scholarly journals THE SEARCH FOR TIME-SERIES PREDICTABILITY-BASED ANOMALIES

2021 ◽  
Vol 0 (0) ◽  
pp. 1-19
Author(s):  
Javier Humberto Ospina-Holguín ◽  
Ana Milena Padilla-Ospina
Keyword(s):  
Time Series ◽  
Asset Pricing ◽  
Market Timing ◽  
The United States ◽  
Trading Rule ◽  
Optimal Parameters ◽  
Pricing Models ◽  
Asset Pricing Model ◽  
Asset Pricing Models ◽  
Out Of Sample

This paper introduces a new algorithm for exploiting time-series predictability-based patterns to obtain an abnormal return, or alpha, with respect to a given benchmark asset pricing model. The algorithm proposes a deterministic daily market timing strategy that decides between being fully invested in a risky asset or in a risk-free asset, with the trading rule represented by a parametric perceptron. The optimal parameters are sought in-sample via differential evolution to directly maximize the alpha. Successively using two modern asset pricing models and two different portfolio weighting schemes, the algorithm was able to discover an undocumented anomaly in the United States stock market cross-section, both out-of-sample and using small transaction costs. The new algorithm represents a simple and flexible alternative to technical analysis and forecast-based trading rules, neither of which necessarily maximizes the alpha. This new algorithm was inspired by recent insights into representing reinforcement learning as evolutionary computation.

Examining Asset Pricing Anomalies: Evidence from Europe

2021 ◽  
pp. 227853372110257
Author(s):  
Asheesh Pandey ◽  
Rajni Joshi
Keyword(s):  
Asset Pricing ◽  
Pricing Model ◽  
Pricing Models ◽  
Investment Rate ◽  
Asset Pricing Model ◽  
Fund Managers ◽  
Asset Pricing Models ◽  
Asset Pricing Anomalies ◽  
Pricing Anomalies ◽  
Portfolio Managers

We examine five important asset pricing anomalies, namely, size, value, momentum, profitability, and investment rate to evaluate their efficacy in major West European economies, that is, France, Germany, Italy, and Spain. We employ four prominent asset pricing models, namely Capital Asset Pricing Model (CAPM), Fama–French three-factor (FF3) model, Carhart model and Fama–French five-factor (FF5) model to evaluate whether portfolio managers can create trading strategies to generate risk-adjusted extra normal returns for their investors. We also examine the prominent anomalies which pass the test of asset pricing in our sample countries and evaluate the best performing asset pricing model in explaining returns in each of these countries. We find that in spite of being matured markets, these countries provide portfolio managers with opportunities to exploit these strategies to generate extra normal returns for their investors. Momentum anomaly for Germany and profitability anomaly for Italy can be exploited by fund managers for generating risk-adjusted returns. For France, except for net investment rate anomaly, all the other anomalies remained unexplained by asset pricing models. We also find CAPM to be the better model in explaining returns of Italy and Spain. While FF3 factor and FF5 factor models explain returns in Germany, our sample asset pricing models failed to work for France. Our study has implications for portfolio managers, academia, and policymakers.

scholarly journals Optimisation of Time-Varying Asset Pricing Models with Penetration of Value at Risk and Expected Shortfall

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 394
Author(s):  
Adeel Nasir ◽  
Kanwal Iqbal Khan ◽  
Mário Nuno Mata ◽  
Pedro Neves Mata ◽  
Jéssica Nunes Martins
Keyword(s):  
At Risk ◽  
Asset Pricing ◽  
Stock Returns ◽  
Value At Risk ◽  
Expected Shortfall ◽  
Time Varying ◽  
Pricing Models ◽  
Asset Pricing Model ◽  
Asset Pricing Models ◽  
Market Beta

This study aims to apply value at risk (VaR) and expected shortfall (ES) as time-varying systematic and idiosyncratic risk factors to address the downside risk anomaly of various asset pricing models currently existing in the Pakistan stock exchange. The study analyses the significance of high minus low VaR and ES portfolios as a systematic risk factor in one factor, three-factor, and five-factor asset pricing model. Furthermore, the study introduced the six-factor model, deploying VaR and ES as the idiosyncratic risk factor. The theoretical and empirical alteration of traditional asset pricing models is the study’s contributions. This study reported a strong positive relationship of traditional market beta, value at risk, and expected shortfall. Market beta pertains its superiority in estimating the time-varying stock returns. Furthermore, value at risk and expected shortfall strengthen the effects of traditional beta impact on stock returns, signifying the proposed six-factor asset pricing model. Investment and profitability factors are redundant in conventional asset pricing models.

scholarly journals Identifying outliers in asset pricing data with a new weighted forward search estimator

