LLM Primer 3: GPT Model Quality Control Portfolio
This report outlines a Quality-Control process to compare the output recommendations from two different AIs: ChatGPT 4.o and o1-preview (uses advanced reasoning) in order to identify and analyze inconsistencies in the AI’s interpretation of the same inputs and prompts. The report from (Part 2)1 is introduced to both AIs as an input with the same instructions.
The output of that process produces two separate Long-Short Portfolio Recommendations. These two reports are then reintroduced to ChatGPT 4o with Prompt Engineering Instructions to ensure the comparison and analytics are done thoroughly to assist in finalizing a high-quality market-neutral portfolio recommendation.
We continue to issue caveats similar to the first Primers (Part 1 and Part 2):
Following the same logic as previous Primers, 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 fact, this entire report is simply a quality-control Step.
We continue to iterate with concise prompt instructions and review its output for reasonableness and consistency with the recommendations from the Trading Strategy with Sellside/Buyside Expert Insights to devise an Alpha Capture Portfolio.
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 4), we will include the detailed step-by-step prompts provided to ChatGPT for iterating over the portfolio to ensure it is Market (Beta) Neutral. Separating the process into concise quality-control steps reduces the risk that the AI will fail to implement an Alpha Capture Portfolio, without taking market risks. We will calculate Betas for the ETFs to provide Beta-adjusted weights. Following that, we will construct a backtest and simulations over various scenarios to get comfortable with the final portfolio we recommend.
1) Prompt Engineering Instructions
You are tasked with conducting a detailed comparison between two portfolio recommendation documents. The goal is to quality control the recommendations and settle on a final consolidated market-neutral portfolio. The two documents to compare are:
4th Iteration of portfolio recommendations (from earlier iterations).
Portfolio recommendations provided by the o1-preview model.
Your task is to:
Compare Long and Short Positions: Identify differences in recommended sectors, regions, or factors for long and short positions in both documents. Provide a side-by-side comparison table of these differences.
Compare Portfolio Allocation: Highlight how the allocation of weights differs between the two documents. If the portfolio allocation differs significantly, explain the rationale for these differences and indicate which allocation aligns better with the Q4 2024 outlook.
Compare Rationale for Recommendations: Review the rationale behind each portfolio's long and short positions. Summarize key differences in reasoning, particularly for sectors that differ in recommendations between the two documents. Ensure that the rationale aligns with the falling interest rate environment, inflation expectations, and other macroeconomic factors described in the Q4 2024 outlook.
Highlight Consistency with the Outlook: Double-check each document's reasoning against the Q4 2024 outlook. For instance, if one portfolio has positions that contradict the expected macroeconomic trends (such as assuming rising interest rates), mark this as a potential issue.
Final Portfolio Recommendation: Based on the comparison, propose a final consolidated portfolio that reflects the best elements of both recommendations. If certain positions from one document are clearly more aligned with the outlook, prioritize those.
Highlight Key Differences: As part of your final summary, highlight the key differences in sector/factor exposure and weight allocation between the two documents. Provide a recommendation on which approach offers more consistency with the market conditions described in the outlook.
Ensure this comparison is done thoroughly to assist in finalizing a high-quality market-neutral portfolio recommendation.
2) Market-Neutral Portfolio Recommendation Comparison Report
This report presents the conviction-based market-neutral portfolio recommendation, reflecting conviction-based adjustments aligned with the Q4 2024 Stock Market Outlook. The report also compares the 4th Iteration Portfolio and the 01-Preview Portfolio, along with rationales for each recommendation.
3. Comparison of Long and Short Positions
We asked the AI to compare its Model Output with a second AI’s Model Output and asked it to highlight the Key Differences
4. Comparison of Portfolio Allocation
A high-level summary of differences in the Portfolios
2. Final AI-Based Consolidated/Merged Portfolio Recommendations
This section demonstrates how the AI merged the two portfolios, integrating the key themes and risks identified in the Q4 2024 Outlook.
