“Quantopian inspires talented people everywhere to write investment algorithms”

 

The Opportunity

Quantitative hedge funds, which instead of human traders use computer algorithms and mathematical models to make investment decisions, are becoming increasingly popular. This is due to the fact that their performance has been much better than that of traditional hedge funds.

As more investment managers seek to implement quantitative strategies, finding people has become a great challenges as many people with the requisite skills to develop trading algorithms have little interest in working for a big, established hedge funds.

Solution

Quantopian, a crowd-sourced quantitative investing firm, solves this issue by allowing people to develop algorithms as a side-job.

On one hand, the company provides access to a large US Equities dataset, a research environment, and a development platform to community members, which are mainly data scientists, mathematicians and programmers,  enabling them to write their own investment algorithms.

On the other, Quantopian acts as an investment management firm, allocating money from individuals and institutions to the community top-performing algorithms.  The allocation is based on the results of each algorithm backtesting and live track record.

If an algorithm receives an allocation, the algorithm developer earns 10% of the net profit over the allocated capital.

 

Effectiveness and Commercial Promise

On the algorithm developer side, Quantopian’s community currently has over 100,000 members including finance professionals, scientists, developers, and students from more than 180 countries from around the world.

Members collaborate with each other through forums and in person at regional meetups, workshops, and QuantCon, Quantopian’s flagship annual event.
On the fundraising side, last year Steven Cohen, one of the hedge fund world’s biggest names, promised up to $250m of his own money to the platform. Moreover, they started managing investor capital last month. The initial allocations ranged from $100k to $3m with a median of $1.5m per algorithm. By the end of 2017, the expect allocations to average $5m-10m per algorithm.

 

Concerns

Trading algorithms sometimes converge on buy or sell signals which can generate systemic events. For example in August 2007 all quant algorithms executed sell orders at the same time. During two weeks quant trading strategies created chaos in the financial markets.

If an event similar to August 2007 occurs again, it might harm Quantopian  returns if the algorithms in which money is allocated converge.

Quantopian can mitigate this risk by regularly analyzing their exposition to particular stocks and the overlap between the different strategies they managed and their portfolio with the portfolios of other quant managers that disclose their positions.

Alterations

We believe that Quantopian approach can be utilized for any “market” that requires accurate predictions. Therefore, Quantopian model can be exported to other markets such as weather prediction, in which users are paid by weather forecasting agencies, or sports results, in which users are paid by sport betting sites, or for public policy solutions as part of open government initiatives.

 

Sources:

1. https://www.ft.com/content/b88e6830-1969-11e7-9c35-0dd2cb31823a
2. https://www.quantopian.com/about
3. https://www.quantopian.com/faq
4. https://www.quantopian.com/home
5. https://www.ft.com/content/0a706330-5f28-11e6-ae3f-77baadeb1c93
6. https://www.novus.com/blog/rise-quant-hedge-funds/
7. http://www.wired.co.uk/article/trading-places-the-rise-of-the-diy-hedge-fund

 

Team members:

Alex Sukhareva
Lijie Ding
Fernando Gutierrez
J. Adrian Sánchez
Alan Totah
Alfredo Achondo

Problem

Within 3 years of release, over 2/3rd of prisoners are re-incarcerated.1  States collectively spend $80 billion a year on correctional costs.2 This is a vicious cycle that must be broken for the sake of these individuals, their families and communities, and the taxpayer dollars that go to support our overcrowded prison system.

These prisoners end up incarcerated as a result of all kinds of crimes and come from all sorts of backgrounds. Studies show that 80% of federal prisoners battle with a history of drug or alcohol abuse, 2/3rds do not have a high school diploma, up to 16 percent have at least one serious mental disorder, and 10% are homeless in the months up to incarceration.3

Each offender is battling with a unique set of issues and has a unique set of goals so they need a unique treatment plan to get back on their feet. For instance, for offenders with children, parental responsibility can interfere with their requirement to attend Alcoholics Anonymous or stick to a curfew or house arrest. On the other hand, regaining custody of their kids can be a major motivating factor for sticking to the program. Those families may benefit from specialized offerings like parenting classes.4

So how do we know what is right for each prisoner?

 

Solution

We will construct an AI model to 1) determine which prisoners are more likely to be re-incarcerated and 2) which re-introduction programs are more effective in keeping which prisoners from being re-incarcerated. The inputs of the model will be demographic data, behavioral data and crime data of prisoners, and the re-introduction programs they received before being released. The output of the model will determine how likely that prison is to be re-incarcerated.

Once we construct the model, we can 1) identify the high-risk prisoners and deploy more resources to help them and 2) create programs that are more likely to succeed in helping a particular set of prisoners.

This solution can be used by federal or state prison systems themselves. It can also be provided by the private sector and sell to the government as a service. Given the significant economic interest at stake, if the solution is effective, the government is highly likely to pay for the solution.

 

Pilot

The objective is to construct, evaluate and implement a model to recommend re immersion programs to people with recent criminal history to reduce their probability of recidivism. The model will be an hybrid between a knowledge model, tell me what fits based on my needs, and a collaborative system, tell me what’s popular among my peers.

The main challenges to implement this solution are the data, as there are thousands of covariates but not so many observations (people that has been part of a program) and timeframe, a person can commit crime again at any given point of life.

In order to train the model, we will collect data from organizations that are already working with men and women that had recent criminal history. Some of this organizations are the Center for Employment Opportunities, Prison Entrepreneurship Program, and The Last Mile.

To validate the model we will run a 2 bin experiment: (i) status quo, (ii) recommended program in order to determine the real effect of our recommendation model. Hopefully, we will reduce recidivism significantly and thus improving the quality of life of people while saving cost to the government.

 

Team members:

Alex Sukhareva
Lijie Ding
Fernando Gutierrez
J. Adrian Sánchez
Alan Totah
Alfredo Achondo

References:

1 Durose, Matthew R., Alexia D. Cooper, and Howard N. Snyder, Recidivism of Prisoners Released in 30 States in 2005: Patterns from 2005 to 2010 (pdf, 31 pages), Bureau of Justice Statistics Special Report, April 2014, NCJ 244205.

2 “Does the U.S. Spend $80 Billion a Year on Incarceration?” Committee for a Responsible Federal Budget. N.p., 23 Dec. 2015. Web. 09 May 2017.

3 Dory, Cadonna. “Society Must Address Recidivism, Officials Say.” USC News. N.p., 11 Nov. 2009. Web. 09 May 2017.

4 Abuse, National Institute on Drug. “What Are the Unique Treatment Needs for Women in the Criminal Justice System?” NIDA. N.p., Apr. 2014. Web. 09 May 2017.