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Evaluating Option-Based Strategies and Dollar-Cost Averaging

Sun, 29 Mar 2026 00:14:22 -0000

In past issues, we discussed popular investment strategies such as covered calls and collars. In this post, we continue by examining other strategies, focusing on their performance, limitations, and how they behave under different market conditions.

Reexamining the Performance of Passive Options Strategies

More than 40 years ago, Merton et al. published two papers [1,2] examining the performance of passive options strategies. They concluded that these strategies outperformed the traditional buy-and-hold approach. At the time of their studies, options data was not widely available, so they used historical volatility to calculate options prices. Merton et al. conducted their research by simulating the impact of options on two portfolios: a broad market proxy of 136 equities and the Dow Jones 30 index. Using a twelve-year period, the backtest incorporated historical volatility and applied the Black–Scholes-Merton model to price the options.

Since then, the options market has become highly liquid, with significant structural changes. A recent article [3] reexamines the strategies studied by Merton et al., along with additional strategies, using actual options data from the period 2012 to 2023. The strategies studied include Call-Write strategies (with seven variants), Put-Write strategies (with two variants), and the Protective Put (PPUT) strategy.

Findings

-Early studies showed that passive option strategies could outperform the underlying index on a risk-return basis.

-The options market has evolved significantly, from open outcry and single listings to a high-frequency, electronic environment.

-The findings suggest that the original strategies no longer provide favorable risk-adjusted returns and that earlier results may have been driven by simplifying assumptions.

-Recent evidence indicates that simple option strategies generally do not add value to portfolios.

-However, certain dynamic option strategies can still outperform the S&P 500 on a risk–return basis.

-Incorporating simple market regime signals can improve the performance of these strategies.

-The PPUT strategy consistently outperforms the S&P 500 on a risk-adjusted basis.

-A modified PPUT strategy, which avoids puts after a one-standard-deviation drawdown, delivers higher returns with lower risk.

-The outperformance may be driven by the widespread use of covered call strategies, which suppress implied volatility and underprice tail risk.

In short, none of the simple options strategies have outperformed the S&P 500. Interestingly, the PPUT strategy outperforms the buy-and-hold approach on a risk-adjusted basis, and the VIX is shown to be an effective regime filter.

Reference

[1] Merton, Robert C., Myron S. Scholes, and Mathew L. Gladstein. 1978. The Returns and Risk of Alternative Call Option Portfolio Investment Strategies. Journal of Business 51: 183–242.

[2] Merton, Robert C., Myron S. Scholes, and Mathew L. Gladstein. 1982. The Returns and Risks of Alternative Put-Option Portfolio Investment Strategies. Journal of Business 55: 1–55.

[3] Andrew Kumiega, Greg Sterijevski, and Eric Wills, Black–Scholes 50 Years Later: Has the Outperformance of Passive Option Strategies Finally Faded?, International Journal of Financial Studies 12: 114.

The Effectiveness of Dollar Cost Averaging Under Varying Market Conditions

Dollar-cost averaging (DCA) is an investment strategy where you invest a fixed amount of money at regular intervals, regardless of market conditions. By consistently buying over time, you smooth out entry prices, reduce the impact of short-term volatility, and avoid the risk of mistiming the market with a single large purchase.

DCA has often been presented as an effective portfolio management technique, and financial advisors and brokers encourage clients to adopt it. But is it truly effective, or merely a marketing scheme?

Reference [4] critically examined this question. The study employed Monte Carlo simulation, rather than historical backtesting, to explore the issue. Specifically, the authors utilized Geometric Brownian Motion (GBM) to simulate stock prices under various market conditions.

Findings

-DCA involves investing a fixed amount at regular intervals and is commonly used for risk mitigation.

-The analysis uses Monte Carlo simulations based on geometric Brownian motion to generate price paths.

-The study compares DCA with a Buy-and-Hold (B&H) strategy across varying levels of market drift and volatility.

-The results show that DCA underperforms B&H in steadily rising and stable markets.

-DCA provides better risk-adjusted performance in highly volatile market environments.

-Market volatility and transaction frequency are key drivers of DCA performance.

-Lower transaction frequency improves the effectiveness of the DCA strategy.

-The study highlights the importance of adjusting DCA parameters based on market conditions.

In brief, DCA is effective when volatility is high; otherwise, it underperforms buy-and-hold. Further, the paper leads to interesting questions about the validity of position-sizing techniques such as scaling in and out.

