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Option-based measures can predict underlying stock returns, due to differences in price discovery and price pressure effects between options and underlying stocks. We investigate stock return predictability by various option price-based measures using REITs. REITs are more transparent and efficiently priced than general stocks, but REIT options are less liquid. Consistent with the model of Easley, O’Hara, and Srinivas (1998), most of the option price-based measures do not significantly forecast REIT stock returns, but changes in option implied volatilities are robust and significant return predictors. We provide further evidence supporting the informed trading channel instead of the price pressure effects.

This paper performs an out-of-sample comparison of linear factor asset pricing models from an economic perspective under predictability. I assess the economic value added of several factor models when a Bayesian investor is faced with a portfolio allocation problem whereby each model imposes cross-sectional restrictions on the parameters of a predictive stock return regression. The empirical framework explicitly accounts for investor skepticism about the model, i.e., mispricing uncertainty. Using several US portfolios as test assets, I find that the q5 model of Hou et al. (2020), as well as the behavioral factor models of Stambaugh and Yuan (2017) and Daniel et al. (2020) outperform competing models across investment horizons. At the longest evaluated horizon (one year), a benchmark portfolio built using historical data produces larger portfolio gains than all the factor models, but in the short run (at the one-month horizon), their performance is comparable.

The paper examines statistical and economic evidence of out-of-sample bond return predictability for a real-time Bayesian investor who learns about parameters, hidden states, and predictive models over time. We find some statistical evidence using information contained in forward rates. However, such statistical predictability can hardly generate any economic value for investors. Furthermore, we find that strong statistical and economic evidence of bond return predictability from fully-revised macroeconomic data vanishes when real-time macroeconomic information is used. We also show that highly levered investments in bonds can improve short-run bond return predictability.

Factor momentum has formed the basis of factor timing strategies. We propose an alternative approach for timing factor portfolio returns by exploiting the information from their portfolio characteristics. Different combinations of dimension reduction techniques are employed to independently reduce the number of predictors and portfolios to predict. Characteristic-based models outperform factor momentum in terms of exact predictability as well as investment performance.

We extend existing studies by considering the higher-order moments relationships among crude oil, gold, and Bitcoin markets. Using high-frequency data from December 2, 2014 to June 10, 2018, we analyze spillovers in jumps and realized second, third, and fourth moments among crude oil, gold, and Bitcoin markets via Granger causality and generalized impulse response analyses. Results suggest evidence of predictability and emphasize, among others, the need of jointly modeling linkages across those three markets with higher-order moments; otherwise, inaccurate risk assessment and investment inferences may arise. The responses of realized volatility shocks are generally positive. Further analyses indicate evidence of a weaker relationship between gold and crude oil and Bitcoin and crude oil compared to the relationship between Bitcoin and gold. Practical implications are also discussed.

We revisit the estimation algorithm of Pettenuzzo and Timmermann (2011) and show how to apply the posterior simulation test of Geweke (2004) to locate and correct an error in the original posterior sampling algorithm. The main modification for the new algorithm is the introduction of a Metropolis–Hasting step to draw the precision parameters of the return and predictor equations, taking into account the correlation between the error terms of the two equations. We find that the modification of the original algorithm has very minor effects on the empirical results reported in Pettenuzzo and Timmermann (2011). In particular, for both the dividend yield and Treasury bill predictors the updated algorithm finds that the models best supported by the data, as measured by the SIC, match those reported in the original paper.

Bias correction in predictive regressions is known to reduce the empirical size problems of OLS-based predictability tests with persistent predictors. This paper shows that bias correction is also achieved in the context of the extended instrumental variable (IVX) predictability testing framework introduced by Kostakis et al. (2015). To be specific, new IVX-based statistics subject to a bias correction analogous to that proposed by Amihud and Hurvich (2004) are introduced. Four important contributions are provided: first, we characterize the effects that bias-reduction adjustments have on the asymptotic distributions of the IVX test statistics in a general context allowing for short-run dynamics and heterogeneity; second, we discuss the validity of the procedure when predictors are stationary as well as near-integrated; third, we conduct an exhaustive Monte Carlo analysis to investigate the small in- and out-of-sample properties of the test procedures and their sensitivity to distinctive features that characterize predictive regressions in practice, such as strong persistence, endogeneity, and non-Gaussian innovations; and fourth, we provide an analysis of real estate return and rent growth predictability in 19 OECD countries.

