Quant Readings: The New Math, Algorithmic trading in India, Money:Tech
- Alpha Magazine: The New Math
Quant shops aren’t sitting around idly. They are pressing into new realms of computational finance, applying concepts from molecular physics, mathematical linguistics, artificial intelligence and other scientific disciplines. Thales, for example, is using computer simulations to replicate human behavior to try to predict the myriad decisions that drive trading activity. Other firms are pinning their hopes on machine learning — statistical methods that allow computers to identify relationships in financial data and make predictions from them.
“ the main flaw of most quant strategies is their heavy reliance on historical data. Even though no strategy works all the time, he explains, this backward-looking approach makes statistical arbitrage especially vulnerable to unforeseen market moves. “If there is an event that is not captured in the prices today, the statistical arbitrage box will completely fall apart,”
- Ajay Shah: Never send a man to do a machine’s job
Chapter 5 of the MIFC report talks about algorithmic trading and the potential for India to achieve a role in international finance here. Direct market access was prohibited by SEBI and hence this kind of development could not take place.
SEBI has removed this constraint, and shifted to the conventional stance of securities regulators worldwide on this question. Here is the SEBI circular, and here is good reportage in Mint . .
- Money:Tech Conference
A Conversation with Michael Stonebraker
Changes in the size, speed, and capabilities of databases underlie every major technology change in capital markets. Entrepreneur and computer scientist Michael Stonebraker will discuss what it all means—on and off Wall Street.
Data Demos: Real Estate, Wikis, and the Web
Data is oxygen for stock markets. The trouble is, interesting new data is increasingly scarce, and existing data—like financial and earnings figures—are like mines picked over the point of exhaustion. Enter the Web.
Data: Making Money from Air Travel
Travel is one of the most technology-enabled industries, with a rapidly increasing amount of information exposed through travel-related sites. What can be done with this data? Rick Seaney will show us.
If You Had Everything Computationally, Where Would You Put It, Financially?
Technology has transformed investment and trading over the past 30 years. Markets have become computer networks, brokers are disintermediated by direct access and algo trading. Reporters are disintermediated when investors have access to primary sources at the same time they do.
Motley Fool CAPS: Investors Helping Investors Beat the Market
About a year ago, we started generating stock ratings from the collective wisdom of the Motley Fool community and Wall Street analysts. In a little more than a year, we’ve collected ~1.5 million stock recommendations on over 7000 stocks. 5300 stocks have met our threshold for achieving a CAPS rating. But can community-generated stock ratings benefit your stock research?
Steve Skiena: Money, the Internet, and Jai-Alai
Back in the 1980s, computer scientist and hacker Steve Skiena thought of a great way to beat jai-alai markets. Trouble was, it required faster computers and more data than he had at the time. That changed in the late 1990s, as Skiena exploited faster computers and web-based data to beat jai-alai markets, at least for a while.
Swimming Successfully in a Flow of Realtime News
Markets are all about changes, about what is different now then what was going on ten minutes ago—and about what’s different from what people expected. Panopticon’s visualization tools process the motherlode of news data from Bloomberg, Reuters, and other real time sources to produce a fascinating picture of what’s different and what matters.
The Future of Online Financial Discussion
AGORACOM is a second-generation financial community that has successfully eliminated epidemic levels of spam, bashing and profanity that plagued first-generation communities.
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