Delta Neutral Model

Adapted from the original work of u/yelyah2

When analyzing stock data, two critical forces often come into play: mean reversion and momentum. Traders aim to predict these forces better than their competitors, and using unique indicators can provide a trading advantage. This model integrates multiple indicators to analyze stock behavior, focusing on key aspects such as reversion to the mean, momentum, and volatility.

  • Delta Neutral (DN): This indicator identifies the price level where the total market delta is zero. It serves as a potential support level for the stock price. When the price falls below this level, it often rebounds as traders recognize the stock as undervalued.
  • Gamma Neutral (GN): This indicator is used to measure momentum and identifies the price where total market gamma is zero. It serves as a potential support/resistance, depending on where the price of the underlying and GN are relative to other indicators in the model.
  • Gamma Maximum (GM): Also used to measure momentum, GM identifies the price with the highest gamma for the total market. A sudden increase in gamma suggests strong upward momentum, and crossing the GM can lead to significant price movements, though failing to cross GM often indicates a potential reversion to mean.
  • Vega Neutral (VN): This indicator measures the price level where the total market vega is zero. Vega represents the sensitivity of an option's price to changes in implied volatility. By identifying the price level at which vega is 0, traders can gauge the impact of volatility changes on the overall market.

Delta Neutral Model

Detailed Explanation

  • Purpose: Identifies price levels where the overall market delta equals zero, which can act as a support level.
  • Behavior: When the stock price is below DN, it often rebounds as it is perceived as undervalued. This is due to increased buying pressure from traders and market makers hedging their positions.
  • Historical Observations: Stocks often bounce off DN levels during drops, although there can be exceptions. For instance, significant pressure may build up below DN, leading to a notable price increase when this pressure is released.
Gamma Neutral (GN) and Gamma Maximum (GM)
  • Purpose: Measures price levels associated with market momentum. GN identifies a zero total gamma level, while GM indicates the maximum gamma level.
  • Behavior:
    • GN acts as a support or resistance level depending on the placement of the indicator, relative to price and the rest of the indicators in the model and can spike during high options volume events.
    • GM generally acts as a maximum potential price target during price improvements, but can be exceeded if there is enough momentum, and/or other conditions of the model support it.
  • Historical Observations: Gamma spikes can indicate potential price increases and are often preceded by high volatility. High gamma can lead to significant price movements, especially when unusual options activity triggers these spikes. GN generally tends to stay within the inner confines of the model indicators and is often indicative of a trading channel when combined with DN.
Vega Neutral (VN)
  • Purpose: VN helps identify price levels where the sensitivity to volatility changes (Vega) is 0. This can act as a reference point for understanding how shifts in implied volatility might affect stock prices.
  • Behavior: When the market Vega is neutral, the impact of changes in implied volatility on the stock price is minimized. This means that fluctuations in volatility will have a reduced effect on the price level of options.
  • Historical Observations:
    • Incorporating VN into the model provides a more holistic view of market dynamics. It allows for a more nuanced understanding of how changes in volatility interact with price and other indicators. This can lead to more informed trading decisions by factoring in volatility effects alongside price movements, hedging requirements, and momentum.
    • Interaction with Delta and Gamma Neutral:
      • Delta Neutral (DN): While DN focuses on price levels where the market delta is zero (indicating support), VN offers insight into how volatility might influence price stability at those levels. When combined, DN and VN can provide a more comprehensive view of price behavior, accounting for both price and volatility factors.
      • Gamma Neutral (GN): GN identifies points of price stability with respect to gamma, while VN focuses on Vega. A Vega-neutral position in conjunction with a gamma-neutral position can offer a balanced perspective on both price momentum and volatility sensitivity, potentially improving trading strategies by incorporating multiple dimensions of risk.

Delta Neutral Model

Methodology and Assumptions

  • Data Sources: Daily options and stock summaries from reliable sources like historical options data providers.
  • Calculations:
    • Implied Volatility Estimations: Uses algorithms to estimate the implied volatility for each strike, type, and expiration combination available on the options chain on any particular date in time. Missing values are filled using linear interpolation.
    • Model Indicator Estimations: Uses optimization algorithms to find price levels achieving target delta, gamma, and vega total market exposure value estimations (DN, GN, VN, GM).
  • Limitations:
    • Open Interest data is typically lagged by one day, impacting real-time analysis. Live OI estimates from other sources may not be entirely accurate.

Delta Neutral Model

Events Affecting Price Outside the Model Boundaries

Understanding Price Deviations: While stock prices generally tend to stay within the boundaries set by the model, certain events or conditions may cause temporary deviations from these predicted ranges. These deviations can occur due to various factors, and it's crucial to understand the forces that drive the price back within the model's confines.

