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In 1997, Statement of Financial Reporting Standards (SFAS) 131 introduced a substantial change in how segment information is reported in US-GAAP (Generally Accepted Accounting Principles) financial statements. We seek to examine whether this change in financial reporting policy increases investors' ability to process information relevant to conglomerate firms (i.e., those operating in multiple industries) more quickly, and whether this increased efficiency in information processing varies cross-sectionally based on firm complexity and the direction of industry news. Our results indicate that SFAS 131 has increased the speed with which stock prices capture information about conglomerates, relative to focused firms, although we find that some frictions remain with regard to disclosing bad news. This study documents an example of how a change in disclosure policy can enhance pricing efficiency, and hence, it may be of interest to the Financial Accounting Standards Board (FASB) in its ongoing initiative to consider potential changes to SFAS 131. [ABSTRACT FROM AUTHOR]

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper proposes a new index of forward looking absolute deviation extracted from option prices. The new index, named absolute deviation index (ADIX), is model‐free and easy to compute using at‐the‐money call and put option prices. It is shown that the spread between volatility index (VIX) and ADIX captures departures from normality in the risk‐neutral distribution and an empirical analysis using S&P 500 options data for the time period 1996–2021 reveals that the spread carries significant forecasting ability with respect to future equity returns at short to medium horizons. Portfolio strategies that use the spread as a predictor of S&P 500 returns outperform buy‐and‐hold strategies in an out‐of‐sample mean‐variance asset allocation exercise. [ABSTRACT FROM AUTHOR]

We propose a model‐guided option‐implied ambiguity measure to capture uncertainty regarding the return distribution of a risky asset underlying a set of options, and investigate its predictive power on the asset return. A representative investor's ambiguous beliefs or prior distributions on the underlying asset returns are extracted from the market prices of options, the expected volatility of which is then defined as the option‐implied ambiguity and is calculated in line with Brenner and Izhakian. Simulated paths of the calibrated models are utilized to compute all pertinent probability characteristics from a forward‐looking perspective. The empirical results with SSE 50 ETF options indicate that the proposed option‐implied ambiguity has strong predictive power for future returns of SSE 50 ETF. Out‐of‐sample tests also verify the significant predictive ability of the option‐implied ambiguity to the equity returns. [ABSTRACT FROM AUTHOR]

Small stocks' time‐varying spreads predict future return gap between small and large stocks. To optimally exploit such predictability, the investor captures current risk premium by purchasing at large spreads with substantially reduced turnover; uses an aim‐in‐front‐of‐the‐target approach to trade‐off between future risk premium and current transaction costs; and meets hedging demand at low costs. Strong interaction between transaction costs and return predictability leads to large losses from myopic trading. Greater variability of the spread is advantageous for investors who trade optimally but detrimental for investors who trade myopically. The spread‐based return predictability significantly increases the investment value of small stocks. [ABSTRACT FROM AUTHOR]

We propose a new predictor—the innovation in the daily return minimum in the U.S. stock market ( Δ MIN US )—for predicting international stock market returns. Using monthly data for a wide range of 17 MSCI international stock markets during the period spanning over half a century from January 1972 to July 2022, we find that Δ MIN US have strong predictive power for returns in most international stock markets: Δ MIN US negatively predicts the next-month stock market returns. The results remain robust after controlling for a number of macroeconomic predictors and conducting subsample and panel data analyses, indicating that Δ MIN US has significant predictive power and it outperforms other variables in international markets. Notably, Δ MIN US demonstrates excellent predictive power even during the periods driven by financial upheavals (e.g., Global Financial Crisis and European Sovereign Debt Crisis). Both panel regressions and out-of-sample tests also support the robust predictive performance of Δ MIN US . The predictive power, however, disappears during the non-financial crisis caused by COVID-19 pandemic, which is originated from the health sector rather than the financial sector. The results provide a new perspective on U.S. extreme indicator in stock market return predictability. [ABSTRACT FROM AUTHOR]

