On the efficiency of the gold market: Results of a real-time forecasting approach

Using a real-time forecasting approach, we study whether publicly available information on a large set of financial and macroeconomic variables help in forecasting out-of-sample monthly excess returns on investing in gold. The real-time forecasting approach accounts for the fact that an investor must reach an investment decision in real time under uncertainty concerning the optimal forecasting model. The real-time forecasting approach also accounts for the possibility that the optimal forecasting model may change over time. We account for transaction costs and show that using forecasts implied by the real-time forecasting approach to set up a simple trading rule does not necessarily lead to a superior performance relative to a buy-and-hold strategy, implying that the gold market is informationally efficient with respect to the predictor variables that we study in this research.     

Stock market momentum, business conditions, and GARCH option pricing models

This paper examines the forecasting performance of GARCH option pricing models from a market momentum perspective, and the possible impacts of financial crises and business conditions are also examined. The empirical results demonstrate that market momentum impacts the forecasting performance of GARCH option pricing models. The EGARCH model performs better under downward market momentum, while the standard GARCH performs better under upward market momentum. In addition, parsimonious models generally outperform richly parameterized ones. The above findings are robust to financial crises, and the results further demonstrate that business conditions influence the forecasting performance of GARCH option pricing models.     

Economic and financial crises and the predictability of U.S. stock returns

We argue that the use of publicly available and easily accessible information on economic and financial crises to detect structural breaks in the link between stock returns and macroeconomic predictor variables improves the performance of simple trading rules in real time. In particular, our results suggest that accounting for structural breaks and regime shifts in forecasting regressions caused by economic and financial crises has the potential to increase the out-of-sample predictability of stock returns, the performance of simple trading rules, and the market-timing ability of an investor trading in the U.S. stock market.     

Institutional arrangements and debt financing

I find that institutional arrangements have an impact on the real economy by affecting firms’ choice between private and public debt and the subsequent financing costs. Using new debt issued by firms in 26 non-US countries, I find, after controlling for firm characteristics predicted by debt agency and information asymmetry theories, that the level of financial market development, the efficiency of bankruptcy procedure, the integrity and enforceability of laws, and the transparency of financial information have significant impacts not only on firms’ debt choice and yield to maturity in domestic debt market, but also their issuance choice in the international debt market.     

Futures volatility forecasting based on big data analytics with incorporating an order imbalance effect

Future markets play vital roles in supporting economic activities in modern society. For example, crude oil and electricity futures markets have heavy effects on a nation’s energy operation management. Thus, volatility forecasting of the futures market is an emerging but increasingly influential field of financial research. In this paper, we adopt big data analytics, called Extreme Gradient Boosting (XGBoost) from computer science, in an attempt to improve the forecasting accuracy of futures volatility and to demonstrate the application of big data analytics in the financial spectrum in terms of volatility forecasting. We further unveil that order imbalance estimation might incorporate abundant information to reflect price jumps and other trading information in the futures market. Including order imbalance information helps our model capture underpinned market rules such as supply and demand, which lightens the information loss during the model formation. Our empirical results suggest that the volatility forecasting accuracy of the XGBoost method considerably beats the GARCH-jump and HAR-jump models in both crude oil futures market and electricity futures market. Our results could also produce plentiful research implications for both policy makers and energy futures market participants.     

Value relevance of analyst earnings forecasts in emerging markets

Even though research in accounting and finance has extensively examined the role of financial analysts in developed economies, this issue has not been thoroughly examined in an emerging market setting. In this paper, I examine whether, following a market opening, analyst forecast accuracy and the market's reliance on analyst forecasts increase with time. Accuracy is expected to increase over time as analysts exert more effort and gain valuable forecasting experience. Results indicate that time is positively related to analyst forecast accuracy after controlling for a number of other firm and country characteristics. Second, I posit that time should also be related to the market's propensity to use analyst forecasts to form earnings expectations. As markets open and investors become more sophisticated, the reliance on analyst forecasts should also increase. Results are consistent with this expectation. In particular, I find that in the first sub-period earnings expectations based on random walk exhibit greater relative information content than earnings expectations based on analyst forecasts. This pattern is reversed in the third sub-period where analyst forecast errors better explain returns. Incremental information content tests produce similar results. Future research should further investigate the relation between financial analysts and other important market characteristics in emerging economies.     

