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Written By

James Barra

Written By

James Barra

James is an investment writer and brokerage expert with a background in financial services. A former management consultant, he's worked on major operational transformation programmes at top European banks. A trusted industry name, James's work at DayTrading.com has been cited in publications like Business Insider.

» James Barra Profile Page

Fact Checked By

Tobias Robinson

Fact Checked By

Tobias Robinson

Tobias is the CEO of DayTrading.com, an active investor, and a brokerage expert. He has over 30 years of experience in financial services, including supervising the reviews of more than 500 trading brokers, and contributing via CySEC to the regulatory response to digital options and CFD trading in Europe. Tobias' expertise make him a trusted voice in the industry, where he's been quoted in various financial organizations and outlets, including the Nasdaq.

» Tobias Robinson Profile Page

Updated

Feb 4, 2026

We regularly conduct research studies on key trends across trading and finance – running tests, crunching data, and uncovering insights. Find our latest reports into topics like the dangers of using AI for trading and how misleading TikTok is for investment information.

Our team of experienced analysts and traders also share their comments on industry events as they unfold.

Viral Investing Tweets: 61% of Trade Calls Result in Losses

• We analyzed 50 viral trading tweets on X, previously Twitter, that were trade ideas related to US stocks and ETFs to see whether they were misleading and the potential results if followed.
• We found that viral trading calls lose much more than they win, with an average loss rate of 61% across the six key misinformation themed we identified.
• The biggest red flag was the most prevalent – posts that had no stop-loss or invalidation level for risk management purposes, making up 42% of posts logged and losing 68% of the time.
• Other common issues were no clear timeframes for trades, charting analysis with insufficient commentary, and important risk details located deep in replies where many users won’t see them.
• We created a three-step checklist retail traders can use to approach trading calls on X with a healthy dose of skepticism: verifying price levels, identifying trade timeframes, and checking for information on what happens if trades don’t go as planned.

Read the report: Viral Investing Tweets: 61% of Trade Calls Result in Losses

Inside Trading Signals Groups: 5 Pressure Tactics

• We joined seven trading signals groups across Discord and Telegram and followed 112 signals shared across a four week period, tracking key details like time of alert, entry, stop/target, edits/deletions, and outcome.
• We saw worrying sales tactics, hidden losses, and artificially improved win rates, while 68% of signals were posted too late into a price expansion to properly capture trends.
• 74% of signals recorded had no stop loss at entry, leaving many traders exposed with little to no thought to risk management.
• “URGENT” alerts pressured users to trade more and actually resulted in worse results (urgent alerts averaged –0.42R expectancy vs +0.07R for non-urgent).
• Results were carefully published and not reflective of the true split between wins and losses (46% in our log vs 62% public win rate). 54% of losing trades were also never acknowledged on groups, 9% were actually removed, while 100% of winners were publicised.
• Don’t join signals groups on social media if you can help it. And if you do, follow your own risk appetite and make use of tools like stop losses.

Read the report: Inside Trading Signals Groups: 5 Pressure Tactics

The Great ETF Mirage: Thematic Funds vs S&P 500

• We took five popular ETFs – ARK Innovation ETF (ARKK), Procure Space ETF (UFO), iShares Global Clean Energy ETF (ICLN), iShares MSCI KLD 400 Social ETF (DSI), and Global X Robotics & Artificial Intelligence ETF (BOTZ), and pitted them against the SPDR S&P 500 ETF Trust (SPY), which tracks the wider U.S. stock market.
• We backtested data over a five year period to see if chasing the latest trend would outperform the S&P 500, and it didn’t. Our data shows if you put $10,000 in SPY, you likely got around $18,000 (+80%, 12.5% CAGR) vs the $10,000 in the themes, which got you $13,200 (+32%, 5.7% CAGR).
• In our test period, which included the 2020 surge, 2021 mania, and the reset that followed, the data indicates investors would have earned less in total, while also suffering much greater drawdowns due to the high volatility associated with themes that can lose momentum.

Read the report: The Great ETF Mirage: Thematic Funds vs S&P 500

Meme Index Tracking The Most Hyped Retail Stocks On Socials

• We built a proprietary Meme Index that tracks the 10 most talked about stocks on social platforms, including Reddit, YouTube, and TikTok.
• Each ticker got a 0–100 rating which reflects how “hot” a meme stock is based on online chatter and engagement levels from our research.
• The Meme Index was designed to reflect the fast-moving nature of investing discussions on digital platforms and reveal new names, the biggest risers and fallers, and spotlight topical meme stocks.
• Popular meme stocks that appear on the index include the likes of AMC Entertainment Holdings Inc. (AMC), GameStop Corporation (GME), and BlackBerry Limited (BB), though names can change monthly.

See the Meme Index for September 2025

Whether Investment Content On TikTok Can Be Trusted

• We reviewed the most-viewed finance and investing TikToks of September 2025 (255K+ total views), analyzing their claims against four metrics: accuracy, risk disclosure, oversimplification, and educational value.
• 70% of the videos failed to reach a B grade; risk disclosure was worst (30% scored F, only 10% earned A), and oversimplification was rampant (60% rated D or F).
• Accuracy came in around middle – just 20% earned an A, while 70% landed at C or below – and only 20% of videos achieved an A for educational value.
• The most common pattern was attention-grabbing oversimplification: “buy these three stocks now” or “get rich fast” messaging stripped away context like time horizon, diversification, or risk.
• Risk-free wealth narratives were commonplace: leverage and trading shortcuts were framed as guaranteed paths to riches, often without sufficient disclaimers or downside warnings.

