Forecasting stock market returns: The sum of the parts is more than the whole

We propose forecasting separately the three components of stock market returns—the dividend–price ratio, earnings growth, and price–earnings ratio growth—the sum-of-the-parts (SOP) method. Our method exploits the different time series persistence of the components and obtains out-of-sample R-squares (compared with the historical mean) of more than 1.3% with monthly data and 13.4% with yearly data. This compares with typically negative R-squares obtained in a similar experiment with predictive regressions. The performance of the SOP method comes mainly from the dividend–price ratio and earnings growth components, and the robustness of the method is due to its low estimation error. An investor who timed the market using our method would have had a Sharpe ratio gain of 0.3.     

Time-dependent lottery preference and the cross-section of stock returns

Highlighting the importance of benchmark to identify lottery-like payoffs of stocks, this study proposes that investors’ lottery preference is formed toward tracking stocks’ performance over time. Accordingly, we develop a strategy based on time-dependent maximum daily return (denoted as TMAX) by buying (short selling) stocks with the most recent maximum daily returns (MAX) ranked in the bottom (top) decile of the historical distribution. The TMAX strategy generates significant premium that subsumes the profitability of Bali, Cakici, and Whitelaw’s (2011) MAX strategy, but not vice versa. A major advantage of the TMAX strategy is its time-invariant profitability across different periods and sentiment states. Further analyses show that the TMAX premium can be explained by shorting flow and behavioral theories, supporting the time-dependent feature of lottery preference.     

Short selling and stock returns: Evidence from the UK

The practice of shorting stocks was put forward as one of the causes of the recent financial crisis whereas Shiller (2003), for example, considers shorting an essential element of an efficient market. Shorting involves selling borrowed stocks and subsequently closing the position by purchasing and returning the stock to the lender. A profit will be realised if the stock's price decreases. Shorting enables investors who do not own a perceived overvalued stock to sell. Using a high-frequency UK dataset for the period between September 2003 and April 2010, our findings suggest shorting indicates evidence of overvalued stocks as significantly negative abnormal stock returns appear to follow an increase in shorting. These results do not hold, however, for shorting which occurs around the ex-dividend date. We further find that these results hold during the recent financial crisis.     

Predicting firm level stock returns: Implications for asset pricing and economic links

We examine the predictive ability of stock price ratios, stock return dispersion and distribution measures for firm level returns. Analysis typically focusses on market level returns, however, for the underlying asset pricing model to hold, firm-level predictability should be present. Additionally, we examine the economic content of predictability by considering whether the predictive coefficient has the theoretically correct sign and whether it is related to future output growth. While stock returns reflect investor expectations regarding future economic conditions, they are often too noisy to act as predictor. We use the time-varying predictive coefficient as it reflects investor confidence in the predictive relation. Results suggest that a subset of stock price ratios have predictive power for individual firm stock returns, exhibit the correct coefficient sign and has predictive power for output growth. Each of these ratios has a measure of fundamentals divided by the stock price and has a positive relation with stock returns and output growth. This implies that as investors expect future economic conditions to improve and earnings and dividends to rise, so expected stock returns will increase. This supports the cash flow channel as the avenue through which stock return predictability arises.     

Geographic dispersion and stock returns

This paper shows that stocks of truly local firms have returns that exceed the return on stocks of geographically dispersed firms by 70 basis points per month. By extracting state name counts from annual reports filed with the Securities and Exchange Commission (SEC) on Form 10-K, we distinguish firms with business operations in only a few states from firms with operations in multiple states. Our findings are consistent with the view that lower investor recognition for local firms results in higher stock returns to compensate investors for insufficient diversification.     

Foreign exchange risk and the cross-section of stock returns

We examine the relation between the cross-section of US stock returns and foreign exchange rates during the period from 1973 to 2002. We find that stocks most sensitive to foreign exchange risk (in absolute value) have lower returns than others. This implies a non-linear, negative premium for foreign exchange risk. Sensitivity to foreign exchange generates a cross-sectional spread in stock returns unexplained by existing asset-pricing models. Consequently, we form a zero-investment factor related to foreign exchange-sensitivity and show that it can reduce mean pricing errors for exchange-sensitive portfolios. One possible explanation for our findings includes Johnson's [2004. Forecast dispersion and the cross-section of expected returns. Journal of Finance, 59, 1957–1978] option-theoretic model in which expected returns are decreasing in idiosyncratic cashflow volatility.     

