Not So Fast Nanex. Debunking the HFT Ford Study


There has been a great deal of discussion recently about whether financial markets are rigged in favor of dark pools, high frequency traders and the large banks. I’ve been following this topic and the material emerging on all sides of the debate, and one of the most interesting aspects is related to liquidity.

I am planning to do a series of blogs exploring various aspects of liquidity. These blogs will include reviews of academic papers and research on the topic, a look at what other markets are doing and also discussions of current events. Please comment if you have additional insights or analysis that you have performed on this topic. I look forward to engaging with you on the topic of liquidity and how it influences and works within the market.

Recently a gentleman by the name of Eric Hunsader (he’s founder of market data company Nanex) published a study talking about what took place during 4.6 milliseconds on July 11, 2014 when a trader attempted to buy 20,000 shares of Ford (F). Hunsader concluded that the incomplete trade was evidence of a rigged market and has promoted his study broadly on social media. He even got the attention of Michael Lewis, author of Flash Boys and self‐appointed monarch of the “market is rigged” camp.

Not so fast. I decided to take this study and attempt to re‐create what happened using industry standard, publicly available information. This blog includes my analysis of what happened before, during, and after those 4.6ms. It seems that Hunsader and Nanex’s data tools aren’t quite up to the task of seeing what really happened. According to my analysis, the market is far from rigged.

The Trade

The buyside trader (Hunsader has not identified the trader, but only said that s/he came to Nanex to study what happened) attempted to execute a trade to buy 20,000 shares of Ford. The algorithm/router performing this trade took over 4.6ms, but successfully obtained over 12,000 shares at the desired price. During this time 12 trades occurred across seven exchanges and there are two notable gaps in trading comprising almost 2ms at the beginning of the series.

Nanex concluded that High Frequency Trading (HFT) is the reason the order was only partially filled—and therefore “the stock market is rigged.” His reason: the “order cancellations (that) happen(ed) far faster than trade executions …. before and during the trader’s order (and) were not a coincidence.” Amping up the rhetoric even more, Hunsader called what happened “premeditated, programmed theft.”

I reviewed Nanex’s analysis of the market in Ford during the time period in question using industry standard, microsecond resolution timestamps from publicly available, direct market data feeds to see what really happened. It was immediately clear that there were some fatal flaws in Nanex’s analysis. One obvious cause for these errors is that Nanex’s analysis did not use the same direct market data feeds used by the Buysider’s router. Ironically, by insisting on only using the SIP (instead of industry standard, direct feeds) in all of its analysis, Nanex’s views of micro‐market structure events are generally flawed.

Nanex’s study included the below chart on the left marked Exhibit 1a (“Nanex”). To provide additional detail, I’ve enlarged the diagram in Exhibit 1a by increasing the time resolution and adding real microsecond timestamps in Exhibit 1b (“Truth”). I have also added the light blue vertical lines, which mark the 12 executions reported during the 4.6 millisecond episode (between 09:47:56.5694 and 09:47:56.574). This will allow us to take a closer look at the correlations between the various charts and trade times.

Note: The numbers on these light blue lines correspond to the trade event numbers in Exhibit 2 below.

Nanex asserts that “HFT reacted faster than the original order could be routed to other exchanges and beat our trader to those shares.” This claim is clearly incorrect. As the chart above reflects, there was a 0.95 millisecond gap in time after the first order was executed where you see no trades and no offers cancelled. The large time gaps (yellow boxes) between the 1st/2nd and 2nd/3rd executions suggest that poor routing decisions impacted execution quality — not an HFT or a liquidity problem. So why did this happen?

Fee Sensitive Routing: In Exhibit 2 above, you can see that the routing strategy deployed by the Buysider acted in a sequential manner that was likely incentivized by the fee/rebate applicable at each venue—rather than employing a “smarter” router that was optimized for execution quality (i.e. removing the available resting offers). In the actual trading sequence, the routing strategy deployed first removed all displayed liquidity on BX (Boston), where the executing broker would have received a rebate of 4 to 15 mills per share. The Buysider’s router then waited almost a full millisecond before removing all displayed liquidity on EdgeA, where the executing broker would have received a rebate of 2 mills per share. It seems reasonable to assume that the Buysider’s router hastened the price level transition when it swept the entire inside offers on BX and EdgeA. It also seems reasonable to assume that the Buysider’s order would have been completely filled had it simultaneously routed to “pay to remove” venues (where there was sizeable resting offers) when it swept the insider offer on EdgeA. The end result of a “smarter,” less fee sensitive router would have been the removal of all of the resting insider offers on BATS, EdgeX, Nasdaq, ARCA and NYSE instead of the partial fills received.