We use a consumption based asset pricing model to show that the predictability of excess returns on risky assets can arise from only two sources: (1) stochastic volatility of fundamental variables, or (2) departures from rational expectations that give rise to predictable investor forecast errors and market inefficiency. While controlling for stochastic volatility, we find that a variable which measures non-fundamental noise in the Treasury yield curve helps to predict 1-month-ahead excess stock returns, but only during sample periods that include the Great Recession. For these sample periods, higher noise predicts lower excess stock returns, implying that a shortage of arbitrage capital in financial markets caused excess returns to drop below the levels justified by fundamentals. The statistical significance of the predictor variables that control for stochastic volatility are also typically sensitive to the sample period. Measures of implied and realized stock return variance cease to be significant when the COVID-influenced data from early 2020 onward is included.

I find no evidence that partial least squares based on disaggregated book-to-market ratios produces a model of market premiums with persistently positive out-of-sample R2, as originally documented for market returns. This is consistent with time variation in predictability, for example, and does not necessarily invalidate the method. The two main drivers of the original performance are: (i) The sample period and (ii) the use of market returns as forecasting targets. Two other drivers are using, as regressors, (iii) the book-to-market ratios of the specific portfolios double-sorted by size and book-to-market (iv) divided by their standard deviations.

We develop a new housing search index (HSI) extracted from online search activity on a limited set of keywords related to the house-buying process. We show that HSI has strong predictive power over subsequent changes in house prices, both in-sample and out-of-sample and after controlling for the effect of commonly used predictors, and relate our findings to models of search-induced frictions. Our results imply that search data can be used as an early indicator of where the market is going. This paper was accepted by David Sraer, finance. Funding: We acknowledge support from the Danish Finance Institute and from the Independent Research Fund Denmark [Grants DFF 7024-00020B and DFF 7015-00017B]. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4672 .

This paper finds positive evidence of return predictability and investment gains for individual corporate bonds for an extended period from 1973 to 2017. Our sample consists of both public and private company bond observations. We have implemented multiple machine learning methods and designed a Fama-Macbeth-type predictive performance evaluation. In addition to robust predictability evidence, there are four main findings. First of all, we find the lagged corporate bond market return as the most important predictor, suggesting a short-term market reversal story. Second, this paper concludes that equity information is conditionally redundant for similar public and private company bond performance. Third, a model-forecast-implied long-short strategy delivers 1.48% monthly returns and 1.4% alpha during the last two decades, which substantially drops if we do not consider private company bonds. Finally, the return predictability is mainly due to the cash flow component instead of the discount rate component.

We show that the difference between the natural rate of interest and the current level of monetary policy stance, which we label Convergence Gap (CG), contains information that is valuable for bond predictability. Adding CG in forecasting regressions of bond excess returns significantly raises the R2, and restores countercyclical variation in bond risk premia that is otherwise missed by forward rates. Consistent with the argument that CG captures the effect of real imbalances on the path of rates, our factor has predictive ability for real bond excess returns. The importance of the gap remains robust out-of-sample and in countries other than the United States. Furthermore, its inclusion brings significant economic gains in the context of dynamic conditional asset allocation. This paper was accepted by Gustavo Manso, finance.

Many modern macro finance models imply that excess returns on arbitrary assets are predictable via the price-dividend ratio and the variance risk premium of the aggregate stock market. We propose a simple empirical test for the ability of such a model to explain the cross-section of expected returns by sorting stocks based on the sensitivity of expected returns to these quantities. Models with only one uncertainty-related state variable, like the habit model or the long-run risks model, cannot pass this test. However, even extensions with more state variables mostly fail. We derive conditions under which models would be able to produce expected return patterns in line with the data and discuss various examples. This paper was accepted by David Simchi-Levi, finance.