We present two valuation models which we use to account for the annual data on price per share and dividends per share for the CRSP Value-Weighted Index from 1929 to 2023. We show that it is a simple matter to account for these data based purely on a model of variation over time in the expected ratio of dividends per share to aggregate consumption under two conditions. First, investors must receive news shocks regarding the expected ratio of dividends per share to aggregate consumption in the long run. Second, the discount rate used to evaluate the impact of this news on the current price per share must be low. We use the approach of Campbell and Shiller (1987) and Campbell and Shiller (1988) to argue that the cash flow news in our model is not a stand-in for changes in expected returns: with our model parameters, returns are not predictable and price dividend spreads and ratios predict dividend growth at modelimplied magnitudes. We illustrate which parameter choices account for differences between our results and prior findings in the literature. We conclude that the answer to Shiller's (1981) question "Do stock prices move too much to be justified by subsequent movements in dividends?" is "not necessarily". [ABSTRACT FROM AUTHOR]

The variance risk premium is a critical predictor of expected returns. However, numerous studies indicate that expected returns depend strongly on the state of the economy. Herein, we examine the effect of the variance risk premium in different market states by using cross – sectional regression and predictability of returns. Our empirical results show that the variance risk premium is a significantly priced factor in bull markets. Additionally, predicted return horizons are shorter in bear markets than in bull markets. Compared with that in bull markets, the predictive ability of the variance risk premium diminishes more rapidly in bear markets when the horizon period is lengthened. [ABSTRACT FROM AUTHOR]

Abstract We propose a new predictor—the innovation in the daily return minimum in the U.S. stock market ( $$\Delta {MIN}^{US}$$ Δ MIN US )—for predicting international stock market returns. Using monthly data for a wide range of 17 MSCI international stock markets during the period spanning over half a century from January 1972 to July 2022, we find that $$\Delta {MIN}^{US}$$ Δ MIN US have strong predictive power for returns in most international stock markets: $$\Delta {MIN}^{US}$$ Δ MIN US negatively predicts the next-month stock market returns. The results remain robust after controlling for a number of macroeconomic predictors and conducting subsample and panel data analyses, indicating that $$\Delta {MIN}^{US}$$ Δ MIN US has significant predictive power and it outperforms other variables in international markets. Notably, $$\Delta {MIN}^{US}$$ Δ MIN US demonstrates excellent predictive power even during the periods driven by financial upheavals (e.g., Global Financial Crisis and European Sovereign Debt Crisis). Both panel regressions and out-of-sample tests also support the robust predictive performance of $$\Delta {MIN}^{US}$$ Δ MIN US . The predictive power, however, disappears during the non-financial crisis caused by COVID-19 pandemic, which is originated from the health sector rather than the financial sector. The results provide a new perspective on U.S. extreme indicator in stock market return predictability.

We examine firm value and performance implications of the growing trend of nontechnology companies engaging in activities relating to digital technologies. We measure digital activities in firms based on the disclosure of digital words in the business description section of 10-Ks. Digital activities are associated with a market-to-book ratio 8%–26% higher than industry peers, and only 25% of the differences in market-to-book is explained by accounting capitalization restrictions. To control for selection bias, we implement lagged dependent variable and IV regressions, and our market-to-book findings are robust to these specifications. Portfolios formed on digital activity disclosure earn a Daniel et al. The Journal of Finance 52 (3): 1035–1058 (1997)-adjusted return of 30% over a three-year horizon and a monthly alpha of 44-basis-points. On the other hand, we find weak evidence of near-term, positive improvements in fundamental performance, as we find some evidence of interim productivity increases but declines in sales growth conditional on digital activities. [ABSTRACT FROM AUTHOR]

We investigate the role of financial uncertainty in forecasting aggregate stock market returns. Our results suggest that financial uncertainty, along with its change, are more powerful predictors of excess US monthly stock market returns than 14 macroeconomic predictors commonly used in the literature. Financial uncertainty is shown to outperform short interest, which has been suggested to be the strongest known predictor of the equity risk premium. These results persist using robust econometric methods in‐sample, and when forecasting out‐of‐sample. [ABSTRACT FROM AUTHOR]

