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Cash Flow Volatility, Prices and Price Volatility: An Experimental Study

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Abstract

The value of an asset is equal to the present value of its expected future cash flows. It is affected by the magnitude, timing and riskiness, or volatility, of the cash flows. We hypothesize that if the expected values of two assets’ cash flows are equal, the value of the asset with more volatile cash flows will be lower. Furthermore, we examine the impact of the volatility of cash flows on the volatility of prices. We consider a simple experimental environment where subjects trade in an asset which provides dividends from a known probability distribution. The expected value of the dividends is identical in all experimental treatments. The treatments vary with respect to the volatility of dividends. We find that when dividends are more volatile, transaction prices are lower. We also find that the volatility of prices is lower in the treatment with highly volatile dividends. In addition, as expected, trading volume is lower when cash flows are less volatile.

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Notes

  1. See, for example, Shiller (1988), Shiller (1989), Cochrane (1991), and Wu (1997).

  2. Some articles reject the null hypothesis of no bubbles, while others report the opposite (e.g. Rappoport and White 1993; West 1987; Dezhbaksh and Demirguc-Kunt 1990; Diba and Grossman 1988)

  3. The details of the three markets are explained in "Experimental Method" section.

  4. All traders in any of our experimental sessions have the same information and expectations. Thus, if the traders were fully rational, then theoretically there should not be any trade. If there is trade, then prices should follow the fundamental values at which the expected gains from trade would be zero. Risk averse traders would be even less willing to trade, and when they do, they would be willing to pay less than the (risk-neutral) fundamental value. So different degrees of risk aversion among players cannot explain the observed deviations of prices from fundamental values either. A number of experimental studies have tried to pin down different reasons for “booms and busts” or “bubbles and crashes”. However, this is not the context of our study. Based on the results from the literature, we take the possibility of trade and volatile prices as a given.

  5. We always recruited more subjects than we needed. However, in that session, only 14 subjects came to the laboratory, even though 18 had signed up.

  6. Properly using rewards helps the experimenter induce salience in the subjects (Friedman and Sunder 1994). In this context, salience refers to “the relation between actions and the reward implements the desired institution, and subjects understand the relation”.

  7. The instructions are available from the authors upon request.

  8. The questionnaire is available from the authors upon request.

  9. Note that if the median price in a period is outside the “feasible range”, then many, if not most, individual trades take place outside the range.

  10. Strictly speaking, for us to make this claim, we must be able to say a reasonable confidence interval (we use a 95% confidence level) of prices falls outside the MinP—MaxP range. More specifically, there would be evidence of irrational behavior when the lower confidence level is above MaxP or when the upper confidence level is below MinP. This is observed in two sessions of the HV treatment. In Session 1, the lower 95% confidence level is above the highest possible dividends (MaxP) in periods 14–18 (see Fig. 1b). In Session 2, the upper 95% confidence level is below the lowest possible dividends (MinP) in the first period (see Fig. 1c).

  11. As before (see footnote 6), to support our claim of irrational behavior, we consider whether 95% confidence intervals fall outside the feasible range. In the Session 2 of the LV treatment, the upper 95% confidence level is below the lowest possible dividends (MinP) in periods 1–3 and the lower 95% confidence level is above the highest possible dividends (MaxP) in periods 8–12 (see Fig. 3b). In Session 3, the upper 95% confidence level is below the lowest possible dividends (MinP) in periods 1–6 and 19–20, and the lower 95% confidence level is above the highest possible dividends (MaxP) in periods 13–17 (see Fig. 3c).

  12. For Fig. 4, as for Fig. 2, the three sessions of the LV treatment are combined to a single imaginary session and median prices for each period are calculated.

  13. Median prices in Fig. 6 are calculated the same way they are calculated for Figs. 2 and 4.

  14. We use this definition (as opposed to largest consecutive number periods when the price is higher than the fundamental value) to count only periods when the median prices are substantially higher than fundamental values. For example, in the first session of the ZV treatment, the largest consecutive number of periods when the median price is higher than the fundamental value is 8. But based on our definition, there is no “Boom”, since median prices are never more than 10% higher than the fundamental value. We use a similar definition for “Busts”.

  15. To be able to compare treatment-level (as opposed to session-level) data, we essentially combine the sessions of a treatment into one. For example, we take the first periods of the three sessions in the HV treatment, and combine all the prices in that period. Then we calculate the average price, standard deviation of prices, trading volume, etc., for each of the 20 periods. This gives us data points for the “average session” for the HV treatment. We then repeat the process for all 3 treatments. The panel regressions used to provide support for results 3–5 are run using these treatment-level independent variables.

  16. Full results of all the regressions discussed here are available from the authors upon request.

  17. The degree of price volatility varies somewhat among sessions. To save space, we picked the more or less representative sessions from each treatment. In each treatment, this “representative” session happened to be the second one.

  18. There is also considerable difference within the three sessions of this treatment. While Sessions 1 and 2 do exhibit very high volatility of prices, they pale in comparison with Session 3, which has by far the highest volatility of individual prices of all nine sessions. We chose Session 2 for the HV treatment, because it is closer to the treatment average.

  19. This distinction (the first 5 periods vs. the last 15 periods) is made somewhat arbitrarily. The results would not be very different if we divided a session to “the first 4 vs. last 16 periods” or “the first 6 vs. last 14 periods”.

  20. When only the last 15 periods are considered, panel regression results are similar to those when all 20 periods are considered.

  21. Both HV and LV treatments are compared to the ZV treatment in this regression.

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Acknowledgements

We thank Brent Ambrose for suggesting this issue, Yuen Leng Chow for her assistance in running the experiments, the editors and the anonymous referee, Erik Devos and the participants of the ARES—AREUEA joint annual conference, Tim Salmon and participants of the Critical Issues in Real Estate Symposium at Florida State University, and participants of the ARES annual conference and the Sacramento Statistical Association Institute for their useful comments and suggestions. We also thank the Insurance and Real Estate Department and the Institute for Real Estate Studies at the Pennsylvania State University for providing funds. This research was supported in part by a Dissertation Research Award from the Smeal College of Business.

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Authors and Affiliations

  1. College of Business Administration, California State University, Tahoe Hall 2047, 6000 J Street, Sacramento, CA, 95819–6088, USA

    Nuriddin Ikromov

  2. School of Business, University of Wisconsin, 5251 Granger Hall, 975 University Avenue, Madison, WI, 53706, USA

    Abdullah Yavas

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  1. Nuriddin Ikromov
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  2. Abdullah Yavas
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Correspondence to Nuriddin Ikromov.

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Ikromov, N., Yavas, A. Cash Flow Volatility, Prices and Price Volatility: An Experimental Study. J Real Estate Finan Econ 44, 203–229 (2012). https://doi.org/10.1007/s11146-011-9320-5

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