Abnormal Returns from Takeover Prediction Modelling: Challenges and Suggested Investment Strategies

While takeover targets earn significant abnormal returns, studies tend to find no abnormal returns from investing in predicted takeover targets. In this study, we show that the difficulty of correctly identifying targets ex ante does not fully explain the below‐expected returns to target portfolios. Target prediction models’ inability to optimally time impending takeovers, by taking account of pre‐bid target underperformance and the anticipation of potential targets by other market participants, diminishes but does not eliminate the potential profitability of investing in predicted targets. Importantly, we find that target portfolios are predisposed to underperform, as targets and distressed firms share common firm characteristics, resulting in the misclassification of a disproportionately high number of distressed firms as potential targets. We show that this problem can be mitigated, and significant risk‐adjusted returns can be earned, by screening firms in target portfolios for size, leverage and liquidity.     

Volatility‐Managed Portfolios

Managed portfolios that take less risk when volatility is high produce large alphas, increase Sharpe ratios, and produce large utility gains for mean‐variance investors. We document this for the market, value, momentum, profitability, return on equity, investment, and betting‐against‐beta factors, as well as the currency carry trade. Volatility timing increases Sharpe ratios because changes in volatility are not offset by proportional changes in expected returns. Our strategy is contrary to conventional wisdom because it takes relatively less risk in recessions. This rules out typical risk‐based explanations and is a challenge to structural models of time‐varying expected returns.     

上市公司综合盈利水平与股票收益

本文基于2000-2017年上市公司的财务及股票交易数据,研究了上市公司综合盈利水平与股票收益之间的关系。我们使用目前资产定价文献中较新的偏最小二乘法和组合预测法,从12个衡量公司盈利能力的指标中提取了一个测度上市公司综合盈利水平的指标。研究结果显示,上市公司综合盈利水平能够显著预测未来股票收益。使用单因子偏最小二乘法、取12个月斜率的平均值构造的综合盈利水平最有效,以其构建的多空对冲投资组合能产生15%的年平均收益,夏普比率达到0.75。与此对应,组合预测法提取的上市公司综合盈利水平的预测能力稍低,但依然显著。在控制了其他公司特征变量后,综合盈利水平对于股票收益的解释能力依然稳健。本文还从经济机制的角度出发,探讨了综合盈利水平对收益的预测来源。我们发现,上市公司综合盈利水平与股票预期回报的正向关系在投资摩擦更低的组中更高,而在错误定价程度更高的组通常更低。这些结果支持了基于投资摩擦的Q理论,而与行为金融的错误定价理论相悖。     

Big is brilliant: Understanding the Chinese size effect through profitability shocks

Taking a dynamic view, this paper assesses the extent to which profitability shocks affect the size premium in the Chinese market. In the short run, there is a significant U-shaped relationship between size and profitability shocks; i.e., both small and large firms experience large and (in most cases) positive profitability shocks, while firms with medium market capitalizations display small profitability shocks. In the long run, profitability shocks in large firms remain large and stable, while profitability shocks in small firms decrease sharply. Adjusting for profitability shocks increases the returns of small firms but decreases the returns of large firms, indicating that large and positive profitability shocks in small firms cannot bring investors sizable returns even though the correlation between profitability shocks and returns is positive. Mismatches between profitability shocks and the per-unit return impact of such shocks (e.g., when firms experience positive shocks but the market reacts to these shocks irrationally) can help explain this phenomenon. Our work reveals that in terms of fundamentals, large firms are very worthy of investment owing to their superb fundamental performance, i.e., large and persistent profitability shocks.     

Empirical Evidence on Capital Investment, Growth Options, and Security Returns

Growth in capital expenditures conditions subsequent classification of firms to portfolios based on size and book‐to‐market ratios, as in the widely used Fama and French (1992, 1993) methods. Growth in capital expenditures also explains returns to portfolios and the cross section of future stock returns. These findings are consistent with recent theoretical models (e.g., Berk, Green, and Naik (1999)) in which the exercise of investment‐growth options results in changes in both valuation and expected stock returns.     

Technical indicators and cross-sectional expected returns

This study shows that 14 widely documented technical indicators explain cross-sectional stock returns. These indicators have lower estimation errors than the three-factor Fama–French and historical mean models. The long-short portfolios based on the cross-sectional technical signals generate substantial excess returns. These remain consistent after controlling for well-known cross-sectional return determinants, including momentum, size, book-to-market ratio, investment, and profitability. Our findings suggest that technical indicators play an important role in determining variation in cross-sectional returns.     

