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Price contingent and price-volume contingent portfolio strategies

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Abstract

Using a partially revealing dynamic equilibrium model, investors adjust their estimates of the expected returns through the price discovery process (past price dynamics) and consequently implement price contingent portfolios based on these estimates. We implement the price contingent portfolio on the U.S. stock market and compare its performance with other common portfolio strategies. We also consider the price-volume contingent strategy, estimating the expected return and covariance matrix from both the past price and observed volume dynamics. We find that these signal-based portfolios outperform the capitalization and equal weighted strategies. They also provide appealing diversification benefits compared to common optimization-based portfolios.

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Notes

  1. Grouping stocks in sub-portfolios has however the unwanted feature of aggregating signals. This methodology would be more effective if for a given stock, we select a few relevant stocks for correlation (i.e., same industry etc.).

  2. Depending on the auto-correlation estimated, both momentum (for positive auto-correlations) and contrarian (negative auto-correlations) strategies are special cases of the price contingent strategies, established under the condition of missing private information, as showed in Biais et al (2010).

  3. Note that the objective consisting of pairs of equal summands, we can lower the number of summands in half by rather minimizing: \(2\sum_{i=1}^{n}\sum_{j=1}^{n}{\left({x}_{i}{\left(\sum x\right)}_{i}-{x}_{j}{\left(\sum x\right)}_{j}\right)}^{2},\forall i<j\).

  4. When allowing short-sales, we preferred the following formulation that minimizes -DR: \(\mathop{{\text{min}}}\limits_{x}\frac{1}{2}\mathrm{ln}\left({x}^{{\prime}}\sum x\right)-\mathrm{ln}\left({x}^{{\prime}}\sigma \right).\)

  5. We compared our FF and data available on Ken French website through multiple metrics (number of firms, ME, BE, returns, etc. for each portfolio and in total, as well as breakpoints). When we do not limit to the 1000 biggest firms and to stocks with at least 60 months of past returns, we find almost identical values. Comparison charts are available upon request.

  6. We estimate the distribution of the statistics by generating 10,000 block bootstrap samples of the returns of the strategies. We present the results with non-annualized figures in order to follow Lo (2002), who shows that annualization is correct only under very special circumstances.

  7. All strategies exhibit non-annualized Sharpe ratios bigger than 0.20 which is significantly different from zero when we consider the value of the standard errors (about 0.04).

  8. The gain in return might not be sufficient to compensate for the increase in volatility for the average risk-averse investor. In this case, she would be reluctant to be invested in the price-volume contingent strategy. To test this hypothesis, we follow Cederburg et al. (2020) and calculate the change in certainty equivalent return (CER) when investors switch from one strategy to another one. For instance, for a risk aversion gamma=5, the loss to switch from the price-volume contingent strategy to the minimum variance strategy is ΔCER = − 0.06% per year. However, this loss in CER is not significant (p–v 0.47) meaning that such a risk-averse investor would remain indifferent between these strategies when considering both risk and return.

  9. Also, the EW portfolio is the less concentrated in weights, and the ERC portfolio is the less concentrated in risk. Note that the Shannon entropy for the weights is equal to 981 (and not 1000) for the EW portfolio—the number of stocks in the sample starting at 1000 and then decreasing each month, with 981 being the average.

References

  • Arnott, Robert D., Jason Hsu, and Philip Moore. 2005. Fundamental Indexation. Financial Analysts Journal 61 (2): 83–99.

    Article  Google Scholar 

  • Arrow, Kenneth J. 1971. Essays in the Theory of Risk Bearing. Chicago: Markham Publishing.

  • Baker, Malcolm, Brendan Bradley, and Jeffrey Wurgler. 2011. Benchmarks as Limits to Arbitrage: Understanding the Low-Volatility Anomaly. Financial Analysts Journal 67 (1): 40–54.

    Article  Google Scholar 

  • Biais, Bruno, Peter Bossaerts, and Chester Spatt. 2010. Equilibrium Asset Pricing and Portfolio Choice Under Asymmetric Information. The Review of Financial Studies 23 (4): 1503–1543.

    Article  Google Scholar 

  • Burlacu, Radu, Alain Guéniche, and Sonia Jimenez-Garcès. 2019. “Testing the CAPM Under Asymmetric Information”. Working Paper, 2019. https://doi.org/10.2139/ssrn.4061052.

  • Cederburg, Scott, Michael S. O’Doherty, Feifei Wang, and Xuemin Yan. 2020. On the Performance of Volatility-Managed Portfolios. Journal of Financial Economics 138 (1): 95–117.

