Equity Options

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 each calendar year based on:

1. Annual nominal and real total returns for Shiller’s long-run S&P Composite Index during 1871-2025, offering 149 overlapping 5-year intervals (only 29.6 independent intervals). We frictionlessly reinvest dividends annually.
2. Annual nominal capital gains for the S&P 500 Index during 1927-2025, offering 94 overlapping 5-year intervals (only 18.8 independent intervals).
3. Annual nominal capital gains for the NASDAQ 100 Index during 1985-2025, offering 93 overlapping 5-year intervals (only 7.0 independent intervals).

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

Is systematically selling covered call options on equity indexes, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider five equity covered call ETFs:

1. Invesco S&P 500 BuyWrite (PBP) – seeks to track the CBOE S&P 500 BuyWrite Index (BXM).
2. Global X S&P 500 Covered Call (XYLD) – seeks to track BXM.
3. Global X NASDAQ 100 Covered Call (QYLD) – seeks to track the CBOE Nasdaq-100 BuyWrite V2 Index (BXNT). We use CBOE NASDAQ-100 BuyWrite Index (BXN) based on availability of historical data.
4. First Trust BuyWrite Income (FTHI) – holds U.S. stocks of all market capitalizations and sells at-the-money to slightly out-of-the-money covered calls on the S&P 500 Index up to 20% of fund assets, laddered with expirations of less than one year (we use BXM as a benchmark).
5. Global X Russell 2000 Covered Call (RYLD) – seeks to track the CBOE Russell 2000 BuyWrite Index (BXRC).

We focus on average monthly return, standard deviation of monthly returns, compound annual growth rate (CAGR) and maximum drawdown (MaxDD) based on monthly data. We consider SPDR S&P 500 ETF Trust (SPY), Invesco QQQ Trust (QQQ) and iShares Russell 2000 ETF (IWM) as underlying stock index proxies. Using monthly dividend-adjusted returns for the five covered call ETFs since inceptions and contemporaneous monthly levels of all benchmarks/underlying index proxies through April 2026, we find that: Keep Reading

A buffer exchange-traded fund (ETF) is designed to limit losses while capping gains over a specific period, usually one year, generally by combining a position in put and call options on a stock index with an ETF that tracks that index. Laddered buffer ETFs smooth this approach by holding a rolling series of buffer ETFs with staggered expiration dates, thereby imposing two layers of fund costs. How do laddered buffer ETFs perform? To investigate, we consider five of the largest such ETFs, all currently available, as follows:

• • FT Vest Laddered Buffer ETF (BUFR)
  • Pacer Swan SOS Fund of Funds ETF (PSFF)
  • FT Vest Laddered Deep Buffer ETF (BUFD)
  • Innovator Laddered Allocation Buffer ETF (BUFB)
  • FT Vest Laddered Nasdaq Buffer ETF (BUFQ)

We use SPDR S&P 500 ETF Trust (SPY) as the benchmark for the first four and Invesco QQQ Trust (QQQ) for the last. We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly total returns for the five laddered buffer ETFs, SPY and QQQ as available through February 2026, we find that:

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Is systematically selling cash-covered equity put options, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider six equity put-write ETFs, three dead and three 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 Equity Premium Income Fund (WTPI) – seeks to track the performance, before fees and expenses, of the Volos US Large Cap Target 2.5% PutWrite Index. Renamed from PUTW in April 2025 with investment policy changes.
4. BMO US Put Write ETF (USD) (ZPW-U.TO) – writes short-dated out-of-the-money put options on large-cap U.S. stocks based on available option premiums, putting proceeds in cash equivalents.
5. Cboe(R) Validus S&P 500(R) Dynamic Putwrite Index ETF (PUTD) – employs a rules-driven approach to maintain a portfolio of put options written on the S&P 500 Index that will beat the index based on total return, with lower volatility and drawdown (dead).
6. Innovator Equity Premium Income – Daily PutWrite ETF (SPUT) – invests at least 80% of net assets (including borrowings) in a daily put-write options strategy.

As performance measurements, we consider monthly return correlation with SPDR S&P 500 ETF Trust (SPY) and CBOE S&P 500 PutWrite Index (PUT), average monthly return, standard deviation of monthly returns and monthly reward/risk (average return divided by standard deviation). We also look at compound annual growth rate (CAGR) and maximum drawdown (MaxDD) based on monthly data. We use SPY and PUT as benchmarks. Using monthly returns for the six ETFs as available through October 2025, and contemporaneous monthly returns for SPY and PUT, we find that: Keep Reading

Do informed put option buyers predict overall U.S. stock market returns? In her November 2025 paper entitled “Put Option Trading Efficiency”, Xiaolin Huo constructs a monthly Put Option Trading Efficiency (POTE) variable to measure the extent to which speculators hold larger put option positions on overpriced stocks than underpriced stocks. She measures POTE monthly as the slope of aggregate put open interest on each stock, weighted by contract size and divided by shares outstanding, versus a mispricing score for each stock based on 11 stock anomalies. A higher POTE suggests that investors are more actively using put options to express bearish views on overvalued stocks. Using monthly put option and short interest data on individual U.S. listed common stocks with price over $5 and market capitalizations above the NYSE 1st percentile, monthly value-weighted excess (relative to the U.S. Treasury bill yield) market returns and monthly values of a variety of market/economic indicators during January 1996 through December 2022, she finds that: Keep Reading

