Modifying a simple agent-based model to disentangle the microstructure of Chinese and US stock markets

We modify a simple agent-based model (ABM) proposed by Franke and Westerhoff [J. Econ. Dyn. Control, 2012, 36(8), 1193–1211] through considering the price limits and the motion of the fundamental value. The method of simulated moments is applied to calibrate both initial and modified ABMs with CSI 300 and S&P 500 respectively, and the goodness-of-fit of each ABMs is tested. The calibration results indicate that the modified model performs better than initial one. Then, we utilize the GSL-div, proposed by Lamperti [Econometrics Stat, 2018, 5, 83–106.], to verify the explanatory power of ABMs. In this procedure, 13 ARCH family models are introduced as benchmarks. The result shows that the explanatory power of modified ABM exceeds ARCH models in both markets, while initial ABM may be defeated by some of the ARCH family models in explaining the microstructure of CSI 300. Finally, a heuristic algorithm is designed to disentangle the insights of Chinese and US stock markets to the observed time horizon through calibrating the initial fundamental value, and Kupiec test is used to check the robustness of the calibration. The result indicates that the explanation of modified model is robust in both markets, while initial model lost its robustness when explaining S&P 500.     

Weighing asset pricing factors: a least squares model averaging approach

Empirical evidence has demonstrated that certain factors in asset pricing models are more important than others for explaining specific portfolio returns. We propose a technique that evaluates the factors included in popular linear asset pricing models. Our method has the advantage of simultaneously ranking the relative importance of those pricing factors through comparing their model weights. As an empirical verification, we apply our method to portfolios formed following Fama and French [A five-factor asset pricing model. J. Financ. Econ., 2015, 116, 1–22] and demonstrate that models accommodated to our factor rankings do improve their explanatory power in both in-sample and out-of-sample analyses.     

Firm characteristics,alternative factors,and asset-pricing anomalies: evidence from Japan

Based on the errors-in-variables-free approach proposed by Brennan et al. [J. Financial Econ., 1998, 49, 345–373], we investigate the competing explanatory capabilities of alternative multi-factor models when examining various asset-pricing anomalies using Japanese data for the period 1978–2006. We find that turnover and book-to-market (BM) ratio are the two major characteristics that significantly explain the average stock returns. A further sub-period analysis reveals that the turnover effect is significant only before 1990, but cannot be explained by any multifactor models. In contrast, the BM premium is significant only after 1990, and can be explained by the Fama–French three-factor model. Thus, the results suggest that asset-pricing anomalies documented in the literature are not universal, and may be different across different markets.     

Participant‐focused analysis: explanatory power of the classic psychometric paradigm in risk perception

Typical psychometric paradigm factors appear to have greater explanatory power for individual participants than previously envisaged. It is possible to acquire interpretable information about single participants using two factors (catastrophic potential and social and personal exposure) from aggregated participant‐focused data. Our results suggest that the classical psychometric model originated by Fischhoff and Slovic in the early 1980s to explain differences among hazards may also be capable of accounting for differences among participants. While socio‐demographic conditions on their own do not have substantial explanatory power, they are statistically significant and appear to dictate the position of participants within the factor space obtained using a participant‐focused analysis. One of the principal criticisms of the psychometric paradigm has been its lack of interpretability when using disaggregated data, but incorporating socio‐demographic variables overcomes this limitation.     

On the validity of the augmented Fama and French’s (1993) model: evidence from the Hong Kong stock market

This paper investigates the performance of four-factor asset pricing model using Hong Kong stock returns. Our four-factor model is constructed by adding a momentum factor into the Fama and French’s (J Finance Econ 33(1):3–56, 1993) three-factor model. We find that the four-factor model may explain return variation using Hong Kong data. Our results show evidence that all the four factors are significant in the model and intercepts are not significant. In addition, the reasonably high values of adjusted R ² and the insignificance of an additional explanatory variable of residual standard deviation provide supportive evidence to the model. The robustness of the model is also checked for two effects: up- and down-market conditions and seasonal behavior.     

A note on the valuation of asset management firms

Market capitalization relative to assets under management is often used to value asset management firms. Huberman’s (2004) dividend discount model implies that cross-sectional variations in this metric are explained by cross-sectional differences in operating margins, and yet we find no evidence of this in our data set. We show that a superior model—inspired by the work of Berk and Green (2004)—includes also the level of fees as an explanatory variable. This approach dramatically increases the fit of our valuation model and casts doubt on the relevance of the so-called Huberman puzzle.     

