Equity Options

Are sentiments conveyed in Seeking Alpha articles useful for stock picking? In their January 2023 paper entitled “Seeking Alpha: More Sophisticated Than Meets the Eye”, Duo Selina Pei, Abhinav Anand and Xing Huan apply two-pass natural language processing to test the informativeness of articles from Seeking Alpha incremental to publicly available earnings data. Specifically, they each month:

• Associate articles with one or more specific stocks.
• Extract positive and negative sentiment at both phrase and aggregate levels for each article/stock.
• Calculate a standardized net sentiment for each article/stock based on the difference between positive and negative mentions, emphasizing event sentiment over general sentiment.
• Rank articles/stocks based on standardized net sentiment over the last month. Reform equal-weighted portfolios of articles/stocks by ranked tenths (deciles). Calculate both immediate [-1,+1] and 90-day future [+2,+90] average gross raw returns and average gross abnormal returns adjusted for size, book-to-market and momentum.
• Sort stocks into 20 groups based on monthly standardized net sentiments up to two days before portfolio selection, excluding stocks with few articles or neutral sentiment. Reform an equal-weighted hedge portfolio that is long stocks with the highest sentiments and short stocks with the lowest (on average, 105 long and 86 short positions).

Using 350,095 articles published on Seeking Alpha since its inception in 2004 through the beginning of October 2018, daily returns of matched stocks and their options and associated earnings surprise data as available, they find that: Keep Reading

Does pricing of options on leveraged exchange-traded funds (ETF) predict future returns of the underlying 1X ETFs? In the March 2024 version of their paper entitled “Lever Up! An Analysis of Options Trading in Leveraged ETFs”, Collin Gilstrap, Alex Petkevich, Pavel Teterin and Kainan Wang examine options trading in leveraged equity ETFs and its implications for future performance of underlying funds. They hypothesize that the compounded leverage of such options attracts especially sophisticated investors. Specifically, they test a risk-on/risk-off strategy that, at the end of each month:

1. Calculates the difference in changes in implied volatilities between at-the-money (ATM) call options and ATM put options on a leveraged ETF (and separately for comparison, on its underlying 1X ETF).
2. If this difference is greater (smaller) than its median value over the prior 12 months, specifies the next month as bullish (bearish) for the 1X ETF, and invests in a synthetic 3X ETF (the risk-free asset) next month. The synthetic 3X ETF earns three times the monthly returns of the underlying 1X ETF.

They also consider a more realistic test using SPDR S&P 500 ETF (SPY) as the underlying 1X ETF and Direxion Daily S&P 500 Bull 3X Shares (SPXL) as the associated leveraged ETF. They assume 0.2% trading frictions for portfolio turnover. Using daily returns for 76 leveraged equity ETFs matched to 30 underlying 1X ETFs and daily implied volatilities for associated ATM call and put options during January 2007 through December 2021, they find that: Keep Reading

Can holders of popular large-capitalization stocks improve portfolio performance by systematically buying or selling options on these stocks? In their February 2024 paper entitled “The Performance of Options-Based Investment Strategies: Evidence for Individual Stocks from 2004 to 2019”, Zhuo Li and Thomas Miller, Jr. compare to buy-and-hold the performances of four strategies that augment a long stock position with options, as follows:

1. Buy and hold the stock.
2. Covered call  – long stock plus short call.
3. Protective put – long stock plus long put.
4. Collar – long stock plus short call plus long put.
5. Covered combination – long stock plus short call plus short put.

They focus on 10 stocks widely held in 401(k) plans: ExxonMobil, Comcast, Berkshire Hathaway (Class B), Oracle, Microsoft, Coca-Cola, Amazon, Wells Fargo, Google (Class A) and Apple. They roll at the end of each calendar month from the standard monthly option that expires during the next month to the one that expires during the subsequent month. They choose option strike prices that are at least 5% out-of-the-money but as close to 5% as possible, with exceptions when no such options are available. They assume option buys and sells are at the daily closing bid-ask midpoint. They ignore the possibility of early option exercise. Using monthly data for the selected 10 stocks and specified options as available during January 2004 through November 2019, they find that: Keep Reading

Do economic data releases trigger predictably large returns in short-term equity index options? In their February 2024 paper entitled “Expected 1DTE Option Returns”, Michael Johannes, Andreas Kaeck, Norman Seeger and Neel Shah analyze returns to one-day-to-expiration (1DTE) S&P 500 Index options. They study 1DTE options rather than zero-day-to-expiration (0DTE) options to capture effects of market moves outside normal trading hours, with focus on economic data releases. Specifically, they calculate 1DTE option returns from 3:55 ET the day before expiration through expiration by moneyness based on quote midpoints. They calculate average option returns separately for days with and without certain economic data releases (CPI, FOMC statements, GDP and monthly payrolls) to assess the premium for the latter. They also analyze the variance risk premium (VRP) to disentangle volatility and economic data release effects. Using 5-minute intraday (9:30 am ET until 4:15 pm ET) quote data for S&P 500 Index options and S&P 500 Index levels, and economic variable announcement dates/times, from the beginning of January 2012 through early December 2023, they find that:

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Given wide bid-ask spreads, do retail option traders systematically bear large losses? In their January 2024 paper entitled “An Anatomy of Retail Option Trading”, Vincent Bogousslavsky and Dmitriy Muravyev characterize retail option trading in the U.S. by exploiting data from a trading journal that attracts retail investors by offering advanced tracking/performance verification tools. When users subscribe and link their brokerage accounts, their trades (past and future) are automatically imported by the journal and verified. Using data for 5,182 traders who initiated 2.1 million trades worth about $20 billion (of which about $8.8 billion involve options) during January 2020 through December 2022, they find that: Keep Reading

A subscriber asked about the historical experience (distribution of outcomes) of an investor with a 5-year horizon (holding period). To investigate we consider returns for 5-year intervals rolled annually at the end of the year based on:

1. Annual nominal and real total returns for Shiller’s long-run S&P Composite Index during 1871-2022, offering 146 overlapping 5-year intervals (only 29.2 independent intervals). We frictionlessly reinvest dividends annually.
2. Annual nominal capital gains for the S&P 500 Index during 1927-2022, offering 91 overlapping 5-year intervals (only 19.2 independent intervals).

Using much shorter samples available for assets such as exchange-traded funds offers results and comparisons of very low reliability. Using annual returns for the two indexes as described, we find that: Keep Reading

Are Zero-Days-to-Expiration (0DTE) options attractive trading vehicles? In his November 2023 paper entitled “0DTE Trading Rules”, Grigory Vilkov surveys outcomes for popular strategies that each day take unhedged positions in cash-settled 0DTE options on the S&P500 Index and hold to expiration. He considers simple trading rules that open positions at a fixed time (10:00 in the main analysis and 13:00 and 15:00 in additional tests) and hold to expiration at 16:00. He augments available prices by interpolation to construct test data for moneyness from 0.98 to 1.02 in 0.00125 increments. He then computes profit/loss statistics for both standalone calls and puts and for selected option strategies, including: at-the-money (ATM) straddles; strangles; bull call and bear put spreads; call and put ratio spreads; and, risk reversals. Excepting the straddle, he considers several combinations of strikes, from in-the-money (ITM) to out-the-money (OTM). He also estimates the variance risk premium as the implied variance minus realized variance while positions are open. Using 0DTE option 30-minute price data during September 2016 through May 2023, he finds that: Keep Reading

Numerous option trading studies report finding strategies with remarkably high mean returns and Sharpe ratios. Are these findings too good to be true? In their October 2023 paper entitled “Too Good to Be True: Look-ahead Bias in Empirical Option Research”, Jefferson Duarte, Christopher Jones, Mehdi Khorram and Haitao Mo investigate the extent to which look-ahead bias drives extraordinarily high average returns and Sharpe ratios in two distinct option strategies: one based on stock characteristics (January 2012 through June 2020), and the other associated with liquidity provision in the options market (January 1996 through April 2016). They first replicate the strategies as published, and then recast the strategies with careful attention to what information is reliably available before trading. Using data over the above sample periods required to replicate and correct the original studies, they find that: Keep Reading

Do covered call strategies, which offer both long equity and short volatility exposures, beat their underlying equities? In their October 2023 paper entitled “A Devil’s Bargain: When Generating Income Undermines Investment Returns”, Roni Israelov and David Ndong test a strategy of each month selling and holding to maturity cash-settled covered calls on the S&P 500 Index selected to meet a target annualized option yield (based on moneyness). For example, if the target annualized option yield is 6%, they sell calls at 0.5% of the index price each month. They consider a full sample period of nearly 25 years and a subsample covering the second half. Using S&P 500 Index and index call options data to test the specified covered call strategy during January 1999 through June 2023, they find that:

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Is systematically selling covered equity put options, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider four equity put-write ETFs, two dead and two living:

1. US Equity High Volatility Put Write (HVPW) – oriented toward individual stocks (dead).
2. ALPS Enhanced Put Write Strategy (PUTX) – index-oriented (dead).
3. WisdomTree PutWrite Strategy Fund (PUTW) – index-oriented (living).
4. BMO US Put Write (ZPW.TO) – oriented toward individual stocks (living).

Because available samples are fairly short, we focus on daily return correlation with SPY, average daily return, standard deviation of daily returns and daily reward/risk (average daily return divided by standard deviation of daily returns). We also look at compound annual growth rates (CAGR) and maximum drawdowns (MaxDD) based on daily data. We consider SPDR S&P 500 ETF Trust (SPY) and CBOE S&P 500 PutWrite Index (PUT) as benchmarks. Using daily returns for the four ETFs as available through early August 2023, and contemporaneous daily returns for SPY and PUT, we find that: Keep Reading