Trading volume and stock returns: A meta-analysis

I examine 468 estimates on the relationship between trading volume and stock returns reported in 44 studies. I study publication bias together with Bayesian and frequentist model averaging to explain the heterogeneity in the estimates. The results yield three key conclusions. First, publication bias distorts the findings of the primary studies. Second, the predictability of stock returns varies with different markets and stock types. Third, different data characteristics, structural variations and methodologies used drive the heterogeneity in the results of the primary articles. In particular, one should be cautious when using monthly data or VAR models.     

Presidential administrations and the day-of-the-week effect in stock returns

This study examines the presence of a day-of-the-week effect over different presidential administrations. The results indicate that the day-of-the-week effect prevails during the Democratic and Republican administrations. However, the pattern of the day-of-the-week effect differs between the two presidential administrations. Specifically, the negative returns on Monday are more pronounced during the Republican than during the Democratic administrations. Therefore, explanations for the day-of-the-week effect should take into account the changing pattern of the day-of-the-week effect across presidential administrations.     

Predicting firm stock returns with customer stock returns: Moderating effects of customer characteristics

Based on the multiple regression model, this study examines the potential predictive effect of customer stock returns to firm stock returns and the moderating effect of diverse customer characteristics on the predictability. By using a sample of Chinese A-share manufacturing firms listed on the Shanghai stock exchange and Shenzhen stock exchange between 2009 and 2017, we find that customer stock returns positively predict firm stock returns in the subsequent month. Additional examinations reveal that the positive predictive effect of customer stock returns on firm stock returns is more intense for firm with high proportion of state-owned customers, customer stability, customer bargaining power and customer concentration than for those with low indicators. Overall, this study contributes to the growing literature on supply chain and predictability of stock returns by shedding light on the forecasting effect of customer stock returns on firm stock returns and the predictive heterogeneity owing to customer characteristics.     

The role of current and non-current accruals in the relation between stock returns and earnings

This study examines the role of accruals in the relation between stock returns and earnings for intervals of one to four years. We argue that the roles of current and non-current accruals differ because the former turn over more frequently while the latter include long term timing differences and permanent differences. Accordingly, the roles of both categories of accruals are examined over intervals within and beyond the cycle of current accruals. The results suggest that accruals strengthen the association between stock returns and earnings and that they are more important for shorter intervals. Further, non-current accruals play a dominant role in the relation between stock returns and earnings while the effect of current accruals is negligible for all intervals examined.     

Earnings volatility,ambiguity, and crisis-period stock returns

Financial crises are marked by substantial increases in ambiguity where prices appear to decouple from fundamentals. Consistent with ambiguity-based asset pricing theories, we find that ambiguity concerns are more severe for firms with higher earnings volatility, causing investors to demand a higher ambiguity premium for such firms. While there is no relation between earnings volatility and stock returns under normal conditions, there is a significant negative relation between crisis-period stock returns and prior earnings volatility. The effect is stronger in firms with low institutional ownership and low analyst following, consistent with ambiguity concerns being greatest amongst firms with unsophisticated investors.     

A nested factor model for non-linear dependencies in stock returns

The aim of our work is to propose a natural framework to account for all the empirically known properties of the multivariate distribution of stock returns. We define and study a ‘nested factor model’, where the linear factors part is standard, but where the log-volatility of the linear factors and of the residuals are themselves endowed with a factor structure and residuals. We propose a calibration procedure to estimate these log-vol factors and the residuals. We find that whereas the number of relevant linear factors is relatively large (10 or more), only two or three log-vol factors emerge in our analysis of the data. In fact, a minimal model where only one log-vol factor is considered is already very satisfactory, as it accurately reproduces the properties of bivariate copulas, in particular, the dependence of the medial point on the linear correlation coefficient, as reported in Chicheportiche and Bouchaud [Int. J. Theor. Appl. Finance, 2012, 15]. We have tested the ability of the model to predict out-of-sample the risk of non-linear portfolios, and found that it performs significantly better than other schemes.     

The impact of internet stock message boards on cross-sectional returns of small-capitalization stocks

We examine the impact of more than 2.5 million HotCopper messages on the Australian stock market. HotCopper is the largest online stock message board in Australia and the sample of messages covers over 2000 companies listed on the Australian Securities Exchange (ASX) from January 2003 through December 2008. We exclude messages surrounding public price-sensitive announcements released centrally by the ASX in order to examine the private information content of internet board messages. We find that the number of board messages and message sentiment significantly and positively relate to the contemporaneous returns of underperforming (low ROE, EBIT margin, EPS) small capitalization stocks with high market growth potential (low book-to-market). Posting activity is positively associated with trading volume for small stocks and negatively associated with bid-ask spreads for small and large stocks in the short term. Bullish small stocks outperform bearish ones significantly in respective days and months, exhibiting no return reversals to pre-message board activity levels in subsequent time periods. Large stocks are not found to be affected by message board activity. We conclude that higher message board activity quickly reflects itself into the prices of small capitalization stocks in a highly regulated market like the ASX.     