The AI Merged the Portfolios and demonstrated a preference for the o1-Preview Model. It did a decent job, but lost sight of some over-arching principals set out by the Expert Outlooks we used to devise the Alpha Capture Outlook.
6) Rationale for AI-Merged Recommendations
4th Iteration Portfolio
U.S. Technology (QQQ) is a long position based on strong momentum in AI and cloud computing but expresses concerns over speculative tech via ARKK as a short position.
European Financials are recommended for their value and improving earnings prospects due to moderating inflation.
Japanese Industrials & Consumer are emphasized for momentum, driven by stimulus and wage growth.
Short positions focus on overvaluation risks (ARKK, speculative tech), geopolitical and energy risks (Europe Industrials), and regional risks in China (real estate, consumer discretionary).
01-Preview Portfolio
U.S. Tech (QQQ) is shorted due to concerns over inflated valuations in AI, with emphasis on overvaluation corrections.
Adds a Consumer Staples long position (XLP), focusing on defensive sectors benefiting from inflation stability and quality.
European Financials and Japanese Industrials align with the 4th Iteration Portfolio, leveraging value and momentum.
Greater focus on currency hedging in Japan (DXJ) and a defensive approach with Chinese large caps (FXI) as a short, avoiding direct long exposure to broader EM.
Consistency with the Q4 2024 Outlook
Interest Rates & Inflation: Both portfolios align with the falling interest rate and inflation environment by prioritizing defensive sectors like healthcare, financials, and consumer staples.
Tech Sector: The 4th Iteration Portfolio is bullish on U.S. tech (long on QQQ), while the 01-Preview is cautious (short on QQQ), reflecting a divergence in views on the valuation risks in tech.
China Exposure: 4th Iteration is more optimistic about China's technology and industrial sectors, whereas 01-Preview is more cautious with heavy short exposure (KWEB, FXI), which might better reflect systemic risks in China's economy.
Final Portfolio Recommendation
A consolidated portfolio would take the following elements from both documents:
Long Positions:
U.S. Healthcare (XLV) for its defensive quality (10-15%).
European Financials (EUFN) for value and earnings prospects (10%).
Japanese Industrials & Consumer (EWJ, DXJ) for momentum and currency hedge (10% each).
U.S. Consumer Staples (XLP) from 01-Preview for defensive exposure (5-10%).
Short Positions:
ARK Innovation (ARKK) to capture overvaluation in speculative tech (10%).
Chinese Large Caps (FXI) and Internet (KWEB) for systemic risks (5-10%).
U.S. Tech (QQQ) reflecting valuation concerns (5-10%).
This approach balances momentum and quality plays in long positions with cautious shorts in overvalued and risk-prone sectors like tech and Chinese equities.
Key Differences
Sector Exposure: The 4th Iteration is more bullish on U.S. tech and China, while 01-Preview takes a more defensive stance on these sectors.
Weight Allocation: 4th Iteration has higher long allocation (80%), while 01-Preview is more balanced (50% long, 50% short).
Currency Hedge: 01-Preview includes currency-hedged positions in Japan (DXJ), which adds risk management flexibility.
Based on the macroeconomic outlook, the 01-Preview Portfolio offers better consistency with a cautious approach, especially on China and U.S. tech, aligning more closely with concerns over systemic risks and overvaluation.
Summary of the Strategy:
Long Positions: Focus on momentum and quality plays in defensive sectors (like U.S. healthcare and consumer staples) and value plays (European financials). Japanese exposure is balanced with both momentum and currency hedge to mitigate FX risks.
Short Positions: Focus on overvaluation risks in speculative sectors like U.S. tech (QQQ) and ARKK. The portfolio also hedges against systemic risks in Chinese equities, including large caps (FXI) and technology companies (KWEB), where regulatory challenges and economic slowdown pose risks.
This portfolio balances growth and defensiveness by combining quality, momentum, and value plays on the long side, while cautiously shorting risk-prone sectors like U.S. tech and Chinese equities.
