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Investing Expertise

Can analysts, experts and gurus really give you an investing/trading edge? Should you track the advice of as many as possible? Are there ways to tell good ones from bad ones? Recent research indicates that the average “expert” has little to offer individual investors/traders. Finding exceptional advisers is no easier than identifying outperforming stocks. Indiscriminately seeking the output of as many experts as possible is a waste of time. Learning what makes a good expert accurate is worthwhile.

Stocktwits Tweeters as Investing Experts

Are there clearly skilled and unskilled stock-picking influencers on social media platforms such as StockTwits? If so, do investor reactions to such influencers drive out the unskilled ones? In their March 2023 paper entitled “Finfluencers”, Ali Kakhbod, Seyed Kazempour. Dmitry Livdan and Norman Schuerhoff examine skillfulness, influence and survival of StockTwits tweeters who have followers. They apply four skill metrics to measure stock-picking skill levels of these influencers to identify those who are: (1) skilled (reliably good advice); (2) unskilled; and, (3) anti-skilled (reliably bad advice). They calculate future (1 to 20 days) abnormal returns for each influencer by comparing factor model-adjusted returns (alphas) of associated stock picks before and after recommendation dates. To assess skill persistence, they compare influencer skill levels in the first and second halves of the sample. Using tweet-level and follower data from StockTwits for 29,477 influencers, matched daily stock returns and daily equity factor returns during July 13, 2013 through January 1, 2017, they find that:

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Evaluating Investment Advisory Service Buy Recommendations

A subscriber requested evaluation of Investment Advisory Service stock picking ability based on a sample newsletter obtained in mid-April 2023. The offerors state that they follow “a sound, buy-and-hold approach to identifying well-managed, high-quality companies” that “highlights emerging and oft-overlooked stocks with excellent growth potential and reasonable valuations.” The sample newsletter, dated January 2022, includes a December 14, 2021 list of 90 stocks apparently representing a recommended portfolio. Of these 90 stocks, 31 have buy recommendations as of that date. To assess the usefulness of the buy recommendations, we calculate total return for each from the close on December 15, 2021 to the close at the end of 2022 and compare the equal-weighted average of those returns to total returns for SPDR S&P 500 ETF Trust (SPY) and (based on the service description) Vanguard U.S. Quality Factor ETF (VFQY) over the same period. For most of the stocks, we use dividend-adjusted price data from Yahoo!Finance. Two of the stocks change name/symbol after the start of the sample period (FB–>META and INS–>CCRD). For one stock with price data no longer available at Yahoo!Finance due to a post-2022 merger (IAA), we use historical data from Barchart.com. Using the specified price data during December 15, 2021 through December 30, 2022, we find that: Keep Reading

ChatGPT as Stock Sentiment Analyst

Can advanced natural language processing models such as ChatGPT extract sentiment from firm news headlines that usefully predicts associated next-day stock returns? In their April 2023 paper entitled “Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models”, Alejandro Lopez-Lira and Yuehua Tang test the ability of ChatGPT to predict next-day returns of individual stocks via analysis of relevant article and press release headlines from RavenPack, a leading provider of news sentiment data. They pre-process the headlines to ensure unique content and high relevance to a specific firm. They next instruct ChatGPT to designate whether each headline is good, bad or irrelevant for firm stock price, as follows:

“Forget all your previous instructions. Pretend you are a financial expert. You are
a financial expert with stock recommendation experience. Answer “YES” if good
news [+1], “NO” if bad news [-1} , or “UNKNOWN” if uncertain in the first line [0]. Then
elaborate with one short and concise sentence on the next line. Is this headline
good or bad for the stock price of _company_name_ in the _term_ term?”

They then compute a ChatGPT score for each stock in the news (averaging if there is more than one headline for a firm) and relate all stock scores to stock returns lagged by one day. They further compare predictive powers of ChatGPT sentiment scores to those provided by RavenPack. Using daily returns for a broad sample of U.S. common stocks and daily news headlines from RavenPack during October 2021 (post-training period for ChatGPT) through December 2022, they find that: Keep Reading

Can Expert Financial Advisors Beat the Market?

Can expert financial advisors beat the market? ChatGPT responds: Keep Reading

Survey-based Stock Market Return Forecasts

Can surveys of various expert and inexpert groups usefully predict stock market returns? In their March 2023 paper entitled “How Accurate Are Survey Forecasts on the Market?”, Songrun He, Jiaen Li and Guofu Zhou assess abilities of the following three surveys to predict S&P 500 Index returns:

For comparison, they also look at two other predictors, one based on a set of economic variables and the other based on aggregate short interest for U.S. stocks. Their benchmark forecast is a simple random walk tethered to the historical mean return. They test forecast accuracies statistically and gauge the economic value of each forecast based on out-of-sample certainty equivalence gain and Sharpe ratio for a portfolio that times the S&P 500 Index based on the forecast (versus buying and holding the index). Using data for the selected surveys, the set of economic variables, aggregate short interest for U.S. stocks and the S&P 500 Index as available (various start dates) through December 2020, they find that: Keep Reading

22 High-conviction Pro Stock Picks for 2022?

In late 2021, Kiplinger “used TipRanks data to unveil the crème de la crème, as viewed by Wall Street’s analyst community. Each stock currently earns a consensus Strong Buy rating based on opinions from analysts surveyed by TipRanks. Here are 22 of the pros’ highest-conviction stocks to invest in for 2022. As of today, these stocks are expected to produce upside of between 10% and 82% over the next 12 months – handily more than consensus S&P 500 projections.” In December 2021, Kiplinger published the list of these 22 stocks in “The Pros’ Picks: 22 Top Stocks to Invest In for 2022”:

The Charles Schwab Corporation (SCHW)
Applied Materials, Inc. (AMAT)
The Coca-Cola Company (KO)
Lowe’s Companies, Inc. (LOW)
Johnson & Johnson (JNJ)
Altria Group, Inc. (MO)
Chevron Corporation (CVX)
Alphabet Inc. (GOOG)
Laboratory Corporation of America Holdings (LH)
DuPont de Nemours, Inc. (DD)
Analog Devices, Inc. (ADI)
Suncor Energy Inc. (SU)
General Motors Company (GM)
Salesforce, Inc. (CRM)
The Beauty Health Company (SKIN)
RH (RH)
Twilio Inc. (TWLO)
JD.com, Inc. (JD)
Zynga Inc Cl A (ZNGA)
Fisker Inc. (FSR)
Sonos, Inc. (SONO)
Under Armour, Inc. (UA)

How did these picks perform? To check, we collect end-of-2021 and end-of-2022 dividend-adjusted prices for the 22 picks (except for Zynga, for which we use the price when the firm became private in May 2022) 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 2021 and 2022, we find that:

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10 Great Stock Picks for 2022?

In late 2021, Forbes “queried Morningstar to identify some of the top-performing fund managers, all of whom consistently beat their benchmarks on a longer-term basis over either a three-year, five-year or ten-year period. Forbes spoke with five top portfolio managers overseeing nearly $25 billion in assets. Here are their best stock ideas for the coming year,” as published in December 2021 as “10 Great Stock Picks for 2022 from Top-Performing Fund Managers”:

ViacomCBS (VIAC)/Paramount (PARA)
Madison Square Garden Entertainment (MSGE)
Signature Bank (SBNY)
SiteOne Landscape Supply (SITE)
Snap (SNAP)
Affirm (AFRM)
Silvergate Capital (SI)
Snowflake (SNOW)
Paramount Resources (PRMRF)
Mirion Technologies (MIR)

How did these picks perform? To check, we collect end-of-2021 and end-of-2022 dividend-adjusted prices for the 10 picks 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 2021 and 2022, we find that:

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Benefit of Complexity in Machine Learning Models

Is model complexity (large number of parameters) more an analytical benefit in predicting asset returns, or more an avenue to discover in-sample luck? In their March 2023 paper entitled “Complexity in Factor Pricing Models”, Antoine Didisheim, Shikun Ke, Bryan Kelly and Semyon Malamud examine the theoretical relationship between input complexity and output accuracy for machine learning asset pricing models. They focus on a complexity wedge, the combination of overfitting (data snooping) and limits to learning that causes in-sample performance of a trained model to exceed out-of-sample performance. They apply ridge shrinkage (controlled by a regularization parameter that sets the strength of an overfitting penalty) to suppress data snooping bias and improve the limits to learning. They assess model performance by out-of-sample Sharpe ratio and out-of-sample pricing errors of optimal portfolios. They test theoretical conclusions on a broad sample of publicly traded U.S. stocks and a set of 110 monthly stock return factors, the latter augmented by a random feature generator that expands the 110 raw factors to any desired number of derivative factors. Using monthly data for the 110 stock return predictors and monthly U.S. stock returns during February 1963 through December 2019, they find that: Keep Reading

Hedge Fund Arbitrage of New Anomalies

Do hedge funds rapidly move to exploit, and thereby weaken/extinguish, newly discovered stock return anomalies? In the December 2022 version of their paper entitled “Anomaly Discovery and Arbitrage Trading”, Xi Dong, Qi Liu, Lei Lu, Bo Sun and Hongjun Yan measure the post-publication role of hedge funds on 99 published stock return anomalies (or latest working paper dates if unpublished). For each anomaly, they:

  1. Calculate a five-year rolling correlation of monthly returns between the extreme tenths (deciles 1 and 10) of anomaly stock sorts, minus the correlation between deciles 5 and 6 to control for unrelated trends.
  2. Analyze via quarterly SEC Form 13F holdings aggregate U.S. hedge fund differential trading of extreme decile stocks.

Using monthly returns for the 99 anomalies as available starting in 1926 and hedge fund SEC Form 13F filings as available starting 1981, both through 2020, they find that: Keep Reading

Suppressing Long-side Factor Premium Frictions

Are their practical ways to suppress the sometimes large reduction in academic (gross) equity factor premiums due to trading frictions and other implementation obstacles? In their March 2023 paper entitled “Smart Rebalancing”, Robert Arnott, Feifei Li and Juhani Linnainmaa first examine the performance and related turnover of seven long-only factor premiums: annually reformed (end of June) value, profitability, investment, and a composite of the three; and, monthly reformed value and momentum, and a composite of the two. Their long-only factor portfolios hold market-weighted stocks in the top fourth of factor signals. They reinvest any dividends in all stocks in the portfolios, such that dividends do not affect portfolio weights. They test three ways to suppress periodic turnover via a turnover limit:

  1. Proportional Rebalancing – trade all stocks proportionally to meet the turnover limit.
  2. Priority Best – buy stocks with the strongest factor signals and sell stocks with the weakest, until reaching the turnover limit.
  3. Priority Worst – buy stocks that only marginally qualify for the factor portfolio and sell those that just barely fall out (with the strongest buy and sell signals last), until reaching the turnover limit.

They also apply these three turnover suppression tactics to non-calendar reformation, triggered when the difference between the current and target portfolios exceeds a specified threshold. They ignore the 100% initial formation turnover common to all portfolios. Using  accounting data and common stock returns for all U.S. publicly listed firms during July 1963 through December 2020, with portfolio tests commencing July 1964, they find that: Keep Reading

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