Day Trading and Overconfidence (EP.121)

Episode Summary

Executive Summary: The episode covers podcast/community updates, then dives into three major investing lessons: technological revolutions and their impact on markets, the evidence against day trading, and how investor overconfidence harms outcomes. The hosts also discuss currency misconceptions, ETF structure changes, factor investing debates, and why indexing criticism often leads to poor stock-picking advice.

Main Topics: Podcast and community updates (Priority: 2/5): The hosts share listener feedback, new merch, community board growth on Discourse, and a shout-out to reviewers. They also joke about rankings, TikTok-driven investing podcasts, and Cameron's 3D printer/lego battle bot project. Technological revolutions and market cycles (Priority: 4/5): Benjamin discusses Carlota Perez's book on technological revolutions, emphasizing how innovation reshapes industries, policy, politics, and wealth concentration. He links the book to today's populism and inequality and to a future episode on innovation and stock returns. Day trading evidence and overconfidence (Priority: 5/5): A large section reviews academic studies showing most day traders lose money after costs, with losses driven by commissions, taxes, market timing, and aggressive trading. The discussion connects day trading to overconfidence, gambling preferences, and attention-driven stock selection. Investor overconfidence and financial knowledge (Priority: 5/5): The planning segment explains overconfidence as confidence exceeding actual ability, using research and FINRA survey questions to show that many investors overestimate their knowledge. It also notes that some confidence can improve action-taking, but excess confidence worsens decisions. Currency and ETF/mutual fund structure misconceptions (Priority: 3/5): The hosts explain that returns are the same once converted to a common base currency, so quoting in USD vs CAD does not change underlying performance. They also discuss why U.S. funds may migrate to ETF structures due to tax efficiency and investor preference. Factor investing debate: size and multi-factor models (Priority: 4/5): Benjamin summarizes a debate on whether size is a true factor, arguing that size matters as part of a multi-factor model even if it has weak standalone significance. The takeaway is to combine factors rather than isolate one and overweight it. Bad advice: anti-indexing and stock-picking (Priority: 4/5): The 'bad advice' segment critiques an article arguing against stock indexes and in favor of selecting 'excellent companies' and managers. The hosts explain why this logic ignores evidence, estimation error, and the difference between good companies and good investments.

Key Arguments: Technological revolutions follow repeatable historical cycles that affect not just markets but inequality, politics, and policy; today’s conditions look eerily similar to late-cycle patterns described in Perez's 2002 book. Day trading is generally a losing proposition after costs; the academic literature consistently shows that higher trading frequency and more aggressive trading are associated with worse outcomes. Some traders may day trade not for expected profit but for entertainment, gambling-like skewness, or social status; stories of winners create a misleading impression of skill. Overconfidence is a major driver of poor investor behavior because people overestimate their knowledge and abilities, trade too much, and under-diversify. Confidence is not always bad; some confidence helps people act, save, and invest, but excess confidence leads to overtrading and lower net returns. Returns quoted in different currencies are not inherently different if they are converted back to the same base currency; the real cost is the FX spread/implementation cost, not a performance advantage from using USD. A stock's industry quality or business quality does not guarantee strong investment returns; expected returns are driven by valuation and estimation error, not just corporate success. Size by itself may not have a strong standalone premium, but it still adds explanatory power in multi-factor asset pricing models and should be used in combination with other factors. Index criticism that focuses only on index composition misses the point: the goal is not to own 'good companies' but to meet financial objectives using evidence-based portfolio construction.

Data Points: Podcast episode: Episode 121 - Rational Reminder Podcast episode number announced mid-show Black Monday anniversary: 33 years - They note the recording date is 33 years after the 1987 market crash SP 500 crash: about 20% in one day - Cameron recalls Black Monday as a near-20% single-day drop Podcast ranking surge: top 3 in Canada - New investment podcasts with celebrity partnerships and energy-drink businesses appeared near the top of the rankings Community board users: over 200 - Discourse community sign-ups mentioned during the update Community activity: over 50 threads - Active discussion volume on the new community board Carlotta Perez book publication: 2002 - Technological Revolutions and Financial Capital was written in 2002 Fidelity Magellan peak assets: about $110 billion - Historical peak under Peter Lynch Fidelity Magellan current assets: around $20 billion - Current scale discussed before ETF transition Magellan prior MER: 77 bps - Fee on the mutual fund before moving to ETF format ARC Innovation ETF inflows: about $8.6 billion - Benjamin cites recent assets gathered this year Small-cap factor paper: all major asset pricing models include market and size - Used to argue size is useful within models even if standalone premium is weak U.S. discount broker sample size: 66,000 households - 1991-1996 trading study on individual investors Household return drag: 1.1% annually after costs - Average household underperformance versus market in the U.S. broker study Risk-adjusted underperformance: 3.7% annually after costs - After adjusting for size, value, and market exposure in the U.S. broker study Average portfolio turnover: 75% annually - Average household turnover in the U.S. broker dataset Most active traders: 200% turnover annually - Top 20% most active households turned over portfolios twice a year Most active traders' underperformance: 5.5% vs market; 10.3% vs risk-appropriate benchmark - Highest-turnover households performed especially poorly Taiwan individual investor loss: 3.8% per year - Day trading reduced aggregate individual investor returns in Taiwan Trading losses as GDP share: 2.2% of Taiwan GDP per year - Aggregate loss attributable to trading activity in the Taiwanese market study Brazil sample size: 19,646 new day traders - Brazilian equity futures study, 2013-2017 follow-up period Brazil day traders trading >300 days: 7.9% of sample - Long-duration day traders in the Brazilian study Brazil long-duration trader losses: 97% lost money - Among those who day traded for at least 300 days Brazil best trader result: US$310 per day on average - Best trader in the Brazilian study earned this with high volatility FINRA survey sample: 2,000 respondents - 2018 U.S. financial capability survey cited in the overconfidence section Financial literacy result: more than half missed at least half the questions - Despite high self-rated knowledge, many respondents answered poorly Self-rated knowledge: 65% said they were above average - Survey result showing overconfidence in financial knowledge Lower-than-average self-rating: 15% - Only a small share of respondents rated themselves below average

Pivotal Quotes: "Overconfident professionals sincerely believe they have expertise, act as experts, and look like experts. You will have to struggle to remind yourself that they may be in the grip of an illusion." — Benjamin Felix (quoting Daniel Kahneman): Used in the planning segment to define and caution against overconfidence "I think the evidence is pretty clear that it doesn't make a whole lot of sense, but there's still people doing it." — Benjamin Felix: Commenting on day trading and technical analysis despite weak evidence "Since indexes have big loopholes and are not indicative of the economy, an investor should aim to obtain returns based on his plans and not based on stock market indices." — Cameron Passmore (reading the article being criticized): Quoted during the bad advice segment to illustrate the anti-indexing argument

Implications: Listeners are reminded to distrust attention-grabbing trading narratives, use evidence-based diversification, and judge investments against goals rather than stories. The episode also reinforces that overconfidence, not just lack of knowledge, is a major risk to long-term returns.