This article considers a stable vector autoregressive (VAR) model and investigates return predictability in a Bayesian context. The bivariate VAR system comprises asset returns and a further prediction variable, such as the dividend-price ratio, and allows pinning down the question of return predictability to the value of one particular model parameter. We develop a new shrinkage type prior for this parameter and compare our Bayesian approach to ordinary least squares estimation and to the reduced-bias estimator proposed in Amihud and Hurvich (2004. "Predictive Regressions: A Reduced-Bias Estimation Method." Journal of Financial and Quantitative Analysis 39: 813–41). A simulation study shows that the Bayesian approach dominates the reduced-bias estimator in terms of observed size (false positive) and power (false negative). We apply our methodology to a system comprising annual CRSP value-weighted returns running, respectively, from 1926 to 2004 and from 1953 to 2021, and the logarithmic dividend-price ratio. For the first sample, the Bayesian approach supports the hypothesis of no return predictability, while for the second data set weak evidence for predictability is observed. Then, instead of the dividend-price ratio, some prediction variables proposed in Welch and Goyal (2008. "A Comprehensive Look at the Empirical Performance of Equity Premium Prediction." Review of Financial Studies 21: 1455–508) are used. Also with these prediction variables, only weak evidence for return predictability is supported by Bayesian testing. These results are corroborated with an out-of-sample forecasting analysis. [ABSTRACT FROM AUTHOR]

In this study, we introduce a newly created sentiment index of individual investors in NZ constructed similar to the well-known sentiment index provided by the American Association of Individual Investors (AAII) in the U.S. This unique setup allows us to compare different aspects of investors' behaviour in both countries. We show that NZ market participants are less confident about the directional movement of the stock market, their expectations are more volatile and their distributions have fatter tails. By contrast, both bullish and bearish sentiment is more persistent among U.S. investors. Furthermore, our analysis of return predictability reveals that both groups of investors behave as noise traders. However, the results for NZ investors are stronger. Overall, our findings call for better financial education, particularly in the area of equity investing. [ABSTRACT FROM AUTHOR]

Using the next-day and next-week returns of stocks in the Korean market, we examine the association of option volume ratios – i.e. the option-to-stock (O/S) ratio, which is the total volume of put options and call options scaled by total underlying equity volume, and the put-call (P/C) ratio, which is the put volume scaled by total put and call volume – with future returns. We find that O/S ratios are positively related to future returns, but P/C ratios have no significant association with returns. We calculate individual, institutional, and foreign investors’ option ratios to determine which ratios are significantly related to future returns and find that, for all investors, higher O/S ratios predict higher future returns. The predictability of P/C depends on the investors: institutional and individual investors’ P/C ratios are not related to returns, but foreign P/C predicts negative next-day returns. For net-buying O/S ratios, institutional net-buying put-to-stock ratios consistently predict negative future returns. Institutions’ buying and selling put ratios also predict returns. In short, institutional put-to-share ratios predict future returns when we use various option ratios, but individual option ratios do not.

We provide novel evidence that equity investors react to currency shocks with a delay. Using the cross-section of currency returns and the relative presence of U.S. firms in foreign economies, we compute a foreign operations-related exchange shock (FOREXS) measure. We find FOREXS to predict firms' future cash flows and stock returns, driving much of the previously documented underreaction to foreign information. An FOREXS-based long-short strategy yields a 6.74% annualized abnormal return. FOREXS predictive power comes from firms' incomplete hedging and investors' limited attention, highlighting the challenges involved when processing information from a different asset class. This paper was accepted by David Sraer, finance. Funding: Z. Da acknowledges financial support from the Beijing Outstanding Young Scientist Program [Grant BJJWZYJH01201910034034] and the 111 Project [Grant B20094]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4778. [ABSTRACT FROM AUTHOR]