Reference

[4] Siyuan Sang, Ru Bai, Haibo Li, The Dynamic Relationship Between Market Volatility and Dollar Cost Averaging Strategy Returns: An Empirical Investigation, in Proceedings of the 2025 3rd International Academic Conference on Management Innovation and Economic Development (MIED 2025)

Closing Thoughts

In this issue, two strands of research on investment strategies are discussed. The first revisits option-based strategies and shows that simple passive approaches no longer deliver attractive risk-adjusted returns, although more dynamic strategies, especially those incorporating regime signals, can still add value. The second examines Dollar-Cost Averaging and shows that its effectiveness is highly dependent on market conditions, underperforming in stable markets but offering advantages in volatile environments. Taken together, the results suggest that simple, static strategies are no longer sufficient, and that performance increasingly depends on adapting to market regimes and implementation details.

Learn More Here: Evaluating Option-Based Strategies and Dollar-Cost Averaging

Large Language Models in Trading: Models and Market Dynamics

Mon, 06 Apr 2026 23:50:36 -0000

I just returned from a two-day conference in New York, FutureAlpha (formerly QuantStrats). This year, the theme focused largely on data, machine learning, and AI. While some speakers were very enthusiastic about the potential of AI to generate alpha, our panel was more conservative. The consensus among the panelists was to use ML and AI to enhance and improve risk management. Along this theme, in this post, I discuss the use of generative AI in trading.

Integrating Structured and Unstructured Data with LLMs and RAG

Traditional quantitative methods often rely on structured data, such as time series. With the emergence of Large Language Models (LLMs), it is now possible to process unstructured data. A new line of research focuses on integrating unstructured data analysis into traditional frameworks.

Along this line, Reference [1] proposed the use of LLMs together with retrieval-augmented generation (RAG) to process both structured and unstructured data concurrently. Specifically, the authors developed a system that first applies LLMs to detect regime shifts using time-series techniques, then employs RAG to integrate external knowledge into the model’s decision-making process. By retrieving relevant information from a vector database and combining it with the model’s capabilities, RAG improves both the interpretability and effectiveness of trading strategies.

Findings

-The paper studies methods for fine-tuning open-source Large Language Models to enhance quantitative trading strategies.

-It integrates numerical data, such as prices and technical indicators, with textual data, including news and sentiment.

-The approach uses Retrieval-Augmented Generation with a vector database to process and contextualize textual information.

-The study focuses on fully fine-tuning smaller models to achieve cost efficiency and scalability.

-It proposes a hybrid framework that combines LLM capabilities with traditional quantitative methods.

-The framework incorporates real-time data pipelines and adaptive model tuning.

-The results show improvements in predictive accuracy and risk-adjusted returns.

-The integration of multimodal data helps address challenges in combining structured and unstructured information.

-Fine-tuned smaller models improve regime detection and trading decision accuracy while maintaining efficiency.

-Additional techniques enhance model performance and robustness, supporting practical applications in quantitative finance.

In short, incorporating RAG into the framework enhances the model’s ability to understand complex macroeconomic environments and adapt trading strategies as conditions evolve. Experimental results show significant gains in predictive accuracy and risk-adjusted returns, demonstrating the practical value of these fine-tuning methods in finance.

Reference

[1] Li, C., Chan, C.H.R., Huang, S.H., Choi, P.M.S. (2025). Integrating LLM-Based Time Series and Regime Detection with RAG for Adaptive Trading Strategies and Portfolio Management. In: Choi, P.M.S., Huang, S.H. (eds) Finance and Large Language Models. Blockchain Technologies. Springer, Singapore.

Can AI Trade? Modeling Investors with Large Language Models

The previous paper focuses on improving trading performance by integrating LLMs with quantitative models and data, while another line of research explores how LLMs behave as autonomous agents within market environments.

Reference [2] utilized LLMs to construct trading agents in the financial markets. Specifically, the author used LLMs to emulate various types of investors: value investors, momentum traders, market makers, retail traders, etc.

Findings

-The paper develops a simulated stock market in which large language models act as heterogeneous trading agents.

-The framework includes realistic market features such as an order book, market and limit orders, partial fills, dividends, and equilibrium clearing.

-Agents operate with different strategies, information sets, and endowments, and communicate decisions using structured outputs while explaining reasoning in natural language.

-The results show that LLMs can consistently follow instructions and implement strategies such as value investing, momentum trading, and market making.

-LLM agents process market information and respond meaningfully to prices, dividends, and historical data.

-The simulated market exhibits realistic dynamics, including price discovery, bubbles, underreaction, and liquidity provision.

-The framework enables controlled analysis of agent behavior under different market conditions, similar to interpretability methods in machine learning.

-It provides a cost-effective way to test financial theories that lack closed-form solutions.

-The study highlights that LLM behavior is highly sensitive to prompts, which can lead to correlated actions across agents.

-This correlation may amplify volatility and introduce systemic risks, emphasizing the need for careful testing before real-world deployment.

In short, the article concluded that trading strategies generated by large language models are effective, but could introduce new systemic risks to financial markets because these agents would act in a correlated manner.

Reference

[2] Alejandro Lopez-Lira, Can Large Language Models Trade? Testing Financial Theories with LLM Agents in Market Simulations, arXiv:2504.10789

Closing Thoughts

In this issue, the discussion highlights two complementary directions in applying LLMs to finance. On one hand, integrating LLMs with quantitative models and multimodal data can improve predictive accuracy and risk-adjusted returns. On the other hand, treating LLMs as autonomous trading agents reveals how their behavior can shape market dynamics, including liquidity, price discovery, and potential instability. Taken together, the results suggest that while LLMs offer meaningful opportunities in trading and risk management, their impact depends critically on implementation, prompting, and control of system-wide behavior.

See Full Article Here: Large Language Models in Trading: Models and Market Dynamics

Nvidia Shares Muted After Fluctuation; Iran Assesses Trump's Proposal | Bloomberg Brief 5/21/2026

Thu, 21 May 2026 11:49:07 -0000

Iran Says Uranium Should Not Be Sent Abroad, Reuters Reports

Thu, 21 May 2026 11:50:19 -0000

Retail investors get direct access to SpaceX IPO through major brokerage platforms

Thu, 21 May 2026 12:16:28 -0000

SpaceX's blockbuster public offering is giving everyday traders access that has traditionally been reserved for Wall Street's biggest clients.

My toddler started having tantrums, and nothing we did was working. Then, I read a book that changed everything.

Thu, 21 May 2026 12:18:01 -0000

The author's daughter had always been bubbly, but started having tantrums at 2 ½.

Courtesy of Sari Caine

When my bubbly toddler hit developmental changes at 2 ½, she suddenly began throwing tantrums.
My husband and I couldn't agree on how to handle it. We argued, and her tantrums continued.
A friend sent me a book with techniques that work for us, and soon our daughter's tantrums ceased.

Neither my husband nor I had tantrums. Grady is of the boomer generation and grew up in the Smoky Mountains. Being tough was a prerequisite for survival, respect for authority started young, and everybody in town could impose it.

I grew up in New York City in the 1980s. Tantrums weren't in my nature. So, despite my husband's previous experience taking parenting classes as a single dad decades ago, and my 30 years of teaching ages 5 and up, our daughter Violet's terrible-tweens rocked us.

Violet's the unexpected bonus from starting our lives over together at ages 56 and 40. People told us how lucky we were, commenting on her joyful disposition. Then, just like the stats predicted, when she turned 2 ½, huge physical, intellectual, and emotional growth spurts arrived, along with tantrums. That was four months ago; we've been in a period of trial and error since then.

The author and her husband during the first week they were dating.

Courtesy of Sari Caine

I researched online and regretted it

Violet is overly logical like her daddy, so it was startling when she'd throw herself to the floor, making demands, and shouting "No!" to things she'd asked for moments ago, like popsicles or bubbles.

At night, I dove into online rabbit-holes that I knew wouldn't help but couldn't stop exploring: "How to teach toddlers self-regulation?" "Is the two-minute timeout rule really viable?" and, perhaps most embarrassingly, one titled, "How to tell if tantrums are normal?" even though Grady and I are both neurodiverse and know "normal" isn't a metric.

Most articles alarmed me with "red flags." Reddit was more good-humored and comforting. I adapted the 12-step recovery acronym H.A.L.T., which stands for "hungry, angry, lonely, or tired," to distinguish meltdowns (requiring care) from tantrums (requiring boundaries). I wish I'd slept instead.

During playgroups, I'd ask other moms about tantrums, but according to them, their kids just occasionally melted down when hungry or over-tired, making me wonder, where were all the kids the internet was obsessed over?

Our techniques failed and stressed our marriage

My husband tried longer timeouts, explanations, and walking away (she followed). I tried teaching her meditation, which she enjoyed, but not enough to practice mid-tantrum. My suggestion that she needed a hug inspired her to quit shouting "NOOOO!" in favor of "I NEED A HUG!!" but didn't ultimately change her tantrums. I began to worry I was contributing to a negative attention spiral, and that giving her any kind of attention while acting out would be seen as a reward for misbehaving and cause her to continue the same behavior.

Those four months were exhausting, strained our marriage, and led to arguments. I told Grady his years working in rehabs with addicts distorted his perspective. He retorted that easygoing families were their biggest enablers. We couldn't agree on what "normal" toddler development was. When Violet shouted "NO!" I'd undermine my husband's responses, saying it was important for little girls to learn to assert themselves.