From the timestamps, it appears that the orders from the Buysider for BATS, EdgeX and NYSE (Trades #3, #4, #5) all hit the exchanges at nearly the same time (after adjusting for approximate network latency), which suggests the router had the sophistication to hit all exchanges at the same time if they really wanted to. Further, Nanex mistakenly claims that “none of this would be possible if the direct feeds weren’t illegally supplying HFT with faster information than the SIP;” however, the SIP updated the EdgeA quote for Trade 2 at 09:47:56.571300, which would have given any trader (regardless of whether they were using “illegal” direct market feeds or the SIP) enough time to cancel offers at BATS, EdgeX, and NYSE (Trades #3, #4, #5) in response to the market impact — if that was the intent.

Exhibit 3 is from Nanex’s “update” to its “research” and is the result of an order executed using a more efficient router, which was supplied by the IEX ATS. In this example, the Buysider reported better execution quality. This further supports the facts that the incomplete execution experienced by the Buysider (Exhibit 1 and Exhibit 2) is simply the result of the Buysider using a less-than-smart order router—not the boogie man or “HFT” or a “rigged” market. Indeed, I would be curious to hear Nanex’s rationalization of how trades like Exhibit 3 can exist in a “rigged” market.

Before the Trade

Before the Buysider’s order enters the market, the relative sizes of the $17.37 bid and the $17.38 offer reflect a large imbalance. In Exhibit 4 below, you can see that the $17.37 bid size is almost double the $17.38 offer size and that the offer size almost doubles in size by the end of the 4.6ms period. The growth of the $17.37 bid size, in conjunction with the fading $17.38 offer size, illustrates a normal price level transition that occurs when the demand exceeds the supply — not “phantom liquidity” or “irrefutable evidence” of HFT front-running. Again, it is important to understand that this transition was in motion before the Buysider’s first trade was executed.

After the Trade

Nanex claims that front-running allowed HFT to artificially raise the price of the underlying security and eroded price stability. The facts, illustrated in the chart below in Exhibit 5 (which depicts all Ford (F) trading activity for the subsequent 30 seconds after the Buysider’s purchase described above) shows that all but 200 shares over this time frame traded at the exact price the purchaser had sought to pay ($17.38).

Conclusion

In sum, contrary to claims by market expert Michael Lewis that Hunsader offers “clean, simple, irrefutable” proof that the markets are rigged, the data actually supports the opposite conclusion. It should also call into question the efficiency of the router being used by the Buysider and a general misunderstanding of the how the market works.

This example and analysis demonstrated the efficiency of a properly functioning market: There was a market imbalance (i.e., there was more interest on one side of the book than the other — in fact almost double as much interest on the bid) and when the Buysider began removing offers in an inefficient way, the offered price eventually did go higher. This example and analysis also demonstrates the importance of using the right order router: A more efficient router likely would have removed more (or all) of the displayed liquidity — as demonstrated in Exhibit 3 — even if that meant prioritizing “pay to remove” venues ahead of “rebate to remove” venues in its liquidity seeking decisions .

Ultimately, Nanex’s study merely tells us what most traders already know—that the market reacts to new information by changing price, and if you don’t choose the right “smart” router, your execution quality will suffer.

Next Story — Let’s Be Clear Wall Street: Direct Feeds Are Not Illegal
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Let’s Be Clear Wall Street: Direct Feeds Are Not Illegal


My first post refuting Nanex’s HFT Study was read by over twelve hundred people in the first day, and covered by numerous columnists including Matt Levine on Bloomberg View. I was shocked at the overwhelming support I received and by how many people had similar concerns about Nanex’s analysis. Thank you!

In the days following my first post Nanex has earnestly and repeatedly cited § 242.600(b)(42) as proof that the use of anything other than the SIP for purposes of identifying protected quotes in order to comply with Reg NMS constitutes a violation of the Order Protection Rule of Reg NMS. This claim conflicted with my own understanding; however, I couldn’t reference any sources to support my understanding. One reason I decided to write this blog entry was to confirm (or correct) what Nanex was claiming. In this post I am exploring Reg NMS and its application within the market. I have inserted portions of the regulation below.

§ 242.600 NMS security designation and definitions.
(b) For purposes of Regulation NMS (§§ 242.600 through 242.612), the following definitions shall apply:
(42) National best bid and national best offer means, with respect to quotations for an NMS security, the best bid and best offer for such security that are calculated and disseminated on a current and continuing basis by a plan processor pursuant to an effective national market system plan; …

Frankly, looking only at that clause without the benefit of any context or history of Reg NMS, I can sort of see where he’s coming from; however, after reading the adopting release of Reg NMS and the FAQs, I think I understand why there is a disconnect between Nanex and the rest of the industry.

The rest of the industry, it seems, reads the reference to a “plan processor” as a way to clarify that a quote that is not submitted to the SIP at any time, (e.g., a quote submitted to a dark pool or some other alternative venue which will not, at any point in time, display the quote) will not qualify as the NBBO (and won’t be protected).

