The Evidence-Based Investor

Tag Archive: attention induced trading

1. The role of social media in stock and bond markets

   July 19, 2022 10:43 am Comments Off on The role of social media in stock and bond markets

    

    

    

   By LARRY SWEDROE

    

   The increased popularity of social media as a forum for market participants to post and exchange opinions has been accompanied by heightened interest from academic researchers who have sought to determine if there is valuable information in the postings. For example, the June 2020 study Do Individual Investors Trade on Investment-related Internet Postings? investigated whether social media postings help individual investors identify investment strategies that deliver superior performance in the future. The authors found that “it is mainly unsophisticated individuals who rely on investment-related Internet postings when making investment decisions, but this does not help them identify traders with superior skills.” These findings are consistent with those of the authors of the November 2020 study Attention Induced Trading and Returns: Evidence from Robinhood Users, who found: “Large increases in Robinhood users are often accompanied by large price spikes and are followed by reliably negative returns.”

   The findings are also consistent with those of the authors of the March 2021 paper The Rise of Reddit: How Social Media Affects Retail Investors and Short-sellers’ Roles in Price Discovery, who found that “Reddit social media activity encourages retail buying behavior, and deters shorting.” They added: “Social media activity and retail flows cultivate price bubbles, while the short-sellers correct the bubbles created by social media activity and retail order flows.” And the author of the August 2020 study Investor Emotions and Earnings Announcements had a particularly interesting finding—investors are typically excited about firms that do end up exceeding expectations, but their enthusiasm was excessive and resulted in negative post-announcement returns.

   Unfortunately, the body of evidence demonstrates that naive retail investors can be easily convinced they have an edge — they know something the market hasn’t yet incorporated into prices. Sadly, the evidence also shows that while these less sophisticated investors can be convinced they “know” something by finding an “expert” on a social media platform, the results of trading activities based on following “experts” show negative outcomes. As usual, the ones benefiting are the platforms (like Robinhood), not the investors who use them. 

    

   Sell-side analysts and social media

   Ann Marie Hibbert, Qiang Kang, Alok Kumar and Suchi Mishra took a different approach to the issue of how social media impacts markets. In their February 2022 study Twitter Information, Analyst Behavior, and Market Efficiency, they examined whether sell-side equity analysts are able to effectively extract information from social media to improve their earnings forecasting performance. They used Bloomberg’s daily Twitter sentiment data on S&P 500 firms over the period 2015-2019 to determine if that was the case. 

   The authors began by noting: “One strand of psychology literature shows that, across a broad range of contexts, negative information is processed more thoroughly than positive information. … Consequently, negative information would be more influential than comparable positive information.” They added: “The same psychology literature also demonstrates that negative information elicits more thorough and careful information processing than positive information. Therefore, negative information may capture more attention and receive more conscious processing.” And finally, they noted: “Due to either conflict of interest or economic incentives, analysts issue overly optimistic earnings forecasts. Innovation in information technology such as the advent and prevalence of social media have intensified competition in information production, which may induce analysts to make less biased forecasts.” And in fact, the authors of the 2016 study The Value of Crowdsourced Earnings Forecasts found such behavior among firms on Estimize, an open platform that crowdsources short-term earnings forecasts.  

   Hibbert, Kang, Kumar and Mishra found that while analysts’ forecasts are too optimistic on average, Twitter information tends to be relatively more pessimistic than traditional news. They also found that while positive Twitter information had little or no impact on analyst forecasts, more negative Twitter information was associated with more pessimistic (less optimistic) and more accurate earnings forecasts—Twitter information reduces forecast optimism and improves forecast accuracy of equity analysts. The effect was also greater for smaller firms with greater information asymmetry. The bottom line was that, in aggregate,  Twitter-sensitive firms have smaller earnings surprises and consequently weaker stock market reaction—the post-earnings-announcement drift (PEAD) anomaly (the tendency for a stock’s cumulative abnormal returns to drift in the direction of an earnings surprise for several weeks, or even several months, following an earnings announcement) was reduced, especially for negative earnings surprises. 

   Their findings led Hibbert, Kang, Kumar and Mishra to conclude: “Collectively, these results suggest that financial analysts extract useful information from Twitter, improving their overall forecasting performance and market efficiency. Investors recognize this relation and respond to this phenomenon accordingly.”

   All of these studies focused on equity markets. And there are major differences between equity and corporate bond markets. For example, equity markets are generally much more liquid because liquidity in the corporate bond market is limited, as most bonds do not trade. In addition, while retail investors play a significant role in equity markets, corporate bonds are almost exclusively the realm of institutional investors, considered to be more sophisticated and less subject to emotion-driven biases. Thanks to Eli Bartov, Lucile Faurel and Partha Mohanram, authors of the April 2022 study The Role of Social Media in the Corporate Bond Market: Evidence from Twitter, we now have evidence on the role of social media in the corporate bond market as well.

    

   Evidence from the corporate bond market

   Bartov, Faurel and Mohanram examined the role of social media information in the corporate bond market by testing the ability of Twitter opinions (OPI) to predict bond returns and changes in credit default swap (CDS) contracts’ spreads around the upcoming quarterly earnings announcements—prior research showed that bond-trading volume increases sharply around earnings announcements. Their test variable was the aggregate OPI during the window [-10;-3], where day 0 was the earnings announcement date. They ended their OPI measurement window on day -3 because some of their tests involved the window [-2;+2]. They focused on the window [10;-3] because prior research documented a concentration of Twitter activity in the period just prior to quarterly earnings announcements. They measured OPI using textual-analysis methodologies focusing on the words that comprised each individual tweet. Their tests controlled for a variety of firm characteristics: size, value, profitability and leverage.

   Their data sample covered 2,692,185 tweets (9,404 firm-quarters; 1,158 unique firms) over the period December 17, 2008-December 31, 2012 and considered all stocks with traded corporate bonds that were ever included in the Russell 3000 Index during the period. Following is a summary of their findings: 

   • Controlling for earnings surprises and announcement stock returns, OPI was significantly positively associated with bond returns and significantly negatively associated with changes in CDS spreads around quarterly earnings announcements. 
   • Bond prices are more sensitive to bad earnings news than good earnings news, consistent with the nonlinear payoff structure of bonds—the association between OPI and announcement bond returns (changes in CDS spreads) was more positive (more negative) for bad news compared to good news.
   • The association between OPI and announcement bond returns/changes in CDS spreads was stronger for tweets containing information directly related to bonds and credit risk and when information uncertainty was high.
   • OPI was significantly negatively associated with the probability of a future credit rating downgrade but insignificantly related to the probability of a future upgrade. 
   • OPI was strongly negatively associated with future changes in implied default probability for three measures of default risk: the Altman Z-score, the Ohlson O-score and the Black-Scholes-Merton models (which view a company’s equity as a call option on its assets). In addition, the association was stronger for speculative-grade bonds, confirming findings in prior studies that speculative-grade bond prices show a greater sensitivity to news compared to investment-grade bonds. Further, this association was stronger for firms with greater information uncertainty, where Twitter information is more likely to be incrementally meaningful.
   • Their findings were robust to various tests of the Twitter data, such as level of Twitter activity and original versus dissemination tweets.

   Their findings led Bartov, Faurel and Mohanram to conclude: “We provide novel evidence that social media appears to convey economically important information to even the presumably sophisticated investors who dominate the corporate bond market, an important financial market that differs significantly from the stock market, as well as to credit rating agencies.”

    

   Investor takeaways

   In our book The Incredible Shrinking Alpha, Andrew Berkin and I provided the evidence demonstrating that it is persistently more difficult for active managers to add alpha (outperform appropriate risk-adjusted benchmarks). We also demonstrated that there are four main themes that explain the shrinking alpha: 

   • Academic research has been converting what was once alpha into beta.
   • The pool of victims that can be exploited has been shrinking.
   • The competition has been getting tougher.
   • The supply of dollars chasing the shrinking pool of alpha has increased.