Until a showdown in a playground: After playing for hours, we had to leave for my work meeting. Violet threw herself into a mudhole, screaming. The other moms watched sympathetically. I knew it was time for a consequence.

I got into my car and pretended to drive away.

Violet came running, but I felt something between us snap.

The author started 'feeding the meter' to avoid her daughter's tantrums.

Courtesy of Sari Caine

We found alternatives that work for us

A New York friend with college-age kids sent us Harvey Karp's, "The Happiest Toddler On The Block." Despondently, I paged through during bathtimes, underlining parts for Grady. As a chess teacher, I taught children from kindergarten through high school, always connecting with them by treating them like little adults.

I realized I'd inherited that from my mom, who refused to talk down to kids. I was dubious about Karp's "Play the boob," or "Feed the meter" techniques to avoid tantrums, which basically suggest pretending to be goofy or clumsy to make them laugh, and making sure to give them small moments with you throughout the day, respectively. But when I accidentally dropped the soap, Violet laughed so hard I did it again. She giggled so much, she peed.

Karp's suggestion of narrating what Violet was doing in cave-speak at eye-level: "Me no room!" made her scream louder. That night, we tried another technique, recounting her wins and trials of the day, then going over expectations for tomorrow. She wound her arms around my neck, saying, "You're my best girl."

Saturday, I "fed the meter": Every hour (or three) for five minutes, I gave Violet my undivided goofiest attention. To my surprise, fewer interruptions followed. But before my class, she grew stormy. After five minutes of pretending she was hot lava and getting chased by her around the kitchen island, she voluntarily went into her room.

While cooking supper, instead of responding to overtures of playtime with "Not right now," I could almost always fake-barf over "stinky feet" or do a quick game of hide-and-seek.

These "pockets of fun" — my term — make all of us more willing to listen, be flexible, and respect rules. The more I invest in them daily (like a savings account in the bank, Karp says), the richer our relationships grow. Violet turned three last Friday. We've barely had a tantrum in months.

These 'pockets of fun' help me, too

Understanding play can happen in short bursts, and not just large blocks, helps me be more playful and relaxed, lessening my anxiety. Grady feels brighter, too, though he swears it's the weather. I don't know what tomorrow holds, but if I respond from my most present place, chances of it being a more positive one are higher.

Read the original article on Business Insider

SpaceX is sitting on a massive hoard of bitcoin

Thu, 21 May 2026 12:26:16 -0000

Elon Musk's SpaceX holds almost 19,000 bitcoins, according to its IPO filing.

WEF

SpaceX owns around 19,000 bitcoins, according to its IPO paperwork.
At Thursday's prices, the bitcoin holding is worth close to $1.5 billion.
Elon Musk has long been an advocate of crypto, with Tesla also holding a major bitcoin pile.

SpaceX's IPO filing on Wednesday revealed many striking details about the company's finances, including its sizable bitcoin holding.

According to the firm's S-1 — the SEC form that officially begins the IPO process — the company owns 18,712 bitcoin units. Bitcoin was trading at just over $77,000 on Thursday morning, giving SpaceX's crypto pile a value of roughly $1.45 billion and paper gains of about $789 million.

The S-1 shows that SpaceX bought the bitcoin for $661 million, meaning the value of each unit of the cryptocurrency at the time of purchase was roughly $35,000. Bitcoin last traded at $35,000 in November 2023. In August that year, it was reported that SpaceX had sold a significant chunk of its bitcoin holdings.

CEO and founder Elon Musk has long been an advocate of digital currencies, backing the viral meme cryptocurrency Dogecoin, and holding a large chunk of bitcoin in the coffers of Tesla.

In a first-quarter filing, Tesla said it held more than 11,000 bitcoins, valued at close to $900 million at Thursday's prices.

In early 2025, Tesla reported a major boost to its quarterly profits, driven by a surge in bitcoin prices, which pushed income in the final quarter of 2024 up by $600 million.

While SpaceX's bitcoin pile is substantial, it pales in comparison to those held by some firms. The largest corporate holder of bitcoin — business intelligence giant Strategy Inc. — holds more than 843,000 bitcoin, worth more than $64 billion.

Musk has embraced the meme side of cryptocurrencies, stating in 2021 that SpaceX would "put a literal Dogecoin on the literal moon."

The rocket company hasn't done so yet, but it is aiming to establish a permanent presence on the moon. Its S-1 filing outlined an even bolder pay package target for Musk: setting up a human colony on Mars with "at least one million inhabitants."

Read the original article on Business Insider