Diversification—investing in imperfectly correlated assets—reduces volatility without sacrificing expected returns. Although the expected return of a diversified portfolio is the weighted average return of its constituent parts, the variance of the portfolio is less than the weighted average variance of its constituent parts. Our results suggest that very few people have correct statistical intuitions about the effects of diversification. The average person in our data sees no benefit of diversification in terms of reducing portfolio volatility. Many people, especially those low in financial literacy, believe diversification actually increases the volatility of a portfolio. These people seem to believe that the unpredictability of individual assets compounds when aggregated together. Additionally, most people believe diversification increases the expected return of a portfolio. Many of these people correctly link diversification with the concept of risk reduction but seem to understand risk reduction to mean greater returns on average. We show that these beliefs can lead people to construct investment portfolios that mismatch investors’ risk preferences. Furthermore, these beliefs may help explain why many investors are underdiversified. This paper was accepted by Yuval Rottenstreich, decision analysis.

We solve a portfolio selection problem in which return predictability, risk predictability and transaction cost are incorporated. In the problem, both expected return, prediction error volatility, and transaction cost are time-varying. Our optimal strategy suggests trading partially toward a dynamic aim portfolio, which is a weighted average of expected future tangency portfolio and is highly influenced by the common fluctuation of prediction error volatility (CPE). When CPE is high, the investor would invest less and trade less frequently to avoid risk and transaction cost. Moreover, the investor trades more closely to the aim portfolio with a more persistent CPE signal. We also conduct an empirical analysis based on the commodities futures in Chinese market. The results reveal that by timing prediction error volatility, our strategy outperforms alternative strategies.

This study conducted a real-time analysis of the health functional food market using big data. To assess the scope of big data in market analysis, big data of the health food category were compared and analyzed with actual market data. Data were first collected using a program to obtain data, through application programming interfaces, followed by SPSS to compare and analyze the actual market index and shopping search word data. The correlation between the online search data and the actual market was high, indicating that online search data can be used to predict the trend of the actual market. Various types of data, such as items and major functional ingredients, can be collected and analyzed through the program developed for this study, which is also used to predict the market trend. The results demonstrate how APIs can be used to predict market size in the food industry effectively.

Stocks with high maximum daily returns (MAX) in a given month yield low returns in the subsequent month. We thoroughly examine the underlying behavioral mechanism based on stock market and individual trading data. We argue that the short-term return predictability is driven by investor overreaction rather than cumulative prospect theory (CPT) preferences. First, we show empirically that high-MAX stocks are comparably not more attractive for CPT-investors. Second, we observe immediate price reversals and no preference-induced price pressure following the MAX return. Third, in line with theories on information-dependent over- and underreaction, the MAX effect reverses if MAX is caused by earnings announcements. Fourth, the MAX effect only exists for stocks far away from an anchoring point as overreaction is mitigated by anchors. Further, discount brokerage data shows that retail investors’ speculative buying pressure for lottery-like stocks cannot explain the specific return patterns associated with the MAX effect.

We are the first to investigate whether close-to-open overnight returns can measure firm-specific investor sentiment in China. Empirically, we find that in the short term, overnight returns persist for up to four weeks, and the persistence is stronger for hard-to-value firms, confirming the result for the US; in the longer run, stocks with high (low) overnight returns underperform (outperform), consistent with the US evidence too. We further show that overnight returns negatively predict future stock returns in the cross-section, and this predictability remains strong after controlling for firm characteristics. Finally, the strategy that buys stocks in the highest overnight-return decile and sells stocks short in the lowest decile generates abnormal returns of 41 and 40 basis points per month for the Fama-French three-factor model and Fama-French-Carhart four-factor model, respectively. Therefore, overnight returns seem to be a suitable proxy for measuring firm-specific sentiment in China.

Moving average (MA) is one of the many indicators that retail investors can be used when trading their investment in the stock market or any financial market. The inclusion of moving average to trade set-up serves as the guidelines of the retail investors to properly execute the trades. The trade set-up with moving average includes different time frames, numbers of MA and MA with combination to other indicators. The 21 Day MA, 3 different timeframes of MA, 21 MA, and 50 MA for the crossover, and indicators like MACD are the most preferred by the retail investors to add in the trade set-up. The retail investor agrees that the moving average indicator is useful when entering and exiting the trades, when finding the Support and Resistance (SAR) area, predicting new trends, a combination of moving average crossover, MA in combination with other technical indicators. The quantitative method is used to emphasize how MA indicators help the investment trading of retail investors and the statistical result shows the significant relationship between the number of time periods or frames like 9, 21, 50, 100, and 200 Moving Average in entering and exiting the trades. Part of the results shows how Moving Average Convergence and Divergence (MACD) in combining to MA results in an insignificant relationship when exiting and entering the trades. Another result about MACD or Volume Analysis in combining to MA when predicting new trends shows an insignificant relationship. This means that moving average indicators and the trade setup with a proper understanding of each can combine and deliver a better investment result to retail investors.

Purpose This paper aims to ask whether a range of stock market factors contain information that is useful to investors by generating a trading rule based on one-step-ahead forecasts from rolling and recursive regressions. Design/methodology/approach Using USA data across 3,256 firms, the authors estimate stock returns on a range of factors using both fixed-effects panel and individual regressions. The authors use rolling and recursive approaches to generate time-varying coefficients. Subsequently, the authors generate one-step-ahead forecasts for expected returns, simulate a trading strategy and compare its performance with realised returns. Findings Results from the panel and individual firm regressions show that an extended Fama-French five-factor model that includes momentum, reversal and quality factors outperform other models. Moreover, rolling based regressions outperform recursive ones in forecasting returns. Research limitations/implications The results support notable time-variation in the coefficients on each factor, whilst suggesting that more distant observations, inherent in recursive regressions, do not improve predictive power over more recent observations. Results support the ability of market factors to improve forecast performance over a buy-and-hold strategy. Practical implications The results presented here will be of interest to both academics in understanding the dynamics of expected stock returns and investors who seek to improve portfolio performance through highlighting which factors determine stock return movement. Originality/value The authors investigate the ability of risk factors to provide accurate forecasts and thus have economic value to investors. The authors conducted a series of moving and expanding window regressions to trace the dynamic movements of the stock returns average response to explanatory factors. The authors use the time-varying parameters to generate one-step-ahead forecasts of expected returns and simulate a trading strategy.

This study sets out to explore the predictability of global foreign exchange rates vis-a-vis the South African rand using daily nominal exchange rates from January 2010 to February 2018. The estima...

In this article, the authors propose a variance-dependent explanation for the contradiction between skewness preference and low expected return concerning lottery stocks. They emphasize an overlooked aspect of skewness as a risk measure: the return uncertainty of extreme events. They show that, during periods of low market volatility, investors dislike large-skewness securities owing to a fear of uncertain results. Thus, one observes a positive relation between skewness and expected return because the security is currently undervalued. Conversely, negative associations occur in high-volatility environments. This conditional skewness–return nexus is demonstrated to possess return predictability and can help in adjusting portfolios with profitable buying and selling decisions. Key Findings ▪ The authors propose variance-dependent skewness to reconcile the skewness preference for lottery stocks with their actual low expected returns. ▪ They emphasize skewness as a risk measure of the return uncertainty of extreme events. ▪ The authors construct portfolios based on the return predictability of skewness conditional on volatility and show that these portfolios remain profitable after considering transaction costs and restrictions on short sales.

We solve a dynamic equilibrium model with generalized disappointment-aversion preferences and continuous state-endowment dynamics. We apply the framework to the term structure of interest rates and show that the model generates an upward-sloping term structure of nominal interest rates and a downward-sloping term structure of real interest rates and that it accounts for the failure of the expectations hypothesis. The key ingredients are preferences with disappointment aversion, preference for early resolution of uncertainty, and an endowment economy with three state variables: time-varying macroeconomic uncertainty, time-varying expected inflation, and inflation uncertainty. This paper was accepted by Karl Diether, finance.

