scholarly journals Does Risk-Neutral Skewness Predict the Cross-Section of Equity Option Portfolio Returns?

2013 ◽  
Vol 48 (4) ◽  
pp. 1145-1171 ◽  
Author(s):  
Turan G. Bali ◽  
Scott Murray
Keyword(s):  
Stock Price ◽  
Stock Options ◽  
Implied Volatility ◽  
Asset Returns ◽  
Options Market ◽  
Short Term ◽  
Portfolio Returns ◽  
Risk Neutral ◽  
Market Factors ◽  
Equity Option

AbstractWe investigate the pricing of risk-neutral skewness in the stock options market by creating skewness assets comprised of two option positions (one long and one short) and a position in the underlying stock. The assets are created such that exposure to changes in the underlying stock price (delta), and exposure to changes in implied volatility (vega) are removed, isolating the effect of skewness. We find a strong negative relation between risk-neutral skewness and the skewness asset returns, consistent with a positive skewness preference. The returns are not explained by well-known market, size, book-to-market, momentum, short-term reversal, volatility, or option market factors.

scholarly journals Option volume and stock returns: evidence from single stock options on the Korea Exchange

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mincheol Woo ◽  
Meong Ae Kim
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Stock Price ◽  
Stock Options ◽  
Trading Volume ◽  
Volume Ratio ◽  
Options Market ◽  
The Future ◽  
Informed Traders ◽  
Option Volume

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.

scholarly journals EFFECTS OF THE PRE-REPURCHASE SYSTEMATIC RISK ON THE RELATIONSHIP BETWEEN INVESTOR BEHAVIOR, MARKET FACTORS AND THE STOCK PRICE RESPONSES

2018 ◽  
Vol 19 (4) ◽  
pp. 673-705
Author(s):  
Ying-Sing Liu
Keyword(s):  
Stock Price ◽  
Abnormal Returns ◽  
Investor Sentiment ◽  
Systematic Risk ◽  
Investor Behavior ◽  
Open Market ◽  
Short Term ◽  
Price Response ◽  
Market Factors ◽  
The Relationship

This study explores the pre-repurchase systematic risk will affect the abnormal returns in the open-market repurchase event period and also change the relationship between the investor sentiment, trading activity, market factors and stock price response during the event on Taiwan stock market. Based on threshold regression models, it is found that the pre-repurchase systematic risk will significantly change the relationship between investor behavior, market factors and stock price responses and the asymmetry of the relationship exists when pre-repurchase systematic risk is lower than a repartition, which supports that institutional investors and credit trading investors differ in these existing relationships. When the pre-repurchase beta is below repartition, it will be detrimental to the returns in event period. But on the contrary, the returns in the short-term shock of news exposure period present the favorable results, which may be related to the fact that there exists sentiment premium in short-term when credit trading investors’ repurchase news exposure occurs. Finally, the study is to confirm the effect of systematic risk for returns and investor sentiment, these results have not been further explored in the past, and can be used as the firm’s evalu-ation reference to the repurchase program in the future.

The Information Content of the Implied Volatility in OTC Individual Stock Options Market

2012 ◽  
Vol 20 (2) ◽  
pp. 195-235
Author(s):  
Yuen Jung Park
Keyword(s):  
Information Content ◽  
Stock Options ◽  
Implied Volatility ◽  
Global Financial Crisis ◽  
Explanatory Power ◽  
Sample Period ◽  
Options Market ◽  
Additional Information ◽  
The Future ◽  
The Difference

This paper investigates the information content of implied volatilities inferred from individual stock options quoted over-the-counter (OTC). First, we examine whether the implied volatility has better explanatory power than historical volatility for forecasting future realized volatility of the underlying stock return. Next, we analyze the properties of volatility spreads, the difference between implied volatilities and realized volatilities. Using near-the-money options for 10 firms over the sample period from April 2005 to April 2010, we first demonstrate that the implied volatilities for most firms don’t have additional information beyond what are already contained in historical volatilities. However, the implied volatilities with some specific remaining maturities for two firms dominate historical volatilities in explaining the future realized volatilities. Second, we find that during the period before global financial crisis, the implied volatilities are systematically lower than the future realized volatilities whereas this reversal disappears after the year 2008. This finding suggests that there’s a possibility of the risk loving behavior of the investors in OTC individual stock options market during the pre-global crisis period. Finally, through the comparative analysis of the KOSPI200 index options quoted OTC over the same sample period, we conclude that the OTC individual stock options market has distinctive characteristics like the KRW/USD OTC currency options market.

Call-Put Volatility Spreads and Option Returns in the KOSPI 200 Index Options Market

2016 ◽  
Vol 24 (4) ◽  
pp. 647-676
Author(s):  
So Jung Kim ◽  
Sun-Joong Yoon
Keyword(s):  
Implied Volatility ◽  
Asset Returns ◽  
Options Market ◽  
Underlying Asset ◽  
Option Returns ◽  
Put Option ◽  
The Us ◽  
The Future ◽  
Overreaction Hypothesis ◽  
Different Characteristics

This study analyzes whether KOSPI200 option returns can be predicted by call-put implied volatility spreads. Doran et al. (2013) show that call-put implied volatility spreads predict the option returns of a specific moneyness as well as underlying asset returns in the US options market. Our study examines whether the same results are shown in the KOSPI200 options market, which has different characteristics in investor compositions and trading behaviors. According to the results, the call-put implied volatility spreads cannot predict the future returns of the underlying index significantly in the KOSPI200 options market. Only, the call-put spreads can predict the future option returns. More specifically, the increase in implied volatility spreads is able to predict the decrease in call option returns and the increase in put option returns in the KOSPI200 options market. This supports the overreaction hypothesis in all ranges of option moneyness, which is in contrast to the result of Doran et al. (2003).

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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