By Wouter Kneepkens.


September continued where August left off. Volatility in the stocks we follow was limited, with our trades ending up in the -0.9% - +1.9% range. We completed six trades in total for the month with a 67% hit rate.

What worked:

We did four trades that made us money this month: TomTom, KPN, Apple and Facebook, two longs and two shorts. We’ll have a quick look at two of these:


The TomTom trade this month was a classic. On 9 September the company announced a deal with Ford, on the 10th it added a deal with Sony and it also resulted in some positive comments by analysts (“positive messages TomTom”). This is the type of talk that our algorithms pick up on, meaning we were able to benefit from (part of) the increase in the share price.


Graph1.: TomTom, 7-13 SEP’13, source: trading dashboard


Our Facebook trade has been our most successful (and second profitable) short trade. The share had been trading upward in the past days/weeks, with sentiment being pretty volatile containing both positive and negative news and points of view. Early in the day on the 26th sentiment broke negatively (while the share was at its intraday high of c. +2%) on the news that insiders were selling their stock. We quickly amassed a short position, which we closed later in the day when the share had decreased around a percent on the insider selling netting our portfolio +1.9%.


Graph2.: Facebook, 23 Sep-2 Oct’13, source: trading dashboard


What didn’t work:

We had two unsuccessful trades this month: Sainsbury and Delta Lloyd.


This was a good lesson for us… We added the FTSE100 on 1 September and were eager to get a trade in, too eager. Our algorithms take a while to settle in, so our trade into Sainsbury on 2 September was just too early. Also UK trades kill us on commission and stamp duty (trading cost 2x NL, 8x US), meaning we need bigger win percentages to make them work. 92% of the loss (-0.85% for the fund) on the Sainsbury trade was commission, while the loss on the position was only the other 8% (-0.07% for the fund).

Delta Lloyd

With our Delta Lloyd both the signal and our reaction to the signal simply seem to have been too slow. We saw some positive sentiment, made the trade, but saw the position move against us. The move was caused by an announcement of a takeover in Belgium, but proved to not be sustainable.


Graph3.: Delta Lloyd, 17-23 Sep’13, source: trading dashboard

Overall performance

Especially the Delta Lloyd trade cost us a good amount of performance for the month. Yet September still came in with a very decent 1.83% (after broker commissions and FX). The portfolio is now up 13.54% for the year and we’re continuing our streak. We’re also happy to have successfully closed our first two profitable shorts, a nice milestone for the strategy.


Table1.: Performance of SNTMNT trading strategy – 30 September 2013     

 By Wouter Kneepkens.


After a spectacular July, driven by the quarterly reporting of the US tech stocks, we realised August was likely to be more quiet. We ended up doing four trades in the month and volatility around these positions was lower than what we saw in July.

What worked:

We did three trades that made us money this month: SBM Offshore, Unilever and TomTom.

SBM Offshore

With SBMO we saw the sentiment spike on the earnings previews (two days before the actual presentation). We decided this was a good time to test the multi-day predictability of our sentiment analysis and took position. The stock declined a little in the further days ahead of the announcement, but sentiment proved to be correct once the numbers hit. The stock finished the day up over 8% and we traded out of our position.


Graph1.: SBM Offshore, 2-12 AUG’13, source: trading dashboard


In the afternoon on 12 Augustus Unilever announced the sale of its American brands Wish-Bone and Western. The divestment was done at an attractive price level leading to positive sentiment in the market. There was also a positive effect on the share price after announcement, from which we managed to benefit.


Graph2.: Unilever, 8-15 Aug’13, source: trading dashboard


Earlier in the week a rumour had started about a possible acquisition of TomTom. After the company denied the rumour the share price declined significantly again. On 23 August the rumour emerged again and we decided to trade into the share once we saw sentiment spike too. Even though the company again dismissed an acquisition the share traded up a couple of percent more during the day, leading to a nice profit.


