Quantamental LLM Primer 2: Convert Sellside/Buyside Ideas into a Long/Short Alpha Capture Portfolio

Ask the AI to convert the Trading Strategy Recommendations from Part 1 into a L/S Alpha Capture portfolio of ETFs. Iterate several times to ensure the portfolio recommendations are sound.

• Sanity Check and provide feedback to AI to iterate on portfolio. Generate report with final recommendations. Run same process with another AI to compare its interpretation of the Trading Strategy.

• Using Prompt Engineer, compare these two separate AI-driven reports, in both AIs to highlight similarities and differences between the models. Resolve to a final portfolio with this Sanity-Check comparison

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Methodology and Risks with LLMs such as ChatGPT:

• As described in Part 1, this report is constructed by re-writing the outputs from several AI-driven consolidated insights and iterating with an AI-assisted Prompt-Engineering tool.

• Leveraging powerful tools such as ChatGPT requires vigilance, and quality controls are required to ensure the insights that the AI derives are reliable.

• In Part 2, we continue to iterate with concise prompt instructions and review its output for reasonableness and consistency with the recommendations from the Trading Strategy devised with Sellside/Buyside Expert Insights.

• Throughout the process, several AI mistakes were caught and corrected because the AI provides unstable, non-deterministic, or incorrect results.

• In Quantamental LLM Primer (Step 3), we will include the detailed step-by-step prompts provided to ChatGPT. Separating the process into concise steps reduces some risks that the AI will "hallucinate" and provide non-sensical, and/or inconsistent results. In this case, we will add quality-controls by comparing the outputs from two parallel processes in addition to the highly manual human process of reviewing the AI's output.

Market-Neutral Long-Short Portfolio Recommendation

Detailed prompt instructions are included in the attached PDF Report we designed in Step one. To see the messy back and forth with ChatGPT's Prompt Engineer, see Portfolio Recommendation Prompt Engineer.

Objective

The goal of this report is to recommend a market-neutral long-short portfolio using ETFs, reflecting global macroeconomic trends, sector-specific insights, and quantitative factors (momentum, value, quality) as outlined in the Q4 2024 Stock Market Outlook Report. The portfolio aims to express views on sectors, regions, and global factors while balancing long and short positions to achieve a neutral market beta.

Key Insights from the Q4 2024 Stock Market Outlook

• The report outlines several important trends that shape the recommendations:
  Interest Rates: Falling rates in the U.S. and Europe are supporting value and quality strategies, especially in defensive sectors like healthcare and consumer staples.

• Momentum: Strong momentum in technology, particularly in AI and cloud computing, is driving returns in the U.S. However, concerns about overvaluation are raised, particularly in speculative sectors like AI.

• Regional Outlook:

  • The U.S. continues to lead in technology and healthcare.

  • Europe presents opportunities in financials and industrials as inflation moderates.

  • Japan shows momentum in industrial and consumer sectors, driven by stimulus and wage growth.

  • China poses a mixed picture with risks in real estate and consumer discretionary but opportunities in technology and industrials.

  • Broader emerging markets like South Korea and India show resilience and growth potential.

  • Sector Focus: Technology, healthcare, and financials are expected to perform well, while risks are high in real estate, consumer discretionary, and speculative tech.

Portfolio Design Criteria

The portfolio must:

1. Capture regional and sector-specific opportunities in alignment with the report’s outlook.

2. Balance long positions (sectors/regions expected to outperform) and short positions (sectors/regions expected to underperform).

3. Utilize quantitative factors like momentum, value, and quality to guide ETF selection.

4. Remain market-neutral by balancing beta exposure through both long and short allocations.

Evolution of the Portfolio Recommendations

1st Iteration Portfolio: Baseline Long/Short Strategy

The initial portfolio focused on capturing momentum in U.S. technology, quality in U.S. healthcare, and value in European financials. It also included emerging markets as a growth opportunity, while shorting U.S. AI stocks (BOTZ) to hedge against overvaluation risks.

2nd Iteration Portfolio: Adding China-Specific Long/Short Positions

In the second iteration, the portfolio incorporated China-specific opportunities, reflecting the report’s mixed view of the country:

• Long: Chinese technology (KWEB) and industrial sectors (CHII), which showed momentum and government backing.

• Short: Chinese real estate (CHIR) and consumer discretionary (KURE), reflecting systemic challenges in the property market and weak consumer confidence.

3rd Iteration Portfolio: Refining Sector Shorts and Emerging Market Exposure

The third iteration further refined the portfolio:

• Reduced exposure to emerging markets to better balance regionally.

• Introduced European industrials (ENOR) as a short, due to geopolitical and energy supply risks.

• Introduced U.S. consumer discretionary (XLY) as a short, reflecting weaker consumer demand and stretched valuations.

4th and Final Iteration Portfolio: Switching to ARKK for Speculative Tech Exposure

The final iteration incorporated the feedback and made the following adjustments:

• Replaced BOTZ (Global X Robotics & AI ETF) with ARKK (ARK Innovation ETF) to capture a broader short position in speculative innovation sectors.

• Refined weights to better balance the portfolio across regions and sectors.

ChatGPT 4.0 ["Final" (but really Preliminary)] Portfolio Recommendation (4th Iteration)

Preliminary AI/LLM Generated Long/Short ETF Portfolio Recommendations.

[Final (but Preliminary)] Conclusion

This [Final (but preliminary)] market-neutral long-short portfolio reflects a well-balanced approach, incorporating insights from global regions, sectors, and quantitative factors. The focus on specific regional opportunities (e.g., China, U.S., Europe) and targeted short positions (e.g., speculative tech, Chinese real estate) allows the portfolio to capture upside momentum while hedging against downside risks. The final iteration includes refined positioning in speculative sectors through ARKK, making it a robust strategy for navigating the macroeconomic landscape outlined in the Q4 2024 Stock Market Outlook.

In Step 3, we will run the same parallel process with ChatGPT's o1-preview, that "Uses advanced reasoning."

In Quantamental LLM Primer (Step 3), we will include the detailed step-by-step prompts provided to ChatGPT to compare the output from ChatGPT 4.0 vs. o1-preview.

Separating the process into concise steps reduces some risks that the AI will "hallucinate" and provide non-sensical, and/or inconsistent results. In this case, we will add quality-controls by comparing the outputs from two parallel processes in addition to the highly manual human process of reviewing the AI's output.

Computers probably shouldn't "assume" anything!

"As of my knowledge cutoff in October 2023, I don't have current information about interest rate trends or the specific economic conditions for Q4 2024. Therefore, I shouldn't have assumed a falling interest rate environment."

I should have asked “How did you know interest rates were falling as at October 2023?”