traded on the London Stock Exchange between 1825 and 1870, we investigate the cross section of stock returns in this early capital market. Unique features of this market allow us to evaluate the veracity of several popular explanations of asset pricing behavior. Using portfolio analysis and Fama–MacBeth regressions, we find that stock characteristics such as beta, illiquidity, dividend yield, and past-year return performance are all positively correlated with stock returns. However, market capitalization and past-three-year return performance have no significant correlation with stock returns.     

Risk,Mispricing, and Value Investing

We evaluate the stock return performance of a modified version of the book-to-market strategy and its implications for market efficiency. If the previously documented superior stock return of the book-to-market strategy represents mispricing, its performance should be improved by excluding fairly valued firms with extreme book-to-market ratios. To attain this, we classify stocks as value or glamour on book-to-market ratios and accounting accruals jointly. This joint classification is likely to exclude stocks with extreme book-to-market ratios due to mismeasured accounting book values reflecting limitations underlying the accounting system. Using both 12-month buy-and-hold returns and earnings announcement returns, our results show that this joint classification generates substantially higher portfolio returns in the post-portfolio-formation year than the book-to-market classification alone with no evidence of increased risk. In addition, this superior stock return performance is more pronounced among firms held primarily by small (unsophisticated) investors and followed less closely by market participants (stock price <$10). Finally, and most importantly, financial analysts are overly optimistic (pessimistic) about earnings of glamour (value) stock, and for a subset of firms identified as overvalued by our strategy, the earnings announcement raw return, as well as abnormal return, is negative. These last results are particularly important because it is hard to envision a model consistent with rational investors holding risky stocks with predictable negative raw returns for a long period of time rather than holding fT-bills and with financial analysts systematically overestimating the earnings of these stocks while underestimating earnings of stocks that outperform the stock market.     

The Other Side of Value: The Effect of Quality on Price and Return in Real Estate

We examine the effect of quality on price and return in real estate. Quality real estate stocks generally trade at higher prices. However, similar to findings in the aggregate stock market (Asness et al. 2013), quality in listed real estate is not fully reflected in price. A long quality, short junk portfolio (QMJ) is found to produce average risk-adjusted returns of 3.6 % per quarter (14.4 % annually). QMJ has a significantly positive coefficient when added as a dependent variable to a 4-factor model explaining U.S. real estate excess returns, including market, size, value and momentum factors, and leads to a small improvement in the overall fit of the model. Further investigation of the components of quality shows that profitability has a consistently positive relationship with real estate excess returns, while higher dividend payout is consistently associated with lower returns, in contrast to findings for conventional firms.     

Stock price synchronicity and analyst coverage in emerging markets

This paper examines the relation between the stock price synchronicity and analyst activity in emerging markets. Contrary to the conventional wisdom that security analysts specialize in the production of firm-specific information, we find that securities which are covered by more analysts incorporate greater (lesser) market-wide (firm-specific) information. Using the R² statistics of the market model as a measure of synchronicity of stock price movement, we find that greater analyst coverage increases stock price synchronicity. Furthermore, after controlling for the influence of firm size on the lead–lag relation, we find that the returns of high analyst-following portfolio lead returns of low analyst-following portfolio more than vice versa. We also find that the aggregate change in the earnings forecasts in a high analyst-following portfolio affects the aggregate returns of the portfolio itself as well as those of the low analyst-following portfolio, whereas the aggregate change in the earnings forecasts of the low analyst-following portfolio have no predictive ability. Finally, when the forecast dispersion is high, the effect of analyst coverage on stock price synchronicity is reduced.     

The Return of the Size Anomaly: Evidence from the German Stock Market

This paper examines the size effect in the German stock market and intends to address several unanswered issues on this widely known anomaly. Unlike recent evidence of a reversal of the size anomaly this study documents a conditional relation between size and returns. I also detect strong momentum across size portfolios. The results indicate that the marginal effect of firm size on stock returns is conditional on the firm's past performance. I use an instrumental variable estimation to address Berk's critique of a simultaneity bias in prior studies on the small firm effect and to investigate the economic rationale behind firm size as an explanatory variable for the variation in stock returns. The analysis in this paper indicates that firm size captures firm characteristic components in stock returns and that this regularity cannot be explained by differences in systematic risk.     

Predicting bear and bull stock markets with dynamic binary time series models

Despite the voluminous empirical research on the potential predictability of stock returns, much less attention has been paid to the predictability of bear and bull stock markets. In this study, the aim is to predict U.S. bear and bull stock markets with dynamic binary time series models. Based on the analysis of the monthly U.S. data set, bear and bull markets are predictable in and out of sample. In particular, substantial additional predictive power can be obtained by allowing for a dynamic structure in the binary response model. Probability forecasts of the state of the stock market can also be utilized to obtain optimal asset allocation decisions between stocks and bonds. It turns out that the dynamic probit models yield much higher portfolio returns than the buy-and-hold trading strategy in a small-scale market timing experiment.     