Graph3.: TomTom, 20-26 Aug’13, source: trading dashboard 

What didn’t work

We also had an unsuccessful trade this month: Apple.


Spurred by the Icahn Apple tweet the share hit positive momentum as it sped to and through the US$500 mark. On the 14th various hedge fund managers were reported to be increasing their stake, leading to positive sentiment signals and us trading into the share too. As we had cut our winners too early in July we decided to try a new tactic and increase our stake while we were right. However, right after we increased our stake the share price sagged, eliminating not just our profits on our earlier positions, but even pushing us to a loss. So far we therefore prefer our July course of action: secure decent profits once they’re there. 


Graph4.: Apple, 12-17 Aug’13, source:

Overall performance

August was quiet compared to July, with a lack of major number releases for us. However, we still managed to close the month with a nice profit. We ended the month up 2.14% (after broker commissions and FX) to bring our YTD to 11.50%. Let’s see whether we can continue this winning streak into September.   


Table1.: Performance of SNTMNT trading strategy – 31 August 2013      

By Vincent van Leeuwen and Wouter Kneepkens.


Part of the SNTMNT mission and vision in analysing sentiment is based around the “wisdom of the crowd”. But what is this wisdom exactly? And how does this work for stock markets?

The theory of wisdom of the crowd argues that the collective opinion of a group of individuals, at times and in specific cases, will outperform the views of a single expert. A classic demonstration of group intelligence is the jelly-beans-in-the-jar experiment. During this experiment, the group’s estimate is invariably superior to the vast majority of the individual guesses. When finance professor Jack Treynor ran an experiment back in 1987 with a jar that held 850 beans, the group estimate was 871. Only one of the fifty-six people in the class made a better guess.

Of course crowd wisdom is not always as accurate as with professor Terynor’s famous experiment. Not all crowds are wise. In many other cases they can actually be quite stupid. Through social influences like groupthink and biases, the crowd can very well be underperforming. The recent tech earnings season provides us with some interesting cases to analyse whether we’ve seen wise or stupid crowds in action.

Testing wise crowds in financial markets: Market sentiment during Tech Earnings Season.

Earnings season is always interesting from a sentiment perspective given the ensuing volatility. By looking at sentiment in the market, you can get a good idea how future earnings are anticipated. This summer we’ve seen some extreme cases where this led to good trade ideas, and a few bad ones.




Google’s earnings paint an interesting picture: both consumer and financial sentiment trend up ahead of the release and upon release follow the share price down. Google clearly didn’t live up to anticipations and the crowd got it wrong by approaching the release with a positive outlook.

Wise crowds 0 - Stupid crowds 1




Another tech giant up next: Apple. Consumer sentiment peaked about 24 hours before the numbers were released (and financial sentiment started to improve), but this seemed to have more to do with rumours on the new iPhone screen size than with the actual upcoming numbers release. From the release onwards financial sentiment moved in unison with the stock’s price. The extreme rise in sentiment (140% @ 10AM) could be considered leading when looking at the 10-11AM spike. Still, the timing is close enough for us to call it a draw.

Wise crowds 0 - Stupid crowds 1




The Yahoo earnings, marking Marissa Mayer’s first anniversary as CEO, were a clear example of financial sentiment outperforming consumer sentiment. While both consumer sentiment and the share price were flat to negative in the anticipation of Yahoo earnings, financial sentiment skyrocketed to more than 500%. Which means that sentiment was five times more bullish than normal. The stock reacted positively with a very strong price increase (10%+).

Wise crowds 1 - Stupid crowds 1




With LinkedIn’s earnings we saw a very similar pattern to that at Yahoo! with financial sentiment leading the share price’s increase and next to no reaction in consumer sentiment. A timely trade would again have been very profitable with more than 10% share price appreciation up for grabs.

