Fundamental Valuation

To determine whether the stock market is expensive or cheap, some experts use aggregate valuation ratios, either trailing or forward-looking, such as earnings-price ratio (E/P) and dividend yield. Under belief that such ratios are mean-reverting, most imminently due to movement of stock prices, these experts expect high (low) future stock market returns when these ratios are high (low). Where are the ratios now and how are they changing during recent months? Using recent actual and forecasted earnings and dividend data from Standard & Poor’s and associated S&P 500 Index levels as available through mid-July 2024, we find that: Keep Reading

Are the industry membership of a firm, as designated by Standard Industrial Classification (SIC) code, and the position of the firm within its industry good predictors of the performance of its stock? In their May 2024 paper entitled “Decoding Cross-Stock Predictability: Peer Strength versus Firm-Peer Disparities”, Doron Avramov, Shuyi Ge, Shaoran Li and Oliver Linton devise the following two industry related stock metrics and test their abilities to predict stock returns:

1. Peer Index (PI) – calculated for each firm via a multi-input, inception-to-date regression to predict next-month stock return, replacing firm characteristics by the contemporaneous average values for all firms in its industry as inputs.
2. Peer-Deviation Index (PDI) – calculated for each firm via a multi-input, inception-to-date regression to predict next-month stock return using firm characteristics minus the contemporaneous average values of these characteristics for all firms in its industry as inputs (indicating the standing of the firm within its industry).

Inputs consist of 94 firm-specific characteristics and 8 industry-related characteristics, organized into six groups: momentum, value versus growth, investment, profitability, trading frictions and intangibles. Using monthly values for the selected 102 firm/industry characteristics and monthly returns for common stocks in the top 80% of AMEX/NYSE/NASDAQ  market capitalizations during January 1980 through March 2022, they find that: Keep Reading

Are large language models such as GPT-4 as effective as professional human analysts in interpreting numerical financial statements? In their May 2024 paper entitled “Financial Statement Analysis with Large Language Models”, Alex Kim, Maximilian Muhn and Valeri Nikolaev investigate whether GPT-4 can analyze standardized, anonymized financial statements to forecast direction and magnitude (large, moderate or small) of changes in future firm earnings and provide the level of confidence in its answer. They withhold management discussions that accompany financial statements, choosing to evaluate the ability of GPT-4 to analyze only numerical data. They anonymize statements by omitting firm names and replacing years with labels (t, t − 1, …) so that GPT-4 cannot use its training data to find actual future earnings. They consider both a simple query and a series of prompts designed to make GPT-4 think like an ideal human analyst by focusing on changes in certain financial statement items, computing financial ratios and generating economic interpretations of these ratios. They compare GPT-4 forecasts to: (1) consensus (median) human earnings forecasts issued during the month after financial statement release; and, (2) forecasts from other benchmarks, including that of a highly focused state-of-the-art artificial neural net (ANN) model. To test economic value of forecasts, they each year on June 30 form portfolios using GPT-4 forecasts based on annual financial statements from the preceding calendar year end, as follows: 

• Sort stocks based on GPT earnings forecasts.
• Select stocks expected to have moderate/large increases or decreases in earnings and separately resort these two groups based on forecast confidence.
• Form an equal-weighted or value-weighted long (short) portfolio of the tenth, or decile, of these stocks with highest confidence in earnings increases (decreases).

Using financial statements for 15,401 firms during  1968 through 2023 (with 2022 and 2023 out-of-sample with respect to the GPT-4 training period), annual returns of associated stocks and consensus human analyst earnings forecasts for 3,152 firms during 1983 through 2021, they find that: Keep Reading

Can investors reliably capture illiquidity-driven stock price noise, short-term deviations in price from some measurable fair value? In their February 2024 paper entitled “Intraday Residual Reversal in the U.S. Stock Market”, Jonathan Brogaard, Jaehee Han and Hanjun Kim investigate returns to a strategy that exploits reversals of short-lived noise in stock prices by buying (selling) stocks with positive (negative) price noise. Specifically, at intervals of 30 minutes, they:

• Regress stock returns cross-sectionally versus 15 standardized/normalized stock return anomalies to predict next-interval return for each stock, with the difference between predicted and actual returns designated as noise (residual). The first daily noise measurement is at 10:00AM and the last (for overnight) at 4:00PM.
• Sort stocks into tenths (deciles) based on noise from most negative to most positive.
• Reform a hedge portfolio, either value-weighted or equal-weighted, that is long (short) the stocks in the bottom (top) noise decile.

They limit their stock universe to the S&P 500 for depth and liquidity, so the hedge portfolio has positions in about 100 stocks. They consider impacts of trading frictions ranging from 0.03% to 0.07%. Using daily returns for each of the 15 anomalies and 30-minute bid-ask midpoints for S&P 500 index stocks during July 1996 through December 2022, they find that:

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Prior to 2015, we tracked performance of an equity market timing model based on real earnings yield (REY). The Simple Asset Class ETF Value Strategy (SACEVS) subsumed that model in 2015. Earnings yield is aggregate corporate earnings divided by corresponding stock index level. The REY model adjusts this earnings yield by subtracting the inflation rate for the same period. Does the REY concept still hold value for equity market timing? Using quarterly S&P 500 operating and as-reported earnings, S&P 500 Index (SP500) level, quarterly inflation as calculated from the U.S. Consumer Price Index, dividend-adjusted SPDR S&P 500 ETF Trust (SPY) and 3-month U.S. Treasury bill (T-bill) yield as available during March 1988 through December 2023, we find that: Keep Reading

Should qualified investors count on global macro (GM) and managed futures (MF, or alternatively CTA for commodity trading advisors) hedge funds to beat the market? In their November 2023 paper entitled “Global Macro and Managed Futures Hedge Fund Strategies: Portfolio Differentiators?”, Rodney Sullivan and Matthew Wey assess the performances of GM and MF hedge fund categories, defined as:

• GM – try to anticipate how political trends and global economic activity will affect valuations of global equities, bonds, currencies and commodities.
• MF – rely systematic trading programs based on historical prices/market trends across stocks, bonds, currencies and commodities.