Of course, the actual text of the Order Protection Rule in Reg NMS (Rule 611 on page 518 of the Adopting Release, pasted below) is what determines what orders are protected. Notice that the SEC uses the term “Protected Quotation” and NOT “National best bid and national best offer”. This is significant because it allows us to focus on understanding what the “Protected Quotation” is and move beyond any fixation on the purpose of the reference to the “plan processor” in the NBBO definition.

§ 242.611 Order protection rule.
(a) Reasonable policies and procedures.
(1) A trading center shall establish, maintain, and enforce written policies and procedures that are reasonably designed to prevent trade-throughs on that trading center of protected quotations in NMS stocks that do not fall within an exception set forth in paragraph (b) of this section and, if relying on such an exception, that are reasonably designed to assure compliance with the terms of the exception.
(2) A trading center shall regularly surveil to ascertain the effectiveness of the policies and procedures required by paragraph (a)(1) of this section and shall take prompt action to remedy deficiencies in such policies and procedures.

The terms trade-through and protected quotation are defined here:

§ 242.600 NMS security designation and definitions.
(b) For purposes of Regulation NMS (§§ 242.600 through 242.612), the following definitions shall apply:
(58) Protected quotation means a protected bid or a protected offer.
(77) Trade-through means the purchase or sale of an NMS stock during regular trading hours, either as principal or agent, at a price that is lower than a protected bid or higher than a protected offer.

And protected bid and protected offer are defined here (read carefully):

§ 242.600 NMS security designation and definitions.
(b) For purposes of Regulation NMS (§§ 242.600 through 242.612), the following definitions shall apply:
(57) Protected bid or protected offer means a quotation in an NMS stock that:
(i) Is displayed by an automated trading center;
(ii) Is disseminated pursuant to an effective national market system plan; and
(iii) Is an automated quotation that is the best bid or best offer of a national securities exchange, the best bid or best offer of The Nasdaq Stock Market, Inc., or the best bid or best offer of a national securities association other than the best bid or best offer of The Nasdaq Stock Market, Inc.

And an automated trading center is defined here:

§ 242.600 NMS security designation and definitions.
(b) For purposes of Regulation NMS (§§ 242.600 through 242.612), the following definitions shall apply:
(4) Automated trading center means a trading center that:
(i) Has implemented such systems, procedures, and rules as are necessary to render it capable of displaying quotations that meet the requirements for an automated quotation set forth in paragraph (b)(3) of this section;
(ii) Identifies all quotations other than automated quotations as manual quotations;
(iii) Immediately identifies its quotations as manual quotations whenever it has reason to believe that it is not capable of displaying automated quotations; and
(iv) Has adopted reasonable standards limiting when its quotations change from automated quotations to manual quotations, and vice versa, to specifically defined circumstances that promote fair and efficient access to its automated quotations and are consistent with the maintenance of fair and orderly markets.

Using the Substitution Principle, we can summarize the Order Protection Rule (Rule 611) as:

A trading center shall establish, maintain, and enforce written policies and procedures that are reasonably designed to prevent (the purchase or sale of an NMS stock during regular trading hours, either as principal or agent, at a price that is lower than a protected bid or higher than a protected offer) on that trading center of (a quotation in an NMS stock that):

(i) Is displayed by an ((4) Automated trading center means a trading center that: (i) Has implemented such systems, procedures, and rules as are necessary to render it capable of displaying quotations that meet the requirements for an automated quotation);

(ii) Is disseminated pursuant to an effective national market system plan; and

(iii) Is an automated quotation that is the best bid or best offer of a national securities exchange, the best bid or best offer of The Nasdaq Stock Market, Inc., or the best bid or best offer of a national securities association other than the best bid or best offer of The Nasdaq Stock Market, Inc.)

in NMS stocks.

I could go on, but I think it would be annoying and I expect that most of you now see what the rest of the world sees: the Protected Quotation is what’s actually protected — regardless of how it’s received (SIP or Direct Feed), as long as the quote is (i) displayed by an automated quotation center, (ii) disseminated to the public in a consolidated stream of data (SIP); and (iii) the best bid or offer on a qualifying national securities exchange.

For those of you still doubting this interpretation: don’t worry — it didn’t click for me the first time either. Thankfully, it seems that the SEC was asked this question enough times that the SEC included it in the Reg NMS FAQ and the Rule 611 FAQ that it published. For convenience I’ve pasted and highlighted 3 Q&A’s that I felt helped me understand that, assuming reasonable data handling policies and procedures, while the SIP’s view of the NBBO would be a useful tool for market participants in gauging their compliance generally, utilizing direct feeds for purposes of identifying protected quotations and complying with the Order Protection Rule is permitted.

