Abstract
We examine whether and how options trading activity curtails real activities manipulation. Using a large sample of U.S. firms suspected of earnings manipulation, we document that an active options trading market significantly reduces real activities manipulation. We confirm our findings by using 2SLS analyses and alternative research designs. Our findings are also robust to using alternative proxies for options trading activity. Further, we find that the deterring impact of options trading on real activities manipulation is more pronounced among firms with low institutional ownership, among firms in highly competitive industries, and among small and young firms. Overall, our findings show that an active options market discourages managers from engaging in real activities manipulation, as informed options trading helps stock prices better reflect adverse consequences of real activities manipulation. Our findings highlight the benefits of options market development in reducing value-destroying activities and thus provide policy, practice, and research implications.
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Notes
The total number of equity options contracts has grown from 174,000 in 1995 to 9,599 million in 2022. (The Options Clearing Corporation: https://www.theocc.com/Market-Data/Market-Data-Reports/Volume-and-Open-Interest/Historical-Volume-Statistics).
Real activities manipulation (often referred to as real earnings management) refers to the overstatement of earnings by taking real economic actions, such as overproduction and cutting discretionary expenses, that temporarily inflate earnings. Real activities manipulation is associated with adverse impacts on firms’ future operations and cash flow (Roychowdhury 2006). For instance, reducing R&D investments or cutting advertising expenses could adversely affect a firm’s competitive advantage.
Financial analysts and short-sellers also reduce information asymmetries in the capital markets and contribute to stock price efficiency. In particular, Lara et al. (2013) shows that financial analysts clearly detect real activities manipulation and fully consider its future consequences in their assessments. However, management engages in more real activities manipulation when analyst coverage is high (Irani and Oesch 2013; Sun and Liu 2016) and there is an active short-selling market (Jiang et al. 2020; Zhang et al. 2020). The reason is that managements switch from accounting-based earnings management to real activities manipulation, as it is more difficult for financial analysts and short-sellers to detect the latter. The same logic may apply to options traders.
Opportunistic overproduction allows managers to delay expensing a large proportion of fixed costs by allocating it to ending inventory, which is a current asset. Real activities manipulation through opportunistic overproduction is also associated with the holding costs of excess inventory. (Gupta et al. 2010).
We do not argue that an active options market totally deters real earnings management, as a host of factors influences the decision to take real economic actions (e.g., auditors, governance mechanisms, etc.).
Ali et al. (2022) also find that options trading is negatively related to the likelihood of accounting restatement (a proxy for accounting-based earnings management). In untabulated results, using the Jones (1991) model of accrual-based earnings management, we also find that options trading is negatively related with accrual-based earnings management.
The excess inventory associated with opportunistic overproduction may also quickly become obsolete in highly competitive markets.
In untabulated results, we obtain qualitatively similar results when we require at least 30, 50, and 120 observations in each industry (two-digit SIC) and each year.
Following the literature (Huang et al. 2020; Zang 2012; Zhang et al. 2020), we do not examine abnormal cash flow from operations, because different real earnings management practices have opposite effects on cash (Huang et al. 2020; Roychowdhury 2006). For instance, reducing R&D, advertising, and SG&A expenses saves cash, and sales manipulation increases cash inflow as well. However, overproduction may increase cash outflows, as purchases of materials and labor increase with the additional production.
As Truong and Corrado (2014) discuss, when options trading volume is low, there are not many trading opportunities for informed options traders. Hence, low options trading volume limits informed options traders’ ability to trade based on their information. Therefore, the impacts of options trading on information environments depend on the volume of options trading. Truong and Corrado (2014) recommend researchers use options trading volume instead of a binary variable (0 & 1) for options listing, as the informational role of options trading hinges on options trading volume. Moreover, the volume of options trading tends to be low in initial years after listing on options exchanges. Following their recommendation, we use options trading volume as a proxy for an active options market rather than a binary variable for options listing.
Moneyness should be exogenous to corporate policies, as exchanges regularly add new options with strike prices close to the recent market price of the underlying stock (Roll et al. 2009).
We also find similar results when we use \({Dis\_DISX}_{i,t}\) and \({Dis\_Prod}_{i,t}\) as dependent variables in 2SLS regressions (untabulated).
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Acknowledgements
We thank workshop participants at the University of Memphis for their comments and suggestions on the earlier draft of this manuscript. We also appreciate helpful comments from Ioannis Tsalavoutas, Mohan Venkatachalam, and the participants of the NTNU Business School conference 2021 and European Financial Reporting conference (EUFIN 2022).