Forces Driving Price Back Inside the Model
  • Reversion to the Mean:
    • Historical Behavior: Historically, stock prices tend to revert to their mean levels over time. Once the initial shock of an event subsides, prices often move back towards the model’s predicted range as market participants reassess the stock’s value.
  • Market Adjustments:
    • Arbitrage Opportunities: When prices move significantly outside the model's range, arbitrageurs may exploit these discrepancies, leading to trades that drive the price back within the model.
    • Market Corrections: Following extreme movements, market corrections often occur, with prices adjusting to align with this model, in a convergent manner.
  • Supply and Demand Dynamics:
    • Call Option Volume: If prices fall below a key level predicted by the model, increased call option buying can drive the price back up as market makers hedge their positions.
    • Hedging Activities: Institutional investors and traders may engage in hedging activities, which can influence price movements and help revert the price to within the model's range.
  • Fundamental Analysis:
    • Reevaluation of Fundamentals: As new information becomes available and analysts reassess the stock’s fundamentals, prices may stabilize and return to the model's predicted levels.
  • Volatility Adjustments:
    • Return to Normal Volatility: After an event-induced spike or drop, volatility often returns to more typical levels. As volatility stabilizes, prices are likely to revert to the model's predicted boundaries.

Understanding Failure to Deliver (FTD)

What Is Failure to Deliver?

Failure to deliver (FTD) occurs when one party in a trading contract—whether involving shares, futures, options, or forward contracts—fails to meet their contractual obligations. This situation arises if a buyer (the party with a long position) lacks sufficient funds to complete the transaction at settlement. Similarly, it can happen if a seller (the party with a short position) does not possess the necessary underlying assets to deliver at settlement.

Key Points
  • Definition: Failure to deliver (FTD) signifies an inability to fulfill trading obligations.
  • Buyers vs. Sellers: For buyers, it means lacking the cash needed; for sellers, it means lacking the required assets.
  • Settlement: These obligations are reconciled at the trade settlement.
  • Context: FTD can occur in derivatives contracts or during naked short selling.

As a remedy for this in the United States, Regulation SHO was designed. Stocks bought and sold in transaction must be settled within one day. The buyer must deliver the cash and the seller the stock. If either party fails, a failure-to-deliver takes place. Sometimes deliberate fails-to-deliver are used to profit from falling stocks, so that the stock can later be purchased at a lower price, then delivered.

Failure to Deliver

Detailed Explanation

In every trade, both parties must transfer cash or assets by the settlement date. If either party fails to do so, an FTD occurs. This can also happen due to technical issues in the settlement process managed by the clearinghouse.

FTD is particularly relevant in the context of naked short selling. In naked short selling, a trader sells a stock they do not own and cannot guarantee access to, which is illegal. Traders who engage in this practice believe the stock will decline or the company will go out of business, allowing them to profit without holding the actual shares. This practice can lead to "phantom shares"—shares that exist only on paper—potentially diluting the stock's price. Consequently, buyers may end up owning shares that, in reality, do not exist.

Understanding T+x Cycles and Market Dynamics

The stock market operates on cycles and dates, essential for understanding transactions and settlements. There has been confusion about T+x cycles and their implications, so here's a clear explanation.

  • Transaction Day (T Day): When someone buys a stock, the market maker sells it at the market value. This marks T day (Transaction Day).
  • Calendar Day (C+): Used for settlement timing, counted in actual days on the calendar..
  • Trading Day (T+): Used for settlement timing, counted in trading days.
  • Settlement Date: The date when settlement is due from the transaction date. Usually T+1, but can be deferred due to different mechanics and regulations in the market.
Market Movers

First, The Market Maker

The market maker's responsibility starts by locating the share they just sold. Remarkably, a Market Maker (MM) can sell a share not currently in their "inventory." From the sale date (T) plus 1 trading day (+1), or T+1 days, they must "locate" the share to settle the trade. Failure to do so results in a Fail To Deliver (FTD).

Note: When the market maker fails to locate the share, it adds to the total shares they need to buy back. This ongoing practice, labeled arbitrage, allows indefinite offsetting of these buys, which can conflict with the investor's interest and the market maker's neutral role.

Examples of Market Makers: Citadel, Virtu, GTS Securities.

Second, The Authorized Participant

Authorized Participants (APs) play a key role in ETF-related activities. APs can generate naked ETF shares, creating phantom shares to suppress stock prices, bundled into a basket for swaps.

APs have from the transaction day (T) plus two trading days (+2) to settle the ETF trade. Additionally, they have another trading day (+1) to locate the shares traded in the ETF, totaling T+3 (T + 2 + T + 1) for ETF-generated FTDs.

Examples of Authorized Participants: Banks, Citadel.

Rule Changes and Future Cycles

In 2017, the rules changed from T+3 to T+2. As of May 28, 2024, the market is now operting in T+1 settlement.

