Christopher G Lamoureux

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Scholarly Contributions

Books

• Lamoureux, C. G. (1985). Three essays in normative portfolio theory. University Microfilms International.

Journals/Publications

• Lamoureux, C. G., & Nejadmalayeri, A. (2015). Costs of Capital and Public Issuance Choice. Journal of Banking and Finance, 61, 27-45.
• Lamoureux, C. G., & Wang, Q. (2015). Measuring Private Information in a Specialist Market. Journal of Empirical Finance, 30, 92-119.
• Lamoureux, C. G., & Schnitzlein, C. R. (2004). Microstructure with multiple assets: An experimental investigation into direct and indirect dealer competition. Journal of Financial Markets, 7(2), 117-143.
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  Abstract: This paper uses the economic laboratory to isolate the effects of direct and indirect competition on dealer profitability. We compare these two settings: (1) three competing dealers in a single asset (direct competition) with (2) three assets with a monopoly dealer in each (indirect competition). We find that: bid-ask spreads are wider, prices are less responsive to order flow (so there is less price discovery), and per-trade dealer profits are larger in the single-asset setting. Important economic differences between these two settings include a heightened adverse selection problem in the three-asset setting and a public good nature of price discovery in the one-asset setting. © 2003 Elsevier B.V. All rights reserved.
• Lamoureux, C. G., & Witte, H. D. (2002). Empirical analysis of the yield curve: The information in the data viewed through the window of Cox, Ingersoll, and Ross. Journal of Finance, 57(3), 1479-1520.
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  Abstract: This paper uses recent advances in Bayesian estimation methods to exploit fully and efficiently the time-series and cross-sectional empirical restrictions of the Cox, Ingersoll, and Ross model of the term structure. We examine the extent to which the cross-sectional data (five different instruments) provide information about the model. We find that the time-series restrictions of the two-factor model are generally consistent with the data. However, the model's cross-sectional restrictions are not. We show that adding a third factor produces a significant statistical improvement, but causes the average time-series fit to the yields themselves to deteriorate.
• Lamoureux, C. G., & Schnitzlein, C. R. (1997). When it's not the only game in town: The effect of bilateral search on the quality of a dealer market. Journal of Finance, 52(2), 683-712.
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  Abstract: We report results from experimental asset markets with liquidity traders and an insider where we allow bilateral trade to take place, in addition to public trade with dealers. In the absence of the search alternative, dealer profits are large-unlike in models with risk-neutral, competitive dealers. However, when we allow traders to participate in the search market, dealer profits are close to zero. Dealers compete more aggressively with the alternative trading avenue than with each other. There is no evidence that price discovery is less efficient when the specialists are not the only game in town.
• Lamoureux, C. G., & Zhou, G. (1996). Temporary components of stock returns: What do the data tell us?. Review of Financial Studies, 9(4), 1033-1059.
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  Abstract: Within the past few years several articles have suggested that returns on large equity portfolios may contain a significant predictable component at horizons 3 to 6 years. Subsequently, the tests used in these analyses have been criticized (appropriately) for having widely misunderstood size and power, rendering the conclusions inappropriate. This criticism however has not focused on the data, it addressed the properties of the tests. In this article we adopt a subjectivist analysis - treating the data as fixed - to ascertain whether the data have anything to say about the permanent/temporary decomposition. The data speak clearly and they tell us that for all intents and purposes, stock prices follow a random walk.
• Sunder, S., Lamoureux, C. G., & Friedman, D. (1995). Experimental Methods: A Primer for Economists.. Journal of Finance, 50(4), 1341. doi:10.2307/2329359
• Lamoureux, C. G. (1994). Comments on Federated's Acquisition and Bankruptcy: Lessons and Implications. Washington University Law Review, 72(3), 1127-1131.
• Lastrapes, W. D., & Lamoureux, C. G. (1994). Endogenous Trading Volume and Momentum in Stock-Return Volatility. Journal of Business & Economic Statistics, 12(2), 253-260. doi:10.1080/07350015.1994.10510012
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  This article examines the ability of volume data to shed light on the source of persistence in stock-return volatility. A mixture model, in which a latent common factor restricts the joint density of volume and returns, is used to relax the assumption of exogenous volume used in previous studies. We use a point-in-time signal-extraction procedure to identify this latent process and a calibrated simulation to conduct analysis of the viability of the model to explain important properties of the data. Using daily returns and volume on individual stocks, our procedure cannot accommodate serial dependence in squared returns.
• Lastrapes, W. D., & Lamoureux, C. G. (1993). Forecasting Stock-Return Variance: Toward an Understanding of Stochastic Implied Volatilities. Review of Financial Studies, 6(2), 293-326. doi:10.1093/rfs/6.2.293
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  We examine the behavior of measured variances from the options market and the underlying stock market. Under the joint hypotheses that markets are informationally efficient and that option prices are explained by a particular asset pricing model, forecasts from time-series models of the stockreturn process should not have predictive content given the market forecast as embodied in option prices. Both in-sample and out-of-sample tests suggest that this hypothesis can be rejected. Using simulations, we show that biases inherent in the procedure we use to imply variances cannot explain this result. Thus, we provide evidence inconsistent with the orthogonality restrictions of option pricing models that assume that variance risk is unpriced. These results also have implicationsfor optimal variance forecast rules.
• Lamoureux, C. G. (1990). Dividends, taxes, and normative portfolio theory. Journal of Economics and Business, 42(2), 121-131.
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  Abstract: This paper presents a normative methodology for selection of optimal portfolios in light of possible differential tax treatments of dividends and capital gains. This methodology is applied to actual market data. It is shown that the selection of optimal portfolios is virtually independent of the investor's tax rate. Implications of this result for understanding the relationship between dividends and rates of return are discussed. It is concluded that dividends are not priced as cash flows; instead they do matter as they relate to the paying firm. Further, portfolio dividend yields are highly positively correlated with investor risk aversion. © 1990.
• Lamoureux, C. G., & Frankfurter, G. M. (1990). Insignificant Betas and the Efficacy of the Sharpe Diagonal Model for Portfolio Selection. Decision Sciences, 21(4), 853-861. doi:10.1111/j.1540-5915.1990.tb01254.x
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  When both practitioners and theorists apply Sharpe's diagonal model [15] to simplify the portfolio selection problem, they assume that the entire covariation structure of each stock (i.e., with all other stocks) is captured in that stock's covariance with the market (or β). Furthermore, it is well known that the selection algorithm itself has a marked tendency to select stocks with the lowest βs, ceteris paribus. When a stock's β is statistically indistinguishable from zero, it is an empirical issue whether the market model is (a) less appropriate for that particular stock relative to those with statistically significant βs; or is (b) a viable model in that the covariance of this stock's rate-of-return with all other stocks' rates-of-return vanishes. The objective of this paper is to distinguish empirically between (a) and (b), and to propose a heuristic which will improve the ex-post performance of the diagonal model. The possible benefits of this heuristic are also demonstrated in a rigorous statistical framework.
• Lastrapes, W. D., & Lamoureux, C. G. (1990). Heteroskedasticity in Stock Return Data: Volume versus GARCH Effects. Journal of Finance, 45(1), 221-229. doi:10.1111/j.1540-6261.1990.tb05088.x
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  This paper provides empirical support for the notion that Autoregressive Conditional HeterQskedasticity (ARCH) in daily stock return data reflects time dependence in the process generating information flow to the market. Daily trading volume, used as a proxy for information arrival time, is shown to have significant explanatory power regarding the variance of daily returns, which is an implication of the assumption that daily returns are subordinated to intraday equilibrium returns. Furthermore, ARCH effects tend to disappear when volume is included in the variance equation. THE AUTOREGRESSIVE CONDITIQNAL HETEROSKEDASTICITY (ARCH) process of Engle (1982) has been shown to provide a good fit for many financial return time series,1 ARCH imposes an autoregressive structure on conditional variance, allQwing volatility shocks to persist over time. This persistence captures the propensity of returns of like magnitude to clu~ter in time and can explain the well documented nonnormality and nonstability of empirical asset return distributions. (See especially Fama (1965).) An appealing explanation for the presence of ARCH is based upon the hypothesis that daily returns are generated by a maiture of distributions, in which the rate of daily information arrival is the stochastic mixing variable. As suggested by Diebold (1986), Gallant, Hsieh, and Tauchen (1988), and Stock (1987, 1988), ARCH might capture the time series properties (e.g., serial correlation) of this mixing variable. HQwever, this linkage has not been broadly documented with the datia. The objective of this study is to examine the validity of this explanation for daily stock returns. The empirical strategy exploits the implication of the mixture model that the variance of daily price increments is heteroskedastic-specifically, positively related to the rate of daily information arrival. Using daily trading volume as a proxy for the mixing variable, we show that, for a sample of 20 common stocks, ARCH effects vanish when volume is included as an explanatory variable in the conditional variance equation.
• Lastrapes, W. D., & Lamoureux, C. G. (1990). Persistence in Variance, Structural Change, and the GARCH Model. Journal of Business & Economic Statistics, 8(2), 225-234. doi:10.1080/07350015.1990.10509794
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  This article examines the persistence of the variance, as measured by the generalized autoregressive conditional heteroskedasticity (GARCH) model, in stock-return data. In particular, we investigate the extent to which persistence in variance may be overstated because of the existence of, and failure to take account of, deterministic structural shifts in the model. Both an analysis of daily stock-return data and a Monte Carlo simulation experiment confirm the hypothesis that GARCH measures of persistence in variance are sensitive to this type of model misspecification.
