Absolute Pricing Error Research Articles

A multi-dimensional assessment of the accuracy of analyst target prices

This paper provides the first assessment for the quality of analyst target forecasts within an emerging market. We find that analysts' target forecasts tend to commit systematically upward bias (9.4%), large absolute pricing error (24.8%), over-prediction of the actual price changes (21%), and a low proportion (54%) of correct directional forecasts. The forecasting quality decays over time as the information in the target prices becomes obsolete, long before the one-year expiry period indicated in the analyst reports. Nevertheless, analysts’ target forecasts consistently outperformed alternative forecasts generated solely based on historical data, which confirms the price discovery function of analyst forecasting. A number of factors are found to be influential to the target performance. Target quality is adversely associated with stock idiosyncratic risk, prior index volatility, the aggressiveness of the target forecasts, and the optimism of consensus targets. In addition, more accurate target prices are delivered by brokerages that have prior experience with the industry and the companies under review, but not by brokerages that possess monopoly power on the stock-specific information.

AN INVESTIGATION OF A MULTIVARIATE SIMILARITY APPROACH FOR PEER SELECTION IN RELATIVE VALUATION

This study presents a multivariate similarity-based peers approach (MSBP) for the selection of peer firms in relative valuation. We select peer firms based on the collective similarity of ten variables which reflect firms’ expected profitability, risk and growth. We test this approach by comparing its valuation accuracy relative to the industry membership selection method. We measure accuracy based on the absolute percentage pricing error and the squared percentage pricing error. We conclude that selecting comparable firms based on our approach offers very sharp improvements in the accuracy of relative valuation using the price-to-book, enterprise-value-to sales and price-to-earnings ratios in comparison to the industry membership selection method. Also, we introduce the use of a multivariate similarity visualization tool which we believe will be very useful in finance research, in general, and firm valuation research in particular.JEL Classification: G320.

Accuracy of dividend discount model and p/e ratio valuation in Indonesia stock exchange

The calculation of the intrinsic value of shares is essential for investors. The result can be used as a guideline for buying, selling, or holding shares. This research was conducted because the stock price on the Indonesia Stock Exchange (IDX) in March 2020 experienced a deep decline, namely a decrease of 38% on average, so it is interesting to study. In determining the intrinsic value of these shares, the Dividend Discount Model (DDM) and Price Earning Ratio (P/E Ratio) methods will be used as well as evaluate the most accurate method. The study uses stock data from all business sectors on the IDX for the 2014-2019 period, especially companies that pay dividends. The sample used in this study were 49 companies (22%) of the average 222 companies that distributed dividend. Based on the results of the study, DDM, and P/E Ratio there are 43 companies (86%) in undervalued positions or only seven overvalued companies (10%). MOS is greater than 30% with DDM and P/E Ratio of 26 companies (52%). Likewise, with the results of the accuracy-test using the Mean Absolute Pricing Error (MAPE), it turns out that DDM is only 0.14% greater than the P/E Ratio. It can be said that DDM is relatively more accurate than the P/E Ratio. Thus overall the calculation of the intrinsic value of shares using DDM and the P/E Ratio in terms of undervalued or overvalued, MOS and accuracy can be concluded that there is no significant difference.

Equity Financing Risk

A risk factor linked to aggregate equity issuance conditions explains the empirical performance of investment factors based on the asset growth anomaly of Cooper, Gulen, and Schill (2008). This new risk factor, dubbed equity financing risk (EFR) factor, subsumes investment factors in leading linear factor models. Most importantly, when substituted for investment factors, the EFR factor improves the overall pricing performance of linear factor models, delivering a significant reduction in absolute pricing errors and their associated t-statistics for several anomalies, including the ones related to R&D expenditures and cash-based operating profitability.

Granular Uncertainty and Disastrous Misallocation

We develop an endogenous growth model with heterogeneous firms, intermediate goods, and financial frictions, in which misallocation emerges analytically as a crucial state variable. In equilibrium, misallocation endogenously generates long-run uncertainty about economic growth by distorting innovation decisions, leading to significant welfare losses and risk premia in capital markets. Empirically, we construct a misallocation measure motivated by our model. We find evidence for misallocation-driven low-frequency movements in both aggregate growth and stock returns. A two-factor model with market and misallocation factors prices size, book-to-market, momentum, and bond portfolios with an R-squared and a mean absolute pricing error close to the Fama-French three-factor model.

