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Bad Timing - Individual and Institutional Investors, Advisors and Consultants

Investment performance can be broken down into several components. On a simple level, the value of a portfolio at the end of a period of time minus the starting value is the absolute return. That return divided by the starting value is the percentage return over the time period.

     For instance, a portfolio that starts at $100,000 and ends two years later at $121,000 has a $21,000 return. The two year return is 21%, and the annual return is 10%. $100,000 times 1.1 = $110,000 after one year and $110,000 times 1.1 again equals $121,000 after two years. But most investors hold multiple investments that fluctuate over time and portfolio performance usually does not follow a straight line over longer periods of time. Some years a riskier portfolio may lose money and other years it may have much stronger returns.

     Portfolio returns can be measured for each individual investment and then aggregated to the total portfolio return by weighting the amounts of each investment. You can then calculate how much of the return was determined by each component and by the asset classes. I'll discuss the importance of asset allocation more in the next chapter, but first it’s useful to recognize that performance is determined by three main components (asset allocation, security selection, and market timing). Asset allocation is the percentage of the portfolio in each asset class, like stocks, real estate, bonds, and other asset classes. Security selection is how well the specific securities in a portfolio did relative to the asset class itself (for instance how well Apple stock performed relative to the broad stock market). Market timing refers to going in and out of asset classes.

     Active management can involve over or under weighting asset classes, which falls in the market timing category. The intention of market timing is to beat a buy and hold strategy whereby the investor just maintains an asset allocation (which may adjust gradually over time based on age and circumstances). Active managers may also try to outperform an asset class by deviating from the benchmark, which in stocks is commonly referred to as beating the market.

     Let’s look at a simple example to help explain the role that asset allocation, security selection, and market timing play in performance. Hypothetically let’s say a child is born in your family and receives several gifts from family members. Your job is to help the child set up their investments for their future. The child receives some toys, clothes, some lottery tickets, and a few other longer-term oriented gifts that might be considered in the investment category.

  1. Cash
  2. A Gold coin
  3. A government bond that matures in many years
  4. $1000 six shares of a stock (let's use Microsoft stock, ticker symbol MSFT since it was the most valuable company in 2002 – Amazon became the most valuable company in 2019 taking over from Apple, both of which represents several percent of the U.S. stock market)

     Let's say you take the cash and invest it a U.S. stock market fund. As I mentioned earlier, for all four of my children I opened mutual fund accounts at Vanguard after they were born and deposited their cash gifts. I bought them a U.S. Total Stock Market Index Fund. So to keep things simple let’s assume the child's portfolio after birth consists of the following.

  1. $1,000 Gold
  2. $1,000 Bond
  3. $1,000 Microsoft Stock
  4. $1,000 U.S. Stocks

     Let's say the child and the parent (or custodian) leave the portfolio alone and do nothing to it for 16 years. I used my childhood savings to buy a used foreign car around the time I started college, which turned out to be quite a learning experience, but hopefully my kids will be smarter than I was and continue to let their accounts grow. Based on historical results, the values might look like roughly like this after 16 years.

  1. $1,500 Gold
  2. $2,000 Bond
  3. $5,000 Microsoft stock
  4. $4,000 U.S. stocks

     With college approaching the child and parent (or custodian) decide to sell some of the stocks for college expenses. Unless they sell each of the investments in proportion to the total portfolio value, the portfolio asset allocation will shift if only stocks are sold. Let's say the child grows up and decides to get married at 24 years and tries to determine the lifetime portfolio performance up to that point. If the stocks were sold at 16 and the stocks continued to perform well from 16 - 24 years old, the portfolio performance would likely be weaker than if they had sold the gold or bonds at age 16.

     In this example, the returns attributed to security selection are the difference between Microsoft stock and U.S. stock performance. The return attributable to market timing would be from having sold the stocks at age 16. So security selection (buying Microsoft instead of the U.S. Stock market) in this case would have added value, but the market timing decision to sell stocks at 16 would have subtracted value (assuming stocks outperformed from the 16th to 24th year).

     In this case, the decision whether to sell the gold, bonds, or stocks depends on how much money is left in the portfolio and whether the remaining portfolio was intended to be invested long term. If all of the money was going to be needed within a few years (for a house down payment, or to start a business) it may be completely appropriate to liquidate all the holdings to have cash available and not risk any losses in the short-term, which arguably is an asset allocation decision.