Section 6: Data Policies and Procedures
Question 6.01: Rule 611 Compliance/Data Latency
In the national market system, trading centers across the U.S. simultaneously display quotations and execute trades in the same NMS stocks. Given the latencies in transmitting data among these trading centers, as well as among broker-dealers that route ISOs to execute against the protected quotations displayed by trading centers, how will regulators assess the compliance of trading centers and broker-dealers with Rule 611?
Answer: In the NMS Release, the Commission stated that, assuming a trading center has implemented reasonable policies and procedures for handling data (see FAQ 6.02 below), a trading center’s compliance with Rule 611 “will be assessed based on the time that orders and quotations are received, and trades are executed, at that trading center.” The same standard will be used to assess the compliance of broker-dealers in routing ISOs under Rule 611(c).
The data that bears on Rule 611 compliance can be divided into three categories: (1) the order and trade data of each trading center or broker-dealer (“Firm”), with internal time stamps reflecting when it was processed by each Firm (“Firm-Specific Order and Trade Data”); (2) the protected quotation data received by each Firm, with internal time stamps reflecting when it was received by the Firm (“Firm-Specific Quotation Data”); and (3) the protected quotation and trade data of the Network processors, with time stamps assigned by such processors (“Network Data”). Assuming reasonable data handling policies and procedures, compliance by individual Firms with Rule 611 will be assessed based on Firm-Specific Order and Trade Data and Firm-Specific Quotation Data, and not on Network Data (the relevance of Network Data to Rule 611 is discussed in FAQ 6.04 below).
Question 6.02: Data Handling Policies and Procedures
What are examples of matters that need to be addressed in a Firm’s data handling policies and procedures?
Answer: As noted in FAQ 6.01 above, compliance by individual Firms with Rule 611 will be based on that Firm’s own data, assuming that the Firm has implemented reasonable data handling policies and procedures. Such policies and procedures should address latencies in obtaining protected quotation data from the sources of such data. The Firm should implement reasonable steps to monitor such latencies on a continuing basis and take appropriate steps to address a problem immediately should one develop.
Question 6.04: Rule 611 Compliance/Relevance of Network Data
What is the relevance of Network Data for assessing compliance with Rule 611?
Answer: As noted in FAQ 6.01 above, the Network processors disseminate to the public a stream of trade and quotation data, with time-stamps, for each NMS stock. In this respect, Network Data is unlike Firm-Specific Order and Trade Data and Firm-Specific Quotation Data, which will have time stamps that vary to some extent from Firm to Firm. As a result, Network Data constitutes a common reference point for quotations and trades in NMS stocks that will be readily available to the public, Firms, and regulatory authorities.
As discussed in FAQs 6.01 and 6.02 above, the compliance of an individual Firm will be assessed based on the data it sees at the time it trades and routes orders, assuming it has implemented reasonable data handling policies and procedures. As a practical matter, however, Firms should be aware that Network Data, as the single available common reference point for quotations and trades in NMS stocks, may be used by regulatory authorities, as an initial matter, to gauge their compliance with Rule 611 generally.
For example, regulatory authorities may examine the Network data for comparable Firms to assess whether any Firm has an exceptionally high trade-through rate. If so, the relevant regulatory authority is likely to contact such Firm and ask for an explanation. At this point, the focus will shift from Network Data to (1) the reasonableness of the Firm’s policies and procedures, particularly for handling data and reviewing for compliance with Rule 611, and (2) the Firm-Specific Order and Trade Data and the Firm-Specific Quotation Data that support the results of the Firm’s compliance reviews. The Firm will want to be in a position to demonstrate that its policies and procedures are reasonable. For example, it could present Firm-specific data from its periodic compliance reviews showing that trades that might appear to be trade-throughs in the Network data are in fact “false positives” that were not trade-throughs at the Firm.
Firms also should recognize that the widely available Network Data could be a valuable external tool for assessing the effectiveness of their internal policies and procedures. For example, an examination of the Network Data might reveal particular types of stocks, times of day, or types of trading conditions in which the Firm appears to generate a high rate of trade-throughs. The Firm could use this information to fashion its compliance reviews to assess these specific potential problem areas. Such compliance reviews could reveal that the trade-throughs in the Network Data are false positives, as well as the explanation for why they appear to be trade-throughs in the Network Data. Conversely, compliance reviews targeted on the problem areas may reveal weaknesses in the Firm’s policies and procedures that the Firm could correct with timely action. In either case, policies and procedures that include the use of Network Data may enable the Firm to provide a more effective response to regulatory inquiries.

As I stated in the beginning, my goal of this blog was to better understand the nuances of the Order Protection Rule in order to determine the validity of the viewpoint that Reg NMS compliance is assessed exclusively on the SIP. I’m not an attorney but I follow the industry generally, I’ve read the rules and the adopting release, and the FAQs. After this review, my conclusion is that this SIP-only viewpoint is not supported by the published material. I’ve also never read about or heard of any HFT, broker-dealers, exchange, or dark pools being investigated or sanctioned for failure to comply with this SIP-only viewpoint of protecting quotes. Especially in the current environment and with today’s 24 hour news coverage, I have to assume that if the SEC/FINRA did not agree with this interpretation, we would have heard about it.

