This document provides a literature review on research examining the information content contained in options markets and their ability to predict stock behavior. The review discusses how option characteristics like implied volatility and option volume/open interest may help forecast future stock movements. While some studies found these option metrics useful predictors, others' results were less conclusive or dependent on market conditions. Overall, the literature suggests options can potentially provide information on future stock returns and volatility, but the predictive power depends on the specific option characteristic and timeframe analyzed.
1. The document discusses the relationship between trading volume, stock returns, and volatility based on an analysis of data from the Pakistan Stock Exchange from 2003-2013. It aims to understand how changes in these variables impact each other.
2. Previous research on the topic in developed markets found a positive relationship between trading volume, returns, and volatility, but little work has been done in Pakistan.
3. The study will analyze daily data from the KSE 100 index and 50 firms using ARCH and GARCH models to explore the explanatory power of past trading volume and returns on current market returns and volatility in Pakistan.
Are good companies good stocks evidence from nairobi stock exchangeAlexander Decker
This document summarizes a research study that examined the relationship between company performance and stock performance on the Nairobi Stock Exchange. The study hypothesized that there would be a strong positive correlation between "good companies", defined as those with strong earnings and sales growth, and their stock performance. The researchers analyzed 32 listed companies using correlation analysis and descriptive statistics. The results indicated there is a strong positive correlation between good company performance and good stock performance on the NSE, supporting the hypothesis that good companies tend to be good stocks.
The presentation on the effect of commodity futuresposhiyaashvin
This document outlines a research proposal submitted by Tejal Navarangani and Parth Shah to Maulik Vasani. The research aims to explore the relationship between commodity futures prices and spot market prices through an exploratory study of 7 commodities. Primary data on commodity and spot prices will be collected from news sources and exchanges, while secondary data on investor preferences will come from questionnaires. The sampling method will be convenience-based. Key dates are outlined for completing proposal, fieldwork, analysis, and final report. The research could benefit investors, brokers, traders and others by increasing awareness of linkages between futures and spot markets.
This document summarizes a study examining IPO underpricing explanations using data from the Stock Exchange of Singapore. The key points are:
1) The study analyzes both application and allocation data from IPOs in Singapore, which allows reconstruction of underlying investor demand schedules.
2) They find large investors preferentially request shares in IPOs with higher initial returns, consistent with them having better information.
3) Inferences differ substantially between looking at just applications versus allocations, since allocations may not reflect true underlying demand due to rationing practices.
This document summarizes a paper that examines behavioral finance versus the efficient market hypothesis and how they can be used to facilitate capital gains. It discusses how behavioral finance incorporates psychological factors that can lead to market inefficiencies and opportunities for gain, unlike the efficient market theory. The paper will analyze works supporting both theories to argue that incorporating behavioral finance can help assess if price movements reflect real changes in company value or irrational investor behavior.
The document discusses the random walk theory as applied to stock prices. It posits that stock prices follow random walks such that their movements cannot be predicted, making it impossible to outperform the market without taking on additional risk. The theory believes that fundamental analysis and technical analysis are futile for predicting prices. It implies the best investment strategy is to invest in a portfolio that reflects the overall stock market. The key aspects of the random walk theory are that stock price changes are independent and have the same probability distribution. Criticisms argue that prices may follow trends in the short run and the theory's basis is flawed.
The document summarizes key ideas from Andrew Lo and Burton Malkiel regarding the efficient market hypothesis. Both authors conclude that while market prices may not always be perfectly efficient in the short-run due to irrational investor behavior, the market conveys information efficiently over the long-run as irrational behaviors are corrected. Lo uses an evolutionary framework to explain how markets adapt dynamically, while Malkiel provides evidence that anomalies tend to disappear as they are exploited by investors. Overall, the document analyzes how investor psychology can lead to short-term inefficiencies that are ultimately corrected through the mechanisms of an adaptive market.
This document summarizes a study that examines the statistical properties and predictive factors of the VKOSPI, South Korea's implied volatility index derived from options on the KOSPI 200 stock index. The study finds that an augmented heterogeneous autoregressive (HAR) model describes the dynamics of the VKOSPI well and some domestic macroeconomic variables help explain it. Most importantly, it determines that US stock market returns and implied volatility (from S&P 500 options) play a dominant role in predicting VKOSPI levels and dynamics, more so than domestic factors. Specifically, while US stock returns significantly predict VKOSPI, Korean stock returns do not. The study thus provides evidence that global factors have substantial influence over implied volatility in emerging markets like
1. The document discusses the relationship between trading volume, stock returns, and volatility based on an analysis of data from the Pakistan Stock Exchange from 2003-2013. It aims to understand how changes in these variables impact each other.
2. Previous research on the topic in developed markets found a positive relationship between trading volume, returns, and volatility, but little work has been done in Pakistan.
3. The study will analyze daily data from the KSE 100 index and 50 firms using ARCH and GARCH models to explore the explanatory power of past trading volume and returns on current market returns and volatility in Pakistan.
Are good companies good stocks evidence from nairobi stock exchangeAlexander Decker
This document summarizes a research study that examined the relationship between company performance and stock performance on the Nairobi Stock Exchange. The study hypothesized that there would be a strong positive correlation between "good companies", defined as those with strong earnings and sales growth, and their stock performance. The researchers analyzed 32 listed companies using correlation analysis and descriptive statistics. The results indicated there is a strong positive correlation between good company performance and good stock performance on the NSE, supporting the hypothesis that good companies tend to be good stocks.
The presentation on the effect of commodity futuresposhiyaashvin
This document outlines a research proposal submitted by Tejal Navarangani and Parth Shah to Maulik Vasani. The research aims to explore the relationship between commodity futures prices and spot market prices through an exploratory study of 7 commodities. Primary data on commodity and spot prices will be collected from news sources and exchanges, while secondary data on investor preferences will come from questionnaires. The sampling method will be convenience-based. Key dates are outlined for completing proposal, fieldwork, analysis, and final report. The research could benefit investors, brokers, traders and others by increasing awareness of linkages between futures and spot markets.
This document summarizes a study examining IPO underpricing explanations using data from the Stock Exchange of Singapore. The key points are:
1) The study analyzes both application and allocation data from IPOs in Singapore, which allows reconstruction of underlying investor demand schedules.
2) They find large investors preferentially request shares in IPOs with higher initial returns, consistent with them having better information.
3) Inferences differ substantially between looking at just applications versus allocations, since allocations may not reflect true underlying demand due to rationing practices.