   The latest research on the impact of social media provides us with yet another explanation: Social media provides analysts with information that reduces their forecasting errors — another example of the benefits of the wisdom of crowds (at least when they act independently, not in herds). The result is that the ability to generate alpha continues to be under assault — trying to outperform the market by stock selection is becoming even more of a loser’s game.   

    

   For informational purposes only and should not be construed as specific investment, accounting, legal, or tax advice.  Certain information is based upon third party data which may become outdated or otherwise superseded without notice. Third party data is deemed to be reliable, but its accuracy and completeness cannot be guaranteed. By clicking on any of the links above, you acknowledge that they are solely for your convenience, and do not necessarily imply any affiliations, sponsorships, endorsements or representations whatsoever by us regarding third-party websites. We are not responsible for the content, availability or privacy policies of these sites, and shall not be responsible or liable for any information, opinions, advice, products or services available on or through them. The opinions expressed by featured authors are their own and may not accurately reflect those of Buckingham Strategic Wealth® or Buckingham Strategic Partners®, collectively Buckingham Wealth Partners. Neither the Securities and Exchange Commission (SEC) nor any other federal or state agency have approved, determined the accuracy, or confirmed the adequacy of this article. LSR-22-314

    

   LARRY SWEDROE is Chief Research Officer at Buckingham Strategic Wealth and the author of numerous books on investing.

    

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2. New evidence on how investor emotions affect markets

   February 18, 2022 2:13 pm Comments Off on New evidence on how investor emotions affect markets

    

    

    

    

   “What defeats investors more than anything is psychology. Specifically, they can be undone by the emotional ups and downs that accompany the ups and downs of the market. For investors, it’s crucial that they understand how closely their emotions track the market, and how such emotions often lead them to do precisely the wrong thing.”

   — Michelle Perry Higgins, financial planner

    

    

   Research (see here, here and here)  has shown that investor sentiment (emotions) plays a significant role in international market volatility and generates return predictability of a form consistent with the correction of investor overreaction; and total sentiment is a contrarian predictor of country-level market returns, as high investor sentiment predicts low future returns and vice versa. In particular, the research shows that younger, smaller, more volatile, unprofitable, non-dividend paying, distressed stocks are likely to be the most sensitive to speculative demands and more affected by shifts in investor sentiment. Conversely, “bond-like” stocks are less driven by sentiment.

   Domonkos Vamossy and Rolf Skog contribute to the literature with their December 2021 study, EmTract: Investor Emotions and MarketBehavior, in which they employed a unique dataset combined with advances in text processing to examine the connection between firm-specific investor emotions and asset price movements. Specifically, they explored the following research questions: 

   1. Do the results of controlled laboratory experiments relating investor emotions to trading behaviour replicate observational data?
   2. Do investor emotions forecast daily price movements?
   3. Whose emotions matter and for what type of firms?
   4. Do investor emotions help explain first-day IPO returns and subsequent long-run underperformance?

   To answer these questions, Vamossy and Skog used data from StockTwits, a social networking platform for investors to share stock opinions, that covered 63 million messages linked to particular stocks spanning the period 2010-May 2021. They developed a tool that quantifies investor emotions from financial social media text data (i.e., informal text containing less than 30 words).

   The authors explained: “Our models are powered by deep learning and a large, novel dataset of investor messages. In particular, our tool takes social media text as inputs, and for each message it constructs emotion variables corresponding to seven emotional states: neutral, happy, sad, anger, disgust, surprise, fear.” They then tested whether firm-specific investor emotions before the market opens predict a firm’s daily price movements. Following is a summary of their findings:

   • Neutral messages constituted about 42 percent, followed by happy posts of about 31 percent. Fear and surprise were the third and fourth most frequent emotions, followed by sad, anger and disgust.
   • Investors are more likely to share their enthusiasm than their pessimism on social media.
   • Investor emotions extracted from social media data behave similarly to those in controlled laboratory experiments, providing validity for previous lab experiments (for example, see here and here).
   • Most posting activity on the platform happened when the markets were open — consistent with investors updating their beliefs in real time as financial events unfold.
   • Social media investors are more interested in discussing firms with higher dollar trading volume, volatility, larger market cap, higher short interest and lower institutional ownership.
   • Within-firm investor emotions can predict the company’s daily price movements — variation in investor enthusiasm is linked with marginally higher daily returns. The result was driven both by messages conveying original information and by those disseminating existing ones. 
   • When considering messages that convey information directly related to earnings, firm fundamentals or stock trading relative to those messages that consist of other information, the latter has a slightly larger impact on daily returns. 
   • The impacts of emotions are larger when volatility or short interest are higher, and when institutional ownership or liquidity are lower. 
   • StockTwits users tend to discuss stocks that have gone up or are currently going up in value: The average past monthly return was 6 percentage points, the close-open return was 0.5 percentage point, and the one-day lag open-close return was 0.1 percentage point higher than in the CRSP sample. These stocks ended up with a 0.4 percentage point lower open-close return, suggesting mean reversion.
   • Emotions before the market opened explained a small fraction of the variation in daily returns — a standard deviation increase in non-market hour happiness (before the market opened) was associated with a 0.7 percent standard deviation increase in daily stock returns. The effects were smaller for larger-cap stocks. There were stronger effects for stocks with larger user engagement (at least 100 messages) — a standard deviation increase in happiness before the market opened was associated with a 3.1 percent standard deviation increase in daily open-close stock returns.
   • When it came to messages disseminating existing information, only the level of fear and happiness was associated with statistically significant differences from the baseline (neutral) level.
   • The predictive power of investor emotions diminishes over a few days.
   • Investor enthusiasm is a predictor for first-day IPO returns and subsequent long-run underperformance — the set of IPOs with low investor enthusiasm prior to the IPO had first-day returns of 16.5 percent on average, while the set of IPOs with high investor enthusiasm had a much higher first-day return of 30.9 percent on average.
   • IPOs with large first-day returns driven by investor enthusiasm underperformed average firms in the same industry over the long run. In contrast, IPOs experiencing large first-day returns without high investor enthusiasm prior to IPO did not experience long-run reversal — neither investor enthusiasm nor first-day return alone predicts long-run IPO underperformance, though the interaction between investor enthusiasm and first-day return does.

   Their findings led Vamossy and Skog to conclude: “Investor emotions extracted from StockTwits provide information relevant to stock valuation not accounted for by unobservable time-invariant stock characteristics, by time patterns, or by recent price movements. ”They added: “These impacts are larger when volatility or short interest are higher, and when institutional ownership and liquidity are lower.” And finally, their findings showed that “investor emotions can help rationalise two stylised facts about IPO returns”.

    

   Evidence From Robinhood investors

   Behavioural finance professors Brad Barber and Terrance Odean have done extensive research on the performance and habits of individual investors. Among their findings is that, on average, individual investors lose money from trading—and not all the losses can be explained by trading costs. In their 2008 study, All that Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors, they made the case that limited attention prevents retail investors from considering all available information and possible stock choices. Instead, many retail investors choose stocks to buy from the subset of stocks that catch their attention. Because most investors own only a few stocks and do not sell short, limited attention plays a smaller role in their sales decision.

   In their November 2020 study, Attention Induced Trading and Returns: Evidence from Robinhood Users, Barber and Odean, with co-authors Xing Huang and Chris Schwarz, examined the behaviour of Robinhood users and found that they are even more subject to attention biases and more likely to chase stocks with extreme performance and volume than other retail investors. Herding by Robinhood investors can be forecasted by attention measures, such as lagged absolute returns and lagged abnormal volume, previously shown to affect the buy-sell imbalances of retail investors. Most importantly, they found that Robinhood herding episodes are followed by abnormal negative returns. Of particular interest is their finding that sophisticated investors were exploiting the patterns created by Robinhood investors by shorting stocks or buying puts in response to Robinhood herding events. They found a marked increase in short selling for stocks involved in Robinhood herding events — for the stocks with the top 25 returns for the period, the average change in short interest was three times greater. They concluded that their results “suggest strongly that market participants examined Robinhood ownership data, knew about the subsequent poor performance caused by Robinhood herding, and traded against Robinhood order flow.” 