2020 ◽  
Vol 31 (84) ◽  
pp. 458-472
Author(s):  
Alexandre Aronne ◽  
Luigi Grossi ◽  
Aureliano Angel Bressan
Keyword(s):  
Asset Pricing ◽  
Factor Model ◽  
The United States ◽  
Estimation Methods ◽  
Annual Data ◽  
Forward Search ◽  
Pricing Models ◽  
Asset Pricing Models ◽  
The Impact ◽  
Three Factor

ABSTRACT The purpose of this work is to present the Weighted Forward Search (FSW) method for the detection of outliers in asset pricing data. This new estimator, which is based on an algorithm that downweights the most anomalous observations of the dataset, is tested using both simulated and empirical asset pricing data. The impact of outliers on the estimation of asset pricing models is assessed under different scenarios, and the results are evaluated with associated statistical tests based on this new approach. Our proposal generates an alternative procedure for robust estimation of portfolio betas, allowing for the comparison between concurrent asset pricing models. The algorithm, which is both efficient and robust to outliers, is used to provide robust estimates of the models’ parameters in a comparison with traditional econometric estimation methods usually used in the literature. In particular, the precision of the alphas is highly increased when the Forward Search (FS) method is used. We use Monte Carlo simulations, and also the well-known dataset of equity factor returns provided by Prof. Kenneth French, consisting of the 25 Fama-French portfolios on the United States of America equity market using single and three-factor models, on monthly and annual basis. Our results indicate that the marginal rejection of the Fama-French three-factor model is influenced by the presence of outliers in the portfolios, when using monthly returns. In annual data, the use of robust methods increases the rejection level of null alphas in the Capital Asset Pricing Model (CAPM) and the Fama-French three-factor model, with more efficient estimates in the absence of outliers and consistent alphas when outliers are present.

Pricing office rents in Sydney CBD: testing the water on automated rent reviews

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Olga Filippova ◽  
Jeremy Gabe ◽  
Michael Rehm
Keyword(s):  
Asset Pricing ◽  
Real Estate ◽  
Percentage Error ◽  
Residential Real Estate ◽  
Pricing Models ◽  
Content Type ◽  
Market Rent ◽  
Absolute Percentage Error ◽  
Asset Pricing Models ◽  
Out Of Sample

PurposeAutomated valuation models (AVMs) are statistical asset pricing models omnipresent in residential real estate markets, where they inform property tax assessment, mortgage underwriting and marketing. Use of these asset pricing models outside of residential real estate is rare. The purpose of the paper is to explore key characteristics of commercial office lease contracts and test an application in estimating office market rental prices using an AVM.Design/methodology/approachThe authors apply a semi-log ordinary least squares hedonic regression approach to estimate either contract rent or the total costs of occupancy (TOC) (“grossed up” rent). Furthermore, the authors adopt a training/test split in the observed leasing data to evaluate the accuracy of using these pricing models for prediction. In the study, 80% of the samples are randomly selected to train the AVM and 20% was held back to test accuracy out of sample. A naive prediction model is used to establish accuracy prediction benchmarks for the AVM using the out-of-sample test data. To evaluate the performance of the AVM, the authors use a Monte Carlo simulation to run the selection process 100 times and calculate the test dataset's mean error (ME), mean absolute error (MAE), mean absolute percentage error (MAPE), median absolute percentage error (MdAPE), coefficient of dispersion (COD) and the training model's r-squared statistic (R2) for each run.FindingsUsing a sample of office lease transactions in Sydney CBD (Central Business District), Australia, the authors demonstrate accuracy statistics that are comparable to those used in residential valuation and outperform a naive model.Originality/valueAVMs in an office leasing context have significant implications for practice. First, an AVM can act as an impartial arbiter in market rent review disputes. Second, the technology may enable frequent market rent reviews as a lease negotiation strategy that allows tenants and property owners to share market risk by limiting concerns over high costs and adversarial litigation that can emerge in a market rent review dispute.

What Do Fund Flows Reveal about Asset Pricing Models and Investor Sophistication?

2020 ◽  
Vol 34 (1) ◽  
pp. 108-148
Author(s):  
Narasimhan Jegadeesh ◽  
Chandra Sekhar Mangipudi
Keyword(s):  
Asset Pricing ◽  
Mutual Fund ◽  
Market Model ◽  
Fund Flows ◽  
Pricing Model ◽  
Pricing Models ◽  
Asset Pricing Model ◽  
Asset Pricing Models ◽  
Investor Sophistication ◽  
Mutual Fund Flows

Abstract Recent evidence indicates that market model alphas are stronger predictors of mutual fund flows than alphas with other models. Some recent papers have interpreted this evidence to mean that CAPM is the best asset pricing model, but some others have interpreted it as evidence against investor sophistication. We evaluate the merits of these mutually exclusive interpretations. We show that no tenable inference about the validity of any asset pricing model can be drawn from this evidence. Rejecting the investor sophistication hypothesis is tenable, but the appropriate benchmark to judge sophistication is different from that used in this literature.

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