Forecasting stock market returns: New technical indicators and two-step economic constraint method

The goal of our paper is to improve the accuracy of stock return forecasts by combining new technical indicators and a new two-step economic constraint forecasting model. Empirical results indicate the stock return forecasts generated by new technical indicators and new economic constraint forecasting model is statistically and economically significant both in-sample and out-of-sample prediction performance. In addition, the prediction performance of new technical indicators and new economic constraint forecasting model is robust for some extension and robustness analysis.     

Efficient predictability of stock return volatility: The role of stock market implied volatility

This study examines the predictability of stock market implied volatility on stock volatility in five developed economies (the US, Japan, Germany, France, and the UK) using monthly volatility data for the period 2000 to 2017. We utilize a simple linear autoregressive model to capture predictive relationships between stock market implied volatility and stock volatility. Our in-sample results show there exists very significant Granger causality from stock market implied volatility to stock volatility. The out-of-sample results also indicate that stock market implied volatility is significantly more powerful for stock volatility than the oil price volatility in five developed economies.     

Overnight stock returns,intraday returns,and firm-specific investor sentiment

In this study, we propose a new index for measuring firm-specific investor sentiment using overnight and intraday stock returns. We use actual equity data to construct the firm-level investor sentiment index and find that the new index has characteristics expected of a sentiment measure. In addition, we propose a novel sentiment-weighted trading strategy and apply it to momentum and short-term reversal strategies. We find that the sentiment-weighted trading strategy generates better performance in momentum and short-term reversal strategies. The sentiment-weighted trading strategy’s superior performance is evidence that our firm-level investor sentiment index possesses predictive powers with regard to future returns.     

Intraday return predictability in the cryptocurrency markets: Momentum,reversal, or both

This paper reports evidence of intraday return predictability, consisting of both intraday momentum and reversal, in the cryptocurrency market. Using high-frequency price data on Bitcoin from March 3, 2013, to May 31, 2020, it shows that the patterns of intraday return predictability change in the presence of large intraday price jumps, FOMC announcement release, liquidity levels, and the outbreak of the COVID-19. Intraday return predictability is also found in other actively traded cryptocurrencies such as Ethereum, Litecoin, and Ripple. Further analysis shows that the timing strategy based on the intraday predictors produces higher economic value than the benchmark strategy such as the always-long or the buy-and-hold. Evidence of intraday momentum can be explained in light of the theory of late-informed investors, whereas evidence of intraday reversal, which is unique to the cryptocurrency market, can be related to investors’ overreaction to non-fundamental information and overconfidence bias.     

Site visit information content and return predictability: Evidence from China

In this paper, we use frequency of related phrases in site visit summary reports to denote the site visit content, and study whether site visit content reflecting institutional investors’ market concerns can predict Chinese stock market return. We find that site visit content has greater forecasting power in Chinese stock market returns than other economic predictors after comparing out-of-sample R². The predictability is both statistically and economically significant. Additionally, our results also suggest that the particular information content has better forecasting power than general content in site visit summary reports.     

Forecasting the direction of the US stock market with dynamic binary probit models

Several empirical studies have documented that the signs of excess stock returns are, to some extent, predictable. In this paper, we consider the predictive ability of the binary dependent dynamic probit model in predicting the direction of monthly excess stock returns. The recession forecast obtained from the model for a binary recession indicator appears to be the most useful predictive variable, and once it is employed, the sign of the excess return is predictable in-sample. The new dynamic “error correction” probit model proposed in the paper yields better out-of-sample sign forecasts, with the resulting average trading returns being higher than those of either the buy-and-hold strategy or trading rules based on ARMAX models.     

Inference with non-Gaussian Ornstein–Uhlenbeck processes for stochastic volatility

Continuous-time stochastic volatility models are becoming an increasingly popular way to describe moderate and high-frequency financial data. Barndorff-Nielsen and Shephard (2001a) proposed a class of models where the volatility behaves according to an Ornstein–Uhlenbeck (OU) process, driven by a positive Lévy process without Gaussian component. These models introduce discontinuities, or jumps, into the volatility process. They also consider superpositions of such processes and we extend that to the inclusion of a jump component in the returns. In addition, we allow for leverage effects and we introduce separate risk pricing for the volatility components. We design and implement practically relevant inference methods for such models, within the Bayesian paradigm. The algorithm is based on Markov chain Monte Carlo (MCMC) methods and we use a series representation of Lévy processes. MCMC methods for such models are complicated by the fact that parameter changes will often induce a change in the distribution of the representation of the process and the associated problem of overconditioning. We avoid this problem by dependent thinning methods. An application to stock price data shows the models perform very well, even in the face of data with rapid changes, especially if a superposition of processes with different risk premiums and a leverage effect is used.     

The VIX,the variance premium and stock market volatility

We decompose the squared VIX index, derived from US S&P500 options prices, into the conditional variance of stock returns and the equity variance premium. We evaluate a plethora of state-of-the-art volatility forecasting models to produce an accurate measure of the conditional variance. We then examine the predictive power of the VIX and its two components for stock market returns, economic activity and financial instability. The variance premium predicts stock returns while the conditional stock market variance predicts economic activity and has a relatively higher predictive power for financial instability than does the variance premium.     

Economic policy uncertainty and illiquidity return premium

In this study, we examine the relation between the price of liquidity, or illiquidity return premium, and the economic policy uncertainty (EPU). On average, an illiquid portfolio earns a 0.597% higher monthly return than a liquid portfolio. The results further show that the EPU index has a positive relationship with the illiquidity return premium. This indicates that investors require higher compensation for holding illiquid stocks when there is a higher economic uncertainty. We also show that EPU affects the illiquidity return premium through the market illiquidity channel. The rise of EPU could increase the risk of illiquid stocks and make investors more risk-averse, thereby requiring higher compensation for illiquidity. Finally, it is found that the relationship between EPU and the illiquidity return premium is stronger when market liquidity is impaired and during crises.     

A Bayesian approach to bandwidth selection for multivariate kernel regression with an application to state-price density estimation

This paper presents a Bayesian approach to bandwidth selection for multivariate kernel regression. A Monte Carlo study shows that under the average squared error criterion, the Bayesian bandwidth selector is comparable to the cross-validation method and clearly outperforms the bootstrapping and rule-of-thumb bandwidth selectors. The Bayesian bandwidth selector is applied to a multivariate kernel regression model that is often used to estimate the state-price density of Arrow–Debreu securities with the S&P 500 index options data and the DAX index options data. The proposed Bayesian bandwidth selector represents a data-driven solution to the problem of choosing bandwidths for the multivariate kernel regression involved in the nonparametric estimation of the state-price density pioneered by Aït-Sahalia and Lo [Aït-Sahalia, Y., Lo, A.W., 1998. Nonparametric estimation of state-price densities implicit in financial asset prices. The Journal of Finance, 53, 499, 547.]     

Geopolitical risks and stock market dynamics of the BRICS

This paper examines the effect of geopolitical uncertainty on return and volatility dynamics in the BRICS stock markets via nonparametric causality-in-quantiles tests. The effect of geopolitical risks (GPRs) is found to be heterogeneous across the BRICS stock markets, suggesting that news regarding geopolitical tensions do not affect return dynamics in these markets in a uniform way. GPRs are generally found to impact stock market volatility measures rather than returns, and often at return quantiles below the median, indicating the role of GPRs as a driver of bad volatility in these markets. While Russia bears the greatest risk exposure to GPRs in terms of both return and volatility, India is found to be the most resilient BRICS nation in the group. Noting that geopolitical shocks and in particular terrorist incidents are largely unanticipated, our findings underscore the importance of a strong financial sector that can help return the market to stability and an open economy that allows local investors to diversify country-specific risks in their portfolios.     

Spurious regressions in technical trading

This paper investigates the spurious effect in forecasting asset returns when signals from technical trading rules are used as predictors. Against economic intuition, the simulation result shows that, even if past information has no predictive power, buy or sell signals based on the difference between the short-period and long-period moving averages of past asset prices can be statistically significant when the forecast horizon is relatively long. The theoretical analysis reveals that both ‘momentum’ and ‘contrarian’ strategies can be falsely supported, while the probability of obtaining each result depends on the type of the test statistics employed.     

Estimation and model selection of semiparametric multivariate survival functions under general censorship

We study estimation and model selection of semiparametric models of multivariate survival functions for censored data, which are characterized by possibly misspecified parametric copulas and nonparametric marginal survivals. We obtain the consistency and root-n$n$ asymptotic normality of a two-step copula estimator to the pseudo-true copula parameter value according to KLIC, and provide a simple consistent estimator of its asymptotic variance, allowing for a first-step nonparametric estimation of the marginal survivals. We establish the asymptotic distribution of the penalized pseudo-likelihood ratio statistic for comparing multiple semiparametric multivariate survival functions subject to copula misspecification and general censorship. An empirical application is provided.     

Empirical modeling of high-income and emerging stock and Forex market return volatility using Markov-switching GARCH models

Using weekly data for stock and Forex market returns, a set of MS-GARCH models is estimated for a group of high-income (HI) countries and emerging market economies (EMEs) using algorithms proposed by Augustyniak (2014) and Ardia et al. (2018, 2019a,b), allowing for a variety of conditional variance and distribution specifications. The main results are: (i) the models selected using Ardia et al. (2018) have a better fit than those estimated by Augustyniak (2014), contain skewed distributions, and often require that the main coefficients be different in each regime; (ii) in Latam Forex markets, estimates of the heavy-tail parameter are smaller than in HI Forex and all stock markets; (iii) the persistence of the high-volatility regime is considerable and more evident in stock markets (especially in Latam EMEs); (iv) in (HI and Latam) stock markets, a single-regime GJR model (leverage effects) with skewed distributions is selected; but when using MS models, virtually no MS-GJR models are selected. However, this does not happen in Forex markets, where leverage effects are not found either in single-regime or MS-GARCH models.     