Asset liquidity and stock returns

We document the significant predictive power of firms' asset liquidity in the cross section of subsequent stock returns. The annual return spread between portfolios featuring the highest and lowest levels of asset liquidity is significantly positive. Our proposed measure of asset liquidity outperforms those measures developed by Gopalan et al. (2012) in predicting returns. The asset liquidity anomaly also provides significantly positive alphas when controlling for the asset pricing factors in the Fama and French (1993) three-factor model and the Carhart (1997) four-factor model. Asset liquidity exhibits strong return forecasting power even after controlling for acknowledged cross-sectional determinants of return. The positive relation between asset liquidity and future returns tends to be stronger for firms with greater asset productivity, higher quality cash flow and lower capital investment.     

WHAT DRIVES INVESTMENT FLOWS INTO SOCIAL TRADING PORTFOLIOS?

We investigate investment flows into more than 5,300 social trading portfolios that are issued as structured products and are tradable at a regular exchange. We find that investment flows chase past performance. However, in contrast to mutual fund flows, the flow–performance relation exists nearly exclusively for the best performing social trading portfolios. Flows follow raw returns rather than factor model alphas. Additionally, flows are highly persistent. Finally, social trading portfolios with higher visibility on the web page of the social trading platform as well as traders communicating actively to investors via public comments attract higher inflows.     

Mutual fund performance persistence: Factor models and portfolio size

We re-examine US mutual fund performance persistence. We investigate persistence (i) using both “academic” factor models and “practitioner” index models, (ii) using decile-size recursive portfolios and also portfolios formed from smaller numbers of funds, (iii) using nonparametric bootstrap p-values as well as conventional t-tests and (iv) using both net-of-fee fund returns (net alphas) and gross alphas. Our key result is that positive net alpha performance persistence can be found using small portfolios of funds together with a holding period of 6 months or less, for both practitioner index models and academic factor models.     

Profitability,investment and average returns

Valuation theory says that expected stock returns are related to three variables: the book-to-market equity ratio (B_t/M_t), expected profitability, and expected investment. Given B_t/M_t and expected profitability, higher expected rates of investment imply lower expected returns. But controlling for the other two variables, more profitable firms have higher expected returns, as do firms with higher B_t/M_t. These predictions are confirmed in our tests.     

Share Issuance and Factor Timing

We show that characteristics of stock issuers can be used to forecast important common factors in stocks' returns such as those associated with book‐to‐market, size, and industry. Specifically, we use differences between the attributes of stock issuers and repurchasers to forecast characteristic‐related factor returns. For example, we show that large firms underperform after years when issuing firms are large relative to repurchasing firms. While our strongest results are for portfolios based on book‐to‐market (i.e., HML), size (i.e., SMB), and industry, our approach is also useful for forecasting factor returns associated with distress, payout policy, and profitability.     

The efficiency of the buy-write strategy: Evidence from Australia

We examine the performance of the buy-write option strategy (BWS) on the Australian Stock Exchange and analyse whether such an investment opportunity violates the efficient market hypothesis on the basis of its risk and returns. This study investigates the relationship between buy-write portfolios returns and past trading volume and other fundamental financial factors including dividend yield, firm size, book to market ratio, earnings per share (EPS), price earnings ratio and value stocks within these portfolios. We also test the profitability of the buy-write strategy during bull and bear markets. Consistent with the literature, it is observed that BWS offers superior risk adjusted returns for low levels of out-of-moneyness and contrary evidence is observed for deeper out-of-money portfolios. Consistent with a preference for options with a maturity of around 3 months in Australia, this research shows that quarterly rebalancing periods offer better returns for the BWS.     

Testing conditional factor models: A nonparametric approach

Some recent studies of conditional factor models do not specify conditioning information but use data from small windows to estimate the time series of conditional alphas and betas. In this paper, we propose a nonparametric method using an optimal window to estimate time-varying coefficients. In addition, we offer two empirical tests of a conditional factor model. Using our new method, we examine the performance of the conditional CAPM and the conditional Fama-French three-factor model in explaining the return variations of portfolios sorted by size, book-to-market ratios, and past returns, for which recent literature has generated controversial results. We find that, although in general the conditional FF model outperforms the conditional CAPM, both models fail to explain well-known asset-pricing anomalies. Moreover, for both models, the failure is more pronounced for the equally-weighted portfolios than for the value-weighted ones.     