Volatility Forecasting in the Hang Seng Index using the GARCH Approach

The aim of this paper is to add to the literature on volatility forecasting using data from the Hong Kong stock market to determine if forecasts from GARCH based models can outperform simple historical averaging models. Overall, unlike previous studies we find that the GARCH models with non-Normal distributions show a robust volatility forecasting performance in comparison to the historical models. The results indicate that although not all models outperform simple historical averaging, the EGARCH based models, with non-normal conditional volatility, tend to produce more accurate out-of-sample forecasts using both standard measures of forecast accuracy and financial loss functions. In addition we test for asymmetric adjustment in the Hang Seng, finding strong evidence of asymmetries due to the domination of financial and property firms in this market.     

Coincident and leading indicators of the stock market

In this paper we have two goals: first, we want to represent monthly stock market fluctuations by constructing a non-linear coincident financial indicator. The indicator is constructed as an unobservable factor whose first moment and conditional volatility are driven by a two-state Markov variable. It can be interpreted as the investors' real-time belief about the state of financial conditions. Second, we want to explore an approach in which investors may use their perceptions of the state of the economy to form forecasts of financial market conditions and possibly of excess returns. To investigate this, we build leading indicators as forecasts of the estimated coincident financial index. The leading indicators yield better within and out-of-sample performance in forecasting, not only the state of the stock market but also of excess stock returns, as compared with the performance obtained using linear methods that have been proposed in the existing literature.     

Financial turbulence,systemic risk and the predictability of stock market volatility

This paper adds a novel perspective to the literature by exploring the predictive performance of two relatively unexplored indicators of financial conditions, i.e. financial turbulence and systemic risk, over stock market volatility using a sample of seven emerging and advanced economies. The two financial indicators that we utilize in our predictive setting provide a unique perspective on market conditions, as they relate directly to portfolio performance metrics from both volatility and co-movement perspectives and, unlike other macro-financial indicators of uncertainty, or risk, can be integrated into diversification models within forecasting and portfolio design settings. Since the data for the two predictors are available at a weekly frequency, and our focus is to produce forecasts at the daily frequency, we use the generalized autoregressive conditional heteroskedasticity-mixed data sampling (GARCH-MIDAS) approach. The results suggest that incorporating the two financial indicators (singly and jointly) indeed improves the out-of-sample predictive performance of stock market volatility models over both the short and long horizons. We observe that the financial turbulence indicator that captures asset price deviations from historical patterns does a better job when it comes to the out-of-sample prediction of future returns compared with the measure of systemic risk, captured by the absorption ratio. The outperformance of the financial turbulence indicator implies that unusual deviations in not only asset returns, but also in correlation patterns play a role in the persistence of return volatility. Overall, the findings provide an interesting opening for portfolio design purposes, in that financial indicators, which are directly associated with portfolio diversification performance metrics, can also be utilized for forecasting purposes, with significant implications for dynamic portfolio allocation strategies.     

Evaluating the performance of futures hedging using factors-driven realized volatility

The complexity and uncertainty of the financial market mainly stem from the rich market internal transaction information and a wide range effect of external factors. To this end, this paper proposes the combination factors-driven forecasting method to predict realized volatilities of the CSI 300 index and index futures. Based on the volatilities predicted by the proposed method, we further evaluate the ex-ante hedging performance in comparison to the conventional HAR model as well as GARCH-type models. The empirical results indicate that the factors-driven realized volatility model significantly dominates the other commonly used models in terms of hedging effectiveness. Furthermore, the superiority of the proposed method is robust in different market conditions, including significant rising or falling and abnormal market fluctuations in the COVID-19 pandemic, and in different index markets. Therefore, this paper improves the prediction accuracy of volatility by integrating market internal transaction information and external factor information, and the proposed method in this paper can be used by investors to obtain an excellent hedging effect.     

