Predictive Value of Estimated Beta

The Capital Asset Pricing Model (CAPM) is widely used in corporate finance to assess expected returns of securities and return on equity, and beta, a measure of systematic risk, is a component of the CAPM equation. Previous studies appear not to have addressed whether beta as a stand-alone metric allows individual investors to effectively assess returns relative to the market, and this study aims to address this. Exchange-traded funds (ETFs) reflecting a range of expected volatilities relative to the S&P 500 index were selected. Betas of XLK (Technology sector), XLE (Energy sector), XLU (Utilities sector), and XLY (Consumer Staples sector) were estimated by regressing their weekly returns over five years against those of the S&P 500 index. Three five-year periods were used (ending in 2005, 2010, and 2015). The betas largely conformed to anticipated values with the exception of that of XLY which was surprisingly greater than the market beta. Estimated and observed betas were compared using a two-tailed paired T-test and no difference was found, suggesting that estimated beta is statistically a good proxy for actual beta. In practical terms though, there were relatatively large variances in several instances between estimated and observed betas, and this could be a concern for investors. Returns using estimated beta and actual returns were also compared over one, two, three, four, and five years with regard to the three five-year periods. Significant variation was observed for expected minus observed returns both in sign and magnitude. A two-tailed paired T-test suggested there was a difference between returns using estimated beta and actual ones over the three five-year periods for all funds except XLE. The observations suggest betas are volatile and individual investors should incorporate additional metrics to forecast returns relative to the market.

The Systematic Risk at the Crisis—A Multifractal Non-Uniform Wavelet Systematic Risk Estimation

In the last decade, many factors, such as socio-political and econo-environmental ones, have led to a perturbation in the timeline of the worldwide development, and especially in countries and regions having political changes. This led us to introduce a new idea of risk estimation taking into account the non-uniform changes in markets by introducing a non-uniform wavelet analysis. We aim to explain the econo-political situation of Arab spring countries and the effect of the revolutions on the market beta. The main novelty is first the construction of a dynamic backward-forward model for missing data, and next the application of random non-uniform wavelets. The proposed procedure will be acted empirically on a sample corresponding to TUNINDEX stock as a representative index of the Tunisian market actively traded over the period from 14 January 2016 to 13 January 2021. The chosen 5-year period is important as it constitutes the first five years after the revolution and depends strongly on the socio-econo-political stability in the revolutionary countries. The results showed the efficiency of non-uniform wavelets in explaining the dynamics of the market well. They therefore may be good tools to explore important phenomena in the market such as the non-stationary aspect of financial series, non-constancy, and time-varying parameters. These facts in turn will have positive implications for investors as well as politicians in front of the evolution of the market. Besides, recommendations to extend the present method for other types of wavelets and markets will be of interest.

Analisis Pengaruh Leverage, Likuiditas, Efisiensi dan Ekspektasi Pertumbuhan Laba terhadap Market-Beta pada Perusahaan Non-Keuangan LQ45

Penelitian ini bertujuan untuk mengetahui pengaruh variabel independen (DOL, DFL, CR, TATO, dan PER) terhadap beta-pasar (market-beta). Sampel yang digunakan dalam penelitian ini adalah perusahaan non-keuangan LQ 45 yang terdaftar di Bursa Efek Indonesia (BEI) selama periode 2016 - 2020. Dalam pengujian pengaruh variabel independen (DOL, DFL, CR, TATO, dan PER) terhadap beta-pasar (market-beta) digunakan data panel dengan menggunakan Pooled least-square dan fixed effect dengan metode cross-section weights. Karena yang dihasilkan diuji dengan uji chow (chow test) diketahui f statistiknya lebih besar dari f tabel. Maka, hipotesis nol ditolak. Dari hasil regresi dengan pooled-least-square dan fixed effect dengan metode cross-section weights berpengaruh positif terhadap beta-pasar (market-pasar). DOL, DFL dan PER menggambarkan sales, net income dan ekspetasi perusahaan terhadap laba perusahaan. Sedangkan CR dan TATO terbukti berpengaruh negatif terhadap beta-pasar. CR dan TATO menggambarkan kemampuan perushaan untuk membayar pinjaman dan efisiensi perusahaan dalam memanfaatkan asetnya untuk menghasilkan keuntungan.