Household Life Insurance Demand

Abstract

What types of households own life insurance? Who owns term life and who owns whole life insurance? These are questions of great interest to insurers that operate in a highly competitive market. To answer these questions, we jointly examine household demand of two types of insurance, term and whole life, using data from the Survey of Consumer Finances, a probability sample of the U.S. population. We model both the frequency and the severity of demand for insurance, building on the work of Lin and Grace by using explanatory variables that they developed. For the frequency portion, the household decisions about whether to own term and whole life insurance are modeled simultaneously with a bivariate probit regression model. Given ownership of life insurance by a household, the amounts of insurance are analyzed using generalized linear models with a normal copula. The copula permits the bivariate modeling of insurance amounts for households who own both term and whole life insurance, about 20% of our sample. These models allow analysts to predict who owns life insurance and how much they own, an important input to the marketing process.

Moreover, our findings suggest that household demand for term and whole life insurance is jointly determined. After controlling for explanatory variables, there exists a negative relationship for a household’s decision to own both whole and term life insurance (the frequency part) and a positive relationship for the amount of insurance purchased (the severity part). This indicates that the greater the probability of holding one type, the smaller the probability of holding the other type of life insurance. However, higher demand for both types of insurance exists when a household decides to own both. This mixed effect extends prior work that established a negative relationship, suggesting that term life insurance and whole life insurance are substitutes for one another. In contrast, our findings reveal that the ownership decision involves substitution, but, for households owning both types of insurance, amounts are positively related. Therefore, term and whole life insurance are substitutes in the frequency yet complements in the severity.     

The Sheldon effect: fixed mindset does not always mean fragile confidence

ABSTRACT

More than just intelligence is needed to learn accounting. We see from prior work that the non-cognitive aspects of learning can influence the experience for accounting students. We investigate by survey both self-efficacy beliefs [Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215] and mindset [Dweck, C. (2006). Mindset: The new psychology of success. Random House] relative to academic performance of accounting students in a first year university course. Analysis of the data shows that mindset is not a predictor of academic success, whereas self-efficacy beliefs have explanatory power. Dweck [2000. Self-theories: Their role in motivation, personality, and development. Psychology Press; 2006. Mindset: The new psychology of success. Random House] claims that students with a fixed mindset also will have fragile confidence. By measuring both self-efficacy beliefs and mindset together, we provide evidence that this may in fact not be the case. Students can have a fixed mindset and high confidence towards learning accounting.     

FIRM CHARACTERISTICS AND ANALYST FOLLOWING IN THE UK

Bhushan (1989) proposed a model of analyst following, which he tested for US companies in 1985. The model was successful, in that the eight variables considered likely to influence analyst following were found to be significant in the regression model used, and an adjusted R²of 70% was achieved. The aim of this paper is to adapt his model and test it for UK companies using survey and other data from 1991. Bhushan found that the level of analyst following for a firm was positively associated with level of institutional investment, variability of returns, the correlation between the firm return and the market return and firm size. There was a negative association with the level of insider shareholdings and industrial diversification. The industrial category of the firm also was significant. This study shows similar results using data on analyst following of UK quoted companies, although industrial diversification does not appear to be an important factor in the UK, and the overall explanatory power of the model is lower than in Bhushan's study. An additional variable, the number of overseas listings, was found to add to the explanatory power of the model.     

Does Money Explain Asset Returns? Theory and Empirical Analysis

A cash-in-advance model of a monetary economy is used to derive a money-based CAPM (M-CAPM), which allows us to implement tests of asset pricing restrictions without consumption data. A test as in Fama and MacBeth of the model suggests that the money betas have some explanatory power for the cross-sectional variation of expected returns; however, the model is rejected using conditional information. Consistent with our predictions, estimates of the curvature parameter are lower than those of the consumption CAPM (C-CAPM) and pricing errors of the M-CAPM tend to be smaller than those of the C-CAPM.     