Informed traders may prefer the options market to the stock market for reasons including the leverage effect, transaction costs, restrictions on short sale. Many studies try to predict future returns of stocks using informed traders' behavior in the options market. In this study, we examine whether the trading volume ratios of single stock options have the predictive power for future returns of the underlying stock. By analyzing the stock price responses to the “preliminary announcement of performance” of 36 underlying stocks on the Korea Exchange from November 2014 to March 2021 and the trading volume of options written on those stocks, we investigate the relation between the option ratios, which are the call option volume to put option volume ratio (C/P ratio) and the option volume to stock volume ratio (O/S ratio), and the future returns of the underlying stock. We also examine which ratio is better in predicting the future returns. The authors found that both option ratios showed the statistically significant predictability about future returns of the underlying stock and that the return predictability of the O/S ratio is more robust than that of the C/P ratio. This study shows that indicators generated in the options market can be used to predict future underlying stock returns. Further, the findings of this study contributed to a dearth of literature pertaining to single stock options. The results suggest that the single stock options market is efficient and influences the price discovery in the stock market.

Economic theory predicts the price dynamics of an unbacked asset to be inherently unforecastable. The same applies to exchange rates of unbacked currencies. Albeit, empirically investors are found to be driven by online and offline news media. This study analyzes the Bitcoin cryptocurrency price series and web search queries with regard to their mutual predictability and cause‐effect delay structure. Chinese Baidu engine searches and compounded Baidu–Google search statistics predict Bitcoin price dynamics at relatively high frequencies ranging from 2 to 5 months. In the other direction, Granger‐causality runs from the cryptocurrency price to queries statistics across nearly all frequencies. In both directions, the reaction time computed from a phase delay measure for the relevant frequency bands with significant causality ranges from about 1 to 4 months. For either direction, out‐of‐sample forecasts are more accurate than forecasts of a benchmark stochastic process. Bivariate models including the Baidu Search Index slightly outperform competing models that include a Baidu–Google composite index. Predictive power seems less diluted if the September 2017 trade regulations by the Chinese government are controlled for.

Purpose This paper aims to achieve two main objectives. The first is to introduce a suitable adjustment to the conventional dividend-price ratio, which would address econometric concerns and improve the predictability of the equity premium. The second is to compare the predictive performance of the newly introduced adjusted dividend-price ratio with the conventional dividend-price ratio. Design/methodology/approach The authors hypothesize that the adjusted dividend-price ratio will have better predictive power and forecasting quality for equity premium compared to the conventional dividend-price ratio. To test the hypothesis, the authors predict equity premium with both variables on a sample of 11 developed and emerging market indexes over a period spanning June 1995 to March 2017. To accommodate time variation in parameter values or structural breaks in the data, the authors conducted a fixed window rolling regressions using both variables. A variety of forecast techniques including magnitude and sign accuracy measures are applied to compare the performance of forecasts. Findings The adjusted dividend-price ratio is shown to be stationary and has both lower persistence and variability compared with the conventional dividend-price ratio. The authors find that the adjusted dividend-price ratio provides superior out-of-sample (OOS) performance compared to the conventional dividend-price ratio, for both size and sign accuracy, in forecasting equity premium for the majority of the countries in the sample. Research limitations/implications This paper introduces an easy-to-follow modification in the conventional dividend-price ratio that can be replicated by researchers and practitioners alike. However, the study has a limitation in that it does not capture the impact of dividend-paying firms within each index on the predictive ability of the adjusted dividend-price ratio. Practical implications The knowledge of equity premium predictability is important in implementing market-timing strategies and could be beneficial for portfolio and risk management. The newly introduced variable is easy to construct using widely available data without the need for complex econometric estimation. Investors can use this variable to predict equity premiums in international markets, both developed and emerging. The findings of this paper will be relevant to financial analysts, portfolio managers, investors and researchers in international finance. For example, by using the adjusted dividend-price ratio, investors would see up to 0.5% improvement in their OOS monthly forecasts of the equity premium. Originality/value To the best of the authors’ knowledge, this is the first paper that proposes adjustment in the conventional dividend-price ratio based on the past observations of the most recent quarter. In this way, the paper offers fresh insight that dividend-price ratio is still useful to predict equity premium albeit, after some adjustments and modifications. The findings of the paper would result in renewed interest in using the dividend-price ratio as a predictor of the equity premium.