Option Pricing with Economic Regime Shifts

Assuming that one-period logarithmic returns of the underlying asset follow a hidden Markov process, we develop a valuation model for European call options. Unlike existing option pricing models, our pricing mechanism relies on the optimal non exponential-affine stochastic discount factor characterized with economic strength. Monthly S\&P 500 index options for the period, January 2014 to October 2018, are used for model validation. It is found that risk/return profiles under the optimal risk neutral probability measure associated with a non exponential-affine stochastic discount factor are drastically different across the regimes of economic strength. We use both the absolute pricing error and the model-implied volatility criteria to examine model performance. In comparison with alternative models, empirically evidenced unbalanced pricing errors for deeply in-the-money and deeply out-of-the-money options are substantially reduced.

Evaluating alternative methods of asset pricing based on the overall magnitude of pricing errors

We are the first pioneers who evaluate the overall fitness of the two-pass Fama–MacBeth regression and the generalized method of moments (GMM) by comparing the R2 or mean absolute pricing error (MAE), using a Monte Carlo simulation of different models and portfolios for hundreds of trials and, in particular, focusing on the case that the expected return is always a gross return in both methods. Our findings reveal an innovative finding that both methodologies achieve approximate overall magnitudes of pricing errors.

The Impact of Make-Take Fees on Market Efficiency

Recently, stock exchanges have altered their trading fees to subsidize liquidity by offering “make” rebates for providing liquidity through limit orders and charging “take” fees for consuming liquidity via marketable orders, leading to debate regarding the impact of these fees on market quality. Using an exogenous experiment performed by NASDAQ in 2015, I employ difference-in-differences analysis on a matched sample and find that a decrease in take fee and make rebate levels leads to greater absolute pricing error and larger variance of mispricing, beyond that expected from widened bid-ask spreads, raising concerns for the SEC’s proposed decrease in fee limits.

Conditional Dynamics and the Multi-Horizon Risk-Return Trade-Off

We propose testing asset-pricing models using multi-horizon returns (MHR). MHR serve as powerful source of conditional information that is economically important and not data-mined. We apply MHR-based testing to linear factor models. These models seek to construct the unconditionally mean-variance efficient portfolio. We reject all state-of-the-art models that imply high maximum Sharpe ratios in a single-horizon setting. Thus, the models do a poor job in accounting for the risk-return trade-off at longer horizons. Across the different models, the mean absolute pricing errors associated with MHR are positively related to the magnitude of maximal Sharpe ratio in the single-horizon setting. Model misspecification manifests itself in strong intertemporal dynamics of the factor loadings in the SDF representation. We suggest that misspecification of the dynamics of loadings arises from the common approach towards factor construction via portfolio sorts.

The relationship between covariance risk and size effects in emerging equity markets

Purpose – The purpose of this paper is to examine whether the conditionally expected return on size-based portfolios in an emerging market (EM) is determined by the country’s world risk exposure. The authors analyze the degree of financial integration of 23 emerging equity markets grouped into five size portfolios using the conditional international asset pricing model with both world and domestic market risks. The authors also compare the model’s fitness on the predictability of portfolio returns by using world and EM indices. Design/methodology/approach – This study investigates whether large-cap stocks are priced globally and whether mid- and small-cap stocks are strongly influenced by domestic risk factors. The authors first examine the predictability of large-, mid-, and small-cap stock portfolio returns by using global and local variables, and next compare the model fitness by using world and EM indices on the prediction of size-based stock returns. Finally, the authors test whether the world price of covariance risk is the same for different portfolios. Findings – The authors find that the conditional expected returns of large-cap stocks should be priced by global variables. Mid- and small-cap stocks are influenced by domestic variables rather than global variables, and their returns are priced by local residual risks. The test of the conditional asset pricing model shows that the largest stocks have the smallest mean absolute pricing errors (MAE), and their pricing errors are lower in large markets than in small markets. Third, the EM index offers more predictability for the excess returns of mid- and small-cap stocks than the world market index, but the explanatory power of this index does not increase for large-cap stocks. Originality/value – EMs in the past were known as segment markets, with local risk factors more important than global risk factors, suggesting significant benefits from adding EMs to global portfolios. It would be interesting to examine whether financial integration differs for various firm sizes in the markets.