Dynamic relations between oil and stock market returns: A multi-country study

We study the relation between the BRENT and seventeen stock market indexes of important oil-dependent economies. We focus on connectedness between these markets and characterize the dynamics of transmission and reception. We use LASSO methods to shrink, select, and estimate the high dimensional network linking these markets between August, 1999 and March, 2018. This methodological innovation allows the inclusion of a significantly larger number of markets in the network, providing finer results regarding connectedness in the oil-stock market nexus. We show that transmission runs mainly from stock markets to the BRENT. Connectedness varies considerably over time, reaching peaks during times of financial distress. Dynamic predictive causality tests show evidence of time-varying bidirectional causality. Causality from stock markets to the BRENT is detected mostly for the last part of the sample period. This finding indicates that the impact of stock market developments on oil markets is growing over time.     

Risk,jumps, and diversification

We test for price discontinuities, or jumps, in a panel of high-frequency intraday stock returns and an equiweighted index constructed from the same stocks. Using a new test for common jumps that explicitly utilizes the cross-covariance structure in the returns to identify non-diversifiable jumps, we find strong evidence for many modest-sized, yet highly significant, cojumps that simply pass through standard jump detection statistics when applied on a stock-by-stock basis. Our results are further corroborated by a striking within-day pattern in the significant cojumps, with a sharp peak at the time of regularly scheduled macroeconomic news announcements.     

Predictability in international stock returns using currency fluctuations and forward rate forecasts

We find that currency risk, specifically dollar exchange rate risk, is a determinant in firm stock returns worldwide. Firms exposed to various dollar exchange rate risks worldwide exhibit strong differences in expected returns, and firms with previously high sensitivity to their home country’s exchange rate fluctuation subsequently outperform during the following six to twelve months. This effect is robust across countries, time, exchange rate policies, and macroeconomic environments. We find that information in currency forward rates provides additional, useful information when predicting future returns of these currency-sensitive firms, and dynamic, state-space estimation of currency forward rate term structures complements the predictability.     

The influence and predictive powers of mixed-frequency individual stock sentiment on stock returns

In this article, we construct mixed-frequency individual stock sentiment using MIDAS model. We first investigate the influence power of mixed-frequency individual stock sentiment on excess returns. The results indicate that the higher the frequency of individual stock sentiment is, the better it explains the variation of excess returns, that mixed-frequency individual stock sentiment, especially mixed high-frequency sentiment, exerts greater influence on excess returns than the same frequency one and that the mixed-frequency sentiment has a stronger explanatory power to the variation of excess returns than size factor, book-to-market factor, profitability factor and investment factor do. Then, we study the predictive content of mixed-frequency individual stock sentiment. The results show that the higher the frequency of individual stock sentiment is, the better the forecast performs. Moreover, by comparing the corresponding statistics in influence and predictive power models, we find that the influence power of mixed-frequency individual stock sentiment is more significant than its predictive power.     

Jump tails,extreme dependencies,and the distribution of stock returns

We provide a new framework for estimating the systematic and idiosyncratic jump tail risks in financial asset prices. Our estimates are based on in-fill asymptotics for directly identifying the jumps, together with Extreme Value Theory (EVT) approximations and methods-of-moments for assessing the tail decay parameters and tail dependencies. On implementing the procedures with a panel of intraday prices for a large cross-section of individual stocks and the S&P 500 market portfolio, we find that the distributions of the systematic and idiosyncratic jumps are both generally heavy-tailed and close to symmetric, and show how the jump tail dependencies deduced from the high-frequency data together with the day-to-day variation in the diffusive volatility account for the “extreme” joint dependencies observed at the daily level.     

Firms’ fundamentals,macroeconomic variables and quarterly stock prices in the US

This paper modeled the effects of firms’ fundamentals such as total assets and long-term debt and of macroeconomic variables such as unemployment and interest rates on quarterly stock prices of over 3000 US firms in the period 2000–07. The merged CRSP/Compustat database was augmented by macroeconomic variables and comprehensive dynamic models were estimated by maximum likelihood taking into account heterogeneity across firms. Likelihood ratio statistics were developed for sequentially testing hypotheses regarding the adequacy of macroeconomic variables in the models. The main findings were that the estimated coefficients of lagged stock prices in simple dynamic random effects models were in the interval 0.90–0.95. Second, comprehensive dynamic models for stock prices showed that the firms’ earnings per share, total assets, long-term debt, dividends per share, and unemployment and interest rates were significant predictors; there were significant interactions between firms’ long-term debt and interest rates. Finally, implications of the results for corporate policies are discussed.