The ability of fundamental signals to enhance the returns from this strategy is important given the sizeable transaction costs relate to the Goyal and Saretto strategy. Indeed, fundamental signals help to achieve positive hedge portfolio returns after transaction cost for the combined strategy for large firms, firms with smaller spreads, and larger option trading volume. Evidence that fundamental signals enhance the combined strategy in the option market complements prior work that uses fundamental signals to enhance strategies in the stock market.

We view our results in a limits-to-arbitrage framework e. Fundamentals do not appear to be fully impounded into options prices, an inefficiency that exists partially because it is too costly to pursue a strategy based solely on fundamentals. Although it is too costly to execute this strategy across all firms due to the large transaction costs in the option market , there are incremental benefits from combining fundamental signals with historical volatility in option trading strategies.

The fact that option prices do not reflect fundamental volatility due to high transaction costs also has implications for parties that use measures derived from option prices, such as using implied volatility to assess risk. Our research contributes to existing literature in multiple ways. First, our study contributes to a growing body of accounting research examining option returns e. These previous studies use implied volatilities to measure the degree to which investors reassess their beliefs about the firm after disclosure Rogers et al.

Whereas previous studies assume that option prices efficiently impound information into implied volatility estimates, we relax this assumption and explore the degree to which option prices reflect accounting information. Our results indicate that accounting-based fundamental signals are not efficiently impounded into options prices. Our work also responds to the call by Richardson, Tuna, and Wysocki to explore the role of fundamental analysis in the pricing of derivatives. Second, our research provides new insight into the type of information that can be gleaned from accounting signals and used in fundamental analysis.

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Prior work on the stock market has focused on the ability of accounting signals to provide information about future cash flows. In contrast, we examine fundamental signals that predict volatility, which is uniquely relevant to option returns and the options market, suggesting that different mechanisms cash flow vs. Biased estimates of implied volatility could also affect managerial decisions, such as investment, compensation structures, and risk management. The remainder of the article is organized as follows.

A growing body of research has examined option returns to make inferences about expected returns and market efficiency. Early work on option returns focused on the returns to option positions based on indexes e. More recently, researchers have explored the returns from options based on individual equity securities. For example, Goyal and Saretto find that the difference between implied and historical volatility can predict straddle option returns.

They argue that implied volatility is incorrect when it deviates too much from historical volatility as volatility tends to be quickly mean-reverting. As a result, straddle option returns tend to be positive when implied volatility is below historical volatility implied volatility is too low and negative when implied volatility is above historical volatility. Following Goyal and Saretto , several concurrent articles explore the cross-section of option returns.

Other articles explore the determinants of put and call returns but not straddle returns. A large literature in accounting examines the extent to which investors effectively interpret and price financial accounting information, although this literature has focused on the predictability of future earnings and future stock returns.

A number of articles have suggested that accounting-based signals or fundamental analysis could generate abnormal returns e. The correlation between fundamental volatility and stock volatility creates the possibility for fundamental analysis to play a role in predicting stock volatility. Although much of the literature on financial statement analysis has focused on the prediction of future earnings and future stock returns, research also examines whether accounting measures provide information about future uncertainty or the magnitude of future price movements.

In direct relation to our study, Beneish et al. Although Beneish et al. Several recent accounting studies have also explored the link between accounting information and options markets with an emphasis on implied volatilities e. But none of these articles examines the link between accounting signals and future option returns, especially after controlling for market-based signals used in the finance literature. Building on the prior literature on accounting signals and future price volatility, this article examines the role of fundamental signals in predicting option returns.

Historical stock volatility and implied volatility in option contracts may not fully reflect such underlying fundamental volatility, which manifests in the future. Similar to Goyal and Saretto , who suggest that options investors underreact to historical volatility i. In tests of our hypothesis, two issues are important to address, both conceptually and empirically. First, we must show that fundamental signals convey incremental information about future option returns beyond what is captured in historical volatility, which the finance literature has shown to predict option returns.

