Crowdsourced stock market trading

Stock market trading has become a dirty word, or if not that, at least uninteresting. Wall Street excesses and the 2008 crash have led to little recent opportunity for financial return (non-existent interest rates for saving, and flat stock markets for equities (the S&P 500 return in 2011 was 0% (S&P 1257 at 12/31/10, 1258 at 12/31/11). Gold has been one of the only asset classes to realize real return (142% five-year return, $632 as of 12/28/06, $1531 as of 12/29/11). The particular subjective day trader gave way to faceless high-frequency computer algorithms as one of the only means of squeezing profits out of the stock market.

One thing that could turn this around, and have the dual benefit of bringing more transparency to markets and market practices is crowdsourcing. The enormous amounts of clean, freely available, computable, straightforward-to-understand data without privacy issues are ideal for crowdsourced manipulation.

Earlier attempts at applying crowdsourcing to stock market trading (for example, Yahoo Prediction Markets with leaderboard-style tracking of traders’ mock portfolios) fell by the wayside with the 2008 crash, but the concept could be reincarnated. There are several obvious ways to deploy crowdsourcing in stock market trading startups:

1. First would be a direct implementation of crowdsourcing as from the Wikinomics, fold.it, eteRNA model: making usable web-based datasets available to the wisdom-of-crowds to apply diverse ideas from different disciplines, often resulting in better results than those produced by the ‘experts’ in any field. Leaderboards, competition, leveling-up, forums, badges, and other gamefication techniques would be expected.
2. Second would be a platform where real-life traders can open source their trades, either before or after execution. Interested traders would grant open access to their trade logs, inviting crowd review to find winning trades, strategies, and traders, and conduct meta-analyses like what strategies work well in a high-volatility environment, a down economy, etc.
3. Third would be prediction markets 2.0, a more social gamefication implementation of prediction markets for stock trading, sales forecasting, movie hit projections, elections, and flu outbreaks through platforms like Iowa Electronic Markets, Intrade, etc.

Black Swan thinking – there’s an app for that!

As mobile apps increasingly mediate human interaction with the outside world, possibly eventually becoming a full buffer layer, there should be an app for Black Swan thinking, or more broadly, for bias reduction.

A Black Swan is an event that is rare, has extreme impact, and is retrospectively (but not prospectively) predictive. As humans with story-based not statistics-based evolutionary-relic perceptual systems, we should think more black swannishly or at least have mechanisms for minimizing exposure to downside black swans (e.g.; stock market crashes, terrorist attacks, health situations), and maximizing exposure to upside black swans (e.g.; startup investments, knowledge, parties).

Antibias App: a decision-making tool based on personal bias
The Antibias App, an on-board bias reduction coach (an extension to the Siri 2.0 personal virtual coach), could improve human perception by allowing randomness to be seen, statistics-based thinking, and a focus on the unknown (antiknowledge) as opposed to the known.

The Antibias App could list the top 5-10 bias areas (e.g.; confirmation bias, decision-making, belief, and behavioral biases, social biases, and memory errors and biases) with your personalized score for each one and a composite score as applied to different contexts (e.g.; personal, professional, political, economic). Even determining personalized biases is valuable; this could be accomplished through automated data collection, sentiment analysis of social media droppings, and online tests.

An advanced feature of the Antibas App could be a click-through to see the top three pro/con arguments on any issue and where different composite bias scores lie (e.g.; your own, your social network, your professional peers, your neighborhood, your nation state, etc.).

The Antibias App could be viewed in different modes such as story mode, statistics mode, graphics mode, and data visualization mode. The meta goal of the Antibias App is to increase liberty and choice by opening up more ways of thinking about bias and improved action-taking as a result.

Detroit 2.0 – cultural transformation

Certainly cultural transformation occurs but to what degree can it be actively catalyzed?

Creative class cities bloom and their opposites become walking Detroits.

How to revitalize your city into Rochester 2.0, Detroit 2.0:

• Free houses for artist communities (the aesthetic future starts now); stimulatory homesteading initiatives
  
• The post-ecotourism fad: ghetto tours; hip hop music and dance classes
  
• Favorable tax policies and free trade zones like Paul Romer’s Charter Cities program (example: Hong Kong in Honduras)
• Singapore/Korea-like targeted industrial policy (Welcome Stem Cell Research!)
• Social policy liberalization: immigration amnesty, gay marriage, euthanasia, decriminalized marijuana use
  

The big data era's flux and pulse

Big data is an important contemporary trend but what does it actually mean?

What is big data?
Big data refers not just to the absolute size of a body of information (which currently can be on the order of terabytes, petabytes, and exabytes), but its usability and manageability. Some of the defining parameters of big data are its large size, high velocity activity (incoming, processing, outgoing), heterogeneous nature (a variety of structured and unstructured data types like video and images), and requirement for real-time analytics.

What is the process of working with big data?
The process of working with big data involves several steps. First there may be an exploration of the data using tools for classification, visualization, and summarization. Then there is the detailed step of data cleaning to make the data consistent and usable. The next step is data reduction, for example defining and extracting attributes, decreasing the dimensions of data, representing the problems to be solved, summarizing the data, and selecting portions of the data for analysis. Then, the steps of predictive analytics, scoring, reporting, publishing, and quality validation and maintenance can be applied.

What are the applications of big data analysis?
Some of the benefits of big data analysis are the ability to summarize information, make predictions, identify trends (for example, consumer spending patterns), and rank and prioritize information. Some of the specific algorithms employed include for summarizing: clustering and associations; for making predictions: tree-based methods, neural networks, and k-nearest neighbors; for identification: anomaly detection, similarities and matches, and change detection; and for ranking: logistics and frequency detection.