PREDICTING SYSTEMATIC RISK WITH MACROECONOMIC AND FINANCIAL VARIABLES

We analyze the predictive power of several macroeconomic and financial indicators in forecasting quarterly realized betas of 30 industry and 25 size and book-to-market portfolios. We model realized betas as autoregressive processes of order 1 and include lagged values of macroeconomic and financial indicators as exogenous predictor variables. In out-of-sample forecasting exercises, forecasts using bond market variables as exogenous predictors statistically outperform forecasts from a benchmark model without any exogenous predictors. These forecasts based on bond market variables also economically outperform benchmark forecasts by providing better performance in hedging the market risk of portfolios.     

Research on the use of financial statement information for forecasting profitability

Research on the use of financial statement information for forecasting profitability has two objectives: (i) to generate improved forecasts of profitability and accurate estimates of firm value; and (ii) to identify market inefficiencies with respect to financial statement information. For these areas of research, this article describes the evolution, provides examples and shares implications of the research. It also discusses opportunities for future research. The article highlights that financial statement analysis research has slowly evolved and has received limited attention from academics. The article argues that there are vast opportunities for impactful research on fundamental analysis and market inefficiencies.     

Less is more? New evidence from stock market volatility predictability

The complex nature of stock market volatility has motivated researchers to apply a variety of predictors to obtain reliable predictive information for precise forecasting. This study seeks to examine the effectiveness of the novel Global Financial Uncertainty (GFU) indices, comprising of only five sub-indices, in predicting stock market volatility using the widely used mixed-data sampling (MIDAS) model. The results demonstrate the remarkable and stable predictive power of GFU, even during crises and global financial uncertainty shocks. Specifically, the financial uncertainty index from Europe plays a significant role in our analysis. Importantly, we find that the GFU index outperforms a large number of other indicators in stock volatility forecasting. The statistical and economic significance of the predictive power of GFU is remarkable. Our study provides significant insights for market participants and policymakers that highlight the need to prioritize global financial uncertainty.     

私人信息、公开信息与中国的金融市场参与

金融市场的发展能够为长期的经济增长提供动力,家庭对金融市场的有限参与是制约金融发展的重要因素之一。2015年中国绝大多数家庭未能参与金融市场。本文采用倾向得分匹配方法考察了私人信息和公开信息对家庭金融市场参与的影响以及两者的影响差异。基于2015年CGSS数据集的分析结果显示,家庭拥有金融市场私人信息或者能够充分获取公开信息都会显著提升其参与金融市场的概率。家庭获取公开信息的渠道越广泛,经由各种渠道获取的公开信息越多,参与金融市场的概率就越高。总体而言,充分获取公开信息比拥有私人信息对家庭金融市场参与的影响更大,这是源于个体对两种信息质量预期的不同。公开信息具有比私人信息更广泛的信息来源和更强的可验证性,这提升了家庭对公开信息质量的预期,为其提供了参与金融市场的更大激励。对中国而言,改善政策制定和执行的效率以及政策承诺的可信性有助于提升家庭对公开信息质量的预期,从而鼓励家庭参与金融市场。     

A cross-country comparison of corporate governance and firm performance: Do financial structure and the legal system matter?

We blend the corporate governance and the financial structure/legal system literature streams to study whether firm performance is enhanced when its governance structure embodies the demands of the host country’s financial structure and legal system. Using a sample of 1736 unique firms representing 22 countries, we find that the joint effect of a country’s financial structure and legal system does matter when explaining the relationship between performance and the overall level of corporate governance in a given country. The results also suggest that firms operating in the market/common combination countries tend to command higher market valuations than firms with a comparable level of corporate governance that operate in the bank/civil combination countries.     