     In prior chapters, I mentioned some research and commentary pointing out that professional money managers have a very difficult time using security selection to beat the market and outperform the broad market itself, after costs. In the last few decades researchers have gotten access to large data sources allowing them to analyze individual investors’ performance, as well as many other investment groups. The studies have documented an overwhelming tendency for individual and professional investors to shoot themselves in the foot by trying to time, or beat the markets. The vast majority of times that investors reduce their allocation to risky assets, they do it at times when the market is weak, and the vast majority of times they increase allocations to risky assets they do it when the market is strong. As a result, most investors that buy and sell actively end up with portfolios performing worse than if they had just bought and held the whole time.

     There are cases when people legitimately shift asset allocation and it’s not because they are trying to time the market. For instance, many people sold stocks in 2008 and 2009 and not necessarily because they were betting the market would drop more than it already had. Many sold because their business or jobs were adversely affected by the crisis and they needed the money, or they could not be sure that the money would remain invested long term. But much of the movement in and out of stocks is related to attempts to time the markets.

     Bing Chen and Frank Stafford released data in 2014 showing that those with less education and smaller accounts were more likely to sell during the global financial crisis. As a result, those with larger accounts did well when the market rebounded, which arguably increased wealth inequality. They also argued that the results held up even after controlling for job loss or mortgage distress, implying some families simply sold at the wrong time.1

     Individual investors had company in selling stocks because financial advisors were also selling stocks for their clients. Allan Roth wrote in 2010 (in a Moneywatch article titled "Financial Advisors Show Poor Market Timing") that accounts managed by advisors also suffered from the same poor timing.2 Citing data on aggregate asset allocation of the entire TD Ameritrade Institutional platform (consisting of over $100 billion), the advised accounts had only 8% in cash and 18% in fixed income on October 9, 2007, but 21% cash and 30% fixed income on March 9, 2009 when the market bottomed.

     The first published academic study (that I am aware of) documenting the negative effects of investor's buying and selling at the wrong times, was published by Stephen Nesbitt in the Fall 1995 issue of The Journal of Portfolio Management. In "Buy High, Sell Low: Timing Errors in Mutual Fund Allocations", Nesbitt found that money tends to flow into mutual funds before they underperform and money tends to flow out before they outperform.3 As a result, timing costs investors roughly 1% per year.

     Since then there have been a steady stream of research on performance gaps and a number of organizations have started regularly publishing data on the gap between fund returns and investors' actual returns (which tend to be lower, due to the poor market timing). Carl Richards coined the term 'behavioral gap' to label the gap between investor returns and investment returns. Morningstar regularly publishes data and commentary on the "gap" and Dalbar also publishes data regularly on the performance differences. However, Dalbar's calculations in the past have been criticized (by many including Wade Pfau) for possibly overestimating the gap due to using some questionable assumptions (like using end of period assets in calculations, which includes money that wasn't in the funds at the start of the period).4

     In April 2000 Terrance Odean and Brad Barber published "Trading is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors" in The Journal of Finance. They documented the fact that investors that trade the most had the weakest returns, and the average investor also underperformed the market. They analyzed over 60,000 households with accounts at a large discount broker from 1991 to 1996 and found that "those that trade most earn an annual return of 11.4 percent, while the market returns 17.9 percent. The average household earns an annual return of 16.4 percent."5

     In March 2007 Ilia Dichev published “What Are Stock Investors' Actual Historical Returns? Evidence from Dollar-Weighted Returns” in American Economic Review. The results indicated that aggregate dollar-weighted returns were systematically lower than buy-and-hold returns by an average of 1.5% for 19 major stock markets around the world from 1973-2004.6

     In September 2007 Geoffrey Friesen and Travis Sapp published “Mutual Fund Flows and Investor Returns: An Empirical Examination of Mutual Fund Investor Timing Ability” in The Journal of Banking and Finance. They found that from 1991-2004 equity fund investor timing decisions reduced fund investor average returns by 1.56% annually.7

     In May 2009 Daniel Bergstresser, John Chalmers, and Peter Tufano published “Assessing the Costs and Benefits of Brokers in the Mutual Fund Industry.” They studied broker-sold and direct-sold funds from 1996 to 2004.8 They could not find support for any benefits of the brokers sold funds. They reached the following conclusions.