7. Summary of Prompt Engineering Iterative Updates
The AI did a good job of comparing the two different model outputs and synthesized a final portfolio recommendation. However, I was unsatisfied with the outcome. We continued testing various assumptions as I asked additional questions to vet the logic it used to implement the Alpha Capture Outlook. I ultimately reached the conclusion that the Consolidated AI View did not adequately construct a market neutral portfolio:
The naive implementation simply provided weights that added up to +50% and -50%. I liked this general goal for a Conviction-based model, but it did not contemplate Market Risk (Beta) in the weighting scheme. So we iterated back over the weighting methodology to better reflect the conviction from the Alpha Capture Outlook.
The Consolidated View did not adequately explain the rationale between ChatGPT 4o’s +15% Long recommendation of QQQ vs. 01-Preview’s -15% Short recommendation.
Additionally, I was unconvinced that the Consolidated Portfolio only includes a Short Position in Tech. It essentially netted the two recommendations and took QQQ out of the portfolio, while maintaining a short position in ARKK -10%. It should further be noted that the 01-Preview Portfolio was short-biased to the entire Sector: Short -15% QQQ and Short -5% ARKK.
I did prefer ChatGPT 4o’s logic to essentially set up a Sector-Hedged Pair trade with +15% QQQ and Short -10% ARKK (Long Large Cap Tech Momentum vs. Short Overvalued “Speculative Innovation Tech”). We implemented my recommendation to provide a Tech Sector-Neutral +20% Long QQQ vs. -20% Short ARKK. Subsequently, I asked the AI to explain the rationale behind this “human-based” decision and I was satisfied with the answer, despite the fact that the same AI needed to be prompted for this very logic. See Section 8 for further elaboration (generated by AI) to summarize the reasoning I applied.
The Model opted to include the o1-preview based recommendation of an unhedged short position in FXI (Chinese Large Caps). This was the last mistake I caught before deciding I was comfortable with the portfolio. Rather than simply go long EEM as ChatGPT 4o vs. Short China Real Estate, I opted to keep some diversity and upside from the Long Exposure Emerging Markets Exposure, but to isolate the upside of China Stimulus by going Long FXI and Long EEM vs. Short China Real Estate.
Once we have a Conviction-based weighting scheme, we can go through the next step of constructing a Beta-Adjusted re-weighting to derive a Beta-Neutral portfolio. Following that step, we will still need to backtest and simulate the portfolio to ensure we are not taking market risk and only betting on the Alpha we want from the Alpha Capture Portfolio. We will inevitably adjust the weights at least twice more.
8) AI-Updated Rationale from Iterative Prompting: “Conviction-Based Adjustments
9) Rationale for Rejecting the Short QQQ Recommendation
Strength in Core Tech Fundamentals: While concerns about overvaluation exist, the large-cap tech companies in QQQ (such as Apple, Microsoft, Alphabet) are fundamentally strong and continue to show robust earnings, cash flow, and growth potential. These companies are market leaders in key growth sectors like AI and cloud computing, which are not speculative bubbles but drivers of future economic growth. We view these firms as essential players in the next phase of tech innovation, supported by tangible earnings growth, rather than speculative excess.
Momentum and Leadership: The momentum in U.S. tech is sustained by real advancements in technology and the structural shift toward digital economies. The Q4 2024 Stock Market Outlook emphasizes a falling interest rate environment, which tends to benefit long-duration assets like tech stocks. Lower interest rates make future cash flows more valuable, supporting the valuation for high-growth tech companies in QQQ.
Pair Trade Opportunity: Rather than shorting the broader tech sector, we saw an opportunity to capitalize on the divergence between high-quality, profitable tech (QQQ) and speculative, unproven innovation stocks (ARKK). By going long QQQ and shorting ARKK, we hedge out the speculative risk in tech while maintaining exposure to the sector's strongest players. This pair trade allows us to capture alpha by isolating quality and growth from overvaluation in speculative tech sectors.
10) Rationale for Rejecting the Short FXI Recommendation
Chinese Government Stimulus: The Q4 2024 Stock Market Outlook highlights the potential for Chinese stimulus efforts to stabilize and support large-cap Chinese companies. The Chinese government has repeatedly shown its willingness to step in and provide support to key sectors, particularly state-owned enterprises (SOEs) and strategic industries, which form a large part of the FXI index. Shorting these firms would be contrary to the current macroeconomic trends, where government intervention is expected to stimulate demand and prop up the larger, higher-quality companies in China.
Quality in Large Caps: FXI is heavily weighted toward large-cap, well-capitalized companies that are more resilient to macroeconomic shocks. These firms tend to benefit the most from stimulus and government support, particularly in sectors like finance, infrastructure, and technology. While there are structural challenges in the Chinese economy, the larger, more diversified firms in FXI are better positioned to navigate these issues compared to smaller, more speculative companies.
Sector-Specific Hedging: Instead of shorting all Chinese large-cap stocks via FXI, we chose to hedge risks in Chinese real estate by going short CHIR (Chinese Real Estate ETF). The real estate sector in China remains the most exposed to debt crises, liquidity risks, and regulatory tightening, making it a more appropriate target for a short position. This allows us to maintain exposure to high-quality Chinese companies while avoiding the most vulnerable sectors. This decision aligns with the macroeconomic view that stimulus will benefit high-quality firms but may not rescue the deeply troubled real estate sector.
11) Tech Sector: Long QQQ / Short ARKK Pair Trade
The long QQQ / short ARKK pair trade captures the divergence within the technology sector. Long QQQ reflects confidence in high-quality, large-cap U.S. tech companies that benefit from strong growth in AI, cloud computing, and digital transformation. Short ARKK captures downside risks from speculative tech companies vulnerable to corrections due to overvaluation.
12) China Exposure: Long FXI / EEM and Short CHIR Pair Trade
This pair trade isolates upside in Chinese quality stocks while hedging against risks in Chinese real estate. Long FXI and EEM capture growth from government stimulus, while shorting CHIR mitigates the sector-specific risks in China's real estate sector.
13) Alignment with the Q4 2024 Outlook
The portfolio aligns with key themes in the Q4 2024 Outlook, focusing on interest rates, inflation, sector divergences, and China's stimulus efforts. Defensive sectors like healthcare (XLV) and consumer staples (XLP) are expected to benefit from falling interest rates, while the tech sector divergence is captured through the QQQ and ARKK pair trade. Chinese real estate risks are hedged by shorting CHIR, while growth is captured through long FXI and EEM positions.
The AI Provided the side-by-side comparison between its Consolidated/Merged Portfolio and the Conviction-Based Portfolio.
Summary of Key Adjustments:
Increased Conviction in U.S. Tech (Long QQQ): The original recommendation was to short QQQ due to concerns about overvaluation. However, conviction-based adjustments led to long QQQ, recognizing the strength in large-cap U.S. tech companies like Apple and Microsoft, driven by momentum in AI and cloud computing.
Focused Hedging on Chinese Real Estate (Short CHIR): Instead of a broad short on Chinese equities (FXI and KWEB), conviction adjustments targeted short Chinese real estate (CHIR), reflecting systemic risks in that sector, such as debt and liquidity challenges.
Increased Weight on Japan: Japan Industrials & Consumer (EWJ) saw its weight increased to 12%, consolidating both unhedged and hedged positions into a single exposure, recognizing strong momentum from stimulus-driven growth.
Doubling Down on Short ARKK: The weight of short ARKK was increased to -20%, reflecting heightened conviction that overvaluation risks in speculative tech sectors would lead to a significant correction.
This table effectively captures the strategic shifts that were made to align the portfolio more closely with the Q4 2024 Stock Market Outlook and the conviction-driven adjustments, providing a balanced approach to alpha generation while managing downside risks.
14. Final Portfolio Overview
Following Iterative Prompting, we settled on the above Portfolio and asked ChatGPT to reiterate the rationale