Cross-sectional dispersion and expected returns

This study investigates whether the cross-sectional dispersion of stock returns, which reflects the aggregate level of idiosyncratic risk in the market, represents a priced state variable. We find that stocks with high sensitivities to dispersion offer low expected returns. Furthermore, a zero-cost spread portfolio that is long (short) in stocks with low (high) dispersion betas produces a statistically and economically significant return. Dispersion is associated with a significantly negative risk premium in the cross section (–1.32% per annum) which is distinct from premia commanded by alternative systematic factors. These results are robust to stock characteristics and market conditions.     

Examination of Conditional Asset Pricing in UK Stock Returns

I examine the empirical performance of various specifications of the capital asset pricing model (CAPM) in UK stock returns, using the stochastic discount framework. When the proxy for the market portfolio includes a proxy for labor income growth in addition to the stock market index, the performance of the CAPM improves. The improvement in performance shows in the magnitude and significance of the pricing errors and in the reduced impact of asset characteristics and other factors in the pricing of assets. There is further improvement when I use conditional versions of the models.     

Institutional investors and the limits of arbitrage

The returns and stock holdings of institutional investors from 1980 to 2007 provide little evidence of stock-picking skill. Institutions as a whole closely mimic the market portfolio, with pre-cost returns that have nearly perfect correlation with the value-weighted index and an insignificant CAPM alpha of 0.08% quarterly. Institutions also show little tendency to bet on any of the main characteristics known to predict stock returns, such as book-to-market, momentum, or accruals. While particular groups of institutions have modest stock-picking skill relative to the CAPM, their performance is almost entirely explained by the book-to-market and momentum effects in returns. Further, no group holds a portfolio that deviates efficiently from the market portfolio.     

Discretely adjusted option hedges

This paper analyses the distribution of returns on a hedged portfolio, consisting of a European call option and its associated stock, when the portfolio is rebalanced at discrete time intervals. Under the assumptions of the Black-Scholes model this distribution is particularly skew. In tests of the average return on a hedged portfolio this skewness leads to biased t-statistics. The paper explores the nature and extent of this bias and suggests procedures for overcoming it. Other aspects of discrete hedging are also discussed.     

Portfolio returns and manager activity: How to decompose tracking error into security selection and market timing

We develop a new method for detecting portfolio manager activity. Our method relies exclusively on portfolio returns and, consequently, avoids the pitfalls associated with disclosed portfolio holdings. We investigate the link between activity and performance of actively managed U.S. equity funds from 2000 to 2007 and document robust evidence that future performance is positively related to past stock picking and negatively associated with past market timing. Finally, we find that portfolio manager activity is highly persistent over time, which supports the conclusion that stock picking increases performance while market timing decreases performance.     

Coincident and leading indicators of the stock market

In this paper we have two goals: first, we want to represent monthly stock market fluctuations by constructing a non-linear coincident financial indicator. The indicator is constructed as an unobservable factor whose first moment and conditional volatility are driven by a two-state Markov variable. It can be interpreted as the investors' real-time belief about the state of financial conditions. Second, we want to explore an approach in which investors may use their perceptions of the state of the economy to form forecasts of financial market conditions and possibly of excess returns. To investigate this, we build leading indicators as forecasts of the estimated coincident financial index. The leading indicators yield better within and out-of-sample performance in forecasting, not only the state of the stock market but also of excess stock returns, as compared with the performance obtained using linear methods that have been proposed in the existing literature.     

Mean–variance and mean–semivariance portfolio selection: a multivariate nonparametric approach

While univariate nonparametric estimation methods have been developed for estimating returns in mean-downside risk portfolio optimization, the problem of handling possible cross-correlations in a vector of asset returns has not been addressed in portfolio selection. We present a novel multivariate nonparametric portfolio optimization procedure using kernel-based estimators of the conditional mean and the conditional median. The method accounts for the covariance structure information from the full set of returns. We also provide two computational algorithms to implement the estimators. Via the analysis of 24 French stock market returns, we evaluate the in-sample and out-of-sample performance of both portfolio selection algorithms against optimal portfolios selected by classical and univariate nonparametric methods for three highly different time periods and different levels of expected return. By allowing for cross-correlations among returns, our results suggest that the proposed multivariate nonparametric method is a useful extension of standard univariate nonparametric portfolio selection approaches.