"Relative to direct-sold funds, broker-sold funds deliver lower risk-adjusted returns, even before subtracting distribution costs. These results hold across fund objectives, with the exception of foreign equity funds. Further, broker-sold funds exhibit no more skill at aggregate-level asset allocation than do funds sold through the direct channel. Our results are consistent with two hypotheses: that brokers deliver substantial intangible benefits that we do not observe and that there are material conflicts of interest between brokers and their clients."

     In the November/December 2009 issue of the Financial Analysts Journal, Scott Stewart, John Neumann, Christopher Knittel, and Jeffrey Heisler published “Absence of Value: An Analysis of Investment Allocation Decisions by Institutional Plan Sponsors.”9 They studied "80,000 yearly observations of institutional investment product assets, accounts, and returns from 1984-2007. Results show that plan sponsors may not be acting in their stakeholders’ best interests when they make rebalancing or reallocation decisions ... Much like individual investors who switch mutual funds at the wrong time, institutional investors do not appear to create value from their investment decisions."

     In February 2010 Andrew Clare and Nick Motson published a working paper titled "Do UK retail investors buy at the top and sell at the bottom?”10

"The UK data that we use here suggest that on average the investment timing decisions of retail investors with regard to equity mutual funds has cost them performance of just under 1.2% per year over the eighteen year period of our study."

     In January 2011 Ilia Dichev and Gwen Yu published “Higher Risk, Lower Returns: What Hedge Fund Investors Really Earn” in the Journal of Financial Economics.11 The authors found that returns to investors in hedge fund were several percent lower than the returns of the funds themselves (significantly worse for "star" funds with the highest returns).

     In Spring 2013 John Haslem published “Mutual Funds Win and Investors Lose” in the Journal of Index Investing.12

"So why do investors persist in earning below market returns? Four possible answers are discussed: (1) investor overconfidence; (2) fund strategic repricing decisions; (3) fund 'sentiment contrarian behavior;' and (4) investor dependence on brokers with agency conflicted broker incentives."

     In May 2013 Vanguard published research on over 58,000 self-directed Vanguard IRA investors over the five years ended December 31, 2012. In "Most Vanguard IRA® investors shot par by staying the course: 2008–2012" they concluded that "investors who exchanged money between funds or into other funds fared considerably worse.13 The resulting performance gap is a good reminder that a simple, broad-based investment solution can minimize the chances that an investor will make a mistake that can reduce returns." They compared the results with 1) an investment in a Vanguard-recommended “policy asset allocation” of stock and bond index funds and, 2) one of the Vanguard Target Retirement Funds.

     Juhani Linnainmaa, Brian Melzer, and Alessandro Previtero published research in 2017 using data from several Canadian firms (more than 4,000 advisors and almost 500,000 clients between 1999 and 2013) that documented high turnover, high costs, bad timing, and under diversification by advisors on behalf of their clients.14 Given all the prior data and studies, those conclusions were unfortunately not surprising. But the researchers also found that the advisers themselves also underperform in their own investing, including after leaving the industry.

     Russel Kinnel summarized Morningstar's 2017 results in "Mind the Gap: Global Investor Returns Show the Costs of Bad Timing Around the World."15

"Although the five-year investor returns gap ranged widely from negative 1.40% to 0.53% for the year ended 2016, some common themes emerged. Investment vehicles that required systematic investment produced better investor returns. Lower-cost funds also proved to produce better returns and a smaller investor returns gap."

     At the start of 2018 YiLi Chien and Paul Morris writing for the Federal Reserve Bank of St. Louis also found return chasing behavior among institutional investors making them more likely to buy high and sell low.16

“When it comes to investing, we are so often our own worst enemy. Countless studies have shown that we tend to chase performance: buying high, selling low, and failing to learn from our mistakes every time. This applies to individual stocks, funds, Beanie Babies, cryptocurrencies, you name it.”
Ben Johnson, April 2018 Morningstar ETFInvestor17

     The huge and overwhelming collection of studies documenting the mostly self-defeating investor (and advisor) behavior presents a simple question. How can investors learn to avoid self-inflicted underperformance? People intuitively hope to buy low and sell high, but they tend to do the opposite.18 Yet we have some evidence that some groups have positive performance gaps. For instance, target dates funds (which set asset allocation for investors) have tended to have positive gaps.19 Those that invest in target date funds often commit to invest regularly and that process of dollar cost averaging tends to result in buying fewer shares when the asset values are higher and more shares when the assets are lower.