We use prevailing machine learning models to investigate the predictability of futures returns in 22 commodities with commodity‐specific and macroeconomic factors as predictors. Out‐of‐sample prediction errors for the majority of futures contracts are lowered compared with those obtained by the baseline models of AR(1) and forecast combinations. Using Shapley values to explain feature importance, we identify dominant predictors for each commodity. A long–short portfolio strategy based on monthly light gradient‐boosting machine predictions outperforms the benchmark linear models in terms of annual return, Sharpe ratio, and max drawdown. [ABSTRACT FROM AUTHOR]

We consider inference for predictive regressions with multiple predictors. Extant tests for predictability (especially for joint predictability) may perform unsatisfactorily and tend to discover spurious predictability as the number of predictors increases. We propose a battery of new instrumental variables-based tests which involve enforcement or partial enforcement of the null hypothesis in variance estimation. A test based on the few-predictors-at-a-time parsimonious system approach is recommended. Empirical Monte Carlos demonstrates the remarkable finite-sample performance regardless of numerosity of predictors and their persistence properties. Empirical application to equity premium predictability is provided. [ABSTRACT FROM AUTHOR]

Using new data from US and global markets, we revisit market risk premium predictability by equity anomalies. We apply a repertoire of machine-learning methods to forty-two countries to reach a simple conclusion: anomalies, as such, cannot predict aggregate market returns. Any ostensible evidence from the USA lacks external validity in two ways: it cannot be extended internationally and does not hold for alternative anomaly sets—regardless of the selection and design of factor strategies. The predictability—if any—originates from a handful of specific anomalies and depends heavily on seemingly minor methodological choices. Overall, our results challenge the view that anomalies as a group contain helpful information for forecasting market risk premia. [ABSTRACT FROM AUTHOR]

Is there a momentum effect in cryptocurrency anomalies? To answer this, we analyze data from over 3900 coins spanning the years 2014 to 2022 and replicate 34 anomalies in the cross-section of cryptocurrency returns. We document a discernible pattern in factor premia: past winners consistently outperform losers. The effect persists across subperiods, withstands various methodological approaches, and its magnitude parallels that of its stock market counterpart. However, the autocorrelation in factor returns is not widespread and primarily stems from size and volatility anomalies. Additionally, unlike in stocks, cryptocurrency factor momentum originates from price momentum, which subsequently transfers to the factor level. [ABSTRACT FROM AUTHOR]

This article finds evidence of return cross‐predictability among trading partners in international financial markets. We show that the predictability of international customers dominates the predictability of domestic customers, and the predictability of international intra‐industry customers dominates the predictability of international inter‐industry customers. This return cross‐predictability decreases with two country characteristics: financial sophistication and size. [ABSTRACT FROM AUTHOR]

Behavioural finance literature argues that stock issuance contains information on equity valuation. If so, does it predict the cross-section of both country and industry stock returns? To answer this question, we investigate data from 68 markets from 1976 to 2022. We find that composite equity issuance negatively correlates with future aggregate stock returns. An equal-weighted quintile of countries (industries) with the highest issuance underperforms those with the lowest by 0.34% (0.58%) per month. Established risk factors and other anomalies cannot subsume this cross-sectional pattern. Furthermore, the effect remains robust to many considerations. This documented issuance anomaly paves the way for an exploitable international investment strategy. [ABSTRACT FROM AUTHOR]

We uncover two channels of effect in the financial market when investors face macroeconomic uncertainty. Conditional on a common mispricing index, we find that economic uncertainty exposure (EUE) induces disagreement, which amplifies mispricing. The highest EUE quintile produces an annualised mispricing alpha of 9.96%, more than double the unconditional mispricing effect. An ambiguity premium of 3.84% alpha is documented in the "non‐mispricing" quintile. The EUE‐induced mispricing effect is different from the existing limits of arbitrage explanations. The ambiguity premium is predictably observed during the unfolding of shocks of COVID‐19 to the market. [ABSTRACT FROM AUTHOR]