To those who find this analysis compelling, especially those with a greater voice than my own, I hope that you will join me in attempting to educate others on this important issue. To those who advocate a different interpretation, I would invite you to share your support for your interpretation using the rules, adopting release, FAQs, and any other materials published by the SEC. For those advocating a different interpretation but unwilling/unable to clearly identify SEC-published materials in support of your position, I can only conclude that your goal is something other than improving investor confidence and market integrity.

Next Story — All your memes are belong to us
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Such meme. Very wow. (Illustration by Harry Malt for The Washington Post)

All your memes are belong to us

The top 25 memes of the web’s first 25 years

By Gene Park, Adriana Usero and Chris Rukan

For more of The Web at 25, visit The Washington Post.

Memes didn’t begin with the Web, but you’d be forgiven for thinking so. The evolutionary biologist Richard Dawkins coined the term in his 1976 book, “The Selfish Gene,” to describe something that already existed. A meme, from the Greek “mimeme” (to imitate) was “a unit of cultural transmission, or a unit of imitation.” This encompassed phenomena from Martin Luther’s “95 Theses” to the famous graffiti drawing “Kilroy Was Here,” which dates to the beginning of World War II.

But the Web has proved to be the most fertile ground, and the site Know Your Meme has confirmed more than 2,600 of them. Below, 25 definitive memes from the Web’s first 25 years.

[1] Dancing Baby

1996: Considered the granddaddy of Internet memes, the baby shuffling to Blue Swede’s “Hooked on a Feeling” filled inboxes and prime-time airwaves, appearing in several episodes of “Ally McBeal.” The file was originally included with early 3D software. LucasFilm developers modified it before it was widely shared, and it was finally compressed into one of the first GIFs.

[2] Hampster Dance

1998: Proving that GIFs were meant for stardom, a Canadian art student made a webpage with 392 hamster GIFs as a tribute to her pet rodent. The infectious soundtrack was a sped-up, looped version of “Whistle Stop” by Roger Miller.

[3] Peanut Butter Jelly Time

2001: A Flash animation featuring an 8-bit dancing banana, “Peanut Butter Jelly Time” became an Internet phenomenon in the early 2000s. The catchy song was written and performed by the Buckwheat Boyz, a rap group.

[4] All Your Base Are Belong to Us

2001: A meme that would echo across the gaming community for years to come, “All your base are belong to us” originated in a cut scene in the Japanese video game “Zero Wing.” The poorly translated quote has persisted as an Internet catchphrase.

[5] Star Wars Kid

2002: Arguably the first victim of large-scale cyberbullying, Ghyslain Raza unwillingly became a meme based on a video of him swinging a golf ball retriever as a weapon, reminiscent of Darth Maul in “Star Wars: The Phantom Menace.” It was an early sign that Internet privacy was not guaranteed for anyone.

[6] Spongmonkeys

2003: Before they became spokesthings for Quiznos, two singing Spongmonkeys catapulted to viral stardom after being featured in a newsletter for b3ta, an early link- and image-sharing site. Their opening line: “We like the moon.”

[7] Numa Numa

2004: The eyebrow lift. The arm pumping when the beat drops. The song (by Moldovan boy band O-Zone). Gary Brolsma, sitting at his desk, showed us all what it means to “dance like no one’s watching.”

[8] O RLY

2005: Originating on the community site 4chan, the wide-eyed owl was used to show sarcasm, becoming a precursor to other reaction memes.

[9] Chuck Norris Facts

2005: Chuck Norris was the Internet’s first “most interesting man in the world,” crowned the avatar for mythical men with impossible strength, attitude and swagger. “There is no theory of evolution,” as one “fact” says. “Just a list of creatures Chuck Norris allows to live.”

[10] I Can Has Cheezburger?

2007: Animal-based memes are a dime a dozen, but the “I Can Has Cheezburger” blog, whose mascot is a surprised, hungry British shorthair cat, brought them into the mainstream. The blog was created by Eric Nakagawa and Kari Unebasami.

Rickroll and Deal With It collide to form an uber-meme

[11] Rickroll

2007: Before there was clickbait, there was the Rickroll. Popularized on 4chan, the gag — springing a Rick Astley video on an unsuspecting victim — has appeared during a session of the Oregon legislature and even on the White House’s Twitter feed.

[12] Success Kid

2007: Based on a photo that Sammy Griner’s mother, Laney, posted to Flickr when he was 11 months old, the meme describes something that goes better than expected. In 2015, Sammy’s fame helped his family raise more than $100,000 to offset the costs of a kidney transplant for his father, Justin.

[13] Dramatic Chipmunk

2007: A simple, five-second video clip of a chipmunk — ahem, actually a prairie dog — suddenly turning its head, from the Japanese TV show “Hello Morning.” The maneuver is set to an exaggerated bit of music from 1974’s “Young Frankenstein.”

