A Link Mining Algorithm for Earnings Forecast and Trading
Germán G. Creamer
Stevens Institute of Technology - Wesley J. Howe School of Technology Management
Columbia University - Computer Science Department
June 1, 2009
Data Mining and Knowledge Discovery, Vol. 18, No. 3
The objective of this paper is to present and discuss a link mining algorithm called CorpInterlock and its application to the financial domain. This algorithm selects the largest strongly connected component of a social network and ranks its vertices using several indicators of distance and centrality. These indicators are merged with other relevant indicators in order to forecast new variables using a boosting algorithm. We applied the algorithm CorpInterlock to integrate the metrics of an extended corporate interlock (social network of directors and financial analysts) with corporate fundamental variables and analysts' predictions (consensus). CorpInterlock used these metrics to forecast the trend of the cumulative abnormal return and earnings surprise of S&P 500 companies.
The rationality behind this approach is that the corporate interlock has a direct effect on future earnings and returns because these variables affect directors and managers' compensation. The financial analysts engage in what the agency theory calls the "earnings game'': Managers want to meet the financial forecasts of the analysts and analysts want to increase their compensation or business of the company that they follow.
Following the CorpInterlock algorithm, we calculated a group of well-known social network metrics and integrated with economic variables using Logitboost. We used the results of the CorpInterlock algorithm to evaluate several trading strategies. We observed an improvement of the Sharpe ratio (risk-adjustment return) when we used "long only'' trading strategies with the extended corporate interlock instead of the basic corporate interlock before the regulation Fair Disclosure (FD) was adopted (1998-2001). There was no major difference among the trading strategies after 2001. Additionally, the CorpInterlock algorithm implemented with Logitboost showed a significantly lower test error than when the CorpInterlock algorithm was implemented with logistic regression. We conclude that the CorpInterlock algorithm showed to be an effective forecasting algorithm and supported profitable trading strategies.
Number of Pages in PDF File: 20
Keywords: Link mining, link analysis, social network, machine learning, computational finance, boosting, time series, pattern analysis, data mining applications
JEL Classification: C49, C63, G14Accepted Paper Series
Date posted: October 17, 2006 ; Last revised: February 20, 2013
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