We develop a new statistical constraint to improve the stock return forecasting performance of predictive models. This constraint uses a new objective function that combines the Huber loss function with the Ridge penalty. Out-ofsample results indicate that our constraint improves the predictive ability of the univariate models. The constrained univariate models significantly outperform the historical average benchmark model assuming no predictability. The forecast improvement based on the new constraint is also evident for multivariate information methods including forecast combination and diffusion index. The model is capable of capturing time-varying risk which serves as the potential economic explanation of the improved return predictability. Our results are robust to different evaluation subsamples, validation sample lengths, and different risk aversion coefficients. [ABSTRACT FROM AUTHOR]

We employ deep learning in forecasting high‐frequency returns at multiple horizons for 115 stocks traded on Nasdaq using order book information at the most granular level. While raw order book states can be used as input to the forecasting models, we achieve state‐of‐the‐art predictive accuracy by training simpler "off‐the‐shelf" artificial neural networks on stationary inputs derived from the order book. Specifically, models trained on order flow significantly outperform most models trained directly on order books. Using cross‐sectional regressions, we link the forecasting performance of a long short‐term memory network to stock characteristics at the market microstructure level, suggesting that "information‐rich" stocks can be predicted more accurately. Finally, we demonstrate that the effective horizon of stock specific forecasts is approximately two average price changes. [ABSTRACT FROM AUTHOR]

In this paper, we examine the Portuguese stock market for indication of time-series momentum effects using a new historical financial dataset that covers about 120 years of data. We find strong time-series momentum effects that cannot be explained by conventional risk factors. The positive return continuation seems to last for a period of 12 months, being heavily concentrated at the first month. At longer investment horizons, returns tend to mean-revert. The market exhibited significant time-series momentum for all look-back and holding periods of 12 months or less. A strategy with a 1-month look-back period and a 12-month holding period is shown to be the most profitable yielding a Sharpe ratio roughly 5.4 times that generated by a passive strategy. Time-series momentum strategies tend to perform best during extreme up-market periods and deliver the worst returns during down markets. This suggests that the strategy may not offer significant diversification benefits. Our findings add to the evidence that time-series momentum effects are not a product of data mining and are difficult to reconcile with the assertion that stock markets follow a random walk.

Abstract The out-of-sample $$R^2$$ R 2 is designed to measure forecasting performance without look-ahead bias. However, researchers can hack this performance metric even without multiple tests by constructing a prediction model using the intuition derived from empirical properties that appear only in the test sample. Using ensemble machine learning techniques, we create a virtual environment that prevents researchers from peeking into the intuition in advance when performing out-of-sample prediction simulations. We apply this approach to robust monitoring, exploiting a dynamic shrinkage effect by switching between a proposed forecast and a benchmark. Considering stock return forecasting as an example, we show that the resulting robust monitoring forecast improves the average performance of the proposed forecast by 15% (in terms of mean-squared-error) and reduces the variance of its relative performance by 46% while avoiding the out-of-sample $$R^2$$ R 2 -hacking problem. Our approach, as a final touch, can further enhance the performance and stability of forecasts from any models and methods.

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper investigates the spillover effect of lagged US daily returns on stock return predictability across 17 developed markets from January 1st, 1972 through August 31st, 2022. Using daily returns series, we find that lagged US returns is a superior predictor for future returns in international markets while including the lagged domestic returns and considering US negative or extreme returns. The predictive power of lagged US daily returns, nonetheless, substantially weakens during the recent COVID period. Our results imply that the degrees of stock return predictability and spillovers across markets are driven by the evolutionary market conditions, the channels of information transmission, and information leadership. [ABSTRACT FROM AUTHOR]