The determinants of secondary market prices for developing country loans: the impact of country risk

With better-defined variables based on Euromoney country risk data as explanatory variables, the determinants of the prices of the debts of less-developed countries (LDCs) in the secondary market are estimated. With the use of cross-sectional data on 27 countries for the years 1992, 1993, and 1994, the regression results indicate that sovereign credit ratings constitute the most important variable influencing prices; other significant variables include the level of external indebtedness and the amount of debt in default. Separate results have been obtained for each of the two categories of countries grouped according to the level of economic development. These results are more meaningful than those of previous studies because the model includes, in addition to debt-servicing capacity, other variables that best explain the prices of LDCs' debt within the context of a risky debt instrument.     

Bivariate Survival Models for Coupled Lives

This article presents alternative models for modeling the dependence of the time of deaths of coupled lives and applies these to a data set from a life annuity portfolio. The data indicates a very high positive correlation in the time of deaths between coupled lives. The analysis concludes that a mixed frailty copula model with Gompertz marginals fits the data very well. Another model that fits the data well is based on a generalized Frank copula.     

Time-varying long-run mean of commodity prices and the modeling of futures term structures

The exploration of the mean-reversion of commodity prices is important for inventory management, inflation forecasting and contingent claim pricing. Bessembinder et al. [J. Finance, 1995, 50, 361–375] document the mean-reversion of commodity spot prices using futures term structure data; however, mean-reversion to a constant level is rejected in nearly all studies using historical spot price time series. This indicates that the spot prices revert to a stochastic long-run mean. Recognizing this, I propose a reduced-form model with the stochastic long-run mean as a separate factor. This model fits the futures dynamics better than do classical models such as the Gibson–Schwartz [J. Finance, 1990, 45, 959–976] model and the Casassus–Collin-Dufresne [J. Finance, 2005, 60, 2283–2331] model with a constant interest rate. An application for option pricing is also presented in this paper.     

Real-time market microstructure analysis: online transaction cost analysis

Motivated by the practical challenge in monitoring the performance of a large number of algorithmic trading orders, this paper provides a methodology that leads to automatic discovery of causes that lie behind poor trading performance. It also gives theoretical foundations to a generic framework for real-time trading analysis. The common acronym for investigating the causes of bad and good performance of trading is transaction cost analysis Rosenthal [Performance Metrics for Algorithmic Traders, 2009]). Automated algorithms take care of most of the traded flows on electronic markets (more than 70% in the US, 45% in Europe and 35% in Japan in 2012). Academic literature provides different ways to formalize these algorithms and show how optimal they can be from a mean-variance (like in Almgren and Chriss [J. Risk, 2000, 3(2), 5–39]), a stochastic control (e.g. Guéant et al. [Math. Financ. Econ., 2013, 7(4), 477–507]), an impulse control (see Bouchard et al. [SIAM J. Financ. Math., 2011, 2(1), 404–438]) or a statistical learning (as used in Laruelle et al. [Math. Financ. Econ., 2013, 7(3), 359–403]) viewpoint. This paper is agnostic about the way the algorithm has been built and provides a theoretical formalism to identify in real-time the market conditions that influenced its efficiency or inefficiency. For a given set of characteristics describing the market context, selected by a practitioner, we first show how a set of additional derived explanatory factors, called anomaly detectors, can be created for each market order (following for instance Cristianini and Shawe-Taylor [An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, 2000]). We then will present an online methodology to quantify how this extended set of factors, at any given time, predicts (i.e. have influence, in the sense of predictive power or information defined in Basseville and Nikiforov [Detection of Abrupt Changes: Theory and Application, 1993], Shannon [Bell Syst. Tech. J., 1948, 27, 379–423] and Alkoot and Kittler [Pattern Recogn. Lett., 1999, 20(11), 1361–1369]) which of the orders are underperforming while calculating the predictive power of this explanatory factor set. Armed with this information, which we call influence analysis, we intend to empower the order monitoring user to take appropriate action on any affected orders by re-calibrating the trading algorithms working the order through new parameters, pausing their execution or taking over more direct trading control. Also we intend that use of this method can be taken advantage of to automatically adjust their trading action in the post trade analysis of algorithms.     

Vol-Bond: an analytical solution

Abstract

We find an analytical solution of the Vol-Bond according to the multi-factor Gaussian Heath-Jarrow-Morton model. We show how to calibrate the model with market data. This solution allows complete (and fast) control of this class of derivatives and of their sensitivities.     