• Lamoureux, C. G., & Akgiray, V. (1989). Estimation of Stable-Law Parameters: A Comparative Study. Journal of Business & Economic Statistics, 7(1), 85-93. doi:10.1080/07350015.1989.10509716
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  The stable distribution has many desirable properties and is applicable in many areas of scientific pursuit (e.g., the study of stock-return behavior). Despite this, little is known about the properties of the various extant estimation techniques for the parameters of the stable laws. This article compares the iterative regression technique with the latest version of the fractile technique, using both simulated and actual data.
• Lamoureux, C. G., & Frankfurter, G. M. (1989). ESTIMATION AND SELECTION BIAS IN MEAN‐VARIANCE PORTFOLIO SELECTION. Journal of Financial Research, 12(2), 173-181. doi:10.1111/j.1475-6803.1989.tb00111.x
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  Much research has focused on the problem of selecting portfolios without the benefit of parametric measures of risk and return. In this paper, a Monte Carlo technique is used to isolate the extent and nature of the problems introduced by this practice. The technique is employed in the context of classical statistical methodology without permitting short sales. It is shown that using estimators of expected return and risk not only obscures parametric values, but also affects portfolio composition in the Markowitz framework. In this study, these two components of bias are isolated and measured.
• Sanger, G. C., & Lamoureux, C. G. (1989). Firm Size and Turn-of-the-Year Effects in the OTC/NASDAQ Market. Journal of Finance, 44(5), 1219-1245. doi:10.1111/j.1540-6261.1989.tb02651.x
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  This paper examines the turn-of-the-year effect, the firm size effect, and the relation between these two effects for a sample of OTC stocks traded via the NASDAQ reporting system over the period 1973-1985. We find results similar to those based solely on listed stocks. The importance of these findings stems from the existence of nontrivial differences between the characteristics of the OTC/NASDAQ sample and the samples of listed firms examined previously in the literature. We also find that NASDAQ quoted bid-ask spreads are highly negatively correlated with firm size, are not highly seasonal, and are large enough to preclude trading profits based upon a knowledge of the seasonality of small firms' returns. BOTH THE "SIZE EFFECT" and the "turn-of-the-year effect" have received much recent attention in the finance literature. Wachtel (1942), Rozeff and Kinney (1976), Branch (1977), and Dyl (1977) have all documented calendar time seasonalities in U.S. stock returns. Particularly striking are the large, regularly observed January returns. Banz (1981) and Reinganum (1981) found that, on average, small capitalization firms experience significantly higher risk-adjusted (vis 'a vis CAPM) returns than large firms. Keim (1983) showed that the two effects are related. A large part of the annual excess risk-adjusted return earned by small firms accrues in January, and in particular during the first few trading days in January.1 Conversely, large firms earn significantly negative excess riskadjusted returns in January.
• Wansley, J. W., & Lamoureux, C. G. (1989). The Pricing of When‐Issued Securities. The Financial Review, 24(2), 183-198. doi:10.1111/j.1540-6288.1989.tb00338.x
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  The observed pricing of when-issued securities would seem to violate the law of one price in financial economics. Generally, when-issued shares sell at a premium over the original shares during the short time when both are traded. This paper examines whether this observed premium could be due to a nonsynchronous trading problem, to the intensity of trading, to the exchange on which the shares are traded, to whether the shares are traded pre-or-post -negotiable commissions, or to the nature of the demand for when-issued relative to the price setting of the specialist. Results indicate that most orders for when-issued securities are buys, that these orders typically take place at the specialist's ask price, and that accounting for this trading mechanism explains the positive premium on when-issued securities.
• Lamoureux, C. G., & Frankfurter, G. M. (1988). Stock Selection and Timing—A New Look At Market Efficiency. Journal of Business Finance & Accounting, 15(3), 385-400. doi:10.1111/j.1468-5957.1988.tb00142.x
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  In this paper it is shown that stock returns do not conform to a random walk model, nor to the more general martingale model, but nevertheless the stock market is weak-form efficient. This result is not surprising when we recall that risk-averse investors are typically concerned with more than the first moment of a security return’s distribution, and market efficiency is appropriately determined in terms of (expected) utility - not profits alone. Extant tests of market efficiency have ignored this point. By examining the first two moments of return distributions, and by not assuming any form of equilibrium pricing model, we provide tests of weakform market efficiency which are more powerful than prior studies. A market is considered informationally efficient if market prices ‘fully reflect’ information. The (sub)set of information presumed to be reflected determines the particular form of market efficiency. The market is said to be efficient in the weak-forms if market prices fully reflect the past realizations of market prices. Extant tests of the weak-form of market efficiency (e.g., Fama, 1970; Fama and Blume, 1966; and Mandelbrot, 