We study the impact of the announcements released by the US Energy Information Administration (EIA) on crude oil storage every Wednesday at 10:30 ET (the beginning of the third half-hour interval) on intraday return predictability, that is, intraday momentum. Our results indicate that returns on the third half-hour on EIA announcement days can significantly and positively predict the returns in the last half-hour, whereas, on non-EIA announcement days, only returns in the first half-hour have significant predictability. The dominant source of prediction in the first half-hour return mainly comes from the overnight component. EIA announcements contribute to intraday momentum because they attract more informed traders and because the period surrounding their release is often associated with a reduction in liquidity. Substantial economic gains can be made by using efficient intraday predictors as trading signals. [ABSTRACT FROM AUTHOR]

This study investigates the lead–lag effects between product futures and raw material futures. Results show that returns on product futures lead returns on raw material futures: lagged product futures returns can significantly predict raw material futures returns in‐ and out‐of‐sample. This product‐material lead–lag effect is mainly driven by bad news and is a short‐term phenomenon. Moreover, returns on product futures, especially those based on bad news, can provide substantial economic gains to investments in raw material futures. The lead–lag effect is associated with frictions in the information diffusion from product futures markets to raw material futures markets and the slower response of raw material futures markets to common market information. [ABSTRACT FROM AUTHOR]

ESG rating dispersion has left responsible investors in great confusion and posed non-negligible barriers to sustainable investment. Despite its importance, there is a lack of research on the role of ESG rating dispersion in portfolio decisions and asset pricing for the Chinese capital market. We reveal the negative return predictability of ESG rating dispersion, which cannot be solely attributed to common risk exposures. We also consider two potential mechanisms based on institutional investor demand and belief dispersion underlying this negative relation. Our findings have important practical implications for asset managers seeking to optimize financial performance while investing responsibly. [ABSTRACT FROM AUTHOR]

We investigate the potential cross-sectional relationship between several equity index attributes and future returns on country-industry indexes in the regions of North America, Europe, Asia-Pacific, South America, MENA, and Japan. Index attributes include the recently documented predictors in the cross-section of stock or index returns such as return range, maximum and minimum returns in a month, idiosyncratic skewness as well as widely documented predictors at the stock level. Maximum and minimum effects are common for all regions. Return range significantly predicts returns in Europe, Asia-Pacific, and South America after controlling for other index attributes. Standard deviation and idiosyncratic volatility have strong predictive ability in Europe, Asia-Pacific, South America, MENA, and Japan. Intermediate term momentum forecasts returns on North American and European portfolios. Earnings-to-price ratio is cross-sectionally linked to returns in Europe. Portfolio sorts show that the predictive power of significant index attributes increases with decreasing index size. [ABSTRACT FROM AUTHOR]

How is technically skilled human capital reflected in firm performance? We leverage a uniquely detailed employer–employee matched dataset to measure US firms' technical human capital in information technology (IT), Software Engineering , Mobile Networks , Data Analysis , and Web Development. All five technical skillsets are associated with higher firm valuations. However, they negatively forecast both financial and operational performance in the future. For example, a one-standard-deviation increase in employees with IT skills corresponds to 2.2% higher Tobin's q but predictable future returns of –10 basis points per month. Our results are stronger in tighter labor markets, in firms with more cash, and during time periods when each technical skillset is especially popular. These patterns suggest that the market expects too much from popular technologies, leading to over-valuation. Overall, our results highlight how corporate over-investment can extend to intangible capital such as skilled employees. [ABSTRACT FROM AUTHOR]

We propose a climate change attention (CCA) index based on Google search volume index (GSVI) from 2004 to 2021 and show that it is an economically and statistically significant negative predictor for next month's energy stock returns. The index is extracted using principal component analysis (PCA), but the results are similar by using the equal-weighted average method. Compared with 14 traditional macroeconomic predictors, CCA performs the best and provides complementary information when added into bivariate and multivariate macro predictive models. When further considering the effect of CCA's forecasting power over different periods, strong evidence is shown that this outperformance is especially prominent in economic depressions and down market conditions. From the asset allocation perspective, CCA can provide a mean-variance investor with significant economic gains under alternative risk aversions. Our empirical results prove that investors' attention to climate change contains predictive information for excess returns of global traditional energy stock index. [ABSTRACT FROM AUTHOR]