    

   Investor takeaways

   The research demonstrates that investor sentiment (emotions) not only plays a significant role in market volatility and generates return predictability of a form consistent with the correction of investor overreaction but also is a contrarian predictor of future returns. This is particularly true for stocks of companies that are younger, smaller, more volatile, unprofitable, non-dividend paying and distressed. Thus, investors are best served by having a well-though-out investment plan, including an asset allocation table that is adhered to (rebalancing along the way). Having and adhering to such a plan provides the greatest chance of not allowing emotions to impact decisions. Forewarned is forearmed. 

   Another takeaway is that while the effects of information on fundamentals can be identified with well-established techniques in finance, studying the emotional component requires new tools, such as the artificial intelligence tool used by Vamossy and Skog (which they made available here).

    

   For informational and educational purposes only and should not be construed as specific investment, accounting, legal, or tax advice. Certain information is based upon third party information and may become outdated or otherwise superseded without notice.  Third party information is deemed to be reliable, but its accuracy and completeness cannot be guaranteed.   By clicking on any of the links above, you acknowledge that they are solely for your convenience, and do not necessarily imply any affiliations, sponsorships, endorsements or representations whatsoever by us regarding third-party websites. We are not responsible for the content, availability or privacy policies of these sites, and shall not be responsible or liable for any information, opinions, advice, products or services available on or through them. The opinions expressed by featured authors are their own and may not accurately reflect those of the Buckingham Strategic Wealth® or Buckingham Strategic Partners®, collectively Buckingham Wealth Partners. LSR-21-20

    

   LARRY SWEDROE is Chief Research Officer at Buckingham Strategic Wealth and the author of numerous books on investing.

    

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3. Why most Robinhood traders earn lousy returns

   December 4, 2020 6:30 am Comments Off on Why most Robinhood traders earn lousy returns

    

    

   By LARRY SWEDROE

    

   Behavioural finance professors Brad Barber and Terrance Odean have done extensive research on the performance and habits of individual investors. Among their findings is that, on average, individual investors lose money from trading — and not all the losses can be explained by trading costs. In their 2008 study All that Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors, they made the case that limited attention prevents retail investors from considering all available information and possible stock choices.

   Instead, many retail investors choose stocks to buy from the subset of stocks that catch their attention. Because most investors own only a few stocks and do not sell short, limited attention plays a smaller role in their sales decision.

   Barber and Odean, with co-authors Xing Huang and Chris Schwarz, add to the behavioural finance literature with their November 2020 study Attention Induced Trading and Returns: Evidence from Robinhood Users. Robinhood was the first to introduce commission-free trading, and its application makes it extremely easy. The authors noted: “Robinhood’s app is simple and engaging, designed to encourage people to invest. Robinhood added features to make investing more like a game. New members were given a free share of stock, but only after they scratched off images that looked like a lottery ticket.” This is important because their prior research had demonstrated that investors who access easier trading by switching from phone-based to online trade more and perform worse for up to two years after switching.

   Drawing on their prior research, Barber, Odean, Huang and Schwarz hypothesised: “When buying stocks, investors with accounts at Robinhood are likely to be more influenced, both individually and as a group, by limited attention than other investors for several reasons”:

    

   — Half of Robinhood users are first-time investors who are unlikely to have developed their own clear criteria for buying a stock.

   — Inexperienced stock investors are likely to be more heavily influenced by attention and by biases that lead to return chasing.

