The QMIT Leveraged Buyout (LBO) Model & Enhancements via Sentiment Based Alternative Data

31 Pages Posted: 7 Aug 2019

See all articles by Milind Sharma

Milind Sharma

QuantZ Capital Management LLC

Aravind Ganesan

Columbia University, Graduate School of Arts and Sciences, Department of Mathematics, Mathematics of Finance, Mathematics of Finance MA Program, Students

Date Written: July 30, 2019

Abstract

This paper introduces the QMIT LBO model and describes its salient characteristics. In addition to a 41% long term hit rate the Top 100 model predictions can be traded quite profitably as an equal weighted long portfolio. A Russell 2000 Value index hedge increases the Sortino ratio to ~2.5 over the 19-year history. It then synopsizes the SESI (sentiment) signal from RavenPack and investigates its merits as an overlay to the base level LBO Top 100 trading signal. Given that SESI captures newsbased sentiment which may include rumors on such LBO names it is logical to ascertain whether benefits may accrue from trading such a combined quantamental signal. We conduct a series of experiments involving the daily overlay of SESI for the 10-year period (2007-16) to the weekly rebalanced LBO Top 100 and find substantial improvements in annualized returns as well as Sharpe and Sortino ratios, not to mention the drawdown profile of the overlaid strategy. The best overlay scenario tested results in a 46% boost to the Sharpe ratio with an absolute +8.6% improvement to annualized returns.

Keywords: Factor Investing, LBO Model, Risk Arbitrage, Machine Learning, Smart Betas, Alternative Data, News Sentiment

JEL Classification: C53, G11, G12, G14, G15, G34

Suggested Citation

Sharma, Milind and Ganesan, Aravind, The QMIT Leveraged Buyout (LBO) Model & Enhancements via Sentiment Based Alternative Data (July 30, 2019). Available at SSRN: https://ssrn.com/abstract=3429492 or http://dx.doi.org/10.2139/ssrn.3429492

Milind Sharma (Contact Author)

QuantZ Capital Management LLC ( email )

44 Wall St., 12th Floor
12th Floor
NY, NY 10005
United States

Aravind Ganesan

Columbia University, Graduate School of Arts and Sciences, Department of Mathematics, Mathematics of Finance, Mathematics of Finance MA Program, Students ( email )

NY
United States

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