Do product market threats affect analyst forecast precision?

We examine how product market threats influence the precision of analyst forecasts. Greater competitive threats may make forecasting more difficult by increasing the uncertainty regarding future cash flows and by influencing the quality of financial disclosure. Using a firm-specific measure of product market threats (i.e., fluidity), we find that analysts are more likely to be less precise forecasting earnings for highly fluid firms and that the lack of precision is not fully explained by performance volatility. Our findings further suggest that firms with fluid products have lower accruals quality and that they are more likely to withheld information regarding contract terms and sales from major customers. Cross-sectional analysis further suggests that the effect of fluidity on analyst forecasts is more pronounced when firms have flexibility in disclosure choices. Using significant changes in tariff rates as a quasi-natural experiment, we find that analyst forecast precision is significantly lower following tariff reductions.     

Bankruptcy Prediction with Industry Effects

This paper investigates the forecasting accuracy of bankruptcy hazard rate models for U.S. companies over the time period 1962–1999 using both yearly and monthly observation intervals. The contribution of this paper is multiple-fold. One, using an expanded bankruptcy database we validate the superior forecasting performance of Shumway's (2001) model as opposed to Altman (1968) and Zmijewski (1984). Two, we demonstrate the importance of including industry effects in hazard rate estimation. Industry groupings are shown to significantly affect both the intercept and slope coefficients in the forecasting equations. Three, we extend the hazard rate model to apply to financial firms and monthly observation intervals. Due to data limitations, most of the existing literature employs only yearly observations. We show that bankruptcy prediction is markedly improved using monthly observation intervals. Fourth, consistent with the notion of market efficiency with respect to publicly available information, we demonstrate that accounting variables add little predictive power when market variables are already included in the bankruptcy model.     

The bright side of market power in Asian banking: Implications of bank capitalization and financial freedom

Using a sample of listed banks in the Asia-Pacific region from 2000 to 2016, this paper documents that higher market power reduces risk taking but increases loan growth and performance in banking. This highlights the "bright side" of bank market power in general. However, the positive effect of market power on bank stability is more pronounced for well-capitalized banks, although their performance tends to decline, and loan growth is unaffected by market power. Hence, bank capitalization plays an important role in strengthening financial stability due to an increase in bank market power. Moreover, banks with higher market power located in countries with a lower degree of financial freedom exhibit lower riskiness, higher loan growth, and better performance. Greater control by authorities in the financial sector is essential, not only to enhance financial stability, but also to boost financial intermediation and bank performance following an increase in bank market power.     

Do financial variables help predict the state of the business cycle in small open economies? Evidence from Switzerland

We analyze the forecasting ability of financial variables to predict the state of the Swiss business cycle up to eight quarters ahead. Overall, our results suggest that financial variables convey leading information for the prediction of business cycles, even when applied to a small open economy. However, we clearly find that model specifications need to be extended to include variables accounting for external shocks, such as exchange rates or international commodity prices. It also appears that the forecasting contribution of individual variables changes over time. Specifically, in the last two decades, stock market liquidity has replaced the term spread as the best single predictor.     

Financial Architecture and Economic Performance: International Evidence

The paper examines the relation between the architecture of an economy's financial system—its degree of market orientation—and economic performance in the real sector. I find that while market-based systems outperform bank-based systems among countries with developed financial sectors, bank-based systems fare better among countries with underdeveloped financial sectors. Countries dominated by small firms grow faster in bank-based systems and those dominated by larger firms in market-based systems. The findings suggest that recent trends in financial development policies that indiscriminately prescribe market-oriented financial-system architecture to emerging and transition economies might be misguided because suitable financial architecture, in and of itself, could be a source of value. Journal of Economic Literature Classification Numbers: G1, G21, O1, 04.