Are good-news firms riskier than bad-news firms?

This paper examines the relative risk of good-news firms, i.e., those with high standardized unexpected earnings (SUE), and bad-news (low SUE) firms using a stochastic discount factor approach. We find that a stochastic discount factor constructed from a set of basis assets helps explain post-earnings-announcement drift (PEAD). The risk exposures on the pricing kernel increase monotonically from the lowest to highest SUE sorted portfolios. Specifically, good-news firms always have higher risk exposures than bad-news firms in both 10 SUE sorted portfolios and 25 size and SUE sorted portfolios. However, the estimated expected risk premium is too small to explain the observed magnitude of returns on the PEAD strategy. Our risk adjustment can explain only about one-fourth of the total magnitude of the average realized return to the PEAD strategy. As a result, the average risk-adjusted returns of earnings momentum strategies are mostly positive and significant. Overall, our results support the view that at least some portion of the returns to the earnings momentum strategies examined represent compensation for bearing increased risk.     

Anomalies,risk adjustment and seasonality: Australian evidence

On the basis of raw return analysis, economically significant anomalies appear to exist in relation to the size, momentum, book-to-market and profitability of Australian firms. However, characteristic-sorted portfolios are shown to load in very particular ways on multiple risk factors. After adjusting for exposure to risk, convincing evidence only remains for the size premium. An analysis of seasonality shows that, rather than being consistent throughout the year, anomaly returns are concentrated in a handful of months. We provide and test preliminary explanations of the observed seasonality in these well-known anomalies.     

What do the Fama–French factors add to C-CAPM?

This study extends standard C-CAPM by including two additional factors related to firm size (SMB) and book-to-market value ratio (HML) — the Fama–French factors. C-CAPM is least able to price firms with low book-to-market ratios. The explanation of these returns, as well as the returns on the SMB and HML portfolios, is significantly improved by the inclusion of the HML factor. The component of the risk premia explained by consumption varies across size. We suggest that a possible explanation for the role of HML is its association with the investment growth prospects of firms.     

Excess Cash and Stock Returns

I document a positive relationship between corporate excess cash holdings and future stock returns. The difference in returns of portfolios of high and low excess cash firms amounts to 5% annually or 6% after standard three-factor risk adjustment. Firms with more excess cash have higher market betas and earn lower returns during market downturns. High excess cash companies invest considerably more in the future than do their low cash peers, but do not experience stronger future profitability. On the whole, this evidence is consistent with the notion that excess cash holdings proxy for risky growth options.     

Dissecting Anomalies

The anomalous returns associated with net stock issues, accruals, and momentum are pervasive; they show up in all size groups (micro, small, and big) in cross-section regressions, and they are also strong in sorts, at least in the extremes. The asset growth and profitability anomalies are less robust. There is an asset growth anomaly in average returns on microcaps and small stocks, but it is absent for big stocks. Among profitable firms, higher profitability tends to be associated with abnormally high returns, but there is little evidence that unprofitable firms have unusually low returns.     

Time series momentum and macroeconomic risk

The time series momentum strategy, previously known as trend following, has been shown to deliver consistent profitability over a long time horizon in futures markets. Funds pursuing this strategy are now a component of many institutional portfolios, due to the expectation of positive returns in equity bear markets. However, the return drivers of the strategy and its performance in other economic conditions are less well understood. We find evidence that the returns to the strategy are connected to the business cycle. Returns are positive in both recessions and expansions, but profitability is higher in expansions. Decomposing asset prices into factor related and idiosyncratic components, we associate a significant portion of returns with exposure to time varying economic factors, consistent with rational asset pricing theories having a role in explaining the profitability of the strategy.     

Mispricing and risk of R&D investment in European firms

We study whether R&D-intensive firms earn superior stock returns compared to matched size and book-to-market portfolios across several financial markets in Europe. Mispricing can arise if investors are not able to correctly estimate the long-term benefits of R&D investment or whether R&D firms are more risky than others. The results confirm that more innovative firms can earn future excess returns. Stocks listed on continental Europe markets and operating in high-tech sectors are more prone to undervaluation. This can be caused in the first case by information asymmetries that are more severe in bank-based countries. No evidence is found for a different risk pattern of R&D-intensive stocks.