Can we infer the "laws" of finance from big data?

A distinction often made between the physical sciences and social sciences is that the latter are not as amenable as the former to controlled experiments for rigorously verifying predictions made from theory. However, astronomy is a hard science in which it is impossible to do experiments and is almost exclusively based on observations. Just as collection and analysis of high-quality data from the period of Tycho Brahe led to the formulation of empirical laws by Kepler and was later followed by the theoretical groundwork of Newton that precisely explains the motion of planetary bodies, it is possible that the use of big-data, especially from financial markets, will eventually lead to a well-established set of "laws" of economic activity. In this talk we will explore the first steps in this direction, focusing on how analysis of price and transaction data from financial markets (including bitcoin as well as more traditional assets such as currencies and equities) suggests the existence of empirical regularities ("stylized facts") that may be universal across space, time and asset classes. In particular, we shall discuss the heavy-tailed distribution of asset price fluctuations (the "inverse cubic/square law"), application of Wishart random matrix spectral statistics to infer cross-correlations in the fluctuations of different assets and the possibility of using graph-theoretic measures such as structural balance as indicators of systemic crises.

  • Prof. Sitabhra Sinha, Institute of Mathematical Sciences, Chennai
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  • 2017-08-24

The formation of partnerships in social networks

This paper analyzes the formation of partnerships in social networks. Agents randomly request favors and turn to their neighbours to form a partnership. If favors are costly, agents have an incentive to delay the formation of the partnership. In that case, for any initial social network, the unique Markov Perfect equilibrium results in the formation of the maximum number of partnerships when players become infinitely patient. If favors provide benefits, agents rush to form partnerships at the cost of disconnecting other agents and the only perfect initial networks for which the maximum number of partnerships are formed are the complete and complete bipartite networks. The theoretical model is tested in the lab. Subjects generally play according to their equilibrium strategy and the efficient outcome is obtained over 78% of the times. Decisions are affected by the complexity of the network. Two behavioural rules are observed during the experiment: subjects accept the formation of the partnership too often and reject partnership offers when one of their neighbours is only connected to them.

  • Prof. Bhaskar Dutta, Ashoka University & University of Warwick
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  • 2017-08-17

Why are fewer married women joining the work force in rural India? A decomposition analysis over two decades

In contrast with global trends, India has witnessed a secular decline in women's employment rates over the past few decades. We investigate this decline in rural areas, where the majority of Indian women reside. Using parametric and semi-parametric decomposition techniques, we show that changes in individual and household attributes fully account for the fall in women's labor force participation in 1987-1999 and account for more than half of the decline in 1999-2011. Our findings underscore increasing education levels among rural married women and the men in their households as the most prominent attributes contributing to this decline. We provide suggestive evidence that a rise in more educated women's returns to home production, relative to their returns in the labor market, may have adversely affected female labor force participation in rural India.

  • Prof. Kanika Mahajan, Ashoka University
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  • 2017-08-04