This document summarizes a paper that examines behavioral finance versus the efficient market hypothesis and how they can be used to facilitate capital gains. It discusses how behavioral finance incorporates psychological factors that can lead to market inefficiencies and opportunities for gain, unlike the efficient market theory. The paper will analyze works supporting both theories to argue that incorporating behavioral finance can help assess if price movements reflect real changes in company value or irrational investor behavior.
The document discusses the random walk theory as applied to stock prices. It posits that stock prices follow random walks such that their movements cannot be predicted, making it impossible to outperform the market without taking on additional risk. The theory believes that fundamental analysis and technical analysis are futile for predicting prices. It implies the best investment strategy is to invest in a portfolio that reflects the overall stock market. The key aspects of the random walk theory are that stock price changes are independent and have the same probability distribution. Criticisms argue that prices may follow trends in the short run and the theory's basis is flawed.
The document summarizes key ideas from Andrew Lo and Burton Malkiel regarding the efficient market hypothesis. Both authors conclude that while market prices may not always be perfectly efficient in the short-run due to irrational investor behavior, the market conveys information efficiently over the long-run as irrational behaviors are corrected. Lo uses an evolutionary framework to explain how markets adapt dynamically, while Malkiel provides evidence that anomalies tend to disappear as they are exploited by investors. Overall, the document analyzes how investor psychology can lead to short-term inefficiencies that are ultimately corrected through the mechanisms of an adaptive market.
This document summarizes a study that examines the statistical properties and predictive factors of the VKOSPI, South Korea's implied volatility index derived from options on the KOSPI 200 stock index. The study finds that an augmented heterogeneous autoregressive (HAR) model describes the dynamics of the VKOSPI well and some domestic macroeconomic variables help explain it. Most importantly, it determines that US stock market returns and implied volatility (from S&P 500 options) play a dominant role in predicting VKOSPI levels and dynamics, more so than domestic factors. Specifically, while US stock returns significantly predict VKOSPI, Korean stock returns do not. The study thus provides evidence that global factors have substantial influence over implied volatility in emerging markets like
: Security and Portfolio Analysis :Efficient market theoryRahulKaushik108
Key Concepts of Efficient market theory: Very Lucid presentation , very Useful for MBA student to understand the Concepts of Efficient Market theory( Random walk hypotheses ) .The key idea of the hypotheses is" no one can efficiently out predict the market" or in other terms, technical analysis or fundamental analysis can not beat "the naive buy and hold strategy".
Forecasting Stocks with Multivariate Time Series Models.inventionjournals
This work seeks to forecast stocks of the Nigerian banking sector using probability multivariate time series models. The study involved the stocks from six different banks that were found to be analytically interrelated. Stationarity of the six series were obtained by differencing. Model selection criteria were employed and the best fitted model was selected to be a vector autoregressive model of order 1. The model was subjected to diagnostic checks and was found to be adequate. Consequently, forecasts of stocks were generated for the next two years.
This document summarizes a study that tested the random walk hypothesis on the Karachi Stock Exchange 100 Index from 1996 to 2006. The random walk hypothesis states that stock prices fully reflect all available information and follow a random pattern, making them unpredictable. The study found no significant differences in average monthly or daily returns, supporting the hypothesis that the KSE follows a random walk pattern. Therefore, past stock prices and returns cannot be used to predict future movements. This finding indicates the KSE behaves as an efficient market.
Determinants of equity share prices of the listed company in dhaka stock exch...MD. Walid Hossain
This is the finance academic project report.This report prepare by MD. WALID HOSSAIN, Patuakhali science and technology University, Faculty of business administration and management. i think that is helpful for business studies students.
The document discusses the efficient market hypothesis (EMH), which states that stock prices already reflect all available public information, making it impossible for investors to outperform the market through strategies based on historical prices, economic news, or other public data. There are three forms of the EMH - weak, semi-strong, and strong - differing in the type of information believed to be reflected in prices. While several studies have found evidence supporting the EMH, others have found anomalies like value and small firm effects that appear to allow above-market returns. The validity of the EMH remains controversial.
The document discusses the random walk theory, which states that stock price movements cannot be predicted because they follow a random path rather than any predictable patterns. It originated in the 1900s and was popularized in a 1973 book. The random walk theory says past stock performance does not indicate future performance and prices reflect all available information. However, some studies have found evidence of predictability based on factors like earnings. The implications are that market timing is difficult and outperforming the market through analysis alone may involve some luck.
Market efficiency survives the challenge from the literature on long-term return anomalies. Consistent with the market efficiency hypothesis that the anomalies are chance results, apparent overreaction to information is about as common as under-reaction, and post-event continuation of preevent abnormal returns is about as frequent as post-event reversal. Most important, consistent with the market efficiency prediction that apparent anomalies can be due to methodology, most long-term return anomalies tend to
disappear with reasonable changes in technique.
This slide set is a work in progress and is embedded in my Principles of Finance course, which is also a work in progress, that I teach to computer scientists and engineers
http://awesomefinance.weebly.com/
The document summarizes research on value investing in emerging markets. It finds that:
1) A simple valuation model can identify emerging markets with higher expected returns compared to average emerging markets.
2) A portfolio of "undervalued" emerging markets identified by the model generates superior returns compared to benchmarks, with statistical significance.
3) Risk measures of the portfolio of undervalued emerging markets are close to risk measures of broader emerging market benchmarks, implying the higher returns are not compensated by significantly higher risk.
According to the EMH, stocks always trade at their fair value on stock exchanges, making it impossible for investors to either purchase undervalued stocks or sell stocks for inflated prices. As such, it should be impossible to outperform the overall market through expert stock selection or market timing, and that the only way an investor can possibly obtain higher returns is by purchasing riskier investments.
The document summarizes key concepts in financial economics, including:
1. Merton Miller identified five "pillars" of finance, including the CAPM, EMH, modern portfolio theory, and options pricing theory.
2. Eugene Fama defined the EMH as a market where prices "fully reflect available information." The EMH implies prices adjust rapidly to new information.
3. Supporters and critics have debated the EMH for decades, with empirical evidence both supporting and contradicting aspects of the hypothesis. The EMH does not claim prices perfectly equal value, but that no trading strategy can consistently beat the market.