Read the report: Finance TikTok Report Card: 70% of Viral Investing Videos Misleading

The Most Dangerous AI Platforms For Trading

• Retail traders are increasingly using AI tools like ChatGPT, Claude, Perplexity, Gemini, Groq, and Meta AI for trading tasks, from checking stock prices to finding trade ideas.
• We ran 100+ trading prompts through popular AI platforms spanning six use cases (market knowledge, macro questions, live data, event summaries, trade advice, signal generation) and scored each model on accuracy, hallucination risk, misleading potential, overconfidence, and risk disclosure.
• Even the best model was unsafe for trading: ChatGPT scored 5.2/10 for danger but still gave misleading or incomplete answers in 6% of tests; Meta AI was riskiest at 8.8/10, often inventing data and giving high-confidence wrong calls.
• Live data was a major weakness: only Perplexity consistently returned current market prices; other models gave stale or fabricated figures without warning.
• Trading advice and signal generation were the most dangerous: 40% of outputs were confidently wrong or lacked risk disclaimers; some suggested trades with no stop losses.
• Misinterpretation was frequent: models misread the Fed’s tone, earnings releases, and chart patterns; technical setups were wrong in various tests and macro sentiment was sometimes flipped.

Read the report: AI Trading Error Rates: Accuracy, Risks, and Reliability

The Greenest Cryptocurrencies

• We analyzed the true environmental impact of major cryptocurrencies, measuring energy use and carbon emissions per transaction, and compared them with traditional payment networks like Visa and PayPal.
• The biggest factor is consensus mechanism: Proof of Work (e.g., Bitcoin) uses hundreds of kWh per transaction, while Proof of Stake and lightweight systems (Solana, Algorand, Nano) cut energy use by over 99%.
• Bitcoin remains an outlier – a single BTC transaction (~700 kWh, ~400,000 g CO₂) uses more power than a UK home in three weeks and dwarfs Visa, PayPal, and modern blockchains.
• Ethereum’s 2022 Merge slashed energy use by 99%+, bringing each transaction down to ~0.025–0.03 kWh, now similar to or better than PayPal.
• Ultra-efficient chains (Nano, Algorand, Solana) use less power per transaction than a Google search; Algorand and Hedera even offset or overcompensate emissions to go carbon-negative.
• Visa and Mastercard remain very efficient (~0.001 kWh, 0.5 g CO₂ per tx), but the cleanest PoS networks now match or outperform them.

Read the report: The Greenest Cryptos: Which Coins Use the Least Energy?

Comments On The Changes To The Pattern Day Trading Rule

• FINRA has voted to revamp the pattern day trading rule, removing the $25,000 minimum equity requirement for day traders in the US.
• The proposed changes to the PDT rule, which have been amongst the strictest applied to retail traders anywhere, could mark the biggest shake up for the US retail day trading market in over 20 years.
• The PDT rule has long trapped skilled albeit small traders with modest accounts. If loosened, experienced traders with an edge could trade more freely without the temptation to turn to higher-risk offshore brokers or trading products.
• With rising financial literacy and huge leaps in technology, both in terms of trading execution and access to market data, some day traders in the US will be well-positioned to capitalize on the changes.
• Day trading brokers operating in the US also stand to gain. Brokerages will likely see more trading volume on their platforms, and thus more order flow and greater commissions.
• But it’s not necessarily all dollar signs for brokers. There could be an increase in the costs associated with customer support as there’ll be more accounts that aren’t as valuable. Costs for risk management and client defaults or margin calls could also rise. This may even lead to a change in fee structures.

Comments On Trump Raising H-1B Visa Fees

• USCIS plans to raise H-1B petition fees from ~$1.7k–$5.4k to $100k per new filing – a seismic cost jump that could reshape how tech firms source global talent. The move will hit startups and mid-tier tech hardest, yet megacap stocks can mostly absorb it.
• Hiking the visa fees is a huge marginal cost increase, especially for entry/mid-level hires. It raises planning risk and will slow talent pipelines, push more domestic hiring/upskilling, and trigger offshoring/near-shoring.
• Megacaps can absorb the cost (deep pockets, premium talent draw), but smaller tech firms and startups will get hit hardest. Scaling AI/software teams will become pricier and more complex.
• Expect fewer H-1B filings overall, skewed toward senior or niche hires with more work routed to global hubs (Canada/Europe), heavier use of consultancies (TCS, Accenture, Infosys), and possibly more automation to reduce junior offshore roles.
• Entry-level ROI will drop; niche skills become harder to fill onshore and on schedule. Some projects could be delayed or reassigned globally.
• It’s mostly headline risk for megacaps; near-term EPS impact will be negligible, though AI/engineering hiring velocity could slow and costs may creep if firms adjust delivery models.

Get In Touch

For questions about our data or requests for expert comments from our experienced analysts covering all things trading, investing, and the stock market, please contact:

James Barra | Media Lead: james.barra@daytrading.com

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