This paper examines, in the time and frequency domains, the quantile dependence and directional predictability of investor attention to cryptocurrency returns. We find that there is significant tai...

Momentum strategies have historically delivered large alphas, yet they also displayed significant time-variation that is not very well understood. I document that expected momentum profits vary negatively with the formation period return difference between past winners and losers, which I term the momentum gap. A one standard deviation increase in the momentum gap predicts a 1.29% decrease in the monthly momentum return after controlling for existing predictors. I find consistent results across 21 international stock markets. Following the simple real-time strategy of investing in momentum only when the momentum gap is below the 80th percentile generates monthly returns of 1.28%. ∗Cox School of Business, Southern Methodist University. This paper is based on my dissertation at Yale University. I would especially like to thank Nick Barberis, Will Goetzmann, Andrew Metrick, and Jake Thomas. I would also like to thank Sriya Anbil, Sandro Andrade, Brad Barber, Michael Brandt, Hui Chen, James Choi, Lauren Cohen, Kent Daniel, Assaf Eisdorfer, Harrison Hong, Sean Hundtofte, Stacey Jacobsen, Eric Kelley, Peter Kelly, Alok Kumar, Mattia Landoni, Charles Lee, Stefan Lewellen, Jim Linck, David McLean, Alan Moreira, Tyler Muir, Marina Niessner, Terry Odean, Mark Ready, Scott Richardson, Geert Rouwenhorst, Ravi Sastry, Clemens Sialm, Rick Sias, Sorin Sorescu, Matt Spiegel, Johan Sulaeman, Paul Tetlock, Sheridan Titman, Rex Thompson, Kumar Venkataraman, Jeff Wurgler, Lei Xie, Hongjun Yan, Frank Zhang, Harold Zhang, Guofu Zhou, as well as seminar participants at the WFA Meetings, the Lone Star Finance Conference, Southern Methodist University, Texas A&M University, the University of Arizona, the University of Connecticut, the University of Miami, the University of Wisconsin, Washington University at St. Louis, Yale University, Algert Global, Cubist Systematic Strategies, Menta Capital, Nipun Capital, QMS Capital Management, and Sensato Investors. I gratefully acknowledge financial support of the Whitebox Advisors Doctoral Fellowship.

Purpose: The given study looks into forecast accuracy of a traditional ARIMA model while comparing it to Autoregressive Neural Network (AR-NN) model for 984 trading days on EURO STOXX 50 Index. Methodology: A hybrid model is constructed by combining ARIMA model and feed-forward neural network model aiming to attain linear and non-linear price fluctuations. The study also incorporates the investigation of component stock prices of the index, that can be selected to improve the predictability of the hybrid model. Findings:The reached ARIMA (1,1,3) model showed higher scores than AR-NN model however integrating selected exogenous stock prices from the index components gave much notable accuracy results. The selected exogenous stocks were extracted after conducting PCA and model scores were compared via MAPE and RMSE. Unique contribution to theory, practice and policy: The major contribution of this work is to provide the researcher and fnancial analyst a systematic approach for development of intelligent methodology to forecast stock market. This paper also presents the outlines of proposed work with the aim to enhance the performance of existing techniques. Therefore, Empirical analysis is employed along with a hybrid model based on a feed-forward Neural Network. Lesser error is attained on the test set of Index stock price by comparing the performance of ARIMA and AR-NN while forecasting. Hence, The components of extracted Index stock price like exogenous features are added to make an influence from the AR-NN model.