Wise crowds 2 - Stupid crowds 1



Amazon showed that it’s stock tends to go its own way. Just after the earnings release we see a very sharp drop in financial sentiment and a small dip in the share price outside of opening hours. However, once trading starts the next day the share rallies a good couple of percent points. Even after the disappointing numbers the company’s shares ended the day higher.

Wise crowds 2 - Stupid crowds 2

The overall result for these four is a slightly disappointing draw this earnings season. However, a different picture shows once you’d look at the share price reactions of the five different events:


Interestingly enough, the (potential) rewards of a momentum trade with the crowd have been significantly higher this summer than the (potential) losses of betting against it.

Another strategy that could be interesting is a straddle strategy where one could profit from earnings volatility rather than one plain direction. Unfortunately we lack the historic option data (for now) to see whether this strategy would be feasible or if the (expected) volatility would be priced in to an extent that it’s not a profitable strategy. This is something we’re looking to share with you in the (near) future, so stay tuned by following us on Twitter or subscribing to this blog.

Photo Credit: Jo@net via Compfight cc

 By Wouter Kneepkens.


July was the first month we started trading our own sentiment signals with a real money account. We thought it would be interesting to display our performance, as well as comment on the why and how of the various trades we did. Since we only started trading from mid July onwards and we track a small sample of stocks we also have a limited amount of trades for the month.

What worked

We did three trades that made us money this month: Yahoo!, LinkedIn and Apple.


In Yahoo! we saw financial sentiment spike after the release of the numbers and the earnings call with CEO Marissa Mayer. Strangely the share price didn’t respond in the after market. This gave us the opportunity to buy into the stock once the market opened and benefit from the great momentum during the day. We could have actually made a bigger killing with our position and closed our profits in at too low a level. Interestingly the consumer sentiment didn’t move at all.


Graph1: Yahoo!, 12-19 Jul’13. Source:


There was no real news for LinkedIn on both 18 and 19 July. However, stories about upcoming positive earnings (due early August), as well as some positive momentum from breaking the US$200 mark made the financial sentiment trend up. We got in with a significant position and closed it within a couple of hours in two profitable steps. 


Graph2: LinkedIn, 12-19 Jul’13. Source:


Whereas our trades in Yahoo! and LinkedIn were intraday, keeping the positions for only a couple of hours our Apple trade took a couple of days. We traded into our position on the 23rd of July, when we saw the financial sentiment starting to follow and quickly crossing the consumer sentiment up. We kept the position open for a couple of days, closing it on the 29th, once both sentiments started waning.  


Graph3: Apple, 19-31 Jul’13. Source:

What didn’t work

Not all trades we made were an instant success. We also had two unsuccessful trades this month: LinkedIn and Amazon.



Here we tried to make a second run at the share on the same day as our earlier trades. The share price came down after we had traded out of the position, while sentiment kept up. Therefore we traded back into a position, looking for a second ride. However the share price never bounced back and continued sliding, costing us some of the day’s profits. Good lesson in trying to control our own greed and being happy with a successful trade.


Another sentiment that backfired was that around Amazon and its numbers. Financial sentiment had just turned slightly positive until Amazon announced disappointing numbers, after which it quickly plummeted. In after hours the share price also came down a bit, but after we had taken up short positions in the opening started a rapid increase. Luckily our stop losses kicked in, as the share closed over 5% higher. In days after the announcement the share price dropped little by little again.


Graph4: Amazon, 17-31 Jul’13. Source:

Overall performance

While we won only three out of five trades, the overall performance was quite satisfactory. Our stop losses on Amazon and LinkedIn limited the losses to -1.8% and -0.7%, while our biggest win was 5.7% (Apple). Overall we closed the month with a gross profit of 9.9% and net profit of 9.2% (after broker commissions). For months to come the swings are likely to be lower, as earnings season provided ample hunting ground for opportunities. Stay tuned for more updates by subscribing to this blog or following us on Twitter.