[14] Philosoraptor

2008: This portmanteau meme was an early example of an “advice animal,” depicting the vicious dinosaur deep in introspection, and pondering wordplay and life’s general paradoxes.

[15] Deal With It

2010: In this GIF, sunglasses slide onto a smug canine’s face. It was around as an emoticon on the SomethingAwful forums for a while, then became a meme when the site Dump.fm held a contest encouraging users to create their own versions, with sunglasses sliding onto various faces and objects.

[16] Hide Your Kids, Hide Your Wife

2010: “So y’all need to hide your kids, hide your wife and hide your husband ’cause they’re raping everybody out here,” Antoine Dodson emphatically told a TV reporter after an intruder attempted to assault his sister. The clip spread quickly on YouTube, leading to Auto-Tuned versions and remixes.

Nyanyanyanyanyanyanyare you going insane yet?

[17] Nyan Cat

2011: The combination of an animated 8-bit cat (originally dubbed “Pop-Tart Cat”) with the insanely catchy tune “Nyanyanyanyanyanyanya!” blew up on YouTube, becoming the site’s fifth-most-viewed video of 2011 and inspiring fan illustrations, designs and games.

[18] Ermahgerd

2012: Originally uploaded as “Gersberms . . . mah fravrit berks” and later “BERKS!,” the text superimposed on this meme mimics the garbled speech of a person with a retainer.

[19] Bad Luck Brian

2012: Takes goofy yearbook photo. Gets face plastered all over the Internet. His real name is Kyle Craven, and he’s Internet famous thanks to his friend Ian Davies, who uploaded the photo to Reddit with the text “Takes driving test . . . gets first DUI.”

[20] Grumpy Cat

2012: The original photo of Tardar Sauce (that’s her name) racked up 1 million views on Imgur in its first two days. The meme has since spawned books, a comic book, an endorsement deal with Friskies cat food and a made-for-TV Christmas movie, “Grumpy Cat’s Worst Christmas Ever,” with Aubrey Plaza voicing Grumpy Cat.

[21] Ridiculously Photogenic Guy

2012: Uploaded to Reddit on April 3, the photo of the handsome runner quickly garnered 40,000 upvotes. Derivatives include Ridiculously Photogenic Metalhead, Ridiculously Photogenic Syrian Rebel, Ridiculously Photogenic Prisoner and Ridiculously Photogenic Running Back.

[22] Doge

2013: In February 2010, a kindergarten teacher in Japan uploaded pictures of Kabosu, her adopted shiba inu, to her personal blog, and a meme was born. It usually features broken English phrases in the comic sans font, representing an inner monologue.

[23] Crying Michael Jordan

2014: The basketball great got a little emotional during his 2009 Hall of Fame induction speech. Around 2014, meme-makers started using an Associated Press photo, superimposing Jordan’s face over failures of all sorts.

[24] Ice Bucket Challenge

2014: While the origins of this one are unclear — people have been doing cold-water challenges for years — the results weren’t. The ALS Association raised more than $100 million in a month, compared with $2.8 million over the same period the previous year.

[25] Left Shark

2015: During the Super Bowl XLIX halftime show, Katy Perry performed with two dancing sharks. One shark stuck to the routine. The other, well, did his own thing — and became an Internet sensation.

And if you’re not over memes like the Internet isn’t over Harambe, we’ve compiled a Spotify meme-themed playlist for you to follow and take with you on the go.

Did we miss your favorite internet meme? Tell us about it — and why it’s so great — in the comments.

Next Story — The Apple-Google shift
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The Apple-Google shift

In the last couple of years, two very distinct things have happened — or, to be more precise, been happening — in the world of consumer tech, in my opinion. A shift has occurred: Apple, once the definition of innovation, has become stale, content to rest on its laurels; while Google, once ugly and disparate, has continually pushed forward with new and better products that are a delight to use.

The result is two-fold: firstly, from a software perspective, Google-authored apps have all but replaced Apple’s defaults on my iPhone; secondly, for the first time ever, I find myself potentially choosing a Google phone over an Apple phone — a choice that represents not just a one-off hardware purchasing decision, but a first tentative step outside of Apple’s ecosystem and, as a result, a break in unashamed Apple fanboy-ism.

Okay, so I’m considering a switch to Android. No big deal. I’m following in the footsteps of many, many, many others. But what I find interesting outside of my own personal decision is that there seems to be a growing discontent with Apple — especially amongst former so-called fanboys/girls — and, at the same time, a growing appreciation of what Google have been doing, especially from a design perspective. In many ways it’s unwise to compare these two companies alone, but few would disagree that these days they’re the two sides of one coin.

So I thought I’d try and pick this apart. What’s actually changed?