Failure to Deliver

Legal & Regulations

Regulation SHO

Regulation SHO, effective January 3, 2005, updated short sale regulations to address persistent FTD issues and abusive naked short selling practices. Key aspects include:

  • Rule 200: Requires trades to be marked as “long,” “short,” or “short exempt.”
  • Rule 201: Implements a price test circuit breaker to prevent short sales from further driving down a stock that has experienced a significant price drop.
  • Rule 203(b)(1) and (2): Mandates that broker-dealers must verify the availability of securities before executing a short sale.
  • Rule 204: Strengthens close-out requirements, requiring prompt action to rectify FTD positions and apply pre-borrowing rules for continued short sales in affected securities.
Historical Amendments
  • 2007: Elimination of the "grandfather" provision.
  • 2008: Removal of the "options market maker" exception.
  • 2009: Finalization of Rule 204, enhancing close-out requirements.
  • 2010: Adoption of Rule 201, introducing price test restrictions during significant price declines.

These regulations are designed to promote market stability and preserve investor confidence by ensuring timely resolution of FTD issues and addressing potential market manipulation. For more information, see the SEC’s Regulation SHO updates and related releases.

Securities Options Contracts

Options are financial derivatives that give buyers the right, but not the obligation, to buy or sell an underlying asset at an agreed-upon price and date.

There are two main types of options:
  • Call Options: These give the buyer the right to buy 100 shares of an asset at the strike price before the expiration date. The seller has the obligation to sell 100 shares at the strike price if the option is exercised.
  • Put Options: These give the buyer the right to sell 100 shares of an asset at the strike price before the expiration date. The seller has the obligation to buy 100 shares at the strike price if the option is exercised.
Terminology
  • Strike Price: The agreed price at which the asset can be bought or sold. For example, a call option with a $420 strike price allows the purchase of 100 shares at $420 before the expiration date.
  • Expiration Date: The date on which the option contract expires.
  • Bid: The market price that buyers are willing to pay for the options contract.
  • Ask: The market price that sellers are willing to accept for the options contract.
  • At The Money (ATM) or Near The Money (NTM): An option is ATM when the strike price is equal to or very close to the underlying stock price.
  • In The Money (ITM): An option is ITM when the strike price is below the underlying stock price for calls, or above the underlying stock price for puts.
  • Out of The Money (OTM): An option is OTM when the strike price is above the underlying stock price for calls, or below the underlying stock price for puts.
  • Extrinsic Value: The difference between the market price of an option (its premium) and its intrinsic value. This rises with market volatility.
  • Intrinsic Value: The value of the option if it were exercised today, based on the difference between the strike price and the underlying asset's price.

Options: It's All Geek to Me

Options Pricing and the Greeks

Options pricing is influenced by several key factors, collectively known as the Greeks, which help traders understand the sensitivity of an option’s price to various underlying variables such as the price of the underlying asset, volatility, and time decay. The greeks that impact price of options most include Delta, Gamma, Theta, Vega, and Rho, each measuring how different factors affect the option’s price:

  • Delta: Measures the change in an option's price relative to the change in the underlying stock price. A delta of 0.8 means that if the stock price increases by $1, the option's value will increase by $0.80. Delta is also used to estimate the probability of the option being ITM at expiration.
  • Gamma: Measures the rate of change of delta over time. Higher for ATM options and lower for deep ITM and OTM options. Gamma indicates how much the delta will change as the underlying stock price changes.
  • Theta: Measures the rate of time decay on the option's value. Theta is always negative because it represents how much value the option loses as time passes. The rate of decay increases as the expiration date approaches.
  • Vega: Measures the sensitivity of the option's price to changes in implied volatility. Higher vega means higher risk of volatility and a higher premium. Vega can increase with quick moves in the underlying asset and decrease as the option nears expiration.
  • Rho: Measures the sensitivity of an option's price to changes in interest rates. Rho indicates how much the price of the option will change with a 1% change in interest rates. Call options generally have positive rho, while put options have negative rho.
  • Vanna: Measures the change in delta with respect to changes in volatility. Vanna shows how delta changes as implied volatility changes, which can affect the option’s price dynamics.
  • Charm (Delta Decay): Measures the rate at which delta changes over time as expiration approaches. Charm indicates how the delta of an option evolves as time passes and the underlying stock price moves.
  • Veta: Measures the rate of change of vega with respect to time. Veta shows how the sensitivity of an option’s price to changes in volatility (vega) changes as the option approaches expiration.
Good Times to Buy Options:
  • When Volatility is Low: Option prices are generally lower, making it an opportune time to buy.
  • When Time Until Expiration is Long: More time until expiration allows for potential price movement and increases the chance of profitable trades.
Good Times to Sell Options:
  • When Volatility is High: Higher volatility increases option prices, which can be beneficial for selling.
  • When Time Until Expiration is Short: Options lose value faster as expiration approaches, benefiting sellers due to time decay (Theta).