Table1: Performance of SNTMNT trading strategy - July 2013

About the author: Wouter Kneepkens, among other things, is co-founder at SNTMNT, blogging and covering financial markets. He has over 15 years of investment experience, some of which as a professional. 

Image credit: Kevin Dooley.

By Vincent van Leeuwen.



Before I start, I should actually rephrase this title a little. It should have read:

Crowdfunding will never be the best option for startups.


With startups, I mean businesses that:

1) are trying to disrupt an industry through creative destruction

2) are in search for a sustainable business model

3) are in the game to eventually become profitable

Don’t get me wrong. For any other kind of business, I believe the potential of crowdfunding is much higher. Crowdfunding has been fantastic for art projects, nonprofits and even for some out-of-the-box products that actually are for-profit. These kind of businesses were often unfundable via traditional ways, so crowdfunding is doing a great job here.

I don’t think though that crowdfunding is very well suited to push startups forward. For early stage startups, I believe this is especially the case.

Think about it.

Did Stripe really needed the cash more than they needed the network when raising their seed round?

Did Airbnb needed the money more than the network when they joined Y Combinator?

Of course the fact that you need your investors money more than their network doesn’t mean anything bad in itself. On the contrary, I believe it’s perfectly normal. But it’s hard to ignore that the best startups out there need the money far less than most others.

If you truly believe in your startups potential, you should be careful to over-value the marketing power of crowdfunding over the network and experience of Accelerators, Angels and VCs. Of course there’s a chance that a few peeps from the crowd can help your business move forward. But I’m happy to bet that he or she is far less likely to walk the extra mile for you and give you that one intro that might supercharge your growth. I know I wouldn’t if I were owning only 0.001 percent of your company!

Why do you think successful businesses, like Facebook and LinkedIn went public long and long after they made their first millions?

Because they had a credible business model. Which is something that startups usually don’t have.

So why go for stupid money that is crowdfunding?

We’ve had stupid money for decades already. It’s called a bank loan.

If you found this post useful, you can help us by sharing it. Also, feel free to leave a comment below, or drop me a line. I love to hear your feedback. Thanks!

Image credit: Boston Public Library.

By Vincent van Leeuwen.


Recently financial markets endured a flash crash social media-style. The very first Twitter flash crash. For those of you who don’t know what I’m talking about: A few weeks ago, the Twitter account of the American Press association was hacked. The hackers used the account to send out the following fake tweet:


Afterward, the Dow Jones plunged 150 points before bouncing back up to “normal” levels. All within half an hour:


As a side note, I think it deserves a mention here that the media (as usual) did its utter best to exaggerate what happened, stimulating panic all around. The Dow Jones plunged 150 points in only a few minutes, which of course is a big deal. But in most images featured in the media it looked like the Dow had a total meltdown, because of the choices made with respect to the axes of the graph shown. For instance, if you were to draw a five-year horizon chart (below), axis would be at quite different levels. From such a perspective, our flash crash would have been nothing but a small bump.


After the crash, the same media exaggerating the scene were the first to scream blue murder about how financial markets have changed for the worse with the rise of social media. All kind of experts were quick to start jabbering away about the risks of Twitter as a user-generated news agency.

Now that most of the dust has settled, I thought I’d share my take on the matter. Please note that I’m not a financial expert. Neither do I have any intent (or the skill set) to become one. My knowledge of financial markets is limited to my finance Masters and my daily work as Founder of SNTMNT (which also means that I’m slightly biased). From my humble point of view though, I don’t believe that the problem here is Twitter. I fully agree with Mark Gongloff that the actual problem is way more fundamental than that: the algorithmic trading systems that are using Twitter as an input source.

I believe that a lot of the buzz and confusion around the events of April 23 can be related to a theory I recently learned about. It’s an interesting theory from 2007 by Nassim Nicholas Taleb, called black swan theory:

Black swan theory

Black swan events are unexpected events of large magnitude and consequence and that have or will have a dominant role in history. According to Taleb’s black swan theory, many major scientific discoveries, historical events, and artistic accomplishments can be qualified as “black swans”. Taleb gives the rise of the Internet, the personal computer, World War I and September 2001 as examples of black swan events.