A Comparative Analysis of Accounting-Based Valuation Models

We empirically compare the reliability of the dividend (DIV) model, the residual income valuation (CT, GLS) model, and the abnormal earnings growth (OJ) model. We find that valuation estimates from the OJ model are generally more reliable than those from the other three models, because the residual income valuation model anchored by book value gets off to a poor start when compared with the OJ model led by capitalized next-year earnings. We adopt a 34-year sample covering from 1985 to 2013 to compare the reliability of valuation estimates via their means of absolute pricing errors ( MAPE) and corresponding t statistics. We further use the switching regression of Barrios and Blanco to show that the average probability of OJ valuation estimates is greater in explaining stock prices than the DIV, CT, and GLS models. In addition, our finding that the OJ model yields more reliable estimates is robust to analysts-based and model-based earnings measures.

Option pricing under truncated Gram–Charlier expansion

This study develops a truncated Gram–Charlier expansion (TGCE) option pricing model, which simultaneously considers the skewness, kurtosis and essentially truncated (bounded) interval in the underlying asset return. In addition to TGCE, a truncated Black–Scholes model is proposed also. The two truncated models are compared to four prevalent option pricing models, including Black–Scholes (constant variance model), Hull–White (a mean-reverting variance model), GARCH (a time-varying volatility clustering model), and Gram–Charlier expansion (GCE) models. Using ARPE (absolute relative pricing error) as the criterion in Taiwan option market (TXO), the result demonstrates that TGCE model overall performs the best. Besides, truncated Black–Scholes model generally dominates Black–Scholes model, and GCE model generally dominates Black–Scholes, Hull–White and GARCH models. This result consists with the fact that stock return distributions are usually asymmetric and nonmesokurtic as well as essentially not unbounded. This finding implies that an accurate option pricing model should simultaneously consider skewness and kurtosis and truncated interval in the underlying asset return. Additionally, the ARPE against moneyness for all option pricing models forms a negative slope smile curve. Moreover, the pricing performance of TGCE model is so stable to moneyness.

Does Accrual Management Impair the Performance of Earnings‐Based Valuation Models?

AbstractThis study examines how accrual manipulations affect firm valuation in the years surrounding the passage of the Sarbanes‐Oxley Act (SOX). We compare the absolute percentage pricing errors of RIM and DCF valuation models for a group of US firms suspected to have engaged in accrual manipulations to avoid a small loss or a small earnings decline vs. ‘Normal’ firms matched on industry, year and size. We find that RIM can better estimate intrinsic value than DCF for the matched Normal firms in the pre‐SOX period, but not so for accrual manipulators, and that SOX mitigates the harmful effect of accrual manipulations, completely eliminating the difference in RIM's accuracy advantage over DCF between Normal firms and accrual manipulators. As a further analysis, we redefine Suspect firms as real‐activity manipulators and find a significant across‐group difference in accuracy wedge in both sample periods, implying that SOX has prompted firms to favor real‐activity manipulations over accrual manipulations.

Variance-constrained canonical least-squares Monte Carlo: An accurate method for pricing American options

The pricing accuracy of the canonical least-squares Monte Carlo (CLM) method can be improved significantly by incorporating innovatively a variance constraint in the derivation of the canonical risk-neutral distribution. This new approach is called the variance-constrained CLM (vCLM) in the paper. Operationally, the forward variance is set to be the square of the volatility implied under vCLM by the option's market price from a previous trading day. For 16,249 American-style S&P 100 index puts, vCLM produced an average absolute pricing error of 5.94%, easily outperforming CLM, a competing nonparametric approach, and a GARCH-based benchmark.

Underpricing Skewed Assets: The Case of Initial IPO Returns

I bring together the issues of offering price accuracy and underpricing and point out that there is a trade-off between the two. If the underwriter intends to set an unbiased offering price, and thereby minimizing the mean absolute pricing error (MAPE), skewness might induce ex ante underpricing (i. e. an offering price below the expected secondary market price). In order to illustrate my rather statistical reasoning, I examine the simplest skewed price distribution one can think of: a discrete price distribution with just two possible prices. In order to come up with more general propositions, I allow for objective functions with differing costs of over- and underpricing including the case of equal costs as a special case. An equal cost of over- and underpricing induces the underwriter to minimize MAPE and thereby underprice on average positively skewed stocks. While skewness induces underpricing, I find that in a situation where underpricing is expected, an increased skewness is associated with less expected underpricing keeping standard deviation und expected value constant.