We document the influence of stock volatility on trend-following profits for a global sample of 1618 blue-chip stocks, across 43911 evaluation subperiods (2004–2018). We use the price sensitivity of trend signals (i.e. signal volatility) to isolate the detrimental impact of high stock volatility manifested through excessive/inefficient trading. Despite its almost null correlation with the stocks' mean-variance characteristics, the signal volatility greatly complements them in explaining the time series variation in trend-following excess returns. The results hold for both the buy and sell excess returns, are robust across stock markets, and conserve after considering explicit and implicit trading costs. Investors can use ex postsignal volatility estimates as a valid criterion to choose across potential trading rules, according to their specific levels of risk aversion and transaction costs. [ABSTRACT FROM AUTHOR]

The out-of-sample R 2 is designed to measure forecasting performance without look-ahead bias. However, researchers can hack this performance metric even without multiple tests by constructing a prediction model using the intuition derived from empirical properties that appear only in the test sample. Using ensemble machine learning techniques, we create a virtual environment that prevents researchers from peeking into the intuition in advance when performing out-of-sample prediction simulations. We apply this approach to robust monitoring, exploiting a dynamic shrinkage effect by switching between a proposed forecast and a benchmark. Considering stock return forecasting as an example, we show that the resulting robust monitoring forecast improves the average performance of the proposed forecast by 15% (in terms of mean-squared-error) and reduces the variance of its relative performance by 46% while avoiding the out-of-sample R 2 -hacking problem. Our approach, as a final touch, can further enhance the performance and stability of forecasts from any models and methods. [ABSTRACT FROM AUTHOR]

We examine the predictability of private and public real estate returns using recursive, out-of-sample, linear and Markov switching models, employing a rich set of predictor variables. We find considerable improved predictive power compared to simple regression models, especially at the intermediate horizon. Next, we test whether such improved forecasting accuracy translates into a positive risk-adjusted out-of-sample performance by performing a recursive mean-variance portfolio allocation analysis. We observe significant improvements in realized Sharpe ratios and mean-variance utility scores, especially when employing Markov switching models and exploiting predictability at intermediate horizons. Furthermore, our results are robust to the inclusion of transaction costs. [ABSTRACT FROM AUTHOR]

In this paper, we examine the Portuguese stock market for indication of time-series momentum effects using a new historical financial dataset that covers about 120 years of data. We find strong time-series momentum effects that cannot be explained by conventional risk factors. The positive return continuation seems to last for a period of 12 months, being heavily concentrated at the first month. At longer investment horizons, returns tend to mean-revert. The market exhibited significant time-series momentum for all look-back and holding periods of 12 months or less. A strategy with a 1-month lookback period and a 12-month holding period is shown to be the most profitable yielding a Sharpe ratio roughly 5.4 times that generated by a passive strategy. Time-series momentum strategies tend to perform best during extreme up-market periods and deliver the worst returns during down markets. This suggests that the strategy may not offer significant diversification benefits. Our findings add to the evidence that time-series momentum effects are not a product of data mining and are difficult to reconcile with the assertion that stock markets follow a random walk. [ABSTRACT FROM AUTHOR]

This paper constructs deep neural network (DNN) models for equity-premium forecasting. We compare the forecasting performance of DNN models with that of ordinary least squares (OLS) and historical average (HA) models. The DNN models robustly work best and significantly outperform both OLS and HA models in both in- and out-of-sample tests and asset allocation exercises. Specifically, DNN models generate monthly out-of-sample R2 of 3.42% and an annual utility gain of 2.99% for a mean-variance investor from 2011:1 to 2016:12. Moreover, the forecasting performance of DNN models is enhanced by adding additional 14 variables selected from finance literature.