An Empirical Assessment of Capital Asset Pricing Model with Reference to Nati...ijtsrd
"This study concentrates on empirical assessment of Capital Asset Pricing Model CAPM on the National Stock Exchange NSE . CAPM assists to determine a well diversified portfolio. The main objective of this research paper is to check the applicability of Nobel laureate’s model in Indian equity market by testing the relationship between risk and return, whether there is any direct proportionality in the expected rate of return and its systematic risk. It relates its results by using the beta systematic risk as a measuring factor. The study was being conducted for a period of 260 weeks from 7 April 2013 to 25 March 2018. 45 companies from NSE were picked as a proxy for the market portfolio. This research was done by using regression analysis on stocks and portfolio to find out the final results. Research of this study nullifies that this model is applicable to the Indian market and also contradicts its expected return and systematic risk which are linearly related to each other. Miss. Yashashri Shinde | Miss. Teja Mane ""An Empirical Assessment of Capital Asset Pricing Model with Reference to National Stock Exchange"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Fostering Innovation, Integration and Inclusion Through Interdisciplinary Practices in Management , March 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23105.pdf
Paper URL: https://www.ijtsrd.com/management/public-sector-management/23105/an-empirical-assessment-of-capital-asset-pricing-model-with-reference-to-national-stock-exchange/miss-yashashri-shinde"
Chp 11 efficient market hypothesis by mahmudulMahmudul Hassan
The document discusses the evolution and different forms of the efficient market hypothesis (EMH). It begins by explaining Maurice Kendall's 1953 study that found stock prices move randomly without predictable patterns. This challenged the notion that markets are irrational, and instead suggested markets are efficient. The document then discusses how the EMH developed, with the idea that markets quickly incorporate all available information into stock prices, making them unpredictable. It outlines Fama's three forms of the EMH based on the information reflected in prices. The implications of EMH for technical analysis, fundamental analysis, and active vs passive portfolio management are also discussed. Finally, empirical tests and evidence related to market efficiency are reviewed.
Determinants of abnormal returns on the ghana stock exchangeAlexander Decker
This document summarizes a research study that examines the determinants of abnormal returns on the Ghana Stock Exchange following dividend initiation announcements. Specifically, it analyzes factors such as a firm's earnings changes, earnings volatility, dividend yield, age, institutional shareholding, size, market-to-book ratio, investment opportunities, and industry to determine if they influence the magnitude of abnormal returns around dividend initiation announcements. The results suggest that older firms and those in the manufacturing industry experience stronger positive investor reactions, while firms with good investment opportunities that decide to initiate dividends see negative reactions from investors.
This paper reviews literature on the debate between the efficient market hypothesis (EMH) and behavioral finance as alternative theories of asset pricing. It discusses studies supporting EMH and its core assumptions of rational investors and stock prices reflecting all available information. It also reviews literature critiquing EMH and providing evidence of market inefficiencies. The paper then examines behavioral finance literature discussing how psychological biases influence investor behavior and stock prices. It aims to provide a comprehensive discussion of both perspectives to establish behavioral finance's potential as a mainstream alternative theory of asset pricing.
This paper examines how accounting information impacts liquidity risk by summarizing and extending prior studies. It finds that higher quality accounting information is associated with lower liquidity risk, supporting findings by Ng and Lang and Maffett. The paper compares these prior studies in a matrix and regression analysis, showing how their results are consistent. It also analyzes the relationship between information quality and liquidity risk during the 2008 financial crisis, finding that liquidity decreased sharply while information quality initially increased as companies disclosed more positive news. However, the paper makes only limited novel contributions and could be strengthened by introducing new variables or developing an original model.
This reviewer report summarizes a research paper that analyzes how information quality impacts the cost of equity capital through liquidity risk. The paper examines the relationship between information quality and liquidity risk of stocks from 1983 to 2008, controlling for other factors. The author finds that higher information quality is negatively related to liquidity risk and the cost of capital. The empirical model builds on past research on information quality, liquidity risk, and the cost of capital. The reviewer comments that while the paper contributes to understanding how information quality affects costs, it could provide more discussion of the mechanisms and evidence to support the theoretical framework.
According to the EMH, stocks always trade at their fair value on stock exchanges, making it impossible for investors to either purchase undervalued stocks or sell stocks for inflated prices. As such, it should be impossible to outperform the overall market through expert stock selection or market timing, and that the only way an investor can possibly obtain higher returns is by purchasing riskier investments.
Stock Market Analysis Provided by Trader & Daily Student of Financial Markets. Shares Education, Insights & Experiences in the world of Investing. Trader's Investment Strategies, & Operational Experiences Revealed.
An intelligent scalable stock market prediction systemHarshit Agarwal
Comparitive study of stock market prediction system using ANN and GONN. Sentiment analysis also done on yahoo news feed. Deployment done on hadoop cluster.
Ridge and random forest regression techniques were used to develop a mathematical model to calculate the cross-validation score and predict stock price volatility of companies. The model aims to determine if a firm's stock prices remain fluctuating or stable and identify trends in real-time price changes over time. Researchers found directional stock price movements were over 90% predictable given past opening and closing prices, though the magnitude of price changes could not be determined with the same certainty.
: Security and Portfolio Analysis :Efficient market theoryRahulKaushik108
Key Concepts of Efficient market theory: Very Lucid presentation , very Useful for MBA student to understand the Concepts of Efficient Market theory( Random walk hypotheses ) .The key idea of the hypotheses is" no one can efficiently out predict the market" or in other terms, technical analysis or fundamental analysis can not beat "the naive buy and hold strategy".
Forecasting Stocks with Multivariate Time Series Models.inventionjournals
This work seeks to forecast stocks of the Nigerian banking sector using probability multivariate time series models. The study involved the stocks from six different banks that were found to be analytically interrelated. Stationarity of the six series were obtained by differencing. Model selection criteria were employed and the best fitted model was selected to be a vector autoregressive model of order 1. The model was subjected to diagnostic checks and was found to be adequate. Consequently, forecasts of stocks were generated for the next two years.
This document summarizes a study that tested the random walk hypothesis on the Karachi Stock Exchange 100 Index from 1996 to 2006. The random walk hypothesis states that stock prices fully reflect all available information and follow a random pattern, making them unpredictable. The study found no significant differences in average monthly or daily returns, supporting the hypothesis that the KSE follows a random walk pattern. Therefore, past stock prices and returns cannot be used to predict future movements. This finding indicates the KSE behaves as an efficient market.
Determinants of equity share prices of the listed company in dhaka stock exch...MD. Walid Hossain
This is the finance academic project report.This report prepare by MD. WALID HOSSAIN, Patuakhali science and technology University, Faculty of business administration and management. i think that is helpful for business studies students.
The document discusses the efficient market hypothesis (EMH), which states that stock prices already reflect all available public information, making it impossible for investors to outperform the market through strategies based on historical prices, economic news, or other public data. There are three forms of the EMH - weak, semi-strong, and strong - differing in the type of information believed to be reflected in prices. While several studies have found evidence supporting the EMH, others have found anomalies like value and small firm effects that appear to allow above-market returns. The validity of the EMH remains controversial.