     For instance in “2015 Target-Date Fund Landscape” Morningstar summarized as follows.20

As target-date funds prosper and grow in assets, it’s important to examine whether investors are using these vehicles well and actually participating in the funds’ gains. Investor returns, which take into account monthly fund flows and monthly returns to estimate a typical investor’s experience in a fund, shed some light from this perspective. The data looks good. On average during the past 10 years, target-date fund’s asset-weighted investor returns are 1.1 percentage points greater than their total returns. The positive gap indicates investors are capturing all of the funds’ total return, plus more. Their roles as default investments for many retirement plans, which brought with it steady steams of inflows throughout recent years’ strong markets, made for additive timing effects as well.

     Several robo-advisory firms have suggested that they will prevent investors from experiencing negative gaps through rebalancing, but it’s still relatively early to determine if they have data to support that argument and whether their customers will experience positive gaps over the long run.

     Jack Bogle suggested in early 2018 that initial evidence even suggests that investors in ETFs also exhibit bad timing.21 Bogle noted that $840 billion had flowed into ETFs in the last 10 years, compared to $400 billion into traditional mutual funds (TIFs). He pointed out that ETFs had $504 billion in market appreciation, and TIFs have had $800 billion. As a result most of the growth in TIFs assets was from investment returns, but less than half of the growth in ETFs has been investment returns. Bogle concluded that “The investor return for TIFs average 7.4%, for ETFs it was 4.6%, even less than the 6.2% investor return in active funds.”

Do newsletters help you time or beat the market?

Some investors are drawn to newsletters (or other sources in the investment industry) after learning about past predictions that appear to have been useful. The following might provide some perspective to keep in mind regarding past predictions.

     Newsletters are exempt from the Investment Advisers Act of 1940 (based on a 1985 U.S. Supreme Court ruling) and are not required to register with the Securities and Exchange Commission as investment advisers. They also don't have to provide proof of the number of subscribers they claim to have, nor do they have to publish complete records of their past recommendations.

     Luckily for anyone interested in newsletters, there is information available about whether they provide valuable advice. Mark Hulbert started tracking newsletters in 1980 by subscribing to the services and tracking their advice independently. He founded the Hulbert Financial Digest (HFD) for that purpose and has been tracking the services ever since. Hulbert writes for various Dow Jones publications (Wall Street Journal, and Barron's and Marketwatch). HFD was acquired by CBS MarketWatch in 2002 and that firm was acquired by Dow Jones in 2005 and renamed MarketWatch. MarketWatch/Dow Jones closed HFD at the end of January 2016, but Mark Hulbert then formed Hulbert Ratings LLC which calculates newsletter performance from the date HFD was closed.

     Summarizing his decades of experience tracking newsletters, Hulbert was quoted in Kiplingers in 2016 stating the following.22 “It is very difficult to beat the market over the long term. Not just very difficult, but extremely so.” Based on his data, fewer than 10% of the advisers for which the HFD has complete data beat the stock market over the entire period of the digest’s existence, from mid-1980 through January 2016. And that represented the newsletters that survived the whole time. In a more recent 2018 article summarizing his career, Hulbert stated “The overwhelming realization is that even among the top performers over one period, only a small fraction of them beat the market in the subsequent period.”23

     Keep in mind that newsletters’ claims regarding Hulbert’s rankings may not be entirely accurate or up to date. Hulbert has cited numerous examples of newsletters claims regarding their rankings that were inaccurate and in some cases entirely false. When in doubt, check with Hulbert directly. Additionally, you should always keep in mind that past performance does not guarantee anything about performance in the future. Newsletters appear to suffer from the same lack of ability to outperform the market and they also exhibit a lack of performance persistence.

