How many factors are important in U.K. stock returns?

I use the sequential approach of Harvey and Liu ([2018]. Lucky factors (Working Paper). Duke University) to build linear factor models in U.K. stock returns among a set of 13 candidate factors using individual stocks and three groups of test portfolios between July 1983 and December 2017. My study finds that the Market factor is the dominant factor in reducing mispricing in individual stocks and test portfolios regardless of the pricing error metric used. The Market factor has a bigger impact when using a value weighting pricing error metric. Whether a second factor is used or not depends upon which metric is used for mispricing and the time period examined. My study finds support for a two-factor model for the whole sample period of the Market factor and the Conservative Minus Aggressive (CMA) factor of Fama and French ([2015]. “A five-factor asset pricing model.” Journal of Financial Economics 116: 1–22) when giving greater weight to the mispricing of larger companies.     

Evaluating asset pricing models in the Korean stock market

This paper evaluates and compares asset pricing models in the Korean stock market. The asset pricing models considered are the CAPM, APT-motivated models, the Consumption-based CAPM, Intertemporal CAPM-motivated models, and the Jagannathan and Wang conditional CAPM model. By using various test portfolios as well as individual stocks, we conduct time-series tests and cross-sectional regression tests based on individual t-tests, the joint F-tests, the Hansen and Jagannathan (1997) distance, and R-squares. Overall, the Fama and French (1993) five-factor model performs most satisfactorily among the asset pricing models considered in explaining the intertemporal and cross-sectional behavior of stock returns in Korea. The Fama and French (1993) three-factor model, the Chen et al. (2010) three-factor model, and the Campbell (1996) model are the next. The results indicate that the two bond portfolios, term spread and default spread, play an important role in explaining stock returns in Korea.     

Long‐Run Stockholder Consumption Risk and Asset Returns

We provide new evidence on the success of long‐run risks in asset pricing by focusing on the risks borne by stockholders. Exploiting microlevel household consumption data, we show that long‐run stockholder consumption risk better captures cross‐sectional variation in average asset returns than aggregate or nonstockholder consumption risk, and implies more plausible risk aversion estimates. We find that risk aversion around 10 can match observed risk premia for the wealthiest stockholders across sets of test assets that include the 25 Fama and French portfolios, the market portfolio, bond portfolios, and the entire cross‐section of stocks.     

An Intertemporal International Asset Pricing Model: Theory and Empirical Evidence

We extend Campbell's (1993) model to develop an intertemporal international asset pricing model (IAPM). We show that the expected international asset return is determined by a weighted average of market risk, market hedging risk, exchange rate risk and exchange rate hedging risk. These weights sum up to one. Our model explicitly separates hedging against changes in the investment opportunity set from hedging against exchange rate changes as well as exchange rate risk from intertemporal hedging risk. A test of the conditional version of our intertemporal IAPM using a multivariate GARCH process supports the asset pricing model. We find that the exchange rate risk is important for pricing international equity returns and it is much more important than intertemporal hedging risk.     

Financial integration and the price of world covariance risk: Large- vs. small-cap stocks

We investigate whether recent country-level evidence of global pricing is particular to large-cap stocks. Specifically, we examine cross-country return correlations and conduct asset pricing tests on three size-based stock portfolios for nine developed countries over the period from 1980 to 2004. We find that large-cap stocks realize significant comovements across countries, whereas small-cap stocks realize smaller average correlations (relative to both large-cap stocks and small-cap stocks across countries). More important, asset pricing tests suggest that while large-cap stocks are priced globally, global pricing is rejected for most small-cap stocks. Finally, the evidence indicates that financial integration deepened in recent years primarily for large-cap stocks. Overall, the results suggest that the global pricing pertains chiefly to large-cap stocks.     

Size effect,book-to-market effect,and survival

Previous studies find that small stocks have higher average returns than large stocks, and the difference between the returns can not be accounted for by the systematic risk, β. In my analysis of Compustat and CRSP data from 1976 to 1995, and simulation experiments based on the data, I find the size effect can be largely explained by data truncation that is caused by survival. Small stocks’ returns are more volatile, and small stocks are more likely to go bankrupt and less likely to meet the stock exchanges’ minimum capitalization requirements for listing. As a result, they are more likely to drop out of the sample. Including small stocks that do well and excluding those that do poorly, ex post, gives rise to higher returns for small-size portfolios. I conclude that the size effect is largely a spurious statistical inference resulting from survival bias, not an asset pricing ‘anomaly’.     