This research specifically reveals the predictability for the volatility on energy futures markets when involving investor sentiment, using the newly launched China’s INE crude oil futures as an incremental evidence. First, we propose a novel investor sentiment index captures the feature of instant sentiment conversion and the internet attention for energy futures markets. Second, we present the nexus between the proposed novel investor sentiment and conditional volatility, which shows that the proposed investor sentiment significantly impacts on return and volatility. It indicates that the emerging markets filled with noise traders like China are more deeply influenced by investor sentiment and bring about more volatility. Third, the results of dual leverage effects in modeling energy futures volatility shows that the optimistic (pessimistic) sentiment shift has negative(positive) effects on volatility, and downward (upward) shocks from bad news (good news) are followed by greater volatility. Fourth, incorporating the novel investor sentiment into models can significantly improve volatility forecasting accuracy because of its smaller loss function results compared with the benchmark models without sentiment. Moreover, high frequency realized volatility and range-based volatility are regarded as real volatility to examine the predictability, and Diebold Mariano test is used to present the difference of forecasting ability. Our results are robust by using the data from China’s Thermal Coal Futures. Overall, we present that the novel investor sentiment can be utilized for monitoring and predicting volatility shocks for emerging markets with lots of noise traders.

This paper investigates a time-consistent investment strategy under the mean-variance criterion for an investor who accumulates retirement savings through a defined contribution (DC) pension plan with stock and bond investment opportunities. The expected return rate on the stock is modulated by an unobservable predictor which follows a mean-reverting stochastic process. The evolution of the instantaneous interest rate is described by the Vasicek model. In addition, the contribution rate of the DC pension plan is stochastic and correlated with financial risks coming from the stochastic interest rate and stock price. In a game theoretic framework, we derive a closed-form equilibrium investment strategy and corresponding equilibrium value function for the mean-variance criterion by adopting the filtering technique and the stochastic control method. Furthermore, we provide an equilibrium investment strategy and equilibrium value function when the expected return rate of the stock is completely observable. Finally, some numerical examples are presented to demonstrate the sensitivity analysis of the equilibrium investment strategy and equilibrium efficient frontier. Numerical analysis confirms that there is non-negligible information loss on the equilibrium investment strategy and equilibrium value function due to partial observation in the stock price dynamics.

I study the return predictability of firm life cycle, originally documented by Dickinson (2011). I show that a hedge portfolio strategy going long on mature firms and short on introduction firms generates a significant hedge portfolio return of 1.29% per month in return-weighted portfolios and 0.72% in value-weighted portfolios. The returns to firm life cycle are related to investors’ and analysts’ expectation errors, are driven by market-wide investor sentiment, and are more pronounced among stocks with low institutional ownership and high idiosyncratic volatility. Quantile regressions show that introduction firms have considerably greater uncertainty and skewness in future earnings growth outcomes than mature firms, such that analysts are better able to justify optimistically biased forecasts for introduction firms compared to mature firms.

This paper studies the dynamic relationship between ship prices and operating profits by using the fact that these two factors are cointegrated. To conduct an empirical analysis, we select Panamax ...

Daily transactions in cryptocurrencies have long been following an ascending tendency, with Bitcoin leading the charge. Daily transactions recorded in the system increased from 7000 trade per day in 2012to more than 1 million nowadays. The study aims to examine the utility of cryptocurrencies specific to Bitcoin and diagnose how predictable its price fluctuations and the volatility of the crypto market. Because the dilemma between risk aversion and return maximization became evident for investors with high yielded digital assets in a zero-lower bound environment. Hence the predictability of its price movements in the short run may shed some light on the price formation of Bitcoin. Using an ARIMA model in forecasting Bitcoin price due to its response to short-term data, the study revealed that ARIMA (1,1,0) is efficient in forecasting quarterly price movements for the last two quarters of 2020, and the deviation of its price in this period might suggest a change in its perceived investment value to investors as a digital asset after the outbreak of COVID-19.