     Campbell Harvey and John Graham analyzed 326 newsletters asset-allocation strategies for the 1983–1995 period and concluded that as a group, newsletters do not appear to possess any special information about the future direction of the market.24 Andrew Metrick studied a sample of 153 newsletters and found no significant evidence of superior stock-picking ability, nor evidence of abnormal short-run performance persistence.25

     The NYTimes had a more favorable commentary in D.I.Y. Retirement Investors Have a Low-Cost Friend: Newsletters (1/30/2020). They noted some relatively highly regarded services cost hundreds of dollars per year, while most advisors charge a percentage of assets (but also tend to manage the account to be fair), and many of those services have tens of thousands of subscribers.

Stock Market Scams

Several books and other media have offered examples of how a stock market scam can be carried out. John Allen Paulos' excellent book Innumeracy includes one example (which he gave me permission to post on my web site at and Martin Fridson's Investment Illusions includes another version. A short online version would go something like this.

     A potential scammer gets ahold of an email list of thousands of consumers. Most spam emails end up in spam or junk folders, or get deleted before being read, but some get through and actually get read. So let's say the list has 320,000 email addresses. The scammer chooses a current topic that is in the news and is more likely to catch someone’s eye. The scammer prepares two emails. One says Bitcoin is about to jump up this week and the other says Bitcoin is about to drop this week. He sends each of the emails to half of the email addresses. If Bitcoin goes up that week he then focuses on those that got the bullish email and disregards the others. If Bitcoin was down he focuses on the email list that got the bearish prediction. The next week he splits the email list in half again and sends half an email predicting that gold will rise this week, while the other half he sends an email that gold will fall. He then continues to split the email list depending on which prediction was correct. After two more rounds he is left with a list of 40,000 that have had four correct predictions. To those he emails a notification that if they subscribe to service he will continue to send his recommendations.

     Paulus elaborated on the consequences with the following.

“There is a strong general tendency to filter out the bad and the failed and to focus on the good and the successful. Casinos encourage this tendency by making sure that every quarter that's won in a slot machine causes lights to blink and makes its own little tinkle in the metal tray. Seeing all the lights and hearing all the tinkles, it's not hard to get the impression that everyone's winning. Losses or failures are silent. The same applies to well-publicized stock market killings vs. relatively invisible stock market ruinations."

     The reality is that the more funds or products a company offers, the better the chances are that one of the funds will rank near the top of its category. The company can then focus its marketing efforts on those funds that have the best track records while keeping quiet about those that underperformed. In succeeding periods, other funds will perform well and then marketing efforts will shift to those funds.

     An important lesson to learn from the scam is that in addition to evaluating individual products, investors should evaluate all of a firm's offerings. Additionally, it can be helpful and often quite illuminating to look at an individual's or firm's previous offerings that have since been eliminated or merged into other funds. An organization that has a history of introducing new products and then discontinuing some of those offerings may be playing the numbers game and should be evaluated with caution.

     Another interesting debate is whether investors that don't touch their accounts do better than those that trade. There is plenty of anecdotal evidence to support that conclusion, but I haven’t seen definitive evidence. There were several mentions in the financial media in recent years about a study supposedly finding that accounts of individuals that had passed away outperforming other accounts. But apparently there was no published study.26

The Wall Street Journal Dartboard Contest

In 1988 the Wall Street Journal began a contest that was inspired by Burton Malkiel’s book A Random Walk Down Wall Street. In the book, Malkiel theorized that "a blindfolded monkey throwing darts at a newspaper’s financial pages could select a portfolio that would do just as well as one carefully selected by experts." The Journal set out to create an entertaining contest to test Malkiel's theory and give its readers some new investment ideas in the process. Wall Street Journal staff members typically played the role of the monkeys (the Journal listed liability insurance as one reason for not going all the way and actually using live monkeys) and they competed against stock picks from industry professionals.

     The contest became a popular feature and ran into 2002, although the WSJ continued letting readers compete with the darts until 2017, when they summarized "Our goal at Heard on the Street is to give readers strong research and analysis to help them make better investing decisions. Maybe we should leave the actual stock picking to the monkeys.”27 The pros beat the darts in 61 of the first 100 contests (although only 51 beat the Dow Jones Industrial Average), but most considered it more entertainment that a true test of the efficient market hypothesis for multiple reasons.28

     Initially the contest lasted one month, but recognizing that the publication of the contest was creating a publicity effect on the pro’s stock picks, the Journal began measuring the results over a six month period beginning in 1990. The rules were changed at various times during the contest, but later the rules settled on four "professionals" per month getting the opportunity to select one stock (long or short). The stocks had to be a decent size, traded on one of the major exchanges, and have a decent amount of trading volume.