Overview

Wisdom on exchange traded funds (ETFs) from u/turdfurg23

The Story of ETFs: Long Overdue Regulation and a Reg SHO Time Machine

ETFs, originally created in the aftermath of the Great Financial Crisis, have now evolved into a trillion-dollar market. Despite their significant growth, these financial products remain largely unaffected by Regulation SHO due to the liquidity provisions embedded within them, specifically through Creation/Redemption in the secondary market. The major players in this space, often referred to as "The Big Three"—BlackRock, Vanguard, and State Street—dominate the market, holding immense power and influence.

The Story of ETFs and the RegSHO Time Machine

Detailed Explanation

The Big Three: Passive Investing Giants

BlackRock, Vanguard, and State Street are the most prominent ETF issuers. Although their funds are categorized as passive, the underlying securities' weights and share counts change frequently to maintain market cap weighting. Most ETFs include high liquidity stocks like Exxon Mobil or Apple, ensuring that deviations in ETF Net Asset Value (NAV) are minimized, preventing arbitrage opportunities.

These institutions are deeply involved in the share lending business, where ETF sponsors and short hedge funds generate steady revenue streams. A significant portion of this revenue comes from the lending of these high-liquidity stocks.

Analyzing ETF Issuers Over Time
  • BlackRock Funds (Excluding IWM): Includes ETFs such as ITOT, IWP, IMCV, IMCB, and several others. Not all of these funds trade options. Chart
  • Vanguard Funds (Excluding VTI): Includes ETFs like VT, ESGV, VCR, VBR, and more. Again, not all trade options. Chart
  • State Street Funds: Includes ETFs like XRT, SPTM, VLU, MMTM, ONEO, and more, with some shedding their positions in GME since the data collection. ` Chart
Market Makers and Authorized Participants

ETF trading is facilitated by numerous Authorized Participants (APs), which are crucial for liquidity. These APs vary by ETF size and issuer. For example, Merrill Lynch, Goldman Sachs, and Citadel Securities are significant APs for BlackRock, while Vanguard's largest APs include Virtu, JP Morgan, and Citigroup. State Street’s primary APs are Merrill Lynch, Virtu, and Citadel Securities.

Arbitrage and Market Dynamics

Arbitrage plays a vital role in the ETF market. When the price of an ETF exceeds its NAV, traders engage in arbitrage by purchasing the underlying securities while shorting the ETF shares. This process brings the ETF price and NAV back into alignment. Only APs can redeem shares with the ETF sponsor, making them central players in this mechanism.

This arbitrage process can affect market volatility, especially when large spreads exist between ETF prices and their NAVs. Increased volatility is often a consequence of rapid trading by arbitrageurs exploiting these mispricings.

Market Manipulation and the Role of ETFs

Large institutions may exploit ETFs through wash sales, known as "The Market Heartbeat." A well-known ETF, XRT, almost faced significant issues during the January 2021 "sneeze," highlighting the vulnerabilities in the system. Market makers benefit from delayed settlement times, allowing them to optimize their trading strategies at the expense of other market participants.

The Story of ETFs and the RegSHO Time Machine

Further Topics

The Impact of GME on ETF Dynamics:
  • As the interest in options wanes and fewer ETF funds hold GME, the stock's price trends downward. Short volatility funds exploit this by leveraging the illiquidity in GME, making it easier to push the price down.
  • GME's illiquidity, due to the removal of shares from the DTC, inadvertently facilitates this price suppression.
The Mechanics of ETF Fund Flows and Failures to Deliver (FTDs):
  • High-volume flows in ETFs, particularly in funds like XRT, are leading indicators of FTDs. These flows often result in FTDs covered in T+6/8 days after large institutional transactions.
  • A recurring pattern exists where large fund flows in ETFs occur roughly every 69 days. If not counterbalanced by opposing flows, these can lead to FTDs, particularly when flow quantities exceed shares outstanding.
The Evolution of Retail Investor Strategies:
  • The retail investor community has evolved from "Buy & Hold" to more complex strategies like "Buy, Hold, Vote" and "Buy, Hold, DRS, Shop." The recent focus on direct registration and company support reflects a deeper engagement with the stock market and its intricacies.

The Story of ETFs and the RegSHO Time Machine

Supporting Materials

Extensive research papers and regulatory documents provide further insights into the complexities of ETF regulations, market dynamics, and the broader impact on capital formation.

Supporting Materials

For further reading and a more detailed breakdown of the unintended consequences of ETF trading, refer to the original Superstonk post.