This study aims to shed light on a freely published mutual fund screening tool—the capture ratio—and its ability to predict future fund performance (i.e., alpha). This analysis is of interest for both financial advisors and retail investors who deploy mutual fund screening tools. We find that capture ratios measured over shorter periods, such as one year, do not exhibit subsequent performance predictability. Conversely, we find that the three-year and five-year capture ratios are useful for investors in the full sample. However, analysis across cap and style-based fund subsamples shows that this return predictability is most consistent in predicting three- and five-year performance. TOPICS:Mutual funds/passive investing/indexing, performance measurement Key Findings ▪ Capture ratios measured over one year are unreliable in predicting mutual fund performance. ▪ Capture ratios measured over three and five years exhibit consistent performance predictability across cap and style fund subsamples. ▪ Mutual fund investors exhibit a real return-chasing behavior as it relates to capture ratios.

Examining the daily Dow Jones Industrial Average (DJI) we find evidence both of higher-order anomalies and predictability. While most researchers are only aware of the relatively harmless anomalies that occur just in the mean, the first part of this article provides empirical evidence of more dangerous kinds of anomalies occurring in higher-order moments. This evidence casts some doubt on the common practice of fitting standard time series models (e.g., ARMA models, GARCH models, or stochastic volatility models) to financial time series and carrying out tests based upon autocorre- lation coefficients without making proper provision for these anomalies. The second part of this article provides evidence in favor of the predictability of the returns on the DJI and, more interestingly, against the efficient market hypothesis. The special value of this evidence is due to the simplicity of the involved methods.

This paper evaluates the predictability of monthly stock return using out-of-sample interval forecasts. Past studies exclusively use point forecasts, which are of limited value since they carry no information about intrinsic predictive uncertainty. We compare the empirical performance of alternative interval forecasts for stock return generated from a naïve model, univariate autoregressive model, and multivariate model (predictive regression and VAR), using U.S. data from 1926. It is found that neither univariate nor multivariate interval forecasts outperform naïve forecasts. This strongly suggests that the U.S. stock market has been informationally efficient in the weak-form as well as in the semi-strong form.

We analyze the predictive power of time-varying risk aversion for the realized volatility of crude oil returns based on high-frequency data. Using random forests, and their extensions to quantile r...

We propose a least squares estimator weighted by a combination of lagged realized semivariances related to positive and negative returns (WLS-CRS) and use univariate models alone and in combination...

Disagreement measures are known to predict cross-sectional stock returns but fail to predict market returns. This paper proposes a partial least squares disagreement index by aggregating information across individual disagreement measures and shows that this index significantly predicts market returns both in- and out-of-sample. Consistent with the theory in Atmaz and Basak (2018), the disagreement index asymmetrically predicts market returns with greater power in high-sentiment periods, is positively associated with investor expectations of market returns, predicts market returns through a cash flow channel, and can explain the positive volume-volatility relationship.

The predictability of asset prices works against the notion of an efficient market where asset prices reflect all available and relevant information. This paper examined the predictability of Bitcoin and 51 other cryptocurrencies that have been classified into the following five categories: Application, Payment, Privacy, Platform, and Utility. Two market efficiency tests (Ljung-Box autocorrelation and Runs tests) were run on the daily returns of the 52 unique cryptocurrencies and the MSCI World index from 28 April 2013 to 30 June 2019. The results showed that Bitcoin was consistently efficient, whereas most of the other cryptocurrencies and even the MSCI World index were not, implying that their prices were predictable. Categorically, Payment altcoins were the most consistent in showing inefficiency. Since altcoins in this category also recorded the third highest risk-adjusted returns, investors with advanced technical trading strategies had a great chance of exploiting the market information to make extremely high abnormal returns.

Using the Trade Policy Uncertainty (TPU) index as a proxy for the level of trade policy uncertainty in the U.S. economy, we study the impact of the level of trade policy uncertainty on the conditional mean of market returns. Additionally, we investigate the predictive power of trade policy uncertainty on future market returns. Our findings show that after accounting for business cycle effects, TPU does not impact contemporaneous market returns. However, TPU is a robust predictor of future market returns in both univariate and bivariate regression tests. Specifically, our findings present unequivocal evidence of a positive relation between TPU and expected market returns.