     At the end of six months, the price appreciation for the pro’s stocks and the dartboard stocks were compared (dividends were not included). Malkiel and other academics responded to those that considered the contest to be a victory for the pros with several critiques. Before the contest even began, Malkiel had suggested that the results would be affected by an announcement effect. In other words, the very act of publishing the pro’s picks in the Journal could cause those stocks to rise as the hundreds of thousands of Journal readers open their morning paper and react to the recommendations of the pros. The Journal’s circulation was listed at over 1.7 million at that time and has since gone higher. Malkiel also suggested to me in 2000 that the pros advantage effectively disappears if you (1) account for the fact that the pros pick relatively riskier stocks and (2) measure returns from the day after the column appears (thereby eliminating the announcement effect).

     There were several very thorough studies that analyzed the contest in great detail. In "The Dartboard Column: Second-Hand Information and Price Pressure," Brad Barber and Douglas Loeffler addressed the question of whether the pro's stock picks created temporary buying pressure by naïve investors (known as the "price pressure hypothesis") or revealed relevant information (otherwise known as the "information hypothesis").29 The authors found evidence for both, but also came to some interesting conclusions.

     Two days following publication, the pro picks had average abnormal returns of 4%. However, those returns partially reversed within 25 days. They also found that the pros picked stocks with (1) lower dividends, (2) higher historic and projected EPS growth, and (3) slightly higher PE ratios and betas.

     Bing Liang studied the contest over an even longer period and published a paper in the January 1999 issue of the Journal of Business titled "Price Pressure: Evidence from the ‘Dartboard’ Column. 30 Liang analyzed almost 5 years of the contests and documented a 2-day announcement effect, which reversed within 15 days. Liang also found that the returns were intertwined with the pro’s track record. That is, returning pros' picks had larger announcement effects. Yet over the full period, even the returning pros picks did not outperform. His research supported the "price pressure hypothesis" or the theory that abnormal returns and volume were driven by noise trading from naïve investors. Liang concluded that the pros neither outperformed the market, nor the darts. According to Liang, the pros supposed superior performance could be explained by the small sample size, the announcement effect, and the missing dividend yields.

     One of the strongest criticisms of the contest was the fact that the Journal measured performance by price appreciation only, despite the fact that total return is measured by both price appreciation and dividends. For the period that Liang studied, the pro’s stocks had an average dividend yield of 1.2% versus yields for the darts of 2.3% and 3.1% for the DJIA average. Liang also found that the pro’s stocks had higher relative strength at the beginning of the contest, and found abnormal volume in the pro's stocks before the contest announcement. This could be coincidental or could indicate that someone knew the pro’s picks were coming and traded on them prior to the columns.

     In “Liquidity Provision and Noise Trading: Evidence from the ‘Investment Dartboard’ Column,” Jason Greene and Scott Smart reached similar conclusions to those of Liang but focused on market maker activity and the bid-ask spread around the column publication.31 They concluded "that the column generates temporary price pressure by increasing noise (i.e., uninformed) trading from its readers."

     The Wall Street Journal created an entertaining contest, unfortunately, as the Journal openly admitted, it was not a perfect test of the efficient market hypothesis. One problem is that the Wall Street Journal is so respected and popular, that the contest itself impacted the results. Perhaps a good comparison that demonstrates the problem with the contest is the system used for testing most medical and pharmaceutical products. Before a product is approved for public use it must complete a series of "double-blind" studies to determine its usefulness and potential side effects. In a double-blind study, neither the test administrators nor the patients know who is getting the real product and who is getting a placebo. This prevents both the study personnel and the patients from being biased and allows for untainted results.