I thought it would be interesting to see whether the recent flash crash can be considered such a black swan event. According to Taleb, an event qualifies as a black swan when:

1) The event is a surprise (to the observer).

2) The event has a major effect.

3) After the first recorded instance of the event, it is rationalized by hindsight, as if it could have been expected.

I don’t believe there will be much call for debate when it comes to the first and second qualifications of black swan theory, even though the media exaggerated point two. The third qualification on the other hand, allows for a lot more discussion in my opinion. I understand that many investors would argue that the Twitter Flash Crash was highly unexpected. The technologies behind the flash crash, mostly text algorithms mining the firehose for specific keyword pairs are all based on AI related concepts, which is in itself a very new science.

Although I do agree that the size of the black swan was unexpected, I strongly disagree (without rationalizing of course :) ) that nobody could have seen it coming though. This is because of two reasons:

1) Back in 2011, the Atlantic wrote a great article about how the buzz surrounding Anne Hathaway’s Oscar was influencing Berkshire Hathaway shares. Although the effects of it were smaller and the methodology behind it is slightly different (sentiment driven versus keyword-pair driven), I believe this was already a perfect example how signals from social media can be misinterpreted by a computer.

2) In a more general sense, history has proven some of the shortcomings of algorithmic trading. Both the flash crash and the above example happened because of shortcomings in computational logic, not human logic. A good example of this were major news outlets like CNN and CNBC who did in fact check their source and quickly learned that the AP tweet was fake.

I think that the history of algorithmic trading is a main reason why the Twitter flash crash is most definitely not a black swan. After all, I believe there’s no false rationalization here that algorithmic trading involves specific risks. Of course most of the time systematic trading strategies will do well and outperform the market. And of course proponents of algorithmic trading will say that it provides more liquidity to the markets.

Until it doesn’t.

Maybe it’s because I am only an outsider on the subject. But when I’m reading the many comments criticizing Twitter after the crash, I can’t help but wonder: Is sending out a fake tweet really that much worse than creating false liquidity by firing off fake bid / ask quotes all day long?

I’m not so sure. I believe that as long as we allow computer systems access to financial markets, there will always be vulnerabilities to extreme events like this. But don’t tell me that nobody could have seen it coming. I believe this crash was no different than in May 2010. Or on the day of the Facebook IPO

If you found this post useful, you can help us by sharing it. Also, feel free to leave a comment below, or drop me a line. I love to hear your feedback. Thanks!

Image credit: Nevit Dilmen (Wikimedia Creative Commons)

Faster insights into ING outage situation thanks to sentiment score

By Wouter Kneepkens.


The last two weeks ING has been hit with various outages of its payment and internet banking services. The worst two hits were Wednesday 3 and Friday 5 April, when ING customers were confronted with faulty transfers as well as balances. During this first Wednesday the size of the outage only became apparent throughout the day. At 16:21 NOS was the first “regular medium” to bring the news of the nation wide, large scale problems. Rapidly followed by among others the NRC (16:26), RTL-Z (16:40) and BNR (16:43). 

Extreme events like this are very interesting to us as sentiment analysts, especially given the impact to a large amount of consumers. They offer us a way to measure our own capabilities up against other channels. As well as a way to improve our analyses based on the outcomes. Therefore we immediately jumped into our ING data on the Thursday.

Our monitors indicated clear skies up until 15:00 on the Wednesday. However, between 15:00 and 16:00 there’s an enormous reversal in sentiment (point A. in graph 1.). From a quiet, positive sentiment, our score switches to a very negative one within this hour. This reversal is followed by a continued and accelerating drop in the next hours (point C. in graph 1.). So, 30 to 60 minutes before regular media started covering the widespread problems (point B. in graph 1.), they were already visible to us.    