Do returns for U.S. stock options exhibit half-day momentum/reversal effects? In their August 2025 paper entitled “In Search of Seasonality in Intraday and Overnight Option Returns”, Turan Bali, Amit Goyal, Mathis Moerke and Florian Weigert examine momentum and reversal patterns in half-day (intraday and overnight) option returns. They specify intraday return as that from 10:00AM to 4:00PM EST and overnight return as that form 4:00PM to 10:30AM EST, calculated from bid-ask midpoints. These intervals take into consideration option price inefficiencies at the equity market open. They focus on individual stock options with moneyness below one and 5 to 50 days to expiration. They aggregate returns across different option contracts for each stock by averaging. They investigate exploitability via equal-weighted, daily reformed long-short momentum or reversal option portfolios based on extreme tenths (deciles) of average half-day option returns over the last five trading days. Using cleaned option and underlying stock data, excluding stocks priced under $5 as a liquidity screen, during January 2004 through December 2021, they find that: Keep Reading

Do the trading activities of especially informed equity and equity option traders predict stock returns? In the June 2025 revision of their paper entitled “An Information Factor: What Are Skilled Investors Buying and Selling?”, Matthew Ma, Xiumin Martin, Matthew Ringgenberg and Guofu Zhou construct an information factor (INFO) using the trades of corporate insiders, short sellers and option traders. Specifically, they each month for each stock calculate:

• To inform the long side of the INFO factor portfolio, net insider purchases (purchases minus sales).
• To inform the short side of the INFO factor portfolio:
  • Short interest (number of shares shorted divided by shares outstanding).
  • Option trading (total option volume divided by total stock volume).
• For each of these three metrics, assign a rank from 1 to 100, with higher rank indicating higher level of positive private information.
• Average the three ranks to compute an information score.
• Reform 10 equal-weighted (decile) portfolios of stocks sorted by information score, with the INFO factor portfolio long the top decile and short the bottom.
• Hold the portfolios for one month.

They assess the impact of stock trading frictions by assuming costs equal to half the respective effective bid-ask spreads. Using insider trading, short interest and option/stock trading volumes during January 1996 through December 2019, they find that: Keep Reading

How consequential are trading frictions for equity options, and how do these frictions vary across brokers? In their September 2024 paper entitled “Some Anonymous Options Trades Are More Equal than Others”, Xing Huang, Philippe Jorion and Christopher Schwarz compare retail option trade executions by placing the same orders across six brokers: E*Trade, Fidelity, Robinhood, Schwab, TD Ameritrade (now Schwab) and Vanguard. These brokers use some or all of the same five wholesalers, but their payments for order flow (PFOF) vary: two brokers receive no PFOF (Vanguard and Fidelity), while the other four receive PFOF at different levels. The authors place intraday market orders at the six brokers that are identical in symbol, strike, expiration, number of contracts, direction (buy or sell) and submission time. They then compare execution prices, measuring performance relative to matched National Best Bid and Offer (NBBO) quotes. Using results from about 7,000 trades in 18 stocks/ETFs that represent 45% of U.S. equity option market volume during mid-March 2024 through June 2024, they find that: Keep Reading

How well do simple option strategies work when applied to equity and bond exchange-traded funds (ETF)? In his April 2024 paper entitled “Effectiveness of Various Options Strategies for Exchange-Traded Funds“, Rishikesh Mahadevan tests five simple option strategies on ETFs, with all options held to expiration:

1. Covered Call – sell a call option on an ETF already owned.
2. Protective Put – buy a put option on an ETF already owned.
3. Long Call – buy a call option on an ETF.
4. Bull Call Spread – buy a call option at a relatively low strike and sell one at a relatively high strike.
5. Bull Put Spread – buy a put option at a relatively low strike and sell one at a relatively high strike.

He considers three equity ETFs (iShares Russell 1000 Growth ETF [IWF]; Vanguard Value Index Fund ETF [VTV]; and, iShares Core S&P 500 ETF [IVV]) and three bond ETFs (BND Vanguard Total Bond Market Index Fund ETF [BND]; SPDR Bloomberg High Yield Bond ETF [JNK]; and, iShares Core US Aggregate Bond ETF [AGG]). He considers three times to expiration for opening positions: 30 days, 60 days and 90 days. He considers three levels of moneyness for opening positions: at-the-money (ATM); 2% out-of-the-money (OTM); and, 5% OTM. His option prices are the average of bid and ask. He excludes extreme outliers from calculations. His benchmark is buying and holding the underlying ETF. Using daily data for the specified ETFs and associated options from the beginning of July 2016 through June 2021, he finds that: Keep Reading

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