Options Trading Based on the Forecasting of Volatility Direction with the Incorporation of Investor Sentiment

Using options price data on the Taiwanese stock market, we propose an options trading strategy based on the forecasting of volatility direction. The forecasting models are constructed with the incorporation of absolute returns, heterogeneous autoregressive-realized volatility (HAR-RV), and proxy of investor sentiment. After we take into consideration the margin-based transaction costs, the results of our simulated trading indicate that a straddle trading strategy that considers the forecasting of volatility direction with the incorporation of market turnover achieves the best Sharpe ratios. Our trading algorithm bridges the gap between options trading, market volatility, and the information content of investor overreaction.     

投资者情绪、期权隐含信息与股市波动率预测——基于上证50ETF期权的经验研究

在异质自回归模型(HAR-RV)中引入中国上证50ETF期权隐含信息和投资者情绪,本文分别对中国股票市场未来日、周和月波动率进行预测。研究发现,期权隐含信息和投资者情绪能够提高HAR-RV模型对股票市场未来波动率的预测效果。投资者情绪对未来波动率的影响存在两种机制:在情绪高涨期间,月已实现波动率与未来波动率正相关,说明以个人投资者占主体所引起的价格信息机制,在中国股票市场交易中占主导作用;风险中性偏度与未来波动率负相关,说明以个人投资者占主体所引起的噪声交易机制占主导作用。     

Investor sentiment indices based on k-step PLS algorithm: A group of powerful predictors of stock market returns

We construct a group of new investor sentiment indices by applying a new dimension reduction technique called k-step algorithm which adopts partial least squares method recursively. With the purpose of forecasting the aggregate stock market return, the new group of investor sentiment indices performs a greater ability in predicting the market return than existing investor sentiment indices in and out of sample by adequately using the information in residuals and eliminating a common noise component in sentiment proxies. This group of new investor sentiment indices beats five widely used economic variables and still has a strong return predictability after controlling these variables. Moreover, they could also predict cross-sectional stock returns sorted by industry, size, value, and momentum and generate considerable economic value for a mean-variance investor. We find the predictability of this group of investor sentiment indices comes from its forecasting power for discount rates and market illiquidity.     

Does social network sentiment influence the relationship between the S&P 500 and gold returns?

This study explored the relationship between investor sentiment (extracted from the StockTwits social network), the S&P 500 Index and gold returns. We investigated bilateral causality between gold prices and S&P 500 prices, the power of investor sentiment and gold returns to predict S&P 500 returns, and the influence of gold returns on S&P 500 volatility. We also considered whether the influence of sentiment varies according to the user's degree of experience. We considered the sentiment of messages that mentioned the S&P 500 Index and that users posted between 2012 and 2016. Granger causality analysis, ARIMA models and GARCH models were used for predicting S&P 500 Index returns and S&P 500 volatility. We observed a causal relationship between gold price and the S&P 500 Index. Our results also suggest that sentiment and gold returns predict S&P 500 Index returns. Finally, we observed that gold returns influence S&P 500 volatility and that the sentiment of experienced users affects S&P 500 returns.     

An examination of U.S. institutional and individual investor sentiment effect on the Turkish stock market

This study examines the effect of rational and irrational components of U.S. institutional and individual investor sentiment on Istanbul Stock Market (ISE) return and volatility. The results show that there is a significant spillover effect of U.S. investor sentiment on stock return and volatility of ISE. A breakdown of sentiment by the type of investor shows that the impact of institutional sentiment is greater than that of individual sentiment. A breakdown of sentiment by rationality shows that the effect of rational sentiment on ISE return is faster though not necessarily greater than that of irrational sentiment. The conclusion from these results is that the effect of U.S. investor sentiment is systemic and cannot be diversified away. U.S. investor sentiment, therefore, constitutes a priced risk factor and must be accounted for accordingly in international asset pricing models. The findings also provide some evidence of a negative relationship between U.S. investor sentiment and ISE return volatility.     

Investor sentiment and stock volatility: New evidence

This study investigates the predictability of sentiment measure on stock realized volatility. We propose a new investor sentiment index (NISI) based on the partial least squares method. This sentiment index outperforms many existing sentiment indicators in three aspects. First, in-sample result shows that the NISI has greater predictive power relative to the others. Most sentiment indicators show predictability in the non-crisis period only while the NISI is also effective in the crisis period. Furthermore, the NISI exhibits more prominent superiority in longer horizons forecasting. Second, further analysis indicates that the NISI has robust predictability before and after the Chinese stock market turbulence periods while the others not. Importantly, the NISI is still effective significantly after considering leverage effect while most of the others not. Finally, out-of-sample analysis demonstrates that the NISI is more powerful than other sentiment measures. This result is reproducible in different robustness checks.     