Graph 1.: share price (indexed) of ING and the AEX versus Sentiment score score, 3 April 2013 (sources: Yahoo Finance & SNTMNT) 

The fact our sentiment recognition managed to beat regular media with such a margin is highly satisfactory. However, that in itself is not our ambition. We’re analysing sentiment because of the impact it has on the share price performance of the companies involved. In this case our signal could have saved an investor from a price drop of 1.42% (table 1.). For a long-term shareholder in ING a drop like that is no more than a bump in the road. For a day trader on the other hand 1.42% in the final hours of trading could mean the difference between a good or bad day of trading.


Table 1.: Sentiment warning and share price performance over the subsequent hours, 3 April 2013

Searching out own hearts

Our analysis and results (1.42% saved in case of timely action) are also cause to critically assess our models. Firstly we need to realise that spotting only part of the downward potential, however timely, only has partial value to investors. Even someone with our current tools would have already lost 2.10% on this ING position between 09:00 and 15:00. While our indicator kept steady at “plain sailing” levels. We’ve therefore started to see how we can analyse sentiment over shorter time intervals (and so even faster). 

Lastly it’s always easier to make calls whilst not being engaged yourself, especially with the benefit of hindsight, instead of having to act in the heat of the moment. Would we have sold out shares in ING at 15:00 on the basis of the shift in sentiment?  We’ll find out in a couple of months from now, when we’ll start testing our own portfolio (of which we’ll obviously share the results). Until that time we’ll have to contend ourselves with predictions… 

Not a one trick pony

We also analysed our performance for the Friday 5 April outage. The results of which can be seen in graph 2. and table 2.. In first instance ING seems to follow the negative development of the AEX at large. However, from c. 12:30 onwards the bank share starts to tank faster than the rest of the market. Our indicator (which had already been negative for the earlier part of the day) registers a significant further drop between 12:00 and 13:00, followed by continued declines in sentiment. 


Graph 2.: share price (indexed) of ING and the AEX versus Sentiment score score, 5 April 2013 


Table 2.: Sentiment warning and share price performance over the subsequent hours, 3 April 2013

About the author: Wouter Kneepkens, among other things, is Financial Guru at SNTMNT, blogging and covering financial markets. He has over 15 years of investment experience, some of which as a professional. 

Disclaimer: Neither the author nor SNTMNT currently holds an interest in ING. This article is not meant as investment advice.  

By Vincent van Leeuwen.


I think it’s funny how people somehow seem to believe that every entrepreneur out there is “an idea guy”. Personally, I’m pretty far from an idea guy. Over the past year, I think I’ve had about one or two startup ideas that could be considered somewhat viable, let alone promising. My current startup is actually mostly the result of my brother’s thesis rather than my own creative sparks. Nevertheless, every now and then, people ask me about my “next great idea”. One of the answers that I often give them is an idea that has always fascinated me. And, as usual, it’s not one of my own. This particular idea actually comes from way back in 2008, when it appeared on the quite famous “idea’s-we’d-like-to fund-list” by Paul Graham:

The Hybrid Database.

“22. A web-based Excel/database hybrid. People often use Excel as a lightweight database. I suspect there’s an opportunity to create the program such users wish existed, and that there are new things you could do if it were web-based. Like make it easier to get data into it, through forms or scraping.

Don’t make it feel like a database. That frightens people. The question to ask is: how much can I let people do without defining structure? You want the database equivalent of a language that makes its easy to keep data in linked lists. (Which means you probably want to write it in one.)”

I believe there is a large gap that could be filled by a Hybrid database structure such as described by the founder of Y-Combinator. Some of the smartest people I know are employed as strategy consultants or investment bankers for evil empires companies like McKinsey, Goldman or JP Morgan. And many of them, including most of my friends from Uni, are using Excel on a daily (if not hourly) basis.

