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Turing Finance | January 20, 2021

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About TuringFinance

TuringFinance has been exploring the exciting possibilities found at the intersection of computer science and quantitative finance for more than three years. In particular, TuringFinance has explored many of the contrarian ideas espoused by evolutionary finance. Evolutionary finance, also known as the adaptive markets hypothesis [1, 2, 3], is a contrarian economic theory which reconciles the efficient market hypothesis with behavioural economics through the application evolutionary principles. The evolutionary finance theory has been championed by notable academics like Andrew Lo and research groups like the Santa Fe Institute.

From the evolutionary finance point-of-view markets are dynamic systems consisting of heterogeneous market agents which interact with one another and are subject to evolutionary forces. It is from these interactions and as a result of evolutionary forces that the statistical properties of markets which quantitative analysts study emerge. Emergent properties of markets often include randomness, momentum, value, and reversion but none of these properties are persistent in nature [4]. Their magnitude changes as the population of market agents adapt, evolve, and succumb. From an evolutionary finance point-of-view these characteristics are not anomalies (as they are called under the efficient markets hypothesis), they are the expected emergent properties of a population of heterogeneous agents.

Evolutionary finance is not widely accepted because it does not shy away from being complex and, in my opinion, researchers and practitioners in the finance industry are inherently complexity averse. Another challenge facing evolutionary finance is the computational complexity of its tools when compared to stochastic process models with elegant closed-form formulas. Tools used in evolutionary finance are agent-based based models [5, 6, 7, 8, 9] (which are sometimes referred to as artificial stock markets) and artificial intelligence algorithms [10] including artificial neural networks, evolutionary algorithms, and swarm intelligence.

Over the past three years TuringFinance has covered many of these topics and the available tools in a series of technical articles ranging in both complexity and depth. These articles have covered topics including, but not limited to: market (in)efficiency [11, 12, 13, 14], financial modelling [15], artificial intelligence [16, 17], evolutionary algorithms and swarm intelligence [18, 19, 20], agent-based models [21, 22], optimization theory [23, 24], algorithmic trading systems [25, 26, 27], and some other arbitrary stuff. In the future we will publish more articles on these topics as well as more philosophical articles which attempt to synthesize the ideas together.

It has been fun to watch this blog gain traction with the established quantitative finance community. In it's first month the site had just 50 views, I'm happy to report that as of 2016 monthly views to the site average 20,000. This would not have been the case without the support of fellow quantitative finance bloggers including many of those aggregated by Lastly, should you wish to submit a blog post to this blog, please feel free to get in touch via the contact form on this site. 

Interesting references for those interested in evolutionary finance

[1] Lo, Andrew W. "The adaptive markets hypothesis: Market efficiency from an evolutionary perspective." Journal of Portfolio Management, Forthcoming(2004).

[2] Lo, Andrew W. "Reconciling efficient markets with behavioral finance: the adaptive markets hypothesis." Journal of Investment Consulting 7.2 (2005): 21-44.

[3] Lo, Andrew W. "Andrew Lo on The Adaptive Markets hypothesis - lecture", Clarendon Lectures 12th June 2013,

[4] LeBaron B, Arthur WB, Palmer R. "Time series properties of an artificial stock market". Journal of Economic Dynamics and control. 1999 Sep 30;23(9):1487-516.

[5] LeBaron, Blake. "Building the Santa Fe artificial stock market." Physica A (2002).

[6] Arthur, W. Brian, et al. "Asset pricing under endogenous expectations in an artificial stock market." Available at SSRN 2252 (1996).

[7] Palmer, Richard G., et al. "Artificial economic life: a simple model of a stockmarket." Physica D: Nonlinear Phenomena 75.1 (1994): 264-274.

[8] Book: Ehrentreich, Norman. "Agent-based modeling: The Santa Fe Institute artificial stock market model revisited." Vol. 602. Springer Science, 2007.

[9] Tesfatsion, Leigh. "Agent-based computational economics: modeling economies as complex adaptive systems." Information Sciences 149.4 (2003): 262-268.

[10] Book: Engelbrecht, Andries P. "Computational intelligence: an introduction". John Wiley & Sons, 2007.

The references which follow are articles published on this blog.

[11]  TuringFinance Article: Reid, Stuart G. "Stock Market Prices Do Not Follow Random Walks", February 2016 -- based on A Lo's paper by the same name.

[12] TuringFinance Article: Reid, Stuart G. "Hacking The Random Walk Hypothesis", September 2015 

[13] TuringFinance Article: Reid, Stuart G. "A Recipe for the 2008 Financial Crisis", May 2015

[14] TuringFinance Article: Reid, Stuart G. "Random Walks Down Wall Street, Stochastic Processes in Python", April 2015

[15] TuringFinance Article: Reid, Stuart G. "All Models are Wrong, 7 Sources of Model Risk", September 2014

[16] TuringFinance Article: Reid, Stuart G. "10 Misconceptions of Neural Networks [in Finance]", May 2014

[17] TuringFinance Article: Reid, Stuart G. "Computational Decision Making Methods for Finance", February 2014

[18] TuringFinance Article: Reid, Stuart G. "Portfolio Optimization Using Particle Swarm Optimization", December 2013 -- related to my Honours paper

[19] TuringFinance Article: Reid, Stuart G. "Using Genetic Programming to evolve Trading Strategies", June 2013

[20] TuringFinance Article: Reid, Stuart G. "Clustering using Ant Colony Optimization", April 2013

[21] TuringFinance Article: Reid, Stuart G. "Agent-based Computational Economic Models", January 2014

[22] TuringFinance Article: Reid, Stuart G. "Perfect Imperfection, Agent Based Models", August 2013

[23] TuringFinance Article: Reid, Stuart G. "Fitness Landscape Analysis for Computational Finance", June 2015

[24] TuringFinance Article: Reid, Stuart G. "Measured of Risk-adjusted Returns", September 2013

[25] TuringFinance Article: Reid, Stuart G. "Algorithmic Trading System System Architecture", November 2013 

[26] TuringFinance Article: Reid, Stuart G. "Algorithmic Trading System Requirements", October 2013 

[27] TuringFinance Article: Reid, Stuart G. "Intelligent Algorithmic Trading Systems", July 2013