The Cost of Capital of Cross‐listed Firms

This paper analyses the cost of capital of firms with foreign equity listings. Our purpose is to shed light on the question whether international and domestic asset pricing models yield a different estimate of the cost of capital for cross‐listed stocks. We distinguish between (i) the multifactor ICAPM of Solnik (1979) and Sercu (1980) including both the global market portfolio and exchange rate risk premia and (ii) the single factor domestic CAPM. We test for the significance of the cost of capital differential in a sample of 336 cross‐listed stocks from nine countries in the period 1980–99. Our hypothesis is that the cost of capital differential is substantial for firms with international listings, as these are often large multinationals with a strong international orientation. We find that the asset pricing models yield a significantly different estimate of the cost of capital for only 12% of the cross‐listed companies. The size of the cost of capital differential is around 50 basis points for the US, 80 basis points for the UK and 100 basis points for France.     

Modelling the Blind Principal Bid Basket Trading Cost

A blind principal bid (BPB) is one of the mechanisms for simultaneously trading a basket of stocks at a pre‐determined execution price. In a BPB, asset managers auction a basket of stocks directly to liquidity providers who do not know the identities of the individual stocks in the basket. Unlike other methods of trading, the cost and composition of the BPB basket are not reported in a standard and timely manner. Complete basket data are available only to the asset manager and the broker who won the auction. The current literature contains very little information on the BPB phenomenon, largely due to a lack of public data for research. This paper analyses a unique dataset of 140 executed baskets, building on the seminal papers of Kavajecz and Keim (2005) and Stoll (1978a, b) to develop empirical and structural models of BPB trading costs. Our research provides novel insights into the dynamics of pricing BPB trading costs, a topic that has rarely been examined in the literature. The research reported here also has significant practical applications. Asset managers obtain a benchmark for evaluating the lowest bid, and brokers obtain qualitative insights that can aid them in formulating their bids.     

Constructing Asset Pricing Models With Specific Factor Loadings

We demonstrate how one can build pricing formulae in which factors other than beta may be viewed as determinants of asset returns. This is important conceptually as it demonstrates how the additional factors can compensate for a market portfolio proxy that is mis‐specified, and also shows how such a pricing model can be specified ex ante. The procedure is implemented by first selecting an ‘orthogonal’ portfolio which falls on the mean‐variance efficient frontier computed from the empirical average returns, variances and covariances on the equity securities of a large sample of firms. One then determines the inefficient index portfolio which leads to a vector of betas that when multiplied by the average return on the orthogonal portfolio, and which when subtracted from the vector of average returns for the firms comprising the sample, yields an error vector that is equal to the vector of numerical values for the variables that are to form the basis of the asset pricing formula. There will then be a perfect linear relationship between the vector of average returns for the firms comprising the sample, the vector of betas based on the inefficient index portfolio and such other factors that are deemed to be important in the asset pricing process. We illustrate computational procedures using a numerical example based on the quality of information contained in published corporate financial statements.     

Asset pricing: A tale of two days

We show that asset prices behave very differently on days when important macroeconomic news is scheduled for announcement. In addition to significantly higher average returns for risky assets on announcement days, return patterns are much easier to reconcile with standard asset pricing theories, both cross-sectionally and over time. On such days, stock market beta is strongly related to average returns. This positive relation holds for individual stocks, for various test portfolios, and even for bonds and currencies, suggesting that beta is after all an important measure of systematic risk. Furthermore, a robust risk–return trade-off exists on announcement days. Expected variance is positively related to future aggregated quarterly announcement day returns, but not to aggregated non-announcement day returns. We explore the implications of our findings in the context of various asset pricing models.     

Is liquidity the missing link?

Durand et al. (2006a ) argue that the Australian market is both internationally integrated and domestically segmented. They find that the US‐based three‐factor model captures returns of the largest stocks in Australia (evidence of international integration), but that it is unable to account for the returns of the smallest stocks (evidence of domestic segmentation). This study resolves the puzzle left by Durand et al. (2006a) . Incorporating a liquidity factor provides the missing link in their analysis: it results in a model that permits both the international integration of the largest stocks and the model can account for the returns of the smallest stocks. Our analysis highlights the important role of liquidity in Australian asset pricing.     

Disagreement and return predictability of stock portfolios

This paper provides evidence that portfolio disagreement measured bottom-up from individual-stock analyst forecast dispersions has a number of asset pricing implications. For the market portfolio, market disagreement mean-reverts and is negatively related to ex post expected market return. Contemporaneously, an increase in market disagreement manifests as a drop in discount rate. For book-to-market sorted portfolios, the value premium is stronger among high disagreement stocks. The underperformance by high disagreement stocks is stronger among growth stocks. Growth stocks are more sensitive to variations in disagreement relative to value stocks. These findings are consistent with asset pricing theory incorporating belief dispersion.     