Funny enough, when I ask them whether they do programming, most of them would tell me that programming is not for them. I found this interesting, as I believe many of them are in fact already programming. I think Paul Graham’s perfectly answered his own question how much you can let people do without defining structure. Excel is in fact a programming language. A low-abstract-level programming language that combines many of the powerful benefits of programming like IF/AND statements and FOR loops with spreadsheet structure. Also, Excel’s macros are basically low-level Cron jobs that are used to push data around in many Websites we know today. I believe this lower level of abstractness in Excel is all that separates it from scripting languages like Python or Ruby that many non-technical people fear.

There is one thing though that Excel has always lacked. Probably a little like its creator Microsoft as a whole, Excel lacks a feeling for connectivity that was brought by the World Wide Web. I think this is why it has never been able to truly fulfill the need as described so wonderfully in Paul Graham’s list. And why the search for the Hybrid Database has always endured.

Since a few weeks though, I believe this search might be all but over. Very recently, I discovered that Google’s clone version of Excel, called Google Spreadsheets, actually has a very important feature that Excel always lacked:


I believe that the Google Spreadsheet API can be the starting point of a Hybrid Database, as the API will make it really easy for techies and non-techies to work together via spreadsheet programming language.

But before we continue, I can imagine that some of you might be thinking:

What do you mean “API”?

For the less technical among us: An application programming interface (API) is a protocol intended to be used as an interface by software components to communicate with each other. In other words, an API enables computer programs to talk with one another, allowing them to exchange data. By making spreadsheets accessible through such an interface, the Google Spreadsheet API enables you to insert and update spreadsheet rows & columns using scripting language.

I found this to be really awesome. By using gdata and a specific Python Wrapper, I was able to set up a script that reads and inserts rows in about 30 minutes. Especially if you’re working on projects that require collaboration on data manipulation between techies and non-techies (like the backtests we do here at SNTMNT for instance), this stuff is gold. It enables you to store data in a database-like environment, but at the same time the data is very accessible and easy to manipulate by Excel gurus. I can imagine this could be cool for a lot of other awesome stuff across many industries.

Off course there are some obvious performance issues when compared to traditional databases. Google Spreadsheets allows for a maximum of 400,000 total cells (not rows!) across all sheets, which is off course peanuts for a traditional database. Also, with such large amounts of data, processing and querying will probably not be lightning fast. Nevertheless, I believe that the Hybrid Database approach taken by Google spreadsheets could be a game changer for many businesses. Especially if Google Spreadsheets is able to make it’s API wrappers even easier to understand for the mainstream crowd. By getting business people to do more of their own programming using hybrid database structures and Excel programming language, I believe many companies could unlock an employee potential they never even knew was there.

Too bad that such a great “startup idea” is operated by a multinational, instead of a startup.

After my post, someone pointed out to me on Twitter that apparently there’s already a (Dutch) startup trying to build some sort of Hybrid Database: They’re still in private beta, but do check them out :)

If you found this post useful, you can help us by sharing it. Also, feel free to leave a comment below, or drop me a line. I love to hear your feedback. Thanks!

Image credit: Tim Morgan.

SNTMNT is bringing Social sentiment analysis to the doorstep of half a million investors in Europe. With its proprietary Trading Indicator API, SNTMNT identifies and visualizes sentiment in social media such as Twitter. Users can see in real-time what exchange traded companies people are talking about online, and what the crowd thinks about them. All boiled down in one single indicator.

As of today, sentiment analytics and social media trend predictions will be accessible to about half a million users at BinckBank, a top-5 European broker with clients in France, Italy, the Netherlands and Belgium.

The rise of social media has made it possible to make emotions in financial markets tangible. By measuring social media sentiment surrounding stocks, SNTMNT has created an indicator that gives real-time insights into financial emotions in the market. The indicator takes away extra noise in asset pricing, and gives investors an additional trading indicator on top of fundamental analysis and/or technical analysis.