The document discusses the random walk theory, which states that stock price movements cannot be predicted because they follow a random path rather than any predictable patterns. It originated in the 1900s and was popularized in a 1973 book. The random walk theory says past stock performance does not indicate future performance and prices reflect all available information. However, some studies have found evidence of predictability based on factors like earnings. The implications are that market timing is difficult and outperforming the market through analysis alone may involve some luck.
Market efficiency survives the challenge from the literature on long-term return anomalies. Consistent with the market efficiency hypothesis that the anomalies are chance results, apparent overreaction to information is about as common as under-reaction, and post-event continuation of preevent abnormal returns is about as frequent as post-event reversal. Most important, consistent with the market efficiency prediction that apparent anomalies can be due to methodology, most long-term return anomalies tend to
disappear with reasonable changes in technique.
This slide set is a work in progress and is embedded in my Principles of Finance course, which is also a work in progress, that I teach to computer scientists and engineers
http://awesomefinance.weebly.com/
The document summarizes research on value investing in emerging markets. It finds that:
1) A simple valuation model can identify emerging markets with higher expected returns compared to average emerging markets.
2) A portfolio of "undervalued" emerging markets identified by the model generates superior returns compared to benchmarks, with statistical significance.
3) Risk measures of the portfolio of undervalued emerging markets are close to risk measures of broader emerging market benchmarks, implying the higher returns are not compensated by significantly higher risk.
According to the EMH, stocks always trade at their fair value on stock exchanges, making it impossible for investors to either purchase undervalued stocks or sell stocks for inflated prices. As such, it should be impossible to outperform the overall market through expert stock selection or market timing, and that the only way an investor can possibly obtain higher returns is by purchasing riskier investments.
The document summarizes key concepts in financial economics, including:
1. Merton Miller identified five "pillars" of finance, including the CAPM, EMH, modern portfolio theory, and options pricing theory.
2. Eugene Fama defined the EMH as a market where prices "fully reflect available information." The EMH implies prices adjust rapidly to new information.
3. Supporters and critics have debated the EMH for decades, with empirical evidence both supporting and contradicting aspects of the hypothesis. The EMH does not claim prices perfectly equal value, but that no trading strategy can consistently beat the market.
An Empirical Assessment of Capital Asset Pricing Model with Reference to Nati...ijtsrd
"This study concentrates on empirical assessment of Capital Asset Pricing Model CAPM on the National Stock Exchange NSE . CAPM assists to determine a well diversified portfolio. The main objective of this research paper is to check the applicability of Nobel laureate’s model in Indian equity market by testing the relationship between risk and return, whether there is any direct proportionality in the expected rate of return and its systematic risk. It relates its results by using the beta systematic risk as a measuring factor. The study was being conducted for a period of 260 weeks from 7 April 2013 to 25 March 2018. 45 companies from NSE were picked as a proxy for the market portfolio. This research was done by using regression analysis on stocks and portfolio to find out the final results. Research of this study nullifies that this model is applicable to the Indian market and also contradicts its expected return and systematic risk which are linearly related to each other. Miss. Yashashri Shinde | Miss. Teja Mane ""An Empirical Assessment of Capital Asset Pricing Model with Reference to National Stock Exchange"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Fostering Innovation, Integration and Inclusion Through Interdisciplinary Practices in Management , March 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23105.pdf
Paper URL: https://www.ijtsrd.com/management/public-sector-management/23105/an-empirical-assessment-of-capital-asset-pricing-model-with-reference-to-national-stock-exchange/miss-yashashri-shinde"
Chp 11 efficient market hypothesis by mahmudulMahmudul Hassan
The document discusses the evolution and different forms of the efficient market hypothesis (EMH). It begins by explaining Maurice Kendall's 1953 study that found stock prices move randomly without predictable patterns. This challenged the notion that markets are irrational, and instead suggested markets are efficient. The document then discusses how the EMH developed, with the idea that markets quickly incorporate all available information into stock prices, making them unpredictable. It outlines Fama's three forms of the EMH based on the information reflected in prices. The implications of EMH for technical analysis, fundamental analysis, and active vs passive portfolio management are also discussed. Finally, empirical tests and evidence related to market efficiency are reviewed.
Determinants of abnormal returns on the ghana stock exchangeAlexander Decker
This document summarizes a research study that examines the determinants of abnormal returns on the Ghana Stock Exchange following dividend initiation announcements. Specifically, it analyzes factors such as a firm's earnings changes, earnings volatility, dividend yield, age, institutional shareholding, size, market-to-book ratio, investment opportunities, and industry to determine if they influence the magnitude of abnormal returns around dividend initiation announcements. The results suggest that older firms and those in the manufacturing industry experience stronger positive investor reactions, while firms with good investment opportunities that decide to initiate dividends see negative reactions from investors.
This paper reviews literature on the debate between the efficient market hypothesis (EMH) and behavioral finance as alternative theories of asset pricing. It discusses studies supporting EMH and its core assumptions of rational investors and stock prices reflecting all available information. It also reviews literature critiquing EMH and providing evidence of market inefficiencies. The paper then examines behavioral finance literature discussing how psychological biases influence investor behavior and stock prices. It aims to provide a comprehensive discussion of both perspectives to establish behavioral finance's potential as a mainstream alternative theory of asset pricing.
This paper examines how accounting information impacts liquidity risk by summarizing and extending prior studies. It finds that higher quality accounting information is associated with lower liquidity risk, supporting findings by Ng and Lang and Maffett. The paper compares these prior studies in a matrix and regression analysis, showing how their results are consistent. It also analyzes the relationship between information quality and liquidity risk during the 2008 financial crisis, finding that liquidity decreased sharply while information quality initially increased as companies disclosed more positive news. However, the paper makes only limited novel contributions and could be strengthened by introducing new variables or developing an original model.
This reviewer report summarizes a research paper that analyzes how information quality impacts the cost of equity capital through liquidity risk. The paper examines the relationship between information quality and liquidity risk of stocks from 1983 to 2008, controlling for other factors. The author finds that higher information quality is negatively related to liquidity risk and the cost of capital. The empirical model builds on past research on information quality, liquidity risk, and the cost of capital. The reviewer comments that while the paper contributes to understanding how information quality affects costs, it could provide more discussion of the mechanisms and evidence to support the theoretical framework.
According to the EMH, stocks always trade at their fair value on stock exchanges, making it impossible for investors to either purchase undervalued stocks or sell stocks for inflated prices. As such, it should be impossible to outperform the overall market through expert stock selection or market timing, and that the only way an investor can possibly obtain higher returns is by purchasing riskier investments.
Stock Market Analysis Provided by Trader & Daily Student of Financial Markets. Shares Education, Insights & Experiences in the world of Investing. Trader's Investment Strategies, & Operational Experiences Revealed.