Sanctions and the Russian stock market

The article presents the robust estimates of extreme movements and heavy-tailedness properties for Russian stock indices returns before and after sanctions were introduced. The obtained results show that almost for all sectoral indices there was a statistically significant increase in volatility. At the same time there is not enough evidence of structural breaks in heavy-tailedness, though some indications of heavier both right and left tails in the post-imposition period can be observed for some indices. However, we cannot with complete certainty directly link the increase in heavy-tailedness with the imposed sanctions. The latter to a considerable extent could be caused by higher country-specific risks due to geopolitical tensions as well as oil prices volatility. Whatever is the cause, any increases in heavy-tailedness can have grave consequences for corporate management, economic modeling and financial stability analysis.     

Forecasting stock returns with large dimensional factor models

We study equity premium out-of-sample predictability by extracting the information contained in a high number of macroeconomic predictors via large dimensional factor models. We compare the well-known factor model with a static representation of the common components with the Generalized Dynamic Factor Model, which accounts for time series dependence in the common components. Using statistical and economic evaluation criteria, we empirically show that the Generalized Dynamic Factor Model helps predicting the equity premium. Exploiting the link between business cycle and return predictability, we find accurate predictions also by combining rolling and recursive forecasts in real-time.     

Unusual operating cash flows and stock returns

The current financial reporting of cash flows from operations does not present individual sources of these cash flows, making it difficult for investors to assess a firm’s future performance. I hand-collect individual cash flows from unusual operations and examine their characteristics for predicting future cash flows. The results show that the unusual individual cash flow items contain a significant incremental predictive ability for future cash flows. Additional return tests show that stock prices fail to fully reflect their predictive value, suggesting that the current reporting practice may mislead investor perceptions of a firm’s cash generating ability and investors could benefit from a more explicit presentation of cash flows from operations.     

Improving the predictability of real economic activity and asset returns with forward variances inferred from option portfolios

This paper presents an option positioning that allows us to infer forward variances from option portfolios. The forward variances we construct from equity index options help to predict (i) growth in measures of real economic activity, (ii) Treasury bill returns, (iii) stock market returns, and (iv) changes in variance swap rates. Our yardstick for measuring predictive ability is both individual and joint parameter statistical significance within a market, as well as across a set of markets.     

Changes in the risk structure of stock returns: Consumer Confidence and the dotcom bubble

Changes in the risk structure of stock returns may sometimes be very revealing. We examine economic variables that help explain principal components in UK stock returns, 01/1985 to 12/2001. The loading pattern on explanatory variables for the first component in a ‘bubble’ period is distinctive and consistent with a bubble/crash market. The second component shows a loading pattern on a Consumer Confidence variable in a pre-bubble period only. We observe apparently systematic changes in the structure of risk, and conjecture that Consumer Confidence captures a change in market sentiment that could be a signal for the evolution of stock prices.     

Models for stock returns

Historically, the normal variance model has been used to describe stock return distributions. This model is based on taking the conditional stock return distribution to be normal with its variance itself being a random variable. The form of the actual stock return distribution will depend on the distribution for the variance. In practice, the distributions chosen for the variance appear to be very limited. In this note, we derive a comprehensive collection of formulas for the actual stock return distribution, covering some sixteen flexible families. The corresponding estimation procedures are derived by the method of moments and the method of maximum likelihood. We feel that this work could serve as a useful reference and lead to improved modelling with respect to stock market returns.     

Jump risk,stock returns,and slope of implied volatility smile

In the presence of jump risk, expected stock return is a function of the average jump size, which can be proxied by the slope of option implied volatility smile. This implies a negative predictive relation between the slope of implied volatility smile and stock return. For more than four thousand stocks ranked by slope during 1996–2005, the difference between the risk-adjusted average returns of the lowest and highest quintile portfolios is 1.9% per month. Although both the systematic and idiosyncratic components of slope are priced, the idiosyncratic component dominates the systematic component in explaining the return predictability of slope. The findings are robust after controlling for stock characteristics such as size, book-to-market, leverage, volatility, skewness, and volume. Furthermore, the results cannot be explained by alternative measures of steepness of implied volatility smile in previous studies.