It’s not that Apple no longer creates great products, but there’s just not that spark there anymore, is there? Remember when a new MacBook or iMac would launch? Or the iPhone? Or pretty much any new product? The buzz was palpable; the hype almost always justified. For years and years, Apple constantly innovated, whether it was with entirely new product lines or updates to existing ones, but recently everything has just felt a little… well, meh, hasn’t it?

Could this feeling because Apple is now so ubiquitous, no longer the underdog? Possibly. And could this be down to some very shrewd business decisions, with Apple deciding to refine and hone rather than experiment, as evidenced by the longer life cycles of designs for their phones and computers? Very likely.

But that doesn’t excuse recent product launches that have (again, in my opinion) fallen flat by their past standards. The MacBook? Well, it’s a lovely little machine (and I’m typing on it right now) and I even took a whole set of photos to capture its beautiful form, but time has revealed it to be irritating in many ways (the keys repeatedly get stuck, for instance, and the removal of a magnetic power connector is genuinely irritating). The Apple Watch? After the initial magic wore off, I came to the conclusion that it’s essentially useless — as did almost every other Apple Watch owner I’ve spoken to. The new Apple TV? A total lack of innovation — both from its previous version and the numerous offerings from competitors. New iPhones aren’t even exciting anymore.

In many ways, I wonder if this all started with the launch of iOS 7: although I was originally one of its supporters when it came out and enraged half the Apple-buying world, when I think about it these days, iOS still doesn’t really encourage interaction. It’s not about flat design versus skeuomorphic design; it’s more about how Apple laid the groundwork for what a great, minimal, mobile operating system could be… and then never really built upon those foundations. The same could be said of their camera technology. The iPhone camera’s noise reduction algorithm has ruined many a photo that would have benefitted from not being put through a paint-like Photoshop filter. Oh, and don’t even get me started on Apple Music. What a mess. Sure, it’s not a total failure from an interaction design point of view, but it’s a sub-par effort from a company that should really be far, far, far better than any other steaming music competitors. That Apple Music has been so successful is only down to the ecosystem they’ve cultivated — not because it offers a superior experience.

Then there’s just all the douche moves Apple has made again and again with proprietary connections — their decision to remove the headphone jack on the forthcoming new iPhone being the latest. All of this has added up to make even this most ardent of Apple fanboys start to question his allegiances.

And all the while this has been going on, Google — which, with each new product launch, whether software or hardware, has become even more of an Apple competitor — has continued to innovate; to make better versions of Apple’s own apps. (I don’t even need to mention Maps, do I? No? Good.) And from a design perspective, Google has well and truly grown up: Material Design offered a lot of promise when it was first announced, and in the time that’s passed since, it’s proven itself to be a strong framework for unifying a the company’s multiple software offerings. Sure, there are times when its incarnation feels a little templated and dry — Google Play Music, for example — and perhaps it’s easy to praise Google for their grown-up new looks when, until recent times, Google web apps were so damn ugly. (Remember how Gmail used to look? For a reminder of that less graceful era, look at the browser version of Google Calendar.) But the difficulty of creating a system that works in so many instances, both in terms of aesthetics and interaction, should not be underestimated.

Beneath all of these apps and interactions and aesthetics, there’s another layer of Google that has become so trusted: its infrastructure. Yes, I get the fears about our data being mined to show us more relevant ads, but who do I trust for reliable cloud syncing: Apple or Google? Who do I trust to backup and share my photo library: Apple or Google? Whose infrastructure do I trust for my emails, documents, calendars, and more: Apple or Google? Granted, the latter could be any service provider vs. Google, but the point is that Google’s infrastructure underpins so much of the internet and our daily lives, it often just doesn’t make sense to let someone else handle what we know Google can handle so well.

(At this point, i’m going to refrain from delving into lengthy praises of particular Google apps and services, but I do want to give a quick mention to the Google Calendar and Google Photos iOS apps. They’re so radically superior to Apple’s equivalents, I’d question anyone’s need to ever open those defaults again.)

All this is to say: if Google can be this good on a competitor’s operating system, how much better can it be in its own environment? This is the question that’s been gaining traction in my head recently.

Android used to be a poor man’s iOS, but it’s obviously grown a lot since then. Unfortunately, fragmentation is a problem that’s plagued Android from the very beginning and is probably the primary factor that’s never allowed me to take switching seriously, but here’s where it gets interesting: with Google making (via OEMs) its own Nexus hardware, it’s possible to use a vanilla version of Android, free of bloat from carrier-installed software. It also removes that weird you-can-only-use-this-particlar-version-of-Android thing that plagues Android phones made by other manufacturers, and, in doing so, puts Google on an evening playing field with Apple: control the hardware and you control the software.It just works.

So it’s this vision of Android — a Google phone in its purest form — that’s making me, and others, consider the switch. And with new Nexus phones rumoured to land (or at least be announced) very soon, the opportunity to do so might be just around the corner.

