Commerce Secretary Howard Lutnick announced the Department of Commerce will begin issuing GDP and other economic statistics on blockchain during a White House cabinet meeting on Aug. 26.

Positioning the technology as a government-wide data distribution tool, Lutnick told President Donald Trump:

“The Department of Commerce is going to start issuing its statistics on the blockchain, cause you [Trump] are the crypto president, and we are going to put the GDP on the blockchain so people can use the blockchain for data distribution.”

Lutnick said the Commerce Department plans to make blockchain-based statistics “available for the entire government” while working through implementation details.

The announcement represents the most prominent federal blockchain deployment under the Trump administration’s crypto-friendly policies.

Existing federal programs

The Commerce initiative builds on existing blockchain pilots across federal agencies.

Treasury tested a grant distribution system using blockchain to track drawdowns with automatic reconciliation and audit trails, though it never launched publicly.

The Commodity Futures Trading Commission operates a pilot program evaluating tokenized collateral and stablecoin-based financial transactions in regulated markets.

At the same time, the Small Business Administration has evaluated blockchain for monitoring fraud and performance metrics in loan programs, according to Government Accountability Office reports.

The Department of Defense and Homeland Security are exploring the use of blockchain for parts tracking, supply chain authentication, and digital documentation.

The Navy and the Defense Logistics Agency collaborate with SIMBA Chain to track high-value parts through blockchain ledgers, thereby reducing manual data entry in defense supply chains.

Customs and Border Protection previously ran blockchain trials to verify intellectual property data on imports and spot counterfeit goods.

Congressional support

The blockchain push aligns with pending congressional legislation. The “Deploying American Blockchains Act of 2025,” sponsored by Rep. Kat Cammack and passed by the House on June 23, moved to the Senate on June 24.

The bill directs the Secretary of Commerce to promote US competitiveness in blockchain deployment and applications.

The legislation would establish a Commerce Department Blockchain Deployment Program and create advisory committees that include federal agencies, private sector representatives, and blockchain infrastructure operators.

The program would examine how federal agencies can benefit from distributed ledger technology while addressing concerns related to cybersecurity and regulatory compliance.

The Commerce Department’s GDP blockchain initiative represents the latest federal commitment to distributed ledger technology for core government functions.

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In a nutshell: Not all television shows are created equal. Even among those that are universally praised, it can take a handful of episodes or more before a series sets its hook and starts reeling you in. This leads to a question that many have no doubt pondered over the years: how many episodes should you give a series before cutting your losses and moving on?

Daniel Parris from Stat Significant recently set out to answer this age-old question. To quantify it, Parris looked at IMDb user ratings (ranging from 1 to 10) for Friends and found the average to be around 8.34. Using this figure as the benchmark, Parris noticed that it wasn’t until episode seven that Friends episodes surpasses this threshold.

Using this method, Parris looked at every show in IMDb’s database and compared its average user rating to individual episodes, then averaged the resulting differential by episode number. Data revealed that most shows need six or seven installments before ratings start to match or exceed the series’ long-term average.

Depending on the series, six or seven episodes could make up the majority or even the entirety of the first season. With so much content to choose from these days, it’s no surprise that some aren’t willing to dedicate that much time to try and get interested in a new show.

While an interesting metric, it is by no means concrete as subjectivity plays a huge role as well. With some shows – especially sitcoms – it can take a season or two for actors to fully flesh out a character. Others manage to instantly click with audiences and get them addicted from the very first episode.

I usually know within the first episode or two whether a show is for me, though occasionally, I’ll give a series more time if it comes highly recommended. How about you? Does the 6-7 episode threshold seem about right, or is it too high? And while we are on the subject, what are a few of your all-time favorite television series?

Analysis of Hyip Projects

“95% of all traders fail” is the most commonly used trading related statistic around the internet. But no research paper exists that proves this number right. Research even suggests that the actual figure is much, much higher. In the following article we’ll show you 24 very surprising statistics economic scientists discovered by analyzing actual broker data and the performance of traders. Some explain very well why most traders lose money.