Comparison of market models for measuring and hedging synthetic CDO tranche spread risks

The recent credit crisis has focused attention on the models used for pricing and assessing risk of structured credit transactions including synthetic CDOs. The market standard one factor Gaussian copula model has been criticized for its unrealistic constant correlation assumption. In this paper, a range of market models that allow a positive relationship between default correlation and default probability, including the correlation mapping methods and the implied copula models, are compared with the Gaussian copula model, based on their relative performance in hedging credit spread risk and pricing bespoke CDOs. The models assessed are calibrated to the traded CDO tranche spreads prior to the credit crisis and then compared based on the mean absolute pricing errors over a time period including the credit crisis. The results of the analysis highlight a number of issues including the accuracy of "mark-to-model" valuations of bespoke CDOs, the value of including past information in pricing and hedging, and the relative performance of the base correlation Gaussian copula model compared to the other market models in this study.

Variance-Constrained Canonical Least-Squares Monte Carlo: An Accurate Method for Pricing American Options

The recently proposed canonical least-squares Monte Carlo (CLM) method is extended by incorporating uniquely a variance constraint in the derivation of the equivalent martingale measure used by CLM. This derivation can be viewed as a change of measure numerically by modifying both the drift term and the volatility term of the underlying stochastic process, for which the analytic form is not explicitly given or assumed. The extended method will be called the variance-constrained CLM (vCLM). In practice, the forward variance used in the pricing of an option is set to be the square of the volatility implied by vCLM at the market price of the option from a previous trading day. With a data set of 16419 put prices for the American-style S&P 100 Index options, vCLM is found to produce an average absolute pricing error of only 5.95%, easily outperforming many competing approaches.

Pricing Taiwan option market with GARCH and stochastic volatility

This study compares the out-of-sample performances among Black–Scholes (B-S), Stochastic Volatility (SV) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models in the Taiwan option market. Using Absolute Relative Pricing Error (ARPE) as the performance criterion, the empirical result reveals that the performance for GARCH is the best, and SV slightly dominates B-S. Additionally, this study performs the regression of ARPE on time-to-maturity, moneyness and a binary variable that is set to unity, if the option is a call and to zero in the case of a put. For the three models, the regression result displays that the pricing error is consistently decreasing in time-to-maturity and moneyness, and the out-of-sample performance in puts are more accurate than those in calls. Since the corresponding R 2 of the regression in GARCH is the smallest, the pricing error for the other two models is relatively severe with respect to the three explanatory variables.

The Impact of Earnings Management on the Performance of Earnings-Based Valuation Models

The purpose of this study is to examine empirically how the presence of earnings management may affect firm valuation. We compare the performance of earnings-based (e.g., Residual Income Model, RIM) and non-earnings-based (e.g., Discounted Cash Flow, DCF) valuation models, measured by absolute percentage pricing errors and absolute percentage valuation errors, for two subsets of publicly traded US firms: Suspect firms that are likely to have engaged in earnings management and “Normal” firms matched on industry, year and size. When valuation models use only analysts' short-term earnings forecasts as model inputs, results indicate that earnings management adversely affects the RIM model’s ability to estimate a firm’s intrinsic value while leaving that of DCF unchanged. We contribute to the valuation literature by showing that the well-known superiority of the RIM model over DCF does not hold when earnings are managed. By comparison, if the valuation model also includes analysts' long-term target price forecasts, RIM does not enjoy any economically significant accuracy advantage over DCF, with or without the presence of earnings management. Over a longer forecast horizon, financial analysts appear to account for the impact of earnings management on firms’ future values by adjusting their target price forecasts. We extend the earnings management literature by demonstrating that the way analysts react to earnings management over short to long-term forecast horizons has different implications for the estimation ability of RIM vis-a-vis DCF models.

Fear of the Unknown: Familiarity and Economic Decisions*

Evidence indicates that people fear change and the unknown. We offer a model of familiarity bias in which individuals focus on adverse scenarios in evaluating defections from the status quo. The model explains the endowment effect, portfolio underdiversification, home and local biases. Equilibrium stock prices reflect an unfamiliarity premium. In an international setting, our model implies that the absolute pricing error of the standard CAPM is positively correlated with the amount of home bias. It also predicts that a modified CAPM holds wherein the market portfolio is replaced with a portfolio of the stock holdings of investors not subject to familiarity bias.