Detecting the presence of insider trading via structural break tests

The occurrence of abnormal returns before the unscheduled announcement of price sensitive information is a potential indicator of insider trading. We identify insider trading with a structural change in the intercept of an extended capital asset pricing model. To detect such a change we introduce a consistent timing structural break test (CTSB) based upon a U-statistic type process. Unlike the traditional CUSUM test, the CTSB test provides a consistent estimator of the timing of a break in the intercept that occurs across the whole evaluation period. We apply our test to a rich data set covering 370 price sensitive announcements relating to FTSE 350 companies. Our test is able to detect potential insider trading far more reliably than the standard CUSUM test. We also show that the majority of suspected insider trading takes place in the 25 days prior to the release of market sensitive information.     

An empirical examination of jump risk in asset pricing and volatility forecasting in China's equity and bond markets

Understanding jump risk is important in risk management and option pricing. This study examines the characteristics of jump risk and the volatility forecasting power of the jump component in a panel of high-frequency intraday stock returns and four index returns from Shanghai Stock Exchange. Across portfolio indexes, jump returns on average account for 45% to 64% of total returns when jumps occur. Market systematic jump risk is an important pricing factor for daily returns. The average jump beta is 62% of the average continuous beta for individual stocks. However, the contribution of jump risk to total risk is limited, indicating that statistically significant jumps in the stochastic process of asset price are rare events but have tremendous impacts on the prices of common stocks in China. We further document that accounting for jump components improves the performance of volatility forecasting for some equity and bond portfolios in China, which is confirmed by in-the-sample and out-of-sample forecasting performance analysis.     

Security prices in a production economy with durable capital

Security prices and physical stocks of capital are determined jointly in a rational expectations economy as functions of a set of exogenous stochastic factors. Investors employ firm marginal productivity of capital to allocate savings across firms. Firm capital stocks adjust to exogenous shocks across many periods. Security price functions in period t are derived in the cases of constrained and unconstrained firm capital in t. The risk premia in security returns include two sets of terms. One set, corresponding to traditional asset pricing models, relates cash flows directly to the stochastic factors. The second set captures interfirm effects which arise because firm capital in each period t is durable.     

Have European Stocks become More Volatile? An Empirical Investigation of Idiosyncratic and Market Risk in the Euro Area

We examine the dynamics of idiosyncratic risk, market risk and return correlations in European equity markets using weekly observations from 3515 stocks listed in the 12 euro area stock markets over the period 1974–2004. Similarly to Campbell et al. (2001) , we find a rise in idiosyncratic volatility, implying that it now takes more stocks to diversify away idiosyncratic risk. Contrary to the US, however, market risk is trended upwards in Europe and correlations are not trended downwards. Both the volatility and correlation measures are pro‐cyclical, and they rise during times of low market returns. Market and average idiosyncratic volatility jointly predict market wide returns, and the latter impact upon both market and idiosyncratic volatility. This has asset pricing and risk management implications.     

Cash, investments and asset returns

We use an investment-based asset pricing model to examine the effect of firms’ investments relative to cash holdings on stock returns, assuming holding cash lowers transaction costs. We find that mimicking portfolios based on investments relative to non-cash capital and based on investments relative to cash capital are priced for various testing portfolios. On average, momentum stocks and growth stocks are more sensitive to the factor constructed using investment relative to cash.     

Comparing Asset Pricing Models

A Bayesian asset pricing test is derived that is easily computed in closed form from the standard F‐statistic. Given a set of candidate traded factors, we develop a related test procedure that permits the computation of model probabilities for the collection of all possible pricing models that are based on subsets of the given factors. We find that the recent models of Hou, Xue, and Zhang (2015a, 2015b) and Fama and French (2015, 2016) are dominated by a variety of models that include a momentum factor, along with value and profitability factors that are updated monthly.     

Is Information Risk a Determinant of Asset Returns?

We investigate the role of information–based trading in affecting asset returns. We show in a rational expectation example how private information affects equilibrium asset returns. Using a market microstructure model, we derive a measure of the probability of information–based trading, and we estimate this measure using data for individual NYSE–listed stocks for 1983 to 1998. We then incorporate our estimates into a Fama and French (1992) asset–pricing framework. Our main result is that information does affect asset prices. A difference of 10 percentage points in the probability of information–based trading between two stocks leads to a difference in their expected returns of 2.5 percent per year.     

The relationship between return and market value of common stocks

This study examines the empirical relationship between the return and the total market value of NYSE common stocks. It is found that smaller firms have had higher risk adjusted returns, on average, than larger firms. This ‘size effect’ has been in existence for at least forty years and is evidence that the capital asset pricing model is misspecified. The size effect is not linear in the market value; the main effect occurs for very small firms while there is little difference in return between average sized and large firms. It is not known whether size per se is responsible for the effect or whether size is just a proxy for one or more true unknown factors correlated with size.