Excerpted from an Association for Computing Machinery (ACM) talk on Big Data & Predictive Analytics (slides).

Craigslist 2.0: task outsourcing to local crowd labor

In the time, reputation, and fun hungry modern world, new tools like task outsourcing are emerging to provide these qualities, offering better ways for the invisible hand to meet in your meatsphere neighborhood.

The concept is that the crowdsourcing workplace meets Craig’s list in a local listing service with a reputation economy and badging/leveling up.

At least two startups offer task outsourcing services, TaskRabbit, which has accepted 1,000 reputation-garnering runners into their network to run tasks on demand, and Zaarly, where a consumer names their price for anything and obtains it from people nearby.

It will be interesting to see if there will be crossover in task outsourcing between physical-world tasks and online tasks, whether tools like TaskRabbit and Zaarly could become a 2.0 version of outsourced labor communities like elance, odesk, and 99designs.

Design and the disruptive startup: dynamic pivoting

In the mashup world of life, business, and web 2.0, spurred on by the Apple-ification of the world (iLife - as a concept not a product), one new idea is applying design to business models, and really by extension, applying design to everything.

Unfortunately, this does not mean as one might think, applying aesthetic principles, conceptually and literally, to business, business models, or any life context, adding beauty to function, and thereby function to function, and questioning the right proportionality of form and function.

Rather, at present applying design to business models means more basically, using design tools and design thinking in a business context, specifically, in the conduct of an iterative prototyping process with users.

In business 1.0, an entrepreneur would dream up an idea and write a business plan. In business 2.0, the claim is that entrepreneurs should interview dozens of potential customers to pivot through value propositions for ideas that solve the biggest customer pain points. Customer acquisition is tantamount, in a 'get, keep, grow' cycle. Elliptical tools like the business model canvas are proposed as support for this iterative prototyping process.

Terahertz information compression era

Information compression eras is an important area of futuretech: the progression from analog to digital and the developing friction for the next era.

Analog and digital are modes of modulating information onto the electromagnetic spectrum with increasing efficacy.

The next era could be characterized by the even greater effectiveness of electromagnetic spectrum control, particularly moving to multidimensional attribute modulation. Already DNA is a potential alternative encoding system with four and maybe eight combinations instead of the 1s and 0s of the digital era. Terahertz networking (Clariphy, Aurrion) and data provenance are early guides in the progress to the next node of information compression.

Excerpted from: "Reality: analog, digital, or information compression continuum?"

Personal social CRM

The new social CRM (customer relationship management) is personal social CRM. Social CRM is when businesses try to access the social network interactions of their customers for the purpose of extending business relationships. An example would be a customer tweeting something about a product, which a customer advocate notices and posts in the company’s online help forum. The company’s marketing staff is flagged and then responds by retweeting or other appropriate measures.

Personal social CRM is applying these corporate social CRM principles to managing interactions within one’s own social network. The latest websites for personal social CRM include Nimble and Contactually; other somewhat similar tools include Rapportive and Highrise. Nimble and Contactually attempt to show who is important in personal email networks through algorithms that count interaction frequency, length of time for response, and CCs versus direct interactions. Presumably future algorithms could include other influence variables like social ‘klout.’

One de facto and perhaps more useful functionality aspect of personal social CRM sites is that they are essentially a web-based API for social networks like LinkedIn. Different kinds of searches, sorting and management of contacts, for example with context tagging, are available with these tools. This could allow a new way to interact with a greater number of people more effectively.

Top 10 technology trends for 2012

1. Mobile is the platform: smartphone apps & device proliferation
2. Cloud computing: big data era, hadoop, noSQL, machine learning
3. Gamification of behavior and content generation
4. Mobile payments and incentives (e.g.; Amex meets FourSquare)
5. Life by Siri, Skyvi, etc. intelligent software assistants
6. Happiness 2.0 and social intelligence: mindfulness, calming tech, and empathy building
7. Social graph prominence in search (e,g.; music, games, news, shopping)
8. Mobile health and quantified self-tracking devices: towards a continuous personal information climate
9. Analytics, data mining, algorithms, automation, robotics
10. Cloud culture: life imitates computing (e.g.; Occupy, Arab Spring)

Further out - Gesture-based computing, Home automation IF sensors, WiFi thermostat, Enterprise social networks

Is it ever coming? - Cure for the common cold, Driverless cars


Looking back at Predictions for 2011: right or wrong?

• Right: Mobile is the platform, Device proliferation, Big data explosion, Group shopping
• On the cusp: Crowdsourced labor, Quantified self tracking gadgets and app, Connected media and on-demand streaming video
• Not yet: Sentiment engines, 3-D printing, Real-time economics

Crowdsourcing the stock market

New market tools are emerging that could be much better (real-time and objective) indicators of performance than the traditional methods of speculation-driven stock market price, quarterly reporting, and financial statements.

These tech tools are a nice response to the perceived social economic malaise of the times, and could help to realize some of the new thinking promulgated by both theorists and activists that markets are more of a Darwinian game of the fittest rather than an invisible hand meeting favorably for all parties.

The new market tools - real-time performance indicators:

• Prediction markets (SimExchange, Hollywood Stock Exchange, Intrade) for unit sales data
  
• Real-time purchasing feeds (Blippy, Google Wallet, social shopping sites) for the aggregation of real-time purchase data
• Social media check-ins (FourSquare, Facebook, etc.) for revenue prediction
• Forum badging and site gamification (Badgeville) for measuring the digital economics of brand sentiment