Returns,volatility and investor sentiment: Evidence from European stock markets

In this study, we show that patterns in returns behave as if investors, influenced by their level of optimism, selected stocks according to their volatility. Our goal is to confirm the contribution of behavioral finance while showing that investor sentiment can be profitably used by practitioners. We incorporate volatility in the relationship between investor sentiment and future returns, this is the main originality of our approach. Our methodology consists in comparing returns, volatility and higher-order moments of portfolios managed with investor sentiment against those obtained either with passive (buy and hold) portfolio management or with a minimum variance portfolio. Portfolios managed with investor sentiment have better returns and involve less risk under certain conditions.     

Investor sentiment as conditioning information in asset pricing

This paper assesses whether incorporating investor sentiment as conditioning information in asset-pricing models helps capture the impacts of the size, value, liquidity and momentum effects on risk-adjusted returns of individual stocks. We use survey sentiment measures and a composite index as proxies for investor sentiment. In our conditional framework, the size effect becomes less important in the conditional CAPM and is no longer significant in all the other models examined. Furthermore, the conditional models often capture the value, liquidity and momentum effects.     

Sentiment and stock market connectedness: Evidence from the U.S. – China trade war

We assess the impact of monthly and daily investor sentiment on stock market return and volatility connectedness during the U.S.-China trade war period. Our analyses focus on the connectedness between the two economies and their major trading partners. We also investigate the asymmetric impact of sentiment on volatility connectedness by exploring the upside and downside markets separately. We consistently document a negative relationship between investor sentiment and stock market connectedness for both return and volatility. We further confirm that investor sentiment exerts a larger impact on volatility connectedness in the downside market compared to the upside market.     

Asymmetric impact of earnings news on investor uncertainty

We describe a model that predicts an asymmetric impact of disclosure on investor uncertainty. We show that good news tends to resolve more uncertainty than bad news, and that uncertainty can be revised upwards if the investors' prior belief is sufficiently strong and the signal is sufficiently bad. This result is in contrast to classical disclosure models, where new information always resolves uncertainty and the change in uncertainty depends only on the relative precision of the news. Using option-implied volatility as a proxy for uncertainty, we find strong support for our predictions. We also show that our results are robust to competing explanations, notably to the leverage effect and volatility feedback, as well as to the jump risk induced in anticipation of the earnings announcements.     

Crowd wisdom and internet searches: What happens when investors search for stocks?

Search engines and social media have become popular among investors as tools for finding and sharing information. The investor social media gathers a large amount of investor-generated content (IGC), which reflects the crowd wisdom of investors, while search engines help investors increase their chances of finding them. In this study, we integrate investor search behavior data from the Baidu Index and investor crowd wisdom data from Eastmoney Guba to assemble a unique data set at the daily level. We then describe and quantify crowd wisdom from investor-generated content (IGC) using three dimensions (IGC average sentiment, IGC sentiment volatility, and IGC increased volume) to investigate the impact of crowd wisdom in the relationship between investors' Internet searches and next-day stock returns. In our empirical analysis, we find that IGC average sentiment strengthens the relationship between investors' Internet searches and next-day stock returns, while IGC sentiment volatility and IGC increased volume have negative effects. These moderating effects are also moderated by institutional investor attention, search terminal preference, and content reading volume. These findings help to explain the value and impact of crowd wisdom when investors search for stock information through the Internet.     

Option-implied information and quality of patents

This research explores how option-implied information predicts quality of patents. Using several measures of option-implied information, we find that only the option to stock volume (O/S) ratio positively and significantly predicts quality of patents around patent grant announcements. The findings are not entirely driven by information from the stock market and the probability of informed trading. Further investigations show that the predictability of O/S on patent quality is stronger when market sentiment is high, firms have a higher short-sale cost, and the quality of patents is relatively high.     

Can the Chinese volatility index reflect investor sentiment?

The volatility index is the implied volatility calculated inversely from the option prices. This study investigates whether the official Chinese volatility index, iVX, can represent investor sentiment. In order to describe investor sentiment comprehensively, we build a three-dimensional investor sentiment measurement system composed of macro, meso and micro level, and decompose iVX into three components to obtain short-term, medium-term fluctuations and long-term trend by EEMD method. The relationships between iVX, its components and sentiment indexes at each level have been analyzed separately, and the empirical results reveal all components of iVX can reflect the investor sentiment at the corresponding level but to which extent they can reflect are not the same. Further we introduce the mixed-frequency dynamic factor analysis to extract the common sentiment factor, which shows stronger correlation with contemporaneous iVX, compared with the sentiment indexes at each level. The ADL model in robustness check also demonstrates the results. Our findings confirm iVX can represent the common sentiment and expectations of Chinese investors in different time scales.     

Option-implied expectations in commodity markets and monetary policy

In this paper we estimate the dynamic interactions between option-implied variance and skewness in agricultural commodity markets and monetary policy. Using a structural vector autoregressive (SVAR) framework, we find that an expansionary (contractionary) monetary policy upwardly (downwardly) revises commodity markets’ expectations about the price and volatility path of agricultural products. On the other hand, our empirical analysis reveals that monetary policy does not have a systematic and timely response to sudden changes in option implied expectations of commodity investors. In addition, we provide empirical evidence showing the robust forecasting power of agricultural option-implied information on monetary policy with R² values reaching almost 52%.     