For comparison, they also look at the long-short equity (LSE) hedge fund category. They decompose category returns into components driven by exposures to U.S. stock and bond market return factors, other factor premiums and unexplained alpha. They focus on how fund categories have changed since the 2008 financial crisis, emphasizing performances during market downtowns. Using index returns from Hedge Fund Research (equal-weighted) and Credit Suisse (asset-weighted) during January 1994 through December 2022, they find that:

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“SACEMS with Margin” investigates the use of target 2X leverage via margin to boost the performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS). “SACEVS with Margin” investigates the use of target 2X leverage via margin to boost the performance of the “Simple Asset Class ETF Value Strategy” (SACEVS). In response, a subscriber requested a sensitivity test of 1.25X, 1.50X and 1.75X leverage targets. To investigate effects of these leverage targets, we separately augment SACEVS Best Value, SACEMS EW Top 2 and the equally weighted combination of these two strategies by: (1) initially applying target leverage via margin; (2) for each month with a positive portfolio return, adding margin at the end of the month to restore target leverage; and, (3) for each month with a negative portfolio return, liquidating shares at the end of the month to pay down margin and restore target leverage. Margin rebalancings are concurrent with portfolio reformations. We focus on gross monthly Sharpe ratio, compound annual growth rate (CAGR) and maximum drawdown (MaxDD) for committed capital as key performance statistics. We use the 3-month Treasury bill (T-bill) yield as the risk-free rate. Using monthly total (dividend-adjusted) returns for the specified assets since July 2002 for SACEVS and since July 2006 for SACEMS, both through October 2023, we find that:

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Is leveraging with margin a good way to boost the performance of the “Simple Asset Class ETF Value Strategy” (SACEVS)? To investigate effects of margin, we augment SACEVS by: (1) initially applying 2X leverage via margin (limited by Federal Reserve Regulation T); (2) for each month with a positive portfolio return, adding margin at the end of the month to restore 2X leverage; and, (3) for each month with a negative portfolio return, liquidating shares at the end of the month to pay down margin and restore 2X leverage. Margin rebalancings are concurrent with portfolio reformations. We focus on gross monthly Sharpe ratio, compound annual growth rate (CAGR) and maximum drawdown (MaxDD) for committed capital as key performance statistics for Best Value (which picks the most undervalued premium) and Weighted (which weights all undervalued premiums according to degree of undervaluation) variations of SACEVS. We use the 3-month Treasury bill (T-bill) yield as the risk-free rate and consider a range of margin interest rates as increments to this yield. Using monthly total returns for SACEVS and monthly T-bill yields during July 2002 through October 2023, we find that:

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Do conventional market capitalization-weighted stock indexes suffer from a long-term buy-high/sell-low performance drag when adding and deleting stocks? In their October 2023 paper entitled “Reimagining Index Funds”, Robert Arnott, Chris Brightman, Xi Liu and Que Nguyen construct alternative indexes that select stocks based on fundamental measures of underlying firm size and then weight them by market capitalization (Fundamental-selection Cap-weighted, FS-CW). Specifically, the each March:

• For each firm, calculate four fundamental measures of firm size as percentages of aggregate values for all U.S. firms:
  1. Current book value adjusted for intangibles.
  2. 5-year trailing average sales adjusted for the equity-to-asset ratio.
  3. 5-year trailing average cash flow plus R&D expenses.
  4. 5-year trailing average dividend plus share repurchases.
• For each firm, average these four measures.
• Rank stocks of these firms based on their respective averages.
• Reform equal-weighted indexes of the top 500 (FS-CW 500) or the top 1000 (FS-CW 1000) stocks.

For perspective, they also reform at the end of each June a portfolio of the top 500 stocks selected purely based on market capitalization (True CW 500). They then compare returns and 4-factor (adjusting for market, size, book-to-market and momentum) alphas of the Russell 1000, True CW 500, FS-CW 500 and FS-CW 1000 measured relative to the S&P 500. Using monthly data as described above for all publicly traded U.S. stocks, S&P 500, Russell 1000 and the four stock factors to support backtesting from July 1991 through December 2022, they find that: Keep Reading

Can artificial intelligence (AI) models help investors quantify vague firm risks through textual analysis? In their October 2023 paper entitled “From Transcripts to Insights: Uncovering Corporate Risks Using Generative AI”, Alex Kim, Maximilian Muhn and Valeri Nikolaev explore the value of generative AI tool ChatGPT 3.5 in quantifying firm risks based on politics, climate change and AI as conveyed in earnings conference call transcripts. For each of the three risks, they generate: (1) risk summaries based solely on the transcripts, and (2) risk assessments in full context based on the transcripts plus all ChatGPT training data. They consider risk analysis both within (before September 2021) and outside (January 2022 through March 2023) ChatGPT’s training period. They test the import of ChatGPT-based risk assessments via 5-factor (accounting for market, size, book-to-market, profitability and investment effects) alphas of hedge portfolios that are that are long the fifth (quintile) of stocks with the highest assessed risks and short the quintile with the lowest. Using earnings transcripts and monthly returns for a broad sample of U.S. stocks during January 2018 through March 2023, they find that: Keep Reading