Fundamental Analysis and Option Returns

Goodman, Theodore H.; Neamtiu, Monica; Zhang, X. Frank

doi:10.1177/0148558x17733593

Cited by 11 publications

(14 citation statements)

References 40 publications

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“…These results suggest that options markets fail to fully process the information available in financial statements when forming volatility expectations. In demonstrating this application, my study complements contemporaneous work by Goodman, Neamtiu, and Zhang (2012) which shows that straddle returns are predictable using the residual expected stock price change based on select accounting-based fundamentals.…”

Section: Introductionmentioning

confidence: 74%

“…As they typically generate negative returns, they are primarily used as a hedge against other investment positions. In a study contemporaneous with this one, Goodman, Neamtiu, and Zhang (2012) demonstrate that, after controlling for implied volatility, the residual expected absolute equity return implied by sales growth and change in earnings per share is also positively associated with long straddle returns.…”

Section: Information Processing In the Options Marketmentioning

confidence: 85%

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Volatility Forecasting Using Financial Statement Information

Sridharan

2015

The Accounting Review

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This paper examines whether financial statement information can predict future realized equity volatility incremental to market-based equity volatility forecasts. I use an analytical framework to identify accounting-based drivers of realized volatility. My main hypothesis is that accounting-based drivers can be used to forecast future realized volatility incremental to either past realized volatility or option-implied volatility. I confirm this empirically and document abnormal returns to an option-based trading strategy that takes a long (short) position in firms with financial statement information indicative of high (low) future realized volatility. These results suggest that accounting-based volatility drivers may serve as useful indicators of variance risk. Finally, I demonstrate that the incorporation of accounting-based fundamental information into forecasting models yields lower forecast errors relative to models based solely on past realized volatility.

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Section: Introductionmentioning

confidence: 74%

Section: Information Processing In the Options Marketmentioning

confidence: 85%

Volatility Forecasting Using Financial Statement Information

Sridharan

2015

The Accounting Review

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“…Moksliniuose darbuose (Chisholm, 2010;Bellalah, 2014;Hull, 2015;Lee et al, 2016;Goodman et al, 2018;Helfenstein, 2017) išskiriami tokie pagrindiniai veiksniai, turintys įtakos pasirinkimo sandorio kainai: 1) bazinio turto rinkos kaina (S); 2) sulygta kaina (X) -kaina, už kurią susitariama pirkti arba parduoti bazinį turtą tam tikrą dieną arba iki jos; 3) nerizikinga palūkanų norma (r) -veiksnys, kuris lemia pasirinkimo sandorio kainą netiesiogiai. Didėjant palūkanų normai rinkoje, yra tikimybė, kad didės ir bazinio turto kaina rinkoje; 4) bazinio turto kainos kintamumas (σ) -visada yra tikimybė, kad bazinio turto kaina keisis sandorio galiojimo metu; 5) sandorio galiojimo laikas (T).…”

Section: Pasirinkimo Sandorių Kainos Ir Jai Darančių Veiksnių Analizėunclassified

Assessment of Option Price Volatility

Sodaunykaitė

Martinkutė-Kaulienė

2020

Science - Future of Lithuania

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Financial derivatives are becoming increasingly popular on a daily basis. As markets become more unpredictable, companies and individual investors are increasingly using these tools to manage risk, leverage, and increase investment returns. The most important aspect of any contract is the contract price, as the financial result of the contract depends on the price. Also for an options. In each case, the option price depends on many factors that are difficult to define and predict in advance. The price sensitivity of the option allows you to determine where and on what the option price depends. Knowing this, the investor can manage the risk of the options. The purpose of the article is to assess the sensitivity of different options to market factors based on scientific literature and real market data. The study uses the Black-Scholes option pricing model, calculating and analyzing the value of Greek letters for the determination and valuation of transaction price sensitivity. The study showed that the most sensitive to changes in the underlying asset price, volatility and risk-free interest rate is the price of the currency option, and the price of the gold option is most sensitive over time (although in theory, gold retains its value in the long run). Knowing which components a particular option is sensitive to and capable of predicting changes in those components, you can predict changes in the option price and avoid additional risk.

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“…(Megharaja, 2015) India It was determined that ICICI Bank has the greatest point. (Goodman, et. al., 2013) S&P 100 index call option It was determined that fundamental analysis is very successful in order to predict option price.…”

Section: (Chapman 2015) Dow Jones Indexmentioning

confidence: 99%

“…To achieve this purpose, the data between the years 1996 and 2001 was used in this study. As a result, it was determined that fundamental analysis is very successful in order to predict option price (Goodman, et. al., 2013).…”

Section: Page48mentioning

confidence: 99%

Comparative Study of Outlier Detection Algorithms via Fundamental Analysis Variables

Emir¹,

Dınçer²,

Hacıoğlu

et al. 2015

IJRBS

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In a data set, an outlier refers to a data point that is considerably different from the others. Detecting outliers provides useful application-specific insights and leads to choosing right prediction models. Outlier detection (also known as anomaly detection or novelty detection) has been studied in statistics and machine learning for a long time. It is an essential preprocessing step of data mining process. In this study, outlier detection step in the data mining process is applied for identifying the top 20 outlier firms. Three outlier detection algorithms are utilized using fundamental analysis variables of firms listed in Borsa Istanbul for the 2011-2014 period. The results of each algorithm are presented and compared. Findings show that 15 different firms are identified by three different outlier detection methods. KCHOL and SAHOL have the greatest number of appearances with 12 observations among these firms. By investigating the results, it is concluded that each of three algorithms makes different outlier firm lists due to differences in their approaches for outlier detection.

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Fundamental Analysis and Option Returns

Cited by 11 publications

References 40 publications

Volatility Forecasting Using Financial Statement Information

Volatility Forecasting Using Financial Statement Information

Assessment of Option Price Volatility

Comparative Study of Outlier Detection Algorithms via Fundamental Analysis Variables

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