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Appendix
Appendix
Variable definitions
Variable | Definition |
|---|---|
Real activities manipulation variables | |
\({REM}_{i,t}\) | Real activities manipulation proxy, calculated as abnormal production costs + (-1* abnormal discretionary expenses) (\({REM}_{i,t}={Dis\_PROD}_{i,t}-{Dis\_DISX}_{i,t}\)) |
\({Dis\_PROD}_{i,t}\) | Abnormal production costs proxy computed by estimation of the residuals of the following model (Roychowdhury 2006): \({(PROD}_{i,t}/{AT}_{i,t-1})={k}_{1}(1/{AT}_{i,t-1})+{k}_{2}({SALES}_{i,t-1}/{AT}_{i,t-1})+{k}_{1}(\Delta {SALES}_{i,t}/{AT}_{i,t-1})+{k}_{1}(\Delta {SALES}_{i,t-1}/{AT}_{i,t-1})+{\varepsilon }_{i,t}\) |
\({Dis\_DISX}_{i,t}\) | Abnormal discretionary expenses proxy, calculated as the residuals of the following model (Roychowdhury 2006): \({(DISX}_{i,t}/{AT}_{i,t-1})={k}_{1}(1/{AT}_{i,t-1})+{k}_{2}({SALES}_{i,t-1}/{AT}_{i,t-1})+{\varepsilon }_{i,t}\) |
\({PROD}_{i,t}\) | Sum of the cost of goods sold and change in inventory (#COGS + \(\Delta\)#INVT) for firm i in year t |
\({DISX}_{i,t}\) | Discretionary expenses is the sum of R&D expenses, advertising expenses, and SG&A expenses (#XAD + #XRD + #XSGA) for firm i in year t |
\({SALES}_{i,t-1}\) | Total sales (#SALE) for firm i in year t-1 |
\({AT}_{i,t-1}\) | Total assets (#AT) for firm i in year t-1 |
Options trading-related variables | |
\({Volume}_{i,t}\) | Options trading volume is the natural logarithm of 1 plus the aggregated annual options trading volume (in $10,000) for firm i in fiscal year t |
\({Money}_{i,t}\) | Moneyness is the annual average of the absolute deviation of the option’s strike price from the stock’s market price (\(\left|\mathrm{ln}(\frac{stock price}{strike price})\right|\)) at close |
\({Open}_{i,t}\) | Open interest is the natural logarithm of 1 plus the annual average of open option contracts |
\({LogNum\_Options}_{i,t}\) | The natural logarithm of 1 plus the annual number of traded options |
O/S | Total options volume (i.e., the annual number of traded options) divided by total stock volume (i.e., annual number of traded stocks) |
Control variables | |
\({Size}_{i,t}\) | Size is the natural logarithm of the total assets at the end of the year (#AT) |
\({LEV}_{i,t}\) | Leverage is the sum of long-term debt (#DLTT) and current debt (#DLC) scaled by total assets at the beginning of the year (#AT) |
\({MB}_{i,t}\) | Market-to-book value is the market value of equity (#CSHO \(\times\)#PRCC_F) divided by book value of equity at the end of the year (#CEQ) |
\({ROA}_{i,t}\) | Financial performance is pretax income (#PI) divided by total assets at the beginning of the year (#AT) |
\({FASSET}_{i,t}\) | Tangibility (or ratio of fixed assets) is property, plant, and equipment—total (gross) (#PPEGT) scaled by total assets at the beginning of the year (#AT) |
\({Capital\_Int}_{i,t}\) | Capital intensity is property, plant, and equipment—total (net) (#PPENT) divided by total assets at the beginning of the year (#AT) |
\({Opcycle}_{i,t}\) | The length of the operating cycle is the sum of the receivables cycle (sales (#SALES) divided by 360 and divided by average receivables (#RECT)) and the inventory cycle (cost of goods sold divided by 360 and divided by average inventory (#INV)) |
\({Sales\_Growth}_{i,t}\) | Sales growth for firm i from year t-1 to year t, measured as the percentage of growth in total sales (#SALE) from year t-1 to year t |
\({Age}_{i,t}\) | Firm age is the natural logarithm of 1 plus the number of years a firm is on Compustat |
\({Coverage}_{i,t}\) | Financial analyst coverage is the natural logarithm of 1 plus the number of financial analysts following the firm |
BigN | A dummy variable that equals 1 if the firm is audited by one of Big N firms, and zero otherwise |
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Delshadi, M., Hosseinniakani, M. & Rezaee, Z. Does options trading deter real activities manipulation?. Rev Quant Finan Acc 61, 673–699 (2023). https://doi.org/10.1007/s11156-023-01162-3
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DOI: https://doi.org/10.1007/s11156-023-01162-3