An intelligent scalable stock market prediction systemHarshit Agarwal
Comparitive study of stock market prediction system using ANN and GONN. Sentiment analysis also done on yahoo news feed. Deployment done on hadoop cluster.
Ridge and random forest regression techniques were used to develop a mathematical model to calculate the cross-validation score and predict stock price volatility of companies. The model aims to determine if a firm's stock prices remain fluctuating or stable and identify trends in real-time price changes over time. Researchers found directional stock price movements were over 90% predictable given past opening and closing prices, though the magnitude of price changes could not be determined with the same certainty.
The document discusses the differences and similarities between stocks, shares, and stock markets. It defines that a share is a unit of ownership in a company that is bought and sold, while stocks refer to the total number of shares a person owns. Both shares and stocks can be traded on a stock market, which is an organized market where securities are bought and sold. The stock market allows individuals and companies to trade shares and includes a primary market for new stock offerings and a secondary market for existing shares.
Stock Market Prediction using Hidden Markov Models and Investor sentimentPatrick Nicolas
This presentation describes hidden Markov Models to predict financial markets indices using the weekly sentiment survey from the American Association of Individual Investors.
The first section describes the hidden Markov model (HMM), followed by selection of features (investors' sentiment) and labeled data (S&P 500 index).
The second section dives into HMMs for continuous observations and detection of regime shifts/structural breaks using an auto-regressive Markov chain
The last section is devoted to alternative models to HMM.
Literature Review of research papers on Stock Market PortfolioAshu Prakash
This literature review summarizes research papers on methods for constructing optimal stock market portfolios. It discusses four papers that describe techniques for minimizing risk and maximizing profits through portfolio optimization. The first paper uses principal component analysis to extract market risk factors from data. The second reviews optimization methods like column generation and decomposition. The third generates scenario trees to model uncertainties in multistage problems. The fourth presents an algorithm for moment matching scenario generation to optimize portfolios based on expected shortfall. The papers outline computational methods for analyzing market data and constructing efficient portfolios to manage risk.
This document discusses using hidden Markov models (HMM) for stock price prediction. HMMs can model time series data as a probabilistic finite state machine. The document explains that HMMs can handle new stock market data robustly and efficiently predict similar price patterns to past data. It provides an overview of HMM components like states, transition probabilities, and emission probabilities. The document also demonstrates building an HMM model on stock data using the RHMM package in R, including training the model with Baum-Welch and predicting state sequences with Viterbi.
This document describes research on using deep learning models to predict stock market movements based on news events. It presents a method to extract event representations from news articles, generalize the events, embed the events, and feed the embedded events into deep learning models. Experimental results show that using embedded events as inputs to convolutional neural networks achieved more accurate stock market predictions than baseline methods, and modeling long, mid, and short-term event effects further improved performance. The research demonstrates that deep learning can effectively capture hidden relationships between news events and stock prices.
This Presentation is about the Financial Market in India.
Aim is to provide basic information regarding Stock market, Bombay Stock Exchange(BSE) and National Stock Exchange of India (NSEI).
The document discusses stock markets and shares. It defines a stock market as a market for trading company stock and derivatives. It explains that shares represent fractional ownership in a company and shareholders have rights like voting and sharing in company profits. A company issues new shares to raise capital for projects or expansion. Share prices are determined by supply and demand on the stock exchange. Investors can analyze companies through fundamental analysis of financials or technical analysis of price trends and patterns. The stock market plays an important role in economies by facilitating business growth and mobilizing savings.
The Predictive Power of Intraday-Data Volatility Forecasting Models: A Case S...inventionjournals
The purpose of this study was to compare the predictive power of various volatility forecasting models. Using intraday high-frequency data, this study investigated the influence of time frequency on the predictive power of a volatility forecasting model. The empirical results revealed that the realized volatility increased when the time frequency of forecasts reduced. The overall results showed that when the forecast range was 1 day, among various volatility forecasting models, the autoregressive moving average-generalized autoregressive conditional heteroskedasticity(1, 1) model presented the optimal forecasting performance and the implied volatility model presented the worst forecasting performance for all time frequencies.
Behavioral Portfolio Theory
Behavioral portfolio theory(BPT), introduced by Shefrin and Statman (2000), provides an alternative to the assumption that the ultimate motivation for investors is the maximization of the value of their portfolios. It suggests that investors have varied aims and create an investment portfolio that meets a broad range of goals such as considering expected wealth, desire for security and potential, aspiration levels, and probabilities of achieving aspiration goals.
Traditional finance is based on three concepts: (1) rational behavior, (2) the capital asset pricing model, and (3) efficient market. While, the behavioral finance argue that psychological force would change decision maker’s mind make it not rational anymore, besides the market is not always efficient as well.
The BPT theory is not follow the same principle as Mean-Variance theory, Capital Asset Pricing Model, and Modern Portfolio Theory. However, authors developed BPT on the foundation of SP/A theory (Lopes, 1987) and prospect theory (Kahneman and Tversky, 1979) and closely related Safety-First Portfolio Theory.
In behavioral portfolio theory, authors build single account version of BPT -SA and multiple account version of BPT –MA. The theory is described as a single account version: BPT-SA, which is very closely related to the SP/A theory. In multiple account version (BPT-MA), investors can have fragmented portfolios, just as we observe among investors. They even propose in their initial article a Cobb–Douglas utility function that shows how money is allocated in the two mental accounts.
The BPT efficient frontiers and the mean-variance frontiers do not coincide. Mean- variance investors choose portfolios by considering mean and variance, which means average and risk. However, investors choose portfolios by considering their expected wealth, security level and potential gain, how to achieve goals. Behavioral portfolio theory is also the observation that investors view their portfolios not as a whole, as prescribed by mean-variance portfolio theory, but as distinct mental account layers in a pyramid of assets, where mental account layers are associated with goals and where attitudes toward risk vary across layers.
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1. Can Options Predict Stock Behavior?: A Literature Review on
the Information Content Contained in Options Markets
By: Steven Kislenko
Abstract
There has been a large amount of research done on options. In particular, there is a growing
amount of literature looking at the information content contained within options. Researchers in
this area of study have looked to see if information priced into options is useful in predicting
future stock behavior. This literature review will analyze the research done regarding the
information content of options. This includes looking at the pricing in of future events like
earnings announcements. In addition, this literature review will look at option characteristics that
may help predict the future performance or direction of stock movement.