Or maybe not. The new iPhone is also due very soon. Maybe it’ll be amazing. Maybe it’ll be the best hardware and software combination that exists in the world. Maybe Apple’s core apps, services, and experiences that underpin the entire iOS / macOS / tvOS ecosystem will up their respective games and I’ll look back on this post as blasphemy.

But — sadly — I’m not sure that’s something the Apple of 2016 is capable of.

Next Story — This 100-Year-Old To-Do List Hack Still Works Like A Charm
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This 100-Year-Old To-Do List Hack Still Works Like A Charm

The “Ivy Lee Method” is stupidly simple — and that’s partly why it’s so effective.

[Photo: Flickr user Billy Millard]

By James Clear, who writes about self-improvement tips based on proven scientific research at JamesClear.com, where this article first appeared. It is adapted with permission.

By 1918, Charles M. Schwab was one of the richest men in the world.

Schwab (oddly enough, no relation to Charles R. Schwab, founder of the Charles Schwab Corporation) was the president of the Bethlehem Steel Corporation, the largest shipbuilder and the second-largest steel producer in the U.S. at the time. The famous inventor Thomas Edison once referred to Schwab as the “master hustler.” He was constantly seeking an edge over the competition.

Accounts differ as to the date, but according to historian Scott M. Cutlip, it was one day in 1918 that Schwab — in his quest to increase the efficiency of his team and discover better ways to get things done — arranged a meeting with a highly respected productivity consultant named Ivy Lee.

Lee was a successful businessman in his own right and is widely remembered as a pioneer in the field of public relations. As the story goes, Schwab brought Lee into his office and said, “Show me a way to get more things done.”

“Give me 15 minutes with each of your executives,” Lee replied.

“How much will it cost me?” Schwab asked.

“Nothing,” Lee said. “Unless it works. After three months, you can send me a check for whatever you feel it’s worth to you.”



THE IVY LEE METHOD

During his 15 minutes with each executive, Lee explained his simple method for achieving peak productivity:

  1. At the end of each workday, write down the six most important things you need to accomplish tomorrow. Do not write down more than six tasks.
  2. Prioritize those six items in order of their true importance.
  3. When you arrive tomorrow, concentrate only on the first task. Work until the first task is finished before moving on to the second task.
  4. Approach the rest of your list in the same fashion. At the end of the day, move any unfinished items to a new list of six tasks for the following day.
  5. Repeat this process every working day.

The strategy sounded simple, but Schwab and his executive team at Bethlehem Steel gave it a try. After three months, Schwab was so delighted with the progress his company had made that he called Lee into his office and wrote him a check for $25,000.

A $25,000 check written in 1918 is the equivalent of a $400,000 check in 2015.

The Ivy Lee Method of prioritizing your to-do list seems stupidly simple. How could something this simple be worth so much?

What makes it so effective?

ON MANAGING PRIORITIES WELL

Ivy Lee’s productivity method utilizes many of the concepts I have written about previously.

Here’s what makes it so effective:

It’s simple enough to actually work. The primary critique of methods like this one is that they are too basic. They don’t account for all of the complexities and nuances of life. What happens if an emergency pops up? What about using the latest technology to our fullest advantage? In my experience, complexity is often a weakness because it makes it harder to get back on track. Yes, emergencies and unexpected distractions will arise. Ignore them as much as possible, deal with them when you must, and get back to your prioritized to-do list as soon as possible. Use simple rules to guide complex behavior.

It forces you to make tough decisions. I don’t believe there is anything magical about Lee’s number of six important tasks per day. It could just as easily be five tasks per day. However, I do think there is something magical about imposing limits upon yourself. I find that the single best thing to do when you have too many ideas (or when you’re overwhelmed by everything you need to get done) is to prune your ideas and trim away everything that isn’t absolutely necessary. Constraints can make you better. Lee’s method is similar to Warren Buffet’s 25–5 Rule, which requires you to focus on just five critical tasks and ignore everything else. Basically,if you commit to nothing, you’ll be distracted by everything.

It removes the friction of starting. The biggest hurdle to finishing most tasks is starting them. (Getting off the couch can be tough, but once you actually start running, it is much easier to finish your workout.) Lee’s method forces you to decide on your first task the night before you go to work. This strategy has been incredibly useful for me: As a writer, I can waste three or four hours debating what I should write about on a given day. If I decide the night before, however, I can wake up and start writing immediately. It’s simple, but it works. In the beginning, getting started is just as important as succeeding at all.

It requires you to single-task. Modern society loves multitasking. The myth of multitasking is that being busy is synonymous with being better. The exact opposite is true. Having fewer priorities leads to better work. Study world-class experts in nearly any field — athletes, artists, scientists, teachers, CEOs — and you’ll discover one characteristic that runs through all of them: focus. The reason is simple. You can’t be great at one task if you’re constantly dividing your time 10 different ways. Mastery requires focus and consistency.

The bottom line? Do the most important thing first each day. It’s the only productivity trick you need.

Read this story at Fast Company.

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