    Article  Google Scholar 

  • Choueifaty, Yves, and Yves Coignard. 2008. Toward Maximum Diversification. The Journal of Portfolio Management 35 (1): 40.

    Article  Google Scholar 

  • Clarke, Roger, Harindra de Silva, and Steven Thorley. 2011. Minimum-Variance Portfolio Composition. The Journal of Portfolio Management 37 (2): 31–45.

    Article  Google Scholar 

  • Dichtl, Hubert, and Wolfgang Drobetz. 2011. Portfolio Insurance and Prospect Theory Investors: Popularity and Optimal Design of Capital Protected Financial Products. Journal of Banking & Finance 35 (7): 1683–1697.

    Article  Google Scholar 

  • Fama, Eugene F., and Kenneth R. French. 1993. Common Risk Factors in the Returns on Stocks and Bonds. Journal of Financial Economics 33 (1): 3–56.

    Article  Google Scholar 

  • Frazzini, Andrea, and Lasse Heje Pedersen. 2014. Betting against Beta. Journal of Financial Economics 111 (1): 1–25.

    Article  Google Scholar 

  • Grossman, Sanford J., and Joseph E. Stiglitz. 1980. On the Impossibility of Informationally Efficient Markets. The American Economic Review 70 (3): 393–408.

    Google Scholar 

  • Haugen, Robert A., and Nardin L. Baker. 1991. The Efficient Market Inefficiency of Capitalization-Weighted Stock Portfolios. The Journal of Portfolio Management 17 (3): 35.

    Article  Google Scholar 

  • Lattimore, Pamela K., Joanna R. Baker, and Ann D. Witte. 1992. The Influence of Probability on Risky Choice. Journal of Economic Behavior & Organization 17 (3): 377–400.

    Article  Google Scholar 

  • Ledoit, Olivier, and Michael Wolf. 2004. Honey, I Shrunk the Sample Covariance Matrix. The Journal of Portfolio Management 30 (4): 110.

    Article  Google Scholar 

  • Ledoit, Oliver, and Michael Wolf. 2008. Robust Performance Hypothesis Testing with the Sharpe Ratio. Journal of Empirical Finance 15 (5): 850–859.

    Article  Google Scholar 

  • Lo, Andrew W. 2002. The Statistics of Sharpe Ratios. Financial Analysts Journal 58 (4): 36–52.

    Article  Google Scholar 

  • Lo, Andrew W., and Jiang Wang. 2006. Trading Volume: Implications of an Intertemporal Capital Asset Pricing Model. The Journal of Finance 61 (6): 2805–2840.

    Article  Google Scholar 

  • Lou, Xiaoxia, and Tao Shu. 2017. Price Impact or Trading Volume: Why Is the Amihud (2002) Measure Priced?. The Review of Financial Studies 30 (12): 4481–4520.

    Article  Google Scholar 

  • Maillard, Sébastien., Thierry Roncalli, and Jérôme. Teïletche. 2010. The Properties of Equally Weighted Risk Contribution Portfolios. The Journal of Portfolio Management 36 (4): 60.

    Article  Google Scholar 

  • Scherer, Bernd. 2011. A Note on the Returns from Minimum Variance Investing. Journal of Empirical Finance 18 (4): 652–660.

    Article  Google Scholar 

  • Shumway, Tyler. 1997. The Delisting Bias in CRSP Data. The Journal of Finance 52 (1): 327–340.

    Article  Google Scholar 

  • Tversky, Amos, and Daniel Kahneman. 1992. Advances in Prospect Theory: Cumulative Representation of Uncertainty. Journal of Risk and Uncertainty 5 (4): 297–323.

    Article  Google Scholar 

  • Vinod, H.D. 2004. Ranking Mutual Funds Using Unconventional Utility Theory and Stochastic Dominance. Journal of Empirical Finance 11 (3): 353–377.

    Article  Google Scholar 

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Authors and Affiliations

  1. Grenoble Ecole de Management, Grenoble, France

    Alain Guéniche & Philippe Dupuy

  2. SKEMA Business School, Valbonne, France

    Wan Ni Lai

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  1. Alain Guéniche
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  2. Philippe Dupuy
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  3. Wan Ni Lai
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Correspondence to Alain Guéniche.

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Guéniche, A., Dupuy, P. & Lai, W.N. Price contingent and price-volume contingent portfolio strategies. J Asset Manag 24, 173–183 (2023). https://doi.org/10.1057/s41260-023-00304-5

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