Second, we must show that predictable option returns are not due to higher risk borne by options investors. In the spirit of Beneish et al. We examine both short-term signals based on the most recent quarter or most recent year and long-term signals based on the previous 5 years.

These signals provide insights into the nature of the mispricing of accounting information in the options market. The long-term signals are meant to examine whether option investors underweight historical data, consistent with finding of Goyal and Saretto that option investors underweight historical volatility. The short-term signals provide insight into the extent to which option investors respond to recent accounting reports in a timely way, which might be expected given the large literature on incomplete market responses to accounting information e.

Our first category of fundamental volatility signals is a collection of earnings signals. Following Beneish et al. Existing research indicates that the process for valuing loss firms differs from that for valuing profit firms Hayn, Our second category of fundamental volatility signals is based on accruals. Both high and low working capital accruals are associated with extreme stock price volatility. Our third category of fundamental volatility signals is a collection of growth signals. We first consider sales growth, defined as the absolute value of seasonally adjusted quarterly sales growth SGR q.

We complement sales growth with asset growth, measured as the absolute value of seasonally adjusted quarterly asset growth AGR q. Naturally, assets growth captures fundamental volatility, with both large positive and large negative asset growth signaling more volatile fundamentals. DuPont analysis is a commonly used technique to evaluate asset turnover or profit margin through which a firm generates a return on its assets. Changes in these variables could complement the analysis of earnings surprises by providing insight into the different dimension of operating surprises i.

Soliman provides evidence that changes in profit margins and asset turnover are associated with future earnings and future returns but that this information is not fully used by equity market participants. In addition to using the most recent realization to calculate short-term signals, we also consider long-term signals on the basis of a long firm-specific time series.

For each corresponding short-term signal aside from the LOSS dummy , we calculate fundamental volatility using the standard deviation of that signal over 5-year window that preceded the measurement of the short-term signal quarters q -1 through q , with a minimum of 10 quarterly observations. Finally, these standard deviations are transformed into a decile rank with a [0, 1] scale. A large body of research e. Thus, managers can use a dividend to signal lower fundamental volatility.

However, we expect that these factors could also lead to greater dividends to signify that a firm is more stable e. We aggregate individual fundamental signals into a single score that reflects the information they convey about stock price volatility relevant to pricing a straddle position. As our analysis explores the returns an investor can earn from buying a straddle contract and holding it to maturity 1 month later, we examine the extent to which our signals are related to the standard deviation of daily returns.

We estimate the historical association between our fundamental signals and the stock price volatility using the following equation:. As noted above, the independent variables in Equation 1 are from three groups: short-term variables measured using data from the earnings announcement at time q e.

### Introduction to Fundamental Analysis

The dependent variable is measured over the 3 months following the month when earnings for quarter q are released. Using the following process, we calculate rolling estimates of Equation 1 on the basis of data available when each firm reports its earnings. For each calendar quarter, we estimate Equation 1 using the 5 years of historical data that are available at the beginning of that calendar quarter.

We begin our analysis of fundamental signals by estimating Equation 1 with all nonfinancial firms SIC [Standard Industrial Classification] codes not in that have sufficient Compustat data to calculate the fundamental signals. We also require that each firm have nonmissing market value of equity at the end of quarter q. As we examine option returns that occur between January and July , we estimate 76 versions of Equation 1 covering rolling windows from the fourth calendar quarter of through the third calendar quarter of Panel A of Table 1 presents the distribution of coefficients from Equation 1 across the 76 samples.

To indicate the explanatory power from each group of signals, columns 1, 2, and 3 present models with short-term, long-term, and dividend signals, respectively. Column 4 presents the full model used in our main analysis. The average coefficients on all the fundamental signals are positive. In addition, variation in coefficients occurs across signals, suggesting that equally weighting the signals may not be optimal. We emphasize that our research purpose is not to test whether these coefficients in Equation 1 are positive.