1966) examine whether information on past price movements can be exploited to enhance profit. These tests assume that investors looking to profit from ‘trend’ data are risk-neutral. However, the normative theory of portfolio selection due to Markowitz (1952), and positive theories of capital asset pricing (e.g., Sharpe, 1964) are developed assuming that investors are risk-averse, This paper fills a gap in the literature by testing the informational efficiency of the stock market by exploring whether or not gains in CXpGGtcd utility are attainable by utilizing the time series of past stock prices. In particular, since Sharpe (1963), a simple algorithm has been available for risk-averse investors to use in selecting optimal portfolios. Normative Portfolio Theory of Markowitz (1952) (N.2“) suggests the use of historical data to obtain estimates of expected return and risk, and then apply a mathematical programming algorithm to build Mean-Variance (E- V) efficient portfolios. Surprisingly, studies that have tackled the timing question have done so in a vacuum vis-B-vis optimal (E- V) portfolio selection.
• Lamoureux, C. G., & Frankfurter, G. M. (1987). The relevance of the distributional form of common stock returns to the construction of optimal portfolios. Journal of Financial and Quantitative Analysis, 22(4), 505-511. doi:10.2307/2330798
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  In this paper, we compare the robustness in application of the Gaussian assumption of security return distributions to the robustness of the general stable assumption. Using actual stock return data to simulate the “real world,†a stock market is constructed in which stock returns conform to a Gaussian distribution as well as to a stable Pareto-Levy distribution. Using these two sets of stock returns, efficient frontiers are generated under both assumptions of parametric environments. It is shown that the Gaussian assumption, and its incumbent statistical techniques, is preferable to the general stable assumption.
• Poon, P., & Lamoureux, C. G. (1987). The Market Reaction to Stock Splits. Journal of Finance, 42(5), 1347-1370. doi:10.1111/j.1540-6261.1987.tb04370.x
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  In this paper, a model of market reaction to stock splits is presented and tested. We argue that the announcement of a split sets off the following chain of events. The market recognizes that, subsequent to the (reverse) split ex-day, the daily number of transactions along with the raw volume of shares traded will increase (decrease). This increase in volume results in an increase in the noisiness of the security's return process. The increase in noise raises the tax-option value of the stock, and it is this value that generates the announcement effect of stock splits. Empirical evidence using security returns, daily trading volume, and shareholder data strongly supports this theory. The evidence, in conjunction with this theory, also agrees with extant literature that splits result in decreased liquidity, but there is no evidence that this reduction in liquidity is priced. STOCK SPLITS HAVE PRESENTED finance theorists with a conundrum. These "nonevents" seem to be a purely cosmetic change; nevertheless, research shows that significant price reaction is attributable directly to splits. Recently, Grinblatt, Masulis, and Titman [12] show that, even in "clean" cases-i.e., where no other firm-specific event coincides with a split announcement-stock splits generate a positive abnormal return of close to three percent upon announcement and an additional one percent abnormal return on the ex-day. The primary contribution of this paper is to provide and empirically support a rational explanation for the split/reverse-split announcement effect. A recent study by Ohlson and Penman [16] documents a statistical aberration, that stock volatilities increase by an average of thirty-five percent subsequent to split ex-days. In this paper, we provide a direct statistical extension of Ohlson and Penman in three directions. First, we examine reverse splits and demonstrate that they exhibit reductions in volatility. Second, we isolate the shift in volatility as between the nonsystematic and systematic components. Finally, we establish a link between trading volume and the increased volatility. Copeland [9] shows that, contrary to "market folklore", the liquidity of a stock is actually reduced by a split. We provide a statistical extension to Copeland by examining the volume pattern of reverse splits. Copeland observes a drop in split-adjusted volume and concludes that liquidity has declined. Once again, we show that the behavior of stocks that reverse split is opposite that of splitting stocks.
• Wansley, J. W., & Lamoureux, C. G. (1987). MARKET EFFECTS OF CHANGES IN THE STANDARD & POOR'S 500 INDEX. The Financial Review, 22(1), 53-69. doi:10.1111/j.1540-6288.1987.tb00318.x
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  This paper examines the impact on stock returns of changes in the Standard & Poor's (S&P) 500 index. S&P states that firms are not added to or deleted from the index for valuation reasons but rather to maintain or improve the index's representative character. Results from market response tests indicate that stocks added to (deleted from) the index since 1975 experience a significant positive (negative) announcement day excess return. No announcement effect occurs in S&P changes prior to 1976. These announcement effects may be explained by a price-pressure hypothesis or by an information effect. Results of tests conducted to isolate which of these phenomena is present are reported.
• Lamoureux, C. G., & Frankfurter, G. M. (1986). THE MARKOWITZ PORTFOLIO MODEL: SOME TEST OF VALIDITY. The Financial Review, 21(3), 28-28. doi:10.1111/j.1540-6288.1986.tb00692.x