BusinessWeek's Inside Wall Street Column

Another interesting case of stock picking was BusinessWeek's “Inside Wall Street” column. The column typically featured three stocks, usually in a favorable light. The potential of a takeover was one of the common arguments presented in the stocks' favor. The Inside Wall Street column drew the attention of's Editor-in-Chief Dave Kansas. In a skeptical article in late 1997, Kansas pointed out that many of the stock tips in the column flopped and few of the predicted takeovers ever panned out.

     Perhaps in response to that article, Business Week offered “A Report Card on 'Inside Wall Street'” in their July 6, 1998 issue. BusinessWeek reported the complete results of all the column's picks in 1997 with summaries for time frames from one day to six months in addition to the best and worst performing picks, which was an example of the growing trend in the investment business toward full accountability. Of course, even when you have full accountability, results are subject to interpretation. We'll always have the optimists (those who see the glass half full) as well as the pessimists (those who see the glass half empty). But generally we can expect the sponsor of a column or contest to portray their own results with a positive spin, so it's usually a good idea to pay close attention to the details.

     In this case, the results don't imply that investors would have benefited from following the column's picks. Wall Street clearly had a tendency to react in a big way to the column, as evidenced by large price moves in the featured stocks. The average gain on publication day was 4.7% - a huge announcement effect. But those gains weren't captured by readers because the price usually gapped up Friday morning (the column was usually available late Thursday). Returns at the end of six months were in line with the broader indexes, but when you subtract the day one returns, the picks underperformed for the one month, three month, and six month periods. The bottom line is that if you bought all of the 1997 Inside Wall Street picks on the close the day after the column appeared, you would have lagged the market (even before any transactions costs).

     BusinessWeek also documented the results of their 1998 picks on August 9, 1999 and found "announcement effect" or average one-day price appreciation was 4.9%. But for the three month and six month time frames, the column's picks had negative returns versus gains for the S&P 500 and DJIA. The losses were smaller than the loss for the Russell 2000 though. Oliver Schnusenberg studied the columns from July 1, 2002 to October 20, 2003 and found announcement period returns of about 3% in the three-day announcement window, but the positive abnormal returns were more than offset by negative cumulative abnormal returns in the subsequent six-month period.32

     In late 2009, Bloomberg L.P. bought BusinessWeek, which was reportedly consistently losing money and they renamed it Bloomberg Businessweek.

TIPS! How people want tips! They crave not only to get them but to give them. There is greed involved, and vanity. It is very amusing, at times, to watch really intelligent people fish for them. And the tip-giver need not hesitate about the quality, for the tip-seeker is not really after good tips, but any tip. If it makes good, fine! If it doesn't, better luck with the next. Larry Livingstone in Reminiscences of a Stock Operator

Chapter 11 Notes - The Footnotes in the Book are sequential and for this chapter start at #208 and end at #239.

2. 29, 2010)
14. The Misguided Beliefs of Financial Advisors December 15, 2017 (Forthcoming in the Journal of Finance per
15. (5/30/2017)
Similar comments were added in "Mind the Gap, Global Edition" (08/08/2017) Per the 2019 edition "allocation funds produced a positive gap of 0.22%" but "the average investor lost 45 basis points to timing" (8/15/2019) 16. Do Institutional Investors Chase Returns? January 1, 2018
17. Ben Johnson "Mind the Gap: Active Versus Passive Edition," May 02, 2018
18. See also Tim Jenkinson, Howard Jones, and Jose Vicente Martinez, “Picking Winners: Investment Consultants’ Recommendations of Fund Managers.” Journal of Finance, October 2016, Bradford Cornell, Bradford, Jason Hsu, and David Nanigian “Does Past Performance Matter in Investment Manager Selection?” Journal of Portfolio Management, Summer 2017 and
20. (page 59)
23. John Bajkowski , Charles Rotblut, and Mark Hulbert, Observations From Decades of Tracking Investment Newsletters, August 2018
24. Campbell Harvey and John Graham, Grading the Performance of Market Timing Newsletters, in the Financial Analysts Journal (November/December 1999)
25. Andrew Metrick, Performance Evaluation with Transactions Data: The Stock Selection of Investment Newsletters in the Journal of Finance (October 1999)
26. 27.
29. Journal of Financial and Quantitative Analysis, June 1993
30. A prior version can be downloaded at
32. A Re-examination of Market Reactions to Business Week's 'Inside Wall Street' Column

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