1. 80% of all day traders quit within the first two years. ¹
2. Among all day traders, nearly 40% day trade for only one month. Within three years, only 13% continue to day trade. After five years, only 7% remain. ¹
3. Traders sell winners at a 50% higher rate than losers. 60% of sales are winners, while 40% of sales are losers.²
4. The average individual investor underperforms a market index by 1.5% per year. Active traders underperform by 6.5% annually. ³
5. Day traders with strong past performance go on to earn strong returns in the future. Though only about 1% of all day traders are able to predictably profit net of fees. ¹
6. Traders with up to a 10 years negative track record continue to trade. This suggests that day traders even continue to trade when they receive a negative signal regarding their ability. ¹
7. Profitable day traders make up a small proportion of all traders – 1.6% in the average year. However, these day traders are very active – accounting for 12% of all day trading activity. ¹
8. Among all traders, profitable traders increase their trading more than unprofitable day traders. ¹
   
9. Poor individuals tend to spend a greater proportion of their income on lottery purchases and their demand for lottery increases with a decline in their income. ⁴
10. Investors with a large differential between their existing economic conditions and their aspiration levels hold riskier stocks in their portfolios. ⁴
11. Men trade more than women. And unmarried men trade more than married men. ⁵
12. Poor, young men, who live in urban areas and belong to specific minority groups invest more in stocks with lottery-type features. ⁵
13. Within each income group, gamblers underperform non-gamblers. ⁴
14. Investors tend to sell winning investments while holding on to their losing investments. ⁶
15. Trading in Taiwan dropped by about 25% when a lottery was introduced in April 2002. ⁷
16. During periods with unusually large lottery jackpot, individual investor trading declines. ⁸
17. Investors are more likely to repurchase a stock that they previously sold for a profit than one previously sold for a loss. ⁹
18. An increase in search frequency [in a specific instrument] predicts higher returns in the following two weeks. ¹⁰
19. Individual investors trade more actively when their most recent trades were successful.¹¹
20. Traders don’t learn about trading. “Trading to learn” is no more rational or profitable than playing roulette to learn for the individual investor.¹
21. The average day trader loses money by a considerable margin after adjusting for transaction costs.
22. [In Taiwan] the losses of individual investors are about 2% of GDP.
23. Investors overweight stocks in the industry in which they are employed.
24. Traders with a high-IQ tend to hold more mutual funds and larger number of stocks. Therefore, benefit more from diversification effects.

Conclusion: Why Most Traders Lose Money Is Not Surprising Anymore

After going over these 24 statistics it’s very obvious to tell why traders fail. More often than not trading decisions are not based on sound research, tested trading methods or their trading journal, but on emotions, the need for entertainment and the hope to make a fortune in no time.

What traders always forget is that trading is a profession and requires skills that need to be developed over the years. Therefore, be mindful of your trading decisions and the view you have on trading. Don’t expect to be a millionaire by the end of the year, but keep in mind the possibilities trading online has. 

We, at Tradeciety, built the Edgewonk trading journal which is a trading tool that allows traders to track and analyze their trades to improve their trading performance. A trading journal is a great way to become a professional trader and start taking trading seriously. 

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– ¹Barber, Lee, Odean (2010): Do Day Traders Rationally Learn About Their Ability?
– ²Odean (1998): Volume, volatility, price, and profit when all traders are above average
– ³Barber, & Odean (2000): Trading is hazardous to your wealth: The common stock investment performance of individual investors
– ⁴ Kumar: Who Gambles In The Stock Market?
– ⁵ Barber, Odean (2001): Boys will be boys: Gender, overconfidence, and common stock investment
– ⁶Calvet, L. E., Campbell, J., & Sodini P. (2009). Fight or flight? Portfolio rebalancing by individual investors.
–⁷Barber, B. M., Lee, Y., Liu, Y., & Odean, T. (2009). Just how much do individual investors lose by trading?
– ⁸Gao, X., & Lin, T. (2011). Do individual investors trade stocks as gambling? Evidence from repeated natural experiments
– ⁹Strahilevitz, M., Odean, T., & Barber, B. (2011). Once burned, twice shy: How naïve learning, counterfactuals, and regret affect the repurchase of stocks previously sol.
– ¹⁰Da, Z., Engelberg, J., & Gao, P. (2011). In search of attention
– ¹¹De, S., Gondhi, N. R. & Pochiraju, B. (2010). Does sign matter more than size? An investigation into the source of investor overconfidence