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on Financial Markets |
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Issue of 2014‒06‒07
four papers chosen by |
| By: | Rodriguez, Abel; Wang, Ziwei; Kottas, Athanasios |
| Keywords: | Social and Behavioral Sciences |
| Date: | 2014–06–05 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:ucscec:qt6dh099g2&r=fmk |
| By: | Aizenman, Joshua; Binici, Mahir; Hutchison, Michael M |
| Keywords: | Social and Behavioral Sciences |
| Date: | 2014–06–05 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:ucscec:qt7n17z9km&r=fmk |
| By: | Fabio Dercole; Davide Radi |
| Abstract: | This paper investigates the effects of the "uptick rule" (a short selling regulation formally known as rule 10a-1) by means of a simple stock market model, based on the ARED (adaptive rational equilibrium dynamics) modeling framework, where heterogeneous and adaptive beliefs on the future prices of a risky asset were first shown to be responsible for endogenous price fluctuations. The dynamics of stock prices generated by the model, with and without the uptick-rule restriction, are analyzed by pairing the classical fundamental prediction with beliefs based on both linear and nonlinear technical analyses. The comparison shows a reduction of downward price movements of undervalued shares when the short selling restriction is imposed. This gives evidence that the uptick rule meets its intended objective. However, the effects of the short selling regulation fade when the intensity of choice to switch trading strategies is high. The analysis suggests possible side effects of the regulation on price dynamics. |
| Date: | 2014–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1405.7747&r=fmk |
| By: | Sercan Arik; Sukru Burc Eryilmaz; Adam Goldberg |
| Abstract: | As the number of publicly traded companies as well as the amount of their financial data grows rapidly, it is highly desired to have tracking, analysis, and eventually stock selections automated. There have been few works focusing on estimating the stock prices of individual companies. However, many of those have worked with very small number of financial parameters. In this work, we apply machine learning techniques to address automated stock picking, while using a larger number of financial parameters for individual companies than the previous studies. Our approaches are based on the supervision of prediction parameters using company fundamentals, time-series properties, and correlation information between different stocks. We examine a variety of supervised learning techniques and found that using stock fundamentals is a useful approach for the classification problem, when combined with the high dimensional data handling capabilities of support vector machine. The portfolio our system suggests by predicting the behavior of stocks results in a 3% larger growth on average than the overall market within a 3-month time period, as the out-of-sample test suggests. |
| Date: | 2014–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1406.0824&r=fmk |