Instead, these coefficients are the first step aggregating our fundamental signals into a single variable in our analysis of option returns. This finding suggests that fundamental signals have strong predictive power with respect to subsequent price volatility. Table 1. Specifically, we consider options that mature in the next month and select the contracts that are close to at-the-money, with moneyness between 0. To form a straddle, for each stock and for each month in the sample, we select the call and the put contracts with the same striking price that are close to at-the-month and expire the next month.

After next-month expiration, we repeat the procedure and select a new pair of call and put contracts. As the straddle has both call and put contracts, the payoff to the straddle is determined purely by the deviation of stock price a month later from its exercise price. Whether the stock price goes up or down is irrelevant, a concept in line with the volatility channel that we emphasize in the article.

Matching our data on fundamental signals and historical volatility to Optionmetrics results in a sample of 98, firm-months composed of 58, firm-quarters 3, unique firms. Ex ante, options investors may partially price the volatility information captured in fundamental signals. Panel A of Table 2 presents descriptive statistics for the monthly regression estimates of Equation 2. In addition, there is a positive association with historical volatility, suggesting that fundamental volatility is one of the drivers of stock return volatility.

However, the average monthly R 2 is Panel B of Table 2 presents descriptive statistics for firm-months with both fundamental signal and straddle return data. Panel C of Table 2 reports the correlation matrix of the main variables of interest in this study. As expected, IVOL and HVOL exhibit a strong positive correlation, suggesting that while implied volatility may not efficiently capture all the information in historical volatility, these two signals overlap considerably. In this section, we use both portfolio and regression approaches to formally investigate whether accounting information predicts future option returns.

Under the portfolio approach, for each month, we sort straddle options into 10 deciles based on the variable of interest. This evidence suggests that fundamental volatility is positively associated with future straddle returns, consistent with option investors underweighting accounting information. We measure stock returns in the same window as we measure option returns from the portfolio formation date to option expiration date.

Finally, we use a multivariate regression framework to supplement our portfolio results. Specifically, we consider the following regression model:. Panel B of Table 3 presents the regression results. In column 3, we include both measures in the model and find that both coefficients are highly significant, providing further evidence that fundamental signals convey incremental information with respect to future straddle returns.

To assess whether our fundamental-based option strategy is risky, we conduct two sets of analysis. First, we examine the comovement between hedge portfolio returns and common risk factors. The finance literature suggests that theoretical asset pricing models, such as CAPM capital asset pricing model , also apply to options e. The intuition is that common state variables, such as consumption, capture all kinds of risks in the capital markets, including the options market. Empirically, prior studies use common return factors to capture systematic risks e. Following prior work, we use the four-factor model below to examine whether our fundamental trading strategy exposes investors to systematic risks.

As our straddle contracts typically span 2 months e. In Models 1 and 2, abnormal returns intercepts and their associated t statistics from the four-factor model are virtually the same as the raw returns reported in Panel A of Table 3. In addition, while the coefficients on the HML factor are negative and significant, the coefficients on the other factors are insignificant regardless of whether the factors for the initiation or the expiration months are considered.

In sum, the results suggest that the hedge portfolio returns from our option strategies are not explained by the four common factors. Table 4. Our second set of analyses examines the time-series properties of our fundamental analysis portfolio. An alternative risk-based perspective suggests that portfolios with higher average returns must have inconsistent performance. Investors may have positive returns over a long window, but the portfolio strategy exposes the investor to large negative outcomes.

This concern particularly arises for options-based trading strategies, where selling a straddle exposes the seller to potentially extreme negative outcomes. To address this concern, we examine the performance of our fundamental analysis portfolio over time, noting the overall frequency of negative returns and the performance by year.

Panel B of Table 4 provides further details on the distribution of monthly returns for the D10 — D1 hedge return. Finally, Figure 2 plots the average monthly hedge returns by year. The average monthly hedge return is positive for all the 19 years in the sample period. Figure 2. Average monthly returns to fundamental-based hedge portfolios over time.

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This figure provides the average monthly return to the hedge portfolio over time. All variables are defined in appendix. The sample includes 98, firm-month observations from January to July Each bar represents the average monthly hedge return in a given year. In summary, we find fairly consistent strong positive returns for the fundamental-based option strategy. Our evidence is unlikely to reflect an appropriate reward for a risky investment strategy caused by the long positions being more risky than the short positions in the strategy.

In this section, we consider how investors could combine fundamental signals and historical volatility together in their trading strategies. As the previous sections show that fundamental signals contain information about future straddle returns that is incremental to what is captured in historical volatility, we expect higher hedge returns by combining historical volatility with fundamental signals. Average straddle returns for each of these groups are reported in Panel A of Table 5. Below we discuss an approach to combine these signals.

Table 5. First, we identify cases where the fundamental and historical volatility signals agree; we label this strategy AGREE. We pick options in D1 with the bottom quartile of fundamental score as our short position and options in D10 with the top quartile of fundamental score as our long position. Panel B of Table 5 provides an analysis of the returns to combining these two types of signals. Consistent with our expectations, incorporating fundamental signals into the historical volatility strategy increases the hedge return from This strategy yields much lower returns Figure 3. Average monthly returns to combined strategy hedge portfolios over time.

In sum, historical volatility and fundamental signals are complements and the two signals can be combined. This suggests that similar to the use of fundamental analysis to enhance the value strategy Piotroski, , fundamental analysis related to fundamental volatility can enhance option-based trading strategies. In this section, we examine the impact of transaction costs on trading strategy performance. Specifically, we consider the impact of the bid—ask spread, which is crucial in interpreting empirical results in option studies. The main results in previous sections are based on the assumption that options are traded at the midpoint of bid and ask prices.

It is possible that investors cannot trade options at that price in every circumstance. Many finance studies e. To examine the impact of transaction costs, we recalculate option returns under the assumption that the effective spread is equal to the quoted spread the effective-to-quoted spread ratio of 1 : investors always buy options at the ask price and write options at the bid price. We view this assumption as conservative because it produces the lowest after transaction cost returns relative to alternative effective spread assumptions used by prior literature.

As tick-by-tick option transaction data are not available, we follow the prior literature and use the closing bid—ask spread to proxy for the average bid—ask spread. We repeat the main analysis using option returns after transaction costs and report empirical results in Table 6.

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The hedge return from historical volatility strategy drops from The returns tests suggest that option prices do not fully capture fundamental or historical volatility signals. We also explore the effect of transaction costs on the returns from our combined strategies. Our second approach to address the transaction cost issue is to examine cross-sectional variations in trading strategy performance based on the proxies for transaction costs and liquidity.

Specifically, we consider three proxies: firm size, the bid—ask spread, and option trading volume. In sum, we find that transaction costs significantly affect the performance of the trading strategy based on fundamental volatility or historical volatility in isolation. For the overall sample, the D10 — D1 hedge returns drop from a significantly positive level before transaction costs to a negative level after transaction costs.

However, the combination of fundamental signals with historical volatility can produce a trading strategy that yields significantly positive hedge returns for options with low transaction costs. We have motivated our analysis from the fundamental volatility perspective. That is, our fundamental signals capture fundamental volatility, and implied volatility may deviate from the true underlying volatility. In portfolios sorted by our fundamental score, implied volatility is too low relative to fundamental volatility for D10 options and too high for D1 options.

If this story holds, a natural prediction is that implied volatility should increase for D10 options and decline for D1 options after the portfolio formation date, given that over time, implied volatility should converge to the true underlying volatility. The results are striking. For D1 options, implied volatility is higher at Time 0 than in previous months. After Time 0, implied volatility decreases to the level of the previous months. Overall, the results are consistent with the story that implied volatility temporally deviates from fundamental volatility, resulting in predictable option returns.

