Introduction

Algorithmic trading systems – whether they are executed by computer programs or used as decision support tools by disciplined individuals – have been shown to significantly outperform traditional methods of investing. (1,2) Take, for example, that algorithmic trading programs do not hold onto losing trades nor do they hold winning trades until they move adversely. When trading volatile markets, intra-day, computers have a decided advantage in response time. But in the realm of algorithmic investing and longer horizons, it will be knowledge-based investors whose decisions are supported by machine algorithms that possess the key to superior returns. (3)

Buy-sell decisions essentially reflect the cognitive ability of investors to make rational decisions about timing and position size. But such decisions are multifaceted, requiring integration of algorithmic trade signals and risk-reward calculations while continuously self-regulating emotions and cognition. Taken together, one’s ability to succeed in this complex environment relies upon three things: (1) good models, (2) quantitative decision support tools, and (3) keen attentional skills and self-awareness.

Empirical Tools for Side-stepping Drawdowns

Ultimately, the only way to avoid drawdowns is to forecast them and then evade them. To mitigate unfavorable return distributions, nextSignals synthesizes data from multiple sources:

  1. asymmetric depth in out-of-the money options and composite gamma exposure as a primary measure of information content [4,5,6,7,8,9],
  2. significant changes in FINRA short sale trade data [10,11],
  3. inversion in VIX futures term structure [12,13,14], and
  4. descriptive statistics that measure volatility clustering (e.g. GJR-GARCH ) for predicting one-day ahead returns in aggregate indexes [15,16].

A Beta Rotation Algo

Close to 90% of investment returns are determined by portfolio asset allocation. nextSignals’ principle strategy amounts to a regime-switching asset allocation model, moving from risk-free assets and defensive equities during periods of high variance to momentum stocks during low variance states.

Combining momentum with defensive signaling through relative performance has been effectively applied to asset allocation and risk positioning. [17] But even the most sophisticated machine code is unable to collect and interpret multiple anticipatory signals ahead of an imminent selloff.

nextSignals‘ original investment algorithm was based on a momentum strategy shown to consistently outperform when the investing environment is more favorable towards higher-beta sectors with a high positive correlation to the S&P 500 Index.  Otherwise, the algorithm rotates into lower-beta sectors with a low correlation to the S&P 500 Index. While capable of outperforming the usual market benchmarks, drawdowns dogged performance potential. A simple method was needed to preserve the performance of beta rotation while temporarily moving to a safe haven (e.g. US Treasuries, Japanese Yen, or gold) when indicated.

Crash Protection through Anticipatory Signaling

Consistent with the work of Gerd Gigerenzer, Director of Risk Literacy and the Max Planck Institute of Human Development, nextSignals has created “fast and frugal trees” – heuristics built on a few key metrics.  Take, for example, the coincident selling by dark pools (DIX) and contraction in long gamma positioning (GEX) that nextSignals has found appear at market highs prior to significant retrenchments. In fact, when asset allocation in the beta rotation algo (above) switches to US Treasuries when DIX-GEX signaling appears, the algorithm’s performance improves substantially and drawdowns are largely be averted.

 

Tools

Portfolio analysis
Asset Correlations
Geopolitical Risk Index (GPR)
FRED Economic Data
Moody’s Baa Corporate Bond Yield vs 10-Year Treasury Yield
FINRA short sale trade data (off-exchange data explained)
Citigroup Economic Surprise Index (Economic Indicators)
Discounted cash flow valuation model
Selection process: Correlations and Dividend Analysis


References

  1. Cartea, Ãlvaro and Jaimungal, Sebastian and Ricci, Jason, Buy Low Sell High: A High Frequency Trading Perspective (April 15, 2014). SIAM Journal of Financial Mathematics.
  2. Dietvorst, Berkeley J. and Simmons, Joseph P. and Massey, Cade, Algorithm Aversion: People Erroneously Avoid Algorithms after Seeing Them Err (July 6, 2014). Journal of Experimental Psychology.
  3. Lin, Tom C. W., The New Investor. 60 UCLA Law Review 678 (2013); 60 UCLA Law Review 678 (2013); Temple University Legal Studies Research Paper No. 2013-45.
  4. Huang, Tao and Li, Junye and Wu, Fei, Asymmetric Variance Premium, Skewness Premium, and the Cross-Section of Stock Returns (November 20, 2018).
  5. Sepp, Artur, Volatility Modelling and Trading (July 17, 2016). Global Derivatives Workshop Global Derivatives Trading & Risk Management, Budapest, 2016.
  6. Augustin, Patrick and Brenner, Menachem and Subrahmanyam, Marti G., Informed Options Trading Prior to M&A Announcements: Insider Trading? (October 26, 2015).
  7. Tsai, Wei-Che and Chiu, Ying-Tzu and Wang, Yaw-Huei, The Information Content of Trading Activity and Quote Changes: Evidence from VIX Options (November 14, 2014). Journal of Futures Markets (2014).
  8. Durham, J. Benson, Betting Against Beta (and Gamma) Using Government Bonds (2015-01-01). FRB of New York Staff Report No. 708.
  9. Madan, Dilip B. and Carr, Peter P. and Schoutens, Wim and Melamed, Michael, Hedging Insurance Books (April 18, 2015). Robert H. Smith School Research Paper No. RHS 2635602; NYU Tandon Research Paper No. 2635602.
  10. Saffi, Pedro A. C. and Sigurdsson, Kari, Price Efficiency and Short Selling (August 30, 2010). AFA 2008 New Orleans Meetings Paper; IESE Business School Working Paper No. 748; Review of Finance Studies, Vol. 24, No. 3, pp. 821-852, 2011.
  11. FINRA Short Sale Volume at www.http://regsho.finra.org/regsho-Index.html
  12. Johnson, Travis L., Risk Premia and the VIX Term Structure (January 27, 2016). Journal of Financial and Quantitative Analysis 52 (2017), 2461-2490.
  13. Donninger, Chrilly, Modeling and Trading the VIX and VSTOXX with Futures, Options and the VXX (May 22, 2015).
  14. Zhu, Yingzi and Zhang, Jin E., Variance Term Structure and VIX Futures Pricing (March 2005).Â
  15. Engle, R. F., 2009. Anticipating Correlations: A New Paradigm for Risk Management. Princeton University Press.
  16. Ederington, Louis H. and Guan, Wei, Forecasting Volatility (January 2004).
  17. Gayed, Michael and Bilello, Charles, An Intermarket Approach to Beta Rotation: The Strategy, Signal, and Power of Utilities (January 2014). 2014 Charles H. Dow Award Winner.