Learning minimum variance discrete hedging directly from the market

Option hedging is a critical risk management problem in finance. In the Black–Scholes model, it has been recognized that computing a hedging position from the sensitivity of the calibrated model option value function is inadequate in minimizing variance of the option hedge risk, as it fails to capture the model parameter dependence on the underlying price (see e.g. Coleman et al., J. Risk, 2001, 5(6), 63–89; Hull and White, J. Bank. Finance, 2017, 82, 180–190). In this paper, we demonstrate that this issue can exist generally when determining hedging position from the sensitivity of the option function, either calibrated from a parametric model from current option prices or estimated nonparametricaly from historical option prices. Consequently, the sensitivity of the estimated model option function typically does not minimize variance of the hedge risk, even instantaneously. We propose a data-driven approach to directly learn a hedging function from the market data by minimizing variance of the local hedge risk. Using the S&P 500 index daily option data for more than a decade ending in August 2015, we show that the proposed method outperforms the parametric minimum variance hedging method proposed in Hull and White [J. Bank. Finance, 2017, 82, 180–190], as well as minimum variance hedging corrective techniques based on stochastic volatility or local volatility models. Furthermore, we show that the proposed approach achieves significant gain over the implied BS delta hedging for weekly and monthly hedging.     

The relative and incremental explanatory powers of dirty surplus items for debt interest rate

This study investigates the relationship between dirty surplus items on the balance sheet and the cost of debt for Japanese firms. We focus on three dirty surplus items—unrealized gains and losses on available-for-sale securities (SEC), foreign currency translation adjustment (FOC), and land revaluation surplus (LAND). While many previous studies on dirty surplus adopted the value-relevance perspective, we examine the effect of dirty surplus items on the interest rate spread of bonds from the bond market perspective. We use the [Vuong, Q. H. (1989). Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica, 57, 307–333] test to evaluate the relative explanatory power of the equity ratio with and without dirty surplus items for the interest rate spread on bonds issued. We find evidence that the equity ratio with dirty surplus items is more strongly associated with the debt interest rate than that without dirty surplus items. The results suggest that the total amount of dirty surplus items have statistically significant explanatory power for the interest rate spread. However, some dirty surplus items do not have explanatory powers for the bond interest rate spread. While FOC has relative and incremental explanatory powers and SEC has only incremental explanatory power, LAND has neither relative nor incremental explanatory power for the bond interest rate spread. The results mean that FOC and SEC are useful to Japanese bond investors.     

Econometric analysis of microscopic simulation models

Microscopic simulation models are often evaluated based on visual inspection of the results. This paper presents formal econometric techniques to compare microscopic simulation (MS) models with real-life data. A related result is a methodology to compare different MS models with each other. For this purpose, possible parameters of interest, such as mean returns, or autocorrelation patterns, are classified and characterized. For each class of characteristics, the appropriate techniques are presented. We illustrate the methodology by comparing the MS model developed by He and Li [J. Econ. Dynam. Control, 2007, 31, 3396–3426, Quant. Finance, 2008, 8, 59–79] with actual data.     

Institutions and Development: The Case of China in Comparative Perspectives

Abstract:

The limited explanatory power of conventional wisdom in understanding the “China miracle“ calls for theories with more universal appeal. The theories might have been considerably enriched if sufficient credit had been given to China’s contribution to the evolution of the modern state in human history. Using conventional wisdom as a benchmark, we demonstrate that an analytical framework might be developed to accommodate characteristics of Chinese society with its unique history and cultural values. Our preliminary results indicate that it was perhaps the set of values emphasizing personal development and rights to property that have enabled the China miracle experienced over the past three decades. Long-standing issues such as excessive reliance on the state for the provision of public goods and omnipresent government control of resource industries are largely consistent with China’s record of civilization state development during various ancient dynasties.     

‘Lights,Camera, Action!’ Video Technology and Students' Perceptions of Oral Communication in Accounting Education

Abstract

This paper examines the influence of an authentic assessment item on three dimensions of oral communication in accounting education: skills, self-efficacy, and relevance. An explanatory mixed methods design is used to explore students' perceptions of their development. The results indicate that an elevator pitch assessment has a positive impact on all three dimensions. In particular, the employed video technology fosters greater self-awareness and a more accurate perception of skill levels, and enhances students' self-efficacy. The contextualised learning experience also enables students to better appreciate the relevance of oral communication to their future careers. The paper extends the literature on video technology, self-efficacy, and generic skills development in accounting education, and provides relevant stakeholders with evidence of an authentic activity that can assist with bridging the skills-expectation gap.