Figure 4. The time-series pattern of implied volatility around the portfolio formation date. Please see appendix for detailed variable definitions. In the main analysis, we aggregate individual fundamental signals into a single score when examining future option returns. To complement this analysis, we transform each signal into a decile rank and include those signals as well as HVOL and IVOL in a regression predicting future straddle returns.

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The coefficients on the individual fundamental signals untabulated suggest a degree of inefficiency with respect to both short-term and long-term fundamental signals. To provide further insight into the pricing of short-term and long-term signals, we estimate a model that includes only the short-term fundamental signals and a model that includes only the long-term fundamental signals.

In each of these specifications, the sum of the coefficients on the fundamental signals is highly significant. These findings are consistent with both the evidence in Goyal and Saretto that option prices underreact to long-term signals and the literature on drift in accounting suggesting that prices underreact to short-term accounting signals.

Our set of fundamental signals is an expanded version of Beneish et al. To avoid the risk of data mining, we repeat our analysis based on eight fundamental signals identified in Beneish et al. We repeat the analysis from Tables 3 and 5 using these signals. We continue to observe a smaller but positive and significant association between straddle returns and the fundamental signals, using the hedge return test or the multivariate regression test Equation 3. This evidence suggests that even a limited set of accounting signals can predict option returns. In addition, we continue to observe that there are benefits from combining signals i.

To complement our analysis using the Black—Scholes model implied volatility provided by Optionmetrics; we also consider an alternative implied volatility measure, model-free implied volatility. Following the technique described in Sridharan , we estimate implied volatility based on the prices of put and call options from the Optionmetrics volatility surface data set. In another contemporaneous article, Sridharan examines the association between fundamental signals and straddle returns. To explore the overlap between her signals, we calculate the natural log of the standard deviation of earnings yields and the natural log of the standard deviation of changes in market-to-book premiums over the window we use to calculate our long window signals.

Hedge portfolios with long and short straddle contract positions based on accounting signals earn statistically and economically significant returns before transaction costs. However, the high level of transaction costs in the options market limits the profitability of fundamental trading strategy in isolation. We also show that a strategy that combining fundamental signals with historical volatility can produce significantly positive hedge returns after transaction costs in cases where transaction costs are low.

Overall, our evidence provides insight into a new dimension of fundamental analysis—using accounting signals to evaluate fundamental volatility and examining whether such information is priced in the options market. Our evidence complements prior fundamental analysis research on equity returns, which focused on fundamental signals to predict future operating performance and stock returns. Z thanks the Yale School of Management for financial support. In the stock market, the link between volatility and expected stock returns is weak at best. Theoretical models suggest that volatility captures risk and thus should be positively correlated with expected stock returns.

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In contrast, in the option market, the numerator or payoff from owning a straddle is directly tied to the volatility of stock prices. Our study also helps to better understand how the capital markets price volatility in general. If the volatility associated with fundamental signals is diversifiable, then theoretical models suggest that equity investors would not use these signals to determine expected returns.

If fundamental volatility provides insight into the systematic component of volatility and thus is relevant for expected returns, empirical tests attempting to identify an association between these signals and realized stock returns would lack power owing to the small variance of expected returns. These two issues partially explain the mixed empirical evidence on the association between volatility proxies and realized returns e. In contrast, our research on option returns identifies a setting where signals related to either systematic or idiosyncratic volatility are highly relevant. Thus, by examining option returns, we provide additional insight into whether investors efficiently use accounting information that is informative about volatility in the capital market.

Cristoffersen, Goyenko, Jacobs, and Karoui provide evidence that illiquidity in the options market is positively associated with both put and call option returns. Similar to other articles on fundamental analysis, the signals examined in this article constrain inferences to those specific signals. As described below, there signals were selected because we view them as providing information about fundamental volatility.

However, this list of signals should not be viewed as exhaustive. NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

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