The Systematic Risk at the Crisis - A Multifractal Non-uniform Wavelet Systematic Risk Estimation

The Capital Asset Pricing Model is a widely applied model to describe risky markets and to deduce their systematic risk. Its estimation, therefore, remains an important task in Econo-financial studies. Empirically, it focuses on the impact of return interval on the betas. Existing studies somehow turn around the same idea of measuring the value of the beta according to the uniform intervals of time during a fixed period. However, it is noticed easily, and especially in the last decade that many factors such as socio-political, and Econo-environmental ones have led to a perturbation in the timeline of the worldwide development, and especially in countries and regions having political changes. This led us to introduce a new idea of risk estimation taking into account the non-uniform changes in markets by introducing a non-uniform wavelet analysis. We aim to explain the Econo-political situation of Arab spring countries and the effect of the revolutions on the market beta. The main novelty is firstly the construction of a dynamic backward-forward model for missing data, and next the application of random non-uniform wavelets. The proposed procedure will be acted empirically on a sample corresponding to TUNINDEX stock as a representative index of the Tunisian market actively traded over the period January 14, 2016, to January 13, 2021. The chosen 5-years period is important as it constitutes the first 5-years-after the revolution and depends strongly on the Socio-Econo-political stability in the revolutionary countries.

The Existence of an Anomaly in the City Indices in Borsa Istanbul

The aim of the study is to reveal the existence of an abnormal return in the city indices in Borsa Istanbul. Three important calculations were made for the detection of an abnormal return. The first was the calculation of adjusted returns. The second was the calculation of beta coefficients for city indices. The third was the determination of the relationship of each city index to the market. According to the findings obtained, there was an abnormal return in the city indices. In other words, each of the city indices made a profit on market returns. However, these returns were almost equal to market returns. When the beta coefficients were analyzed, it was seen that the coefficients were equal to the theoretically-expressed average market beta coefficient. Thus, the city indices and the market are moving in the same direction, and the results are statistically significant.

The Impact of Dieselgate on the Required Rate of Return on Equity of VW, BMW and Daimler

Our paper studies the impacts of the Dieselgate scandal on the required rate of return on equity investments into VW, Daimler, and BMW. The object of investigation is the beta coefficient that determines the risk premium in the Capital Asset Pricing Model (CAPM). Our research takes a deep dive into the developments from the turning point of the scandal (the EPA NOTICE 2015) on September 18, 2015 – when a Notice of Violation of the Clean Air Act was issued to Volkswagen by the EPA – to the end of February 2016. This period also covers FORMAL COMMENCEMENT 2016, when the U.S. Department of Justice first sued Volkswagen on behalf of the EPA. The spillover (contagion) effect of fraudulent practices of VW impacted BMW, Daimler and other companies in the industry that share a similar business model and market segment. Our research of historical market betas has not confirmed the expectation that in the context of the Dieselgate scandal the return required on equity investments into VW, Daimler, and BMW would soar. The Dieselgate scandal proves that the reliability of beta estimates is an inverse function of market volatility. Historical market beta is, therefore, not a good estimate of the required rate of return for the companies in question.

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

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.

Firm-specific News and Anomalies

This study investigates the relation between idiosyncratic volatility and future returns around the firm-specific news announcements in the Korean stock market from July 1995 to June 2018. The excess returns of decile portfolios that are formed by sorting the stocks based on news and non-news idiosyncratic volatility measures. The Fama and French three-factor model is also examined to see whether systematic risk affects news and non-news idiosyncratic volatility profits. The pricing of our news and non-news idiosyncratic volatility are confirmed in the cross-sectional regression using the Fama and MacBeth method. Market beta, size, book to market, momentum, liquidity, and maximum return are controlled to determine robustness. Our empirical evidence suggests that the pricing of the non-news idiosyncratic volatility is more strongly negative compared to the news idiosyncratic volatility, which is contrary to the limited arbitrage explanation for the negative price of the idiosyncratic volatility. We find that the non-news idiosyncratic volatility has a robust negative relation to returns in non-January months. Macro-finance factors drive the conditioned on the missing risk factor hypothesis, the pