Investing Expertise

Can Large Language Models (LLM) inject lookahead bias into backtests when rigor is lacking in generation of LLM training samples? In their preliminary and incomplete March 2024 paper entitled “Lookahead Bias in Pretrained Language Models”, Suproteem Sarkar and Keyon Vafa examine the potential for lookahead bias in backtests using the Llama-2 LLM to identify future firm risks based on content of earnings calls. They consider cases for which: (1) the backtest falls within the LLM training sample, but the researcher tells the LLM to consider only information before the test period; and, (2) the researcher specifies a training sample that ends before the backtest but generates it long after the end of the training sample. Using Llama-2 to interpret transcripts of selected firm earnings calls from 2018, they find that:

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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

Can artificial intelligence platforms such as ChatGPT be good stock day traders? In his March 2024 paper entitled “Can ChatGPT Generate Stock Tickers to Buy and Sell for Day Trading?”, Sangheum Cho tests whether ChatGPT 3.5 turbo supports profitable day trading. He instructs ChatGPT to pretend to be a professional day trader who picks from among U.S. listed stocks 100 to buy and 100 to sell for short-term returns based on daily Bloomberg and the Wall Street Journal news blurbs on Twitter. Each day, prior to the market open, he:

• Uses the Refinitiv Eikon News Monitor to collect the selected tweets from the past 24 hours. He removes hyperlinks and duplicate tweets.
• Segments the tweets into batches to accommodate ChatGPT processing limitations.
• For each batch, asks ChatGPT to generate 100 BUY and 100 SELL signals, with 30 iterations for each batch to amplify signals by suppressing spurious selections. He then constructs equal-weighted long and short portfolios of stocks with signals.
• For each stock with signals:
  • Sums BUY and SELL signals across batches/iterations to calculate SUM_BUY and SUM_SELL signals. He constructs signal count-weighted long and short portfolios from these summed signals.
  • Subtracts SUM_SELL from  SUM_BUY to calculate NET_BUY and NET_SELL signals. He constructs signal magnitude-weighted long and short portfolios from these netted signals.

For each portfolio, he excludes stocks with zero daily volume, missing daily prices or incomplete trading histories for the previous five trading days. He measures returns from the market open to the market close. Using 222,659 tweets (only 16,359 of which are firm-specific) and daily opening and closing prices for U.S. listed common stocks during December 2022 through December 2023 (271 trading days), he finds that:

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Does finance professor David Kass, who presents annual lists of stock picks on Seeking Alpha, make good selections? To investigate, we consider his picks of:

• “10 Stocks for 2020”: APPL, BAC, BRK-A, WBD, JPM, MSFT, OXY, PH, SWK, STNE
• “16 Stocks For 2021”: GOOG, ABBV, AMZN, APPL, BAC, BRK-A, CABO, CHTR, LSXMK, META, MU, MSFT, PCG, PSTH, RBAC, RH
• “12 Stocks For 2022”: GOOG, AMZN, APPL, BAC, BRK-A, CVS, GPN, META, MSFT, NU, NVDA, TECK
• “10 Stocks For 2023”: ATVI, APPL, BRK-A, CVX, DHR, HSY, KHC, OXY, PEP, PGR

We compare the average return for stocks picks each year with that for SPDR S&P 500 ETF Trust (SPY) for the same year as a benchmark. Using dividend-adjusted returns from Yahoo!Finance for SPY and most stock picks and returns from Barchart.com and Investing.com for three picks during their selection years, we find that: Keep Reading

Some exchange-traded funds (ETF) focus on capturing potentially attractive factor premiums or thematic niches. Their histories offer a way to test these concepts live. We have conducted many such tests, listed here to offer a global view.

1. “U.S. Equity Premium?” – evidence from simple tests on about 21 years of data suggests that stock market leadership shifts between the U.S. and other developed markets over time, but the U.S. may be better overall.
2. “Tech Equity Premium?” – evidence from simple tests on 24 years of data suggests long boom, short bust for a tech/innovation-concentrated portfolio. It does not support belief in risk-adjusted outperformance.
3. “Measuring the Size Effect with Capitalization-based ETFs” – evidence from simple tests of capitalization-based ETFs with nearly 22 years of data offers little support for belief in a long-term, reliably exploitable size effect among U.S. stocks.
4. “Do Equal Weight ETFs Beat Cap Weight Counterparts?” – evidence from simple tests on some equal-weight U.S. equity ETFs offers little support for belief that equal weighting substantially and reliably beats capitalization weighting on a net basis.
5. “Measuring the Value Premium with Value and Growth ETFs” – evidence from simple tests with 21.6 years of available data does not support belief that investors reliably capture a value premium via popular value-growth ETFs.
6. “Are Equity Momentum ETFs Working?” – available evidence on attractiveness of momentum-oriented U.S. stock and sector ETFs is less than compelling.
7. “Are Stock Quality ETFs Working?” – available evidence offers little support for belief that quality ETFs reliably beat respective benchmarks.
8. “Are Low Volatility Stock ETFs Working?” – available evidence on attractiveness of low volatility stock ETFs is mixed, with recent data undermining belief in reliability of low volatility outperformance.
9. “Are Equity Multifactor ETFs Working?” – available evidence offers very little support for belief that equity multifactor ETFs beat their benchmarks, or that they offer material diversification with comparable performance.
10. “Are Hedge Fund ETFs Working?” – evidence on attractiveness of hedge fund-oriented ETFs is mostly negative.
11. “Are Managed Futures ETFs Working?” – available evidence on attractiveness of managed futures ETFs in aggregate (but with recent short-sample exceptions) suggests that any benefits from diversification of equities and fixed income are unlikely to compensate for poor absolute returns.
12. “Best Safe Haven ETF?” – evidence from simple tests over available and common sample periods suggests that silver, gold, longer-term U.S. Treasuries and investment grade corporate bonds are safe havens, while crude oil is clearly not.
13. “Do High-dividend Stock ETFs Beat the Market?” – evidence from data for high-dividend U.S. stock ETFs does not support belief that high-dividend stocks reliably outperform the broad U.S. stock market.
14. “Are ESG ETFs Attractive?” – available evidence suggests that ESG ETFs do not perform much differently from selected benchmarks.
15. “How Are Renewable Energy ETFs Doing?” – available evidence on attractiveness of renewable energy ETFs is adverse overall, but with bursts of market outperformance perhaps due to novelty.
16. “How Are Robotics-AI ETFs Doing?” – available evidence is that robotics-AI ETFs are less attractive than the broader technology exposure offered by QQQ.
17. “How Are AI-powered ETFs Doing?” – available evidence does not support belief that ETFs using AI to select and weight assets are particularly attractive.
18. “Are iShares Core Allocation ETFs Attractive?” – available evidence regarding attractiveness of iShares Core Asset Allocation ETFs is mixed to negative.
19. “Are Target Retirement Date Funds Attractive?” – evidence offers little support for belief that target retirement date mutual funds are preferable to simple stocks-bonds diversification.
20. “How Are TIPS ETFs Doing?” – available evidence on attractiveness of TIPS ETFs is mostly favorable after the recent inflation burst, with shorter duration funds offering more reliable inflation protection.
21. “Are Equity Index Covered Call ETFs Working?” – available evidence on attractiveness of equity index covered call ETFs as either substitutes for or diversifiers of underlying stock indexes is generally adverse.
22. “Are Equity Put-Write ETFs Working?” – available evidence on attractiveness of equity put-write ETFs is adverse.
23. “Are IPO ETFs Working?” – available evidence on attractiveness of IPO ETFs is mixed, requiring very high risk tolerance of interested investors.
24. “Are Preferred Stock ETFs Working?” – available evidence on attractiveness of preferred stock ETFs relative to a 60-40 stocks-bonds portfolio is largely negative.
25. “Do Convertible Bond ETFs Attractively Meld Stocks and Bonds?” – available evidence suggests that convertible bond ETFs sometimes outperform and sometimes underperform a conventional 60-40 stocks-bonds portfolio.
26. “Do ETFs Following Gurus/Insiders Work?” – available evidence on attractiveness of guru/insider-following stock ETFs is mostly adverse.
27. “Congressional Trade Tracking ETFs” – limited available evidence suggests that investors should choose a fund mimicking holdings of Democrat rather than Republican members of Congress.
28. “The Long and Short of Jim” – available evidence does not support belief that funds based on Jim Cramer’s stock/market recommendations reliably produce attractive short-term returns.
29. “Live Test of the Stock Market Overnight Move Effect” – early evidence does not support belief in exploitability of the overnight move effect.

The upshot of the above items is that academic factor research and thematic speculations rarely translate to outperformance when implemented with ETFs.

A global caution is that the period since 2009 is strong for broad equity indexes, driven by a few large-capitalization firms. This trend may not persist.

How do exchange-traded-funds (ETF) that employ artificial intelligence (AI) to pick assets perform? To investigate, we consider six such ETFs, all currently available, as follows:

• • Amplify AI Powered Equity ETF (AIEQ) – picks U.S. stocks based on a quantitative model that runs on the IBM Watson platform.
  • VanEck Social Sentiment ETF (BUZZ) – picks U.S. stocks with the most positive sentiment as determined from online sources via a rules-based quantitative methodology.
  • WisdomTree International Al Enhanced Value Fund (AIVI) – picks developed market value stocks outside the U.S. and Canada via a quantitative AI model. [We use data only since retention of an AI-oriented advisor at the beginning of 2022.]
  • QRAFT AI-Enhanced U.S. Large Cap Momentum ETF (AMOM) – picks momentum stocks via an investment strategy enhanced by the use of artificial intelligence.
  • Teucrium Aila Long-Short Agriculture Strategy ETF (OAIA) – takes long and short positions in agricultural securities via a strategy employing machine learning.
  • Teucrium Aila Long-Short Base Metals Strategy ETF (OAIB) – takes long and short positions in base metals securities via a strategy employing machine learning.

We use SPDR S&P 500 ETF Trust (SPY) for comparison, though it is not conceptually matched to some of the ETFs. We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly total returns for the six AI-powered ETFs and SPY as available through January 2024, we find that: Keep Reading

A reader asked: “I am wondering how come you have not rated Motley Fool guys. Any insight?” To augment the test of Motley Fool public stock picks in “‘Buy These Stocks for 2019’ Forward Test”, we look at three more lists of stock picks:

1. “10 Top Stocks That Will Make You Richer in 2021” published 1/5/2021: Alphabet (GOOG), Amazon (AMZN), Costco Wholesale (COST), Cresco Labs (CRLBF), Kirkland Lake Gold (KL), NextEra Energy (NEE), salesforce.com (CRM), Teladoc Health (TDOC), UnitedHealth Group (UNH) and Visa (V).
2. “7 Stocks That Could Make You Richer in 2022” published 1/5/2022: Devon Energy (DVN), Innovative Industrial Properties (IIPR), Mastercard (MA), PayPal (PYPL), Sea Limited (SE), Teladoc Health (TDOC) and Vertex Pharmaceuticals (VRTX).
3. “Got $1,000? 5 Sensational Stocks to Buy to Start 2023 With a Bang” published 12/26/22: AstraZeneca (AZN), Broadcom (AVGO), Innovative Industrial Properties (IIPR), NextEra Energy (NEE) and Novavax (NVAX).

We calculate total (dividend-reinvested) returns for the first list during 1/5/2021 through 12/31/2021, for the second list during 1/5/2022 through 12/30/2022 and for the third list during 12/30/22 through 12/29/23. We compare average returns of these lists to returns for SPDR S&P 500 ETF Trust (SPY) over matched sample periods. Using dividend-adjusted closing prices for SPY and each of the stocks in the three lists on the specified dates from Yahoo!Finance, except for Kirkland Lake Gold, for which prices are from Barchart.com, we find that:

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In late 2022, Forbes “tapped Morningstar to identify top-performing fund managers who have either beat their benchmarks this year or on a longer-term basis over three-year, five-year or ten-year periods. Here are their best stock ideas for the coming year…” as published at the beginning of January 2023 in “20 Great Stock Ideas for 2023 from Top-Performing Fund Managers”:

Air Lease (AL)
Atlanta Braves Holdings Inc Series C (BATRK)
Becton Dickinson (BDX)
Bill.com Holding (BILL)
CACI International (CACI)
Chord Energy (CHRD)
Comcast (CMCSA)
Dish Network (DISH)
DoubleVerify (DV)
Duolingo (DUOL)
HCA Healthcare (HCA)
Insulet (PODD)
Permian Basin Royalty Trust (PBT)
Philip Morris International (PM)
Royal Caribbean Cruises (RCL)
Shopify (SHOP)
TJX (TJX)
UnitedHealth Group (UNH)
Waste Connections (WCN)
Zebra Technologies (ZBRA)

How did these ideas perform? To check, we collect end-of-2022 and end-of-2023 dividend-adjusted prices for the 20 ideas and calculate the annual total return for each. We then compare the average of these returns to the annual total return for SPDR S&P 500 ETF Trust (SPY). Using the specified annual data for 2022 and 2023, we find that:

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Is ChatGPT useful for predicting stock market returns based on financial news headlines? In the December 2023 version of their paper entitled “ChatGPT, Stock Market Predictability and Links to the Macroeconomy”, Jian Chen, Guohao Tang, Guofu Zhou and Wu Zhu investigate whether ChatGPT 3.5 can predict U.S. stock market (S&P 500 Index) returns based on Wall Street Journal front-page news headlines/alerts. The instruction they give ChatGPT 3.5 is:

“Forget all previous instructions. You are now a financial expert giving investment advice. I’ll give you a news headline, and you need to answer whether this headline suggests the U.S. stock prices are GOING UP or GOING DOWN. Please choose only one option from GOING UP, GOING DOWN, UNKNOWN, and do not provide any additional responses.”

They first compute monthly ratios of good news to total news (NR^G) and bad news to total news (NR^B) and then relate these ratios to S&P 500 Index excess returns over the next 1, 3, 6, 9 or 12 months. They compare the ability of ChatGPT to predict returns to that of traditional human interpretation and to those of BERT and RoBERTa as ChatGPT alternatives. Using daily Wall Street Journal front-page news headlines/alerts and monthly S&P 500 Index excess returns during January 1996 through December 2022, they find that:

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Can enhanced machine learning models accurately time popular equity factors? In their January 2024 paper entitled “Multi-Factor Timing with Deep Learning”, Paul Cotturo, Fred Liu and Robert Proner explore equity factor timing via a multi-task neural network model (MT) to capture the commonalities across factors and a dynamic multi-task neural network model (DMT) to extract financial and macroeconomic states. They attempt to time six well-known factors: (1) excess market return, size, value, profitability, investment and momentum. They employ 272 model inputs (123 macroeconomic and 149 financial) to predict each month:

1. The sign of next-month return for each factor.
2. The return for an equal-weighted portfolio that holds the factors (the risk-free asset) for factors with positive (negative) predicted returns.

The compare performances of MT and DMT with those of seven simpler off-the-shelf machine learning models: logistic regression (LR), penalized logistic regression (EN), random forest (RF), extremely randomized trees (XRF), gradient boosted trees (GBT), support vector machine (SVM) and feed-forward neural network (NN). For all models, they use the first 20 years of their sample period for training, the next five years for validation and the remaining years for out-of-sample testing. Their benchmark is an equal-weighted portfolio of all six factors. Using monthly data for the 272 model inputs and monthly returns for the six factors during January 1965 through December 2021, with out-of-sample testing starting January 1990, they find that: Keep Reading