Presentations

• Lamoureux, C. G. (2019, September). An Empirical Assessment of Characteristics and Optimal Portfolios. Department Seminar. University of Wyoming.
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  Invited seminar presentation in the Finance Department.
• Lamoureux, C. G., & Zhang, H. (2014, May). Risk, Return, and the Optimal Exploitation of Stock Characteristics. Invited seminar at the Finance Department, Ivey College of Business. London Ontario: University of Western Ontario.
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  I presented this paper as well as some extensions I am working on in the finance department's seminar series.

Others

• Lamoureux, C. G., & Nejadmalayeri, A. (2014, December). Costs of Capital and Public Issuance Choice. First Conference on Recent Developments in Financial Econometrics and Applications. http://lamfin.arizona.edu/rsrch/ln.pdf
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  Co-author presentation.
• Lamoureux, C. G., & Theocharides, G. (2014, June). Dimensions of Limits to Arbitrage: Evidence from Coupon Spreads and Repo Specials in the 10-Year US Treasury Market. Twenty-First Annual Conference of the Multinational Finance Society. http://lamfin.arizona.edu/rsrch/ltlta.pdf
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  My co-author, George Theocharides, presented this paper in Prague.
• Lamoureux, C. G., & Zhang, H. (2014, July). Risk, Return, and the Optimal Exploitation of Stock Characteristics. China International Conference in Finance. http://lamfin.arizona.edu/rsrch/RROESC.pdf
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  Co-author presentation.