   — The Robinhood app directs Robinhood users’ attention to the same small subset of stocks, such as the 20 “Top Movers,” while offering limited additional information that might lead to more heterogeneous choices.

   — The simplification of information on the Robinhood app is likely to provide cognitive ease to investors, leading them to rely more on their intuition and less on critical thinking.

   — Robinhood users may deliberate and hesitate less than other investors when trading due to a lack of frictions because it is easy to place trades on the app and commissions are zero.

   — As evidenced by turnover rates many times higher than at other brokerage firms, Robinhood users are more likely to be trading speculatively and less likely to be trading for reasons such as investing their retirement savings, liquidity demands, tax-loss selling and rebalancing. The lack of non-speculative trading motives increases the potential for attention-driven trading.

   — Because Robinhood users are more likely than other investors to be influenced by attention, their purchase behavior is more likely to be correlated; that is, they herd more than other investors.

    

   Their analysis focused on examining abnormal returns following events in which the number of Robinhood users owning a particular stock increased dramatically in one day. While herding by a few investors is unlikely to move prices in all but the least liquid stock, by May 2020, there were 13 million Robinhood users, more users than Schwab (12.7 million) or E-Trade (5.5 million).

   Additionally, Robinhood users are unusually active. In the first quarter of 2020, Robinhood users “traded nine times as many shares as E-Trade customers, and 40 times as many shares as Charles Schwab customers, per dollar in the average customer account in the most recent quarter.” Their data set is from the Robintrack website, which scrapes stock popularity data from Robinhood between May 2, 2018, and August 13, 2020. The following is a summary of their findings:

    

   — Robinhood users are more subject to attention biases and more likely to chase stocks with extreme performance and volume than other retail investors — for Robinhood users about 35 (25) percent of all net buying (selling) was in the top 10 stocks versus 24 (14) percent for other retail investors.

   — Robinhood herding is influenced by information that is prominently displayed on the Robinhood app.

   — There is persistence in the herding episodes: a stock which was heavily bought by Robinhood investors was 10 percent more likely to experience another episode the next day. However, negative returns were less likely to generate extreme herding.

   — Robinhood herding can be forecasted by attention measures, such as lagged absolute returns and lagged abnormal volume, previously shown to affect the buy-sell imbalances of retail investors.

   — When Robinhood experienced outages, they observed the largest decrease in retail trading among stocks that attract the attention of Robinhood users (the most popular stocks on Robinhood and stocks with a high probability of a herding event).

   — Robinhood users are more aggressive buyers of stocks on Robinhood’s Top Mover list than other retail investors — Robinhood investors buy both extreme gainers and losers, while other retail investors prefer to buy extreme gainers rather than losers.

   — Robinhood herding episodes are followed by abnormal negative returns. Defining herding events as the top 0.5 percent of positive user changes as a percent of prior day user count each day, or a user increase of more than 1,000 and more than 50 percent relative to the previous day, the return and user patterns are similar over a 31-day period from 10 trading days before the event day to 20 trading days after — average abnormal return on the herding day was 14 percent (42 percent). Most of the abnormal return occurred at the open of trading — the mean opening return was 11 percent. Despite the large positive mean daily returns, about one-third of the stocks had large negative returns on the day of herding events. However, over the subsequent month, the average return was about -5 percent (-9 percent). These results are economically and statistically significant and were not driven by just a few stocks. In addition, portfolio alphas were more negative during the 2020 pandemic period, ranging from -79 to -94 basis points per day.

   — Returns were also negative following a day when they observed both a surge in Robinhood users and the stock’s price went down.

   — A strategy of selling after a Robinhood herding event and repurchasing five days later would have resulted in a return of 3.5 percent (6.4 percent for extreme herding events). For the 4,884 herding events observed, this strategy would have yielded a positive return 63 percent of the time.

   — Returns were negative following Robinhood herding events for stocks with market caps under $1 billion but not for stocks with market caps over $1 billion.

   — Retail trading has increased significantly at Robinhood and elsewhere in the post-Covid period (after March 13, 2020), and the negative return effect following Robinhood herding events is more pronounced in the post-Covid period.