The information content of risk-neutral skewness for volatility forecasting

The paper investigates whether risk-neutral skewness has incremental explanatory power for future volatility in the S&P 500 index. While most of previous studies have investigated the usefulness of historical volatility and implied volatility for volatility forecasting, we study the information content of risk-neutral skewness in volatility forecasting model. In particular, we concentrate on Heterogeneous Autoregressive model of Realized Volatility and Implied Volatility (HAR-RV-IV). We find that risk-neutral skewness contains additional information for future volatility, relative to past realized volatilities and implied volatility. Out-of-sample analyses confirm that risk-neutral skewness improves significantly the accuracy of volatility forecasts for future volatility.     

Chinese institutional investors’ sentiment

We use daily survey data on Chinese institutional investors’ forecasts to measure investors’ sentiment. Our empirical model uncovers that share prices and investor sentiment do not have a long-run relation; however, in the short-run, the mood of investors follows a positive-feedback process. Hence, institutional investors are optimistic when previous market returns were positive. Contrarily, negative returns trigger a decline in sentiment, which reacts more sensitively to negative than positive returns. Investor sentiment does not predict future market movements—but a drop in confidence increases market volatility and destabilizes exchanges. EGARCH models reveal asymmetric responses in the volatility of investor sentiment; however, Granger causality tests reject volatility-spillovers between returns and sentiment.     

Optimal asset allocation using a combination of implied and historical information

This paper investigates the contribution of option-implied information for strategic asset allocation for individuals with minimum-variance preferences and portfolios with a variety of assets. We propose a covariance matrix that exploits a mixture of historical and option-implied information. Implied variance measures are proposed for those assets for which option-implied information is available. Historical variance and correlation measures are applied to the remaining assets. The performance of this novel approach for constructing optimal investment portfolios is assessed out-of-sample using statistical and economic measures. An empirical application to a sophisticated portfolio comprised by a combination of equities, fixed income, alternative securities and cash deposits shows that implied variance measures with risk premium correction outperform variance measures constructed from historical data and implied variance without correction. This result is robust across investment portfolios, volatility and portfolio performance metrics, and rebalancing schemes.     

Research on stock volatility risk and investor sentiment contagion from the perspective of multi-layer dynamic network

In the post-epidemic period, the international economic structure has been readjusted, with risks contagious across financial and economic systems. This paper primarily uses the high-frequency TENET network and the Granger-causality network to describe the interconnectedness between the tail risk of stock volatility and investor sentiment, then the two-layer network is constructed by the generalized variance decomposition method to examine the inter-layer connectedness. Based on the two-layer network, the heterogeneity frequency response of network connectedness and dynamic network structure are further analyzed from the perspective of frequency domain. The study found that the tail risk of high-frequency stock volatility displays industry heterogeneity and time-varying property, and investor sentiment contagion network provides information transmission medium for stock risk. The double-layer network study found that stock volatility in consumer goods industry exhibits higher risk spillover to investor sentiment. The diversified financial industry, real estate industry and energy industry in the two-layer network are systemically important industries. In addition, the study of the frequency domain dynamic network found that the connectedness volatility in the short-term risk network of stock volatility was significantly higher than that of the investor sentiment network, and the short-term risk spillover effect of the network played a leading role in the total risk spillover. The research conclusions provide reference for preventing systemic risks from the perspective of systemically important industries and cyclical fluctuations.     

Does investor sentiment predict bitcoin return and volatility? A quantile regression approach

The study investigates hypotheses relating to the effect of investor sentiment on predicting bitcoin returns and volatility. Using moments quantile regression, we present robust empirical evidence for the period 2017–2021. Our findings demonstrate that investor interest and emotions are significant predictors of bitcoin returns and volatility, while VIX and Bitcointalk.org forum are the most suitable predictors for representing investor emotions and interest, respectively. The findings also indicate a nonlinear relationship between investor sentiment and bitcoin returns and volatility, with predictable power changing based on the market conditions. Thus, the study enriches existing literature by providing empirical evidence to affirm the viability of behavioral finance theories in the bitcoin market and complements investors with more information to seek profits in different market conditions.     

Examining the volatility of soybean market in the MIDAS framework: The importance of bagging-based weather information

Based on the close relationship between the global soybean market and weather variables, current studies regarding soybean volatility forecasting under weather information are limited. The aim of our study is to fill this gap and examine the predictive power of soybean volatility by separately adding normal and bagging-based weather information. Methodologically, two types of extended GARCH-MIDAS approaches with weather variables, the GARCH-MIDAS-W and GARCH-MIDAS-W-MBB models, are first introduced into soybean volatility forecasting. By using the prices of soybean futures and weather information including clear-sky index, cloud cover, relative humidity, atmospheric pressure, precipitation, temperature and wind speed, our findings provide fresh evidence that predictive models that incorporate bagging-based weather information significantly outperform the models with raw weather indicators and the model without weather information. Finally, our conclusions are robust to further robustness checks. Our novel bagging-related GARCH-MIDAS-W-MBB model with weather indicators can provide fresh insights into soybean volatility forecasting.