Where sentiment analysis so far has been solely available to larger hedge funds, we’ve just taken a huge step in making these kind of tools available to retail investors. As of today, we’re proud to announce sentiment analytics and social media trend predictions will be integrated for about half a million users at BinckBank across the Netherlands, Belgium, France and Italy. As far as we know, this is the first time that social sentiment analysis is integrated by an online broker on such a large scale.

But, maybe most important of all: Binck sentiment is up on the announcement :)

By Vincent van Leeuwen.


Do you know what I hate most about network events?

That no matter how hard I try to escape them: Sooner or later I always find myself trapped between the very people I try to avoid. The type of people who keep on bragging and bragging about their “successful Internet businesses”.

I recently found myself at one of these joyful events. I had the pleasure of hearing one of these poor souls brag away to me about her daily whereabouts (which were insignificant to say the least). Unfortunately for me, the inevitable moment came when she asked me the quasi-interested W-D-Y-D question. After which she took my answer as a starting sign to begin bragging away once more. About her boyfriend this time:

“Oh really? My boyfriend is an Entrepreneur too! Pretty successful actually. He started out by doing Website development and then he also started doing SEO and SEA. Now he’s moving more into social media consultancy and Mobile development.”

“Ah, I see. That’s very interesting.” (thank god for my parent’s good nurturing)

“I know right? Yeah, he does so many different things. He’s quite a visionary.”

And I couldn’t help myself thinking:

He’s not a visionary. He’s just a fucking copycat.

Whenever I open the Deloitte Fast 50 anywhere in the world, there are plenty of them. SEO and SEA agency’s who are employing armies of monkeys placing shitty links all day. Full-service Web agency’s that-now-also-do-apps-and-shit. Or even worse: social media douchebags (who typically like to call themselves “evangelists”).

All businesses that profit from people who don’t know the Internet that well. All creating next to no value for anybody except themselves.

Please note that I’m talking about true copycats here. Not to be mistaken with disruptive innovation, which I believe is a beauty. I admire founders like Sir Stelios Haji-Ioannou (Easyjet), Michiel Muller (Tango) or Sean Parker (Napster). They created value for millions by shifting industries entirely upside down. In some cases even without a large financial reward in return.

These founders remind me a lot of another group of innovators in a slightly different ball game a long time ago:

Do you know what painters like Van Gogh, Mondriaan and Monet have in common?

They all had to face rejection for their work during life. And they all died in almost absolute poverty. This must have been frustrating to say the least, as there were plenty of painters out there who could come by with their work just fine.

But they were not remembered.

You know why?

Because most of them were merely sticking to the painting status quo of their generation. The ones that we still remember and cherish today though, are the ones who were brave enough to fight this status quo. The ones who were able to see something that nobody else saw. The very ones who were so passionate about their work, that poverty didn’t stop them from doing what they truly loved.

Off course there are exceptions like Pablo Picasso and Salvador Dali, both great artists who were already immensely wealthy and famous during life. I guess this has a lot to do with the more modern age both lived in. An age where dissemination of information simply went faster. I believe the lesson from this is that, in the end, innovation always wins. It just takes time.

And let’s be fair. If all of us were trying to be innovators, the success rate of innovation would probably be even lower than it is today. Still, I often find it quite frustrating to see how shamefully little creativity is rewarded with so much credits and coin by society. Especially when I see some frighteningly ambitious startup ideas (my favorites by far) struggle for traction. The reality for many entrepreneurs is that most copycats will probably make tons more money than you and me will ever do in a lifetime.

Still, I see no reason to envy them. Whenever I find myself at some snobbish party again, with someone jabbering away about his copycat voodoo, I always remember this one little thought:

True entrepreneurs are artists. And that’s something a copycat will never get.

If you found this post useful, you can help us by sharing it. Also, feel free to leave a comment below, discuss on HN, or drop me a line. I love to hear your feedback. Thanks!

Image credit: ekenitr.