This study examines the predictive power of tail risk measures in stock indices returns using a comprehensive dataset covering 50 countries from 1926 to 2021. Our findings reveal that tail risk measures exhibit predictive power when considered independently. However, their forecasting abilities disappear when other risk and return factors are incorporated. This suggests that tail risk measures do not contain incremental information about the cross-section of stock returns beyond the commonly used global factors. Our findings are robust across various considerations, holding for alternative tail risk measure types, estimation periods, and different control variables subsets.

This paper studies the predictive power of the trend strategy in the international stock market. Using data from 49 markets, we find that a trend signal exploiting the short‐, intermediate‐, and long‐term price information can predict stock returns cross‐sectionally in the international market. The significance of the trend strategy is associated with market‐level characteristics such as macroeconomic conditions, culture, and the information environment. The trend premium is more pronounced in markets with a more advanced macroeconomic status, a higher level of information uncertainty and individualism, and better accessibility to foreign investors. Nevertheless, the trend strategy only outperforms the momentum strategy in a relatively short horizon. [ABSTRACT FROM AUTHOR]

This article investigates for the first time the role of idiosyncratic tail risk in the cross-sectional pricing of bond returns. We use the idiosyncratic return of the bond to measure the idiosyncratic tail risk based on the extreme value theory. The results show that bonds in the highest idiosyncratic tail risk quintile generate 3.5% more annual return compared to bonds in the lowest idiosyncratic tail risk quintile. In addition, we found that idiosyncratic tail risk is cross-sectionally positive correlated with bond expected returns in Chinese bond market, even when the downside risk, bond rating, liquidity, size, maturity, bond market beta, short-term reversals, and coupon rate are controlled. The positive correlation is in line with the traditional risk-return tradeoff theory. Because of their aversion to extreme losses, investors are willing to accept the low returns from low idiosyncratic tail risk bonds. [ABSTRACT FROM AUTHOR]

This study reveals a new stock return predictability that relates to firms' geographic features in the Chinese A-share market. Using a text-based measure of the degree of localness to capture the economic ties between firms and their provinces, we find that low-localized firms are slow to incorporate local information into stock prices. Specifically, there is a significant lead-lag effect in stock returns between high- and low-localized firms in the same region, and a portfolio that exploits this pattern can generate a monthly alpha of about 1%. This effect cannot be explained by geographic or industry return momentum, investors' inattention, and limits to arbitrage. We find that this return predictability is mainly driven by investors' limited information-processing capacity, and the evidence of predictability is stronger among low-localized firms with highly complicated business structures. [ABSTRACT FROM AUTHOR]

From 2007 to 2020, unlisted Chinese firms paid an average of over US $500 million to listed firms for their shell value in reverse merger transactions. We show that this large shadow price for a public listing sheds light on other features of Chinese markets, including (i) near-zero mortality rates, (ii) frequent major-asset restructurings (MARs), (iii) insensitivity of small-firm prices to corporate earnings, and (iv) a large size effect. A firm-level measure of expected shell probability (ESP) predicts stock returns, MARs, earnings-to-price sensitivity, and short-window returns to initial public offering-related regulatory news. Furthermore, adding ESP to existing pricing models for Chinese stocks significantly improves model performance. [ABSTRACT FROM AUTHOR]

This article studies the driving sources of cross‐firm lead–lag relations in returns which are generated by the Granger‐causality test with a long estimation window. The authors observed that economic links such as customer–supplier relationships and peer effects only account for a small share of the frequently observed cross‐firm lead–lag relations. Instead, most of the cross‐firm lead–lag relations are driven by several classical factors. On average, 95% of the cross‐firm lead–lag relations disappear after controlling for the three classical Fama–French factors, "betting against beta" and a few industry factors such as utility, oil, construction, and finance. This article represents novel evidence that classical factors designed for explaining the contemporaneous stock returns also contains important information for future returns. Investors trading on the signals generated by the relevant classical factors can obtain significant and positive returns. [ABSTRACT FROM AUTHOR]