    

   Barber, Odean, Huang and Schwarz observed that sophisticated investors could exploit the patterns created by Robinhood investors by shorting stocks, or buying puts, in response to Robinhood herding events. In fact, they found a marked increase in short selling for stocks involved in Robinhood herding events — for the stocks with the top 25 returns for the period, the average change in short interest was three times greater. They concluded that their results “suggest strongly that market participants examined Robinhood ownership data, knew about the subsequent poor performance caused by Robinhood herding, and traded against Robinhood order flow.”

   Another interesting finding was that the average number of stocks held by Robinhood investors was just three, displaying a lack of knowledge of the benefits of diversification. The lack of diversification could be explained by the all-too-human trait of overconfidence.

   Their findings led, Barber, Odean, Huang and Schwarz to conclude: “Large increases in Robinhood users are often accompanied by large price spikes and are followed by reliably negative returns. While some users profit from these episodes, we find that, in aggregate, Robinhood users who establish new positions during these episodes incur losses.”

   They added that their findings contribute to the literature demonstrating price reversals following attention-grabbing events such as Jim Kramer’s stock recommendations, Google stock searches and repeat news stories. This result “fits into the emerging literature that emphasises the display of information can affect investor behaviour.” (Note: Robinhood discontinued the reporting of stock popularity data on August 13, 2020.)

   The above findings are entirely consistent with those from the 2014 study The Cross-Section of Speculator Skill: Evidence from Day Trading. Co-authored with Yi-Tsung Lee and Yu-Jane Liu, Barber and Odean studied the performance of day traders (almost exclusively individual investors) in Taiwan (where there were about 450,000 day traders) for the 15-year period 1992 to 2006.

   Among their findings was that the vast majority of day traders lose money: “While about 20% earn profits net of fees in the typical year, the results of our analysis suggest that less than 1% of day traders (4,000 out of 450,000) are able to outperform consistently.” The other 99 percent would be better off abandoning their day-trading efforts. In other words, day-trading is hazardous to your financial health.

    

   Summary

   Robinhood, with commission-free trading, has certainly been successful in its stated mission, having attracted 13 million users with its app that makes trading easy. Unfortunately, its application also leaves naïve individual investors more susceptible to well-documented biases that lead to speculative trading and poor results, with the winner being Robinhood itself.

   The historical evidence on efforts of individual investors to generate alpha clearly show that while it’s not impossible to generate alpha on a consistent basis, the odds of doing so are so poor it’s not prudent to try. In other words, if you look in the mirror and see Warren Buffett, go ahead and try to pick stocks that will outperform.

   But unless you live in Lake Wobegon, where everyone has Buffett-like abilities, you’re not likely to see the Oracle of Omaha in the mirror. For those who don’t, the winning strategy is to build a globally diversified portfolio that reflects your unique ability, willingness and need to take risk, and stay the course, rebalancing and tax managing as events dictate.

    

   Important Disclosure:  The information contained in this article is for educational purposes only and should not be construed as specific investment, accounting, legal or tax advice.  The analysis contained in this article is based upon third party information available at the time which may become outdated or otherwise superseded at any time without notice.  Certain third-party information is based upon is deemed to be reliable, but its accuracy and completeness cannot be guaranteed.  By clicking on any of the links above, you acknowledge that they are solely for your convenience, and do not necessarily imply any affiliations, sponsorships, endorsements or representations whatsoever by us regarding third-party websites. We are not responsible for the content, availability or privacy policies of these sites, and shall not be responsible or liable for any information, opinions, advice, products or services available on or through them. The opinions expressed by featured authors are their own and may not accurately reflect those of the Buckingham Strategic Wealth®, Buckingham Strategic Partners® (collectively Buckingham Wealth Partners). R-20-1541

    

   LARRY SWEDROE is Chief Research Officer at Buckingham Strategic Wealth and the author of numerous books on investing.

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