Keywords: Information content, Options, Stock returns
Submitted under the supervision of Professor Aamir Khan, Carlson School of Management,
University of Minnesota, Spring 2015
2. Introduction
Traders consistently look for opportunities to generate returns above and beyond market
returns. Traders look at different asset classes (including stocks, bonds, and treasuries to name a
few) and strategies to trade the market. Options are one type of security that traders use to bet on
the market to generate positive returns for themselves and/or for investors. In essence, an option
is the right, but not the obligation, to buy or sell some underlying asset at a future date at a
specified price. The most common type of option is a stock option, which gives the owner of the
option the right to buy or sell a stock sometime in the future at a specified price (known as a
strike price). The former is called a call option. The latter is called a put option.
A growing body of literature has looked at the information content contained within
option prices. In essence, because options are valued based on stocks movements and because
option volume is so large, researchers have theorized that option behavior may help predict
future movements in stock prices. Therefore, if studied carefully, a trader may be able to profit
by looking at option patterns that can predict future increases or decreases in stock prices.
This literature review will look at this growing body of research regarding the
information content priced into options. This literature review will focus on two different
relationships. First, this literature review will discuss how inherent characteristics of options (like
open interest and implied volatility) help predict future stock behavior. Second, the literature
review will discuss the relationship between external events and option prices and how options
potentially price in the event prior to the stock doing so, which may mean that options lead future
stock behavior. Through this analysis, a better understanding of the relationship between stock
options and stocks will be developed.
3. Implied Volatility and Stock Predictions
The volatility of an option may play a key factor in predicting future stock behavior. In
essence, many researchers have theorized that the implied volatility contained within options
leads future stock volatility. Initial research done by Chiras and Manaster (1978) saw that the
implied volatility of stock options (measured as implied standard deviation) does a better job in
predicting future volatility of a stock as compared to a stock’s historical volatility. This
evaluation is disputed by Canina and Figlewski (1993), as they state there is no correlation
between implied volatility on S&P 100 index options and the future volatility of the S&P 100.
Therefore, implied volatility by itself may or may not be useful in predicting future volatility.
It is also important to compare implied volatility with other models that may forecast
future volatility. Day and Lewis (1992) compared implied volatility with GARCH (Generalized
Autoregressive Conditional Heteroscedasticity) and EGARCH (Exponential GARCH) models.
They determined that none of the models have a clear advantage in forecasting future volatility,
as implied volatility adds more information to GARCH and EGARCH conditional volatilities in
some circumstances and GARCH and EGARCH models add information to implied volatilities
in other circumstances. However, this may be the result of less mature option markets at the time
of research, according to Mayhew and Stivers (2002). Mayhew and Stivers (2002) conducted an
analysis of implied volatility and compared this to other models that help forecast future
volatility. They found that in active options markets, implied volatility contains more
information (and therefore outperforms) GARCH models and other time-series volatility models.
However, if there is a less actively trade option market (or there is a firm with low option
volume), implied volatility performance deteriorates relative to other time-series models.
Therefore, depending on the situation and circumstance, overall implied volatility may do a
4. better job in modelling the future volatility of stocks as opposed to other models, although this
idea is debated based on the research done in the preceding paragraph.
Implied volatility may also predict return characteristics. Diavatopoulos, Doran, and
Peterson (2008) looked to see whether firm-specific (i.e. idiosyncratic) implied volatility does a
better job in predicting future returns than other measurements of volatility (like historical
volatility). The researchers concluded, based on the information provided, that implied volatility
seemingly does a better job of predicting future returns as compared to other measures of
volatility. Because of this, historical volatility isn’t as important in modelling future returns.
Furthermore, the researchers created three sample portfolios: one that follows the market (i.e.,
buying and holding the S&P 500), one that involves going long high implied volatility stocks and
shorting low implied volatility stocks, and one that involves going long high realized (i.e.
historical) volatility stocks and shorting low realized volatility stocks. Starting with a $10,000
investment in 1996 and holding each portfolio through 2005, the results showed that the implied
volatility strategy nearly tripled in value while the historical volatility strategy did not gain at all.
The market strategy only gained 50% in that time period. Therefore, following an implied
volatility strategy may benefit an investor, as implied volatility may better predict future stock
returns. However, as stated before, the lack of consistency in predicting future volatility may
limit the overall effectiveness of implied volatility.
Option Volume and Open Interest Impact Stock Behavior
Option trading volume may also play a key factor in predicting future stock behavior.
Easley, O’Hara, and Srinivas (1998) showed that informed traders (i.e., traders with private
information) drive information content into options. Furthermore, the researchers saw a stronger
information content effect when there was bad news about a stock as opposed to good news.
5. Coupled with the fact that, at the time, there were limits on short selling, traders may be attracted
to options markets to act on private negative information. Therefore, informed traders, in driving
option volume, may give hints as to where a stock is heading. However, this relationship
between options and stocks is not one-dimensional, as stock price changes lead option volume,
but not vice versa. The researchers attribute this to hedging of different option strategies
Pan and Poteshman (2006) also see option volume leading stock behavior, although both
researchers looked at this idea through the framework of ratios. In this case, the researchers were
interested in seeing whether put-call ratios based on option volume lead stock behavior (i.e.,
dividing put option volume by call option volume for a stock). The researchers determined that
stocks that have low put-call ratios outperform stocks with high put-call ratios by 0.40% over
one day and about one percent over one week. This is primarily driven by informed traders with
nonpublic information trading in the stock market.
Johnson and So (2012) also looked at option volume through the framework of ratios, but
instead chose to focus on the option to stock volume ratio. They state that firms with low option
to stock volume ratios outperform against firms with high option to stock volume ratios by
0.34% per week. This follows a similar pattern seen in the preceding paragraph, although the
return performance is slightly weaker on a weekly basis. However, as was stated in the preceding
paragraph, private information drives the results seen in the research. Johnson and So also add
that this ratio leads outperformance in an environment where short sales are costly and where
leverage is low.
Both options ratios may help predict future stock behavior. However, Blau, Nguyen, and
Whitby (2014) saw that these ratios work best under different timeframes of analysis. In this
case, put-call volume ratios predict future stock returns better when looking at option volume at a
6. daily level. This means that option to volume stock ratios better predict stock returns when
looking at option volume at a weekly or monthly level. When one looks at negative information
in relation to option volume ratios, the relation between the put-call volume ratio and future
stock returns is not very strong. By contrast, the option to stock volume ratio better predicts
future stock returns at all time horizons (daily, weekly, and monthly) when there is negative
information. Therefore, although both predict future stock behavior, the put-call volume ratio
does not appear to be as effective when an individual has a longer time horizon for analyzing
options. This may mean that the option to stock volume ratio is more robust in many situations
for predicting future stock returns.
