Equity Options

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 2025, we find that:

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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

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 ETF (USD) (ZPW-U.TO) – oriented toward individual large-capitalization U.S. 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 July 2024, and contemporaneous daily returns for SPY and PUT, 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 2024, we 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

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