Like option volume, option open interest may also be useful in helping predict stock
behavior. Bhuyan and Chaudhary (2005) looked at CBOE open interest and compared stock
trading strategies based on open interest with other types of passive and active strategies. The
researchers determined that open interest trading strategies provide better accuracy and overall
better returns than other active and passive trading strategies in United States stock markets. In
addition, by trading open interest, an investor limits risky behavior. This amplifies the value of
trading on open interest. Therefore, beyond price, even open interest seem to be very useful in
providing information about future behavior of equity stocks.
Stock Order Imbalance Predicts Future Stock Returns
Hu (2014) looked at another feature of options: order imbalance. Before diving into this
topic, order imbalance must be defined. Order imbalance means that there is an excess of buy or
sell orders in the market that other buyers and sellers are not able to fill. In essence, it becomes
impossible to match all buy and sell orders between different market participants. This is
especially a problem for market makers that take on the opposite side of an order. If transactions
7. come in to sell options, market makers are usually on the other end to buy the options. However,
it is difficult for market makers to offload options given infrequent option transactions in
general. To reduce risk exposure, most market makers delta hedge, meaning they buy or sell
short shares to offset the risk of holding a naked option position. This translates the order
imbalance over to the stock market, which creates a stock order imbalance.
Hu (2014) was particularly interested in the information content that stock order
imbalances have as a result of market makers dealing with the risk exposure from holding
options in a naked position. When looking at stock order imbalances caused by delta hedging
options, he saw that this stock order imbalance positively predicts future stock returns. This trend
does not reverse in the long run, so in theory stock order imbalances induced by options
permanently predict future stock behavior. This behavior is amplified when there is an active
market for the stock and where there are informed investors trading the stock. Stock order
imbalances caused by things other than delta hedging options do not have this same information
content. Therefore, when looking to predict future stock returns, an investor should also look at
stock order imbalances caused by option trading in an actively traded stock, as this may help
predict future stock returns.
Firm-Specific Events/Announcements and Options
Different types of firm announcements may lead future stock prices. Take earnings
announcements. Billings and Jennings (2011) looked to see how sensitive option prices are to
upcoming earnings announcements. Given that options are useful for trading on private
information, the researchers found that option prices do lead future stock prices when there is a
lot of pre-announcement private information available (including whether or not management
will trade different types of stocks). In addition, option prices are also very sensitive to the
8. number of analysts that follow a stock. According to the researchers, the more analysts there are,
the more information is priced into the option. Finally, the built-in anticipation for the impact of
an earnings announcement on stock prices will be greater if more sophisticated investors trade
the stock. Therefore, firm-specific characteristics and firm-specific private information most
likely drive future stock behavior before an earnings announcement.
Hayunga and Lung (2014) looked deeper into how market analyst consensus factors into
option pricing and anticipation of future stock behavior. When looking at revisions in market
analysts’ consensus recommendations, the researchers saw that options correctly price the
direction of the stock movement three to four days in advance of the revision, regardless as to
whether or not there is a downgrade or upgrade. Research previously done by Doran, Fodor, and
Krieger (2010) confirms this general finding, although their research did not specify how early
the options priced in the market analyst consensus revision. This seems to suggest that options
may have already priced in the recommendation change in advance of the actual event and
therefore, based on the type of announcement, may lead future stock price increases (in the case
of an upgrade) or future stock price decreases (in the case of a downgrade).
Veenman, Hodgson, Van Praag, and Zhang (2011) looked more closely at how
management behavior in exercising options and trading stocks may give an indication of future
stock behavior. The researchers theorized that management may be opportunistic in taking
advantage of private information about the firm. They saw that a stock option exercise coupled
with the liquidation of the shares from the exercise leads weak performance of the stock. Sales of
previously held stocks by management do not predict anything. On the other end, straight stock
purchases by management lead future positive earnings, while the purchase of stocks through an
option exercise does not lead future positive earnings behavior. Therefore, depending on the
9. context and situation, management may give an indication as to where the firm is heading, which
may lead future stock price downtrend and uptrends.
Finally, stock splits should also be looked at to determine their impact on stock behavior.
Chern, Tandon, Yu, and Webb (2008) looked at stocks on the NYSE, AMEX, and NASDAQ to
see how stock split announcements impacted stocks that have options and stocks that do not have
options. In general, the researchers noted that stock split announcements tend to be associated
with positive abnormal returns. However, stocks with options exhibit significantly lower returns
relative to non-option stocks, although there is some dispute as to whether or not this is true for
stocks on the NASDAQ. Regardless, this seems to suggest that options may have already priced
in stock split information in advance of the announcement. Therefore the options may mute
future stock movements up or down, as all information has already been traded on the options
market.
Conclusion
In short, options most likely have information content useful in predicting future stock
behavior. Certain option characteristics do a good job in predicting future stock behavior. In this
case, research seems to show that open interest and stock order imbalance induced by options
trading do a good job in predicting future stock returns. Option volume also does a good job,
although the efficacy of different ratios is debated. Implied volatility seems to be the weaker
option characteristic to use, as other volatility models may do a better job than implied volatility
in predicting future stock behavior.
In addition, options seem to price in events earlier than stocks do, indicating that options
may be a vector for trading on private information. Overall, options seem to do a better job in
10. correctly anticipating future announcements. In addition, management trading strategies also lead
future earnings announcements. Therefore, an investor should also look to management to see if
their trading behavior predicts future movements in stocks that will be caused by future events.
An investor can trade options and stocks based on just option characteristics. He or she
can also trade on future announcements or on management cues. Trading on both, however, will
allow an investor to take advantage of the full breadth of information content that options afford.
Therefore, when trading stocks, an investor should look at options first. Most likely, the options
have already priced in something that will lead stock behavior in the future.
11. References
Bhuyan, R., & Chaudhary, M. (2005). Trading on the information content of open interest:
Evidence from the US equity options market. Derivatives Use, Trading & Regulation,
11(1), 16-36. Retrieved from http://web.b.ebscohost.com.ezp1.lib.umn.edu/ehost/
pdfviewer/pdfviewer?sid=0c6a12d0-f470-47c7-b1a6-369832b46e11%40sessionmgr
198&vid=1&hid=115
Billings, M., & Jennings, R. (2012). The option market’s anticipation of information content
in earnings announcements. Review of Accounting Studies, 16(3), 587-619. Retrieved
from http://link.springer.com.ezp1.lib.umn.edu/article/10.1007/s11142-011-9156-5
Blau, B. M., Nguyen, N., & Whitby, R. J. (2014). The information content of option ratios.
Journal of Banking & Finance, 43, 179-187. doi: 10.1016/j.jbankfin.2014.03.023
Canina, L., & Figlewski, S. (1993). The informational content of implied volatility. The Review
of Financial Studies, 6(3), 659-681. Retrieved from http://www.jstor.org/stable/2961982
Chern, K., Tandon, K., Yu, S., & Webb, G. (2008). The information content of stock split
announcements: Do options matter?. Journal of Banking & Finance, 32(6), 930-946.
doi: 10.1016/j.jbankfin.2007.07.008
Chiras, D. P., & Manaster, S. (1978). The information content of option prices and a test of
market efficiency. Journal of Financial Economics, 6(2), 213-234. doi:
12. 10.1016/0304-405X(78)90030-2
Day, T. E., & Lewis, C. M. (1992). Stock market volatility and the information content of
stock index options. Journal of Econometrics, 52(1), 267-287. doi:
10.1016/0304-4076(92)90073-Z
Diavatopoulos, D., Doran, J. S., & Peterson, D. R. (2008). The information content in implied
idiosyncratic volatility and the cross-section of stock returns: Evidence from the option
markets. Journal of Futures Markets, 28(11), 1013-1039. doi: 10.1002/fut.20327
Doran, J. S., Fodor, A., & Krieger, K. (2010). Option market efficiency and analyst
recommendations. Journal of Business Finance & Accounting, 37(5-6), 560-590. doi:
10.1111/j.1468-5957.2010.02189.x
Easley, D., O’Hara, M., & Srinivas, P. S. (1998). Option volume and stock prices: Evidence on
where informed traders trade. The Journal of Finance, 53(2), 431-465. Retrieved from
http://www.jstor.org/stable/117358
Hayunga, D. K., & Lung, P. P. (2014). Trading in the options market around financial analysts’
consensus revisions. Journal of Financial & Quantitative Analysis, 49(3), 725-747. doi:
10.1017/S0022109014000295
Hu, J. (2014). Does option trading convey stock price information?. Journal of Financial
Economics, 111(3), 625-645. doi: 10.1016/j.jfineco.2013.12.004
13. Johnson, T. L., & So, E. C. (2012). The option to stock volume ratio and future returns. Journal
of Financial Economics, 106(2), 262-286. doi: 10.1016/j.jfineco.2012.05.008
Mayhew, S, & Stivers, C. (2003). Stock return dynamics, option volume, and the information
content of implied volatility. Journal of Futures Markets, 23(7), 615-646. doi:
10.1002/fut.10084
Pan, J., & Poteshman, A. M. (2006). The information in option volume for future stock prices.
The Review of Financial Studies, 19(3), 871-908. Retrieved from
http://www.jstor.org/stable/3844016
Veenman, D., Hodgson, A., Van Praag, B., & Zhang, W. (2011). Decomposing executive stock
option exercises: Relative information and incentives to manage earnings. Journal of
Business Finance & Accounting, 38(5-6), 536-573. doi: 10.1111/j.1468-
5957.2011.02239.x
14. Methodology of “Trading in the Options Market around
Financial Analysts’ Consensus Revision”
By: Steven Kislenko
Hayunga and Lung (2014) looked to see how options behave in anticipation of financial
analysts’ consensus recommendations and revisions. In order to do this, the researchers used an
event study design. In essence, the researchers were looking to collect data on options based
around an event, which in this case is the consensus recommendation or revision. From this, the
researchers hoped to understand how options act before and during the event to see whether or
not options contain information about these consensus revisions in advance of the actual event.
Hayunga and Lung (2014) used four dependent variables to understand this mechanism.
First is option-implied stock price and abnormal return. Starting with the put-call parity equation
to calculate stock price, the researchers added in a factor for early exercise of calls and puts to
the put-call parity equation to come up with an option-implied stock price based on American
options. They then compared the option-implied return with the benchmark return of the stock
through a linear regression. The benchmark is calculated based on the average return seen 11 to
150 days before the event. They also controlled for the moneyness of the option and the maturity
of the option, as they do include delta and maturity as factors in their regression as part of the
sensitivity analysis. Finally, the benchmark established in the linear regression above was
subtracted from the observed stock price to get the abnormal option return.
The second dependent variable is volatility spread (Hayunga & Lung, 2014). The
volatility spread is the average difference between the implied volatility of a pair of put and call
15. options at time t. In other words, the researchers first subtract the implied volatility of a put
option from the implied volatility of a call option in a put-call pair. The researchers then multiply
this difference by the open interest of the put-call pair. The researchers calculate this for several
put-call pairs. They then sum these calculations together to come up with the volatility spread.
The average volatility spread is then calculated based on the average spread seen 11 to 150 days
prior to the event. The abnormal volatility spread becomes the difference between the average
volatility spread and the volatility seen in the period under research.
The third dependent variable is volatility skewness (Hayunga & Lung, 2014). Volatility
skewness is defined as the difference between the implied volatility of an out-of-the-money put
option and the implied volatility of an at-the-money call option. Restated another way, it is the
implied volatility of an out-of-the-money put option minus the implied volatility of an at-the-
money call option. An out-of-the-money put option has a delta between -0.125 and -0.375, while
an at-the-money call option has a delta between 0.375 and 0.675. The abnormal volatility
skewness is then determined by comparing the average volatility skewness seen 11 to 150 days
prior to the recommendation/revision to the volatility skewness seen in the period under research.
The last dependent variable is option volume. In this case, Hayunga and Lung (2014)
looked to the option-to-stock volume ratio. According to the researchers, Roll, Schwartz, and
Subrahmanyam (2010) saw that post-announcement returns are positively correlated with the
pre-announcement option-to-stock volume ratio. Because of this, Hayunga and Lung believe that
the ratio would be useful in analyzing option behavior before consensus recommendations or
revisions. To understand this behavior, the average option-to-stock volume ratio is calculated
based on the option-to-stock volume ratios seen 11 to 150 days prior to the
16. recommendation/revision. These average ratios are then compared to the ratios seen in the period
under research.
To collect all the data necessary, Hayunga and Lung (2014) got upgrade and downgrade
information from the Institutional Brokers’ Estimate System (IBES). They got options data from
OptionMetrics. Finally, they got stock prices and accounting data from the Center for Research
in Security Prices (CRSP). Option prices are considered to be the midpoint of the bid and ask
prices.
The researchers then cleaned the data by removing several types of options, including
options with zero open interest and call or put options with no corresponding put or call option,
respectively, that has the same time to maturity and strike price. The researchers then found the
average implied stock price for a pair of options. Next, the researchers cleaned the data regarding
the analysts’ recommendation/revisions so there were no duplicate recommendations and so that
the recommendations in the data analysis didn’t display typical analyst behavior like
piggybacking or herding. Finally, if other events occur within three days of the consensus
recommendation revision, the revision events were removed from the analysis. This created a
sample of about 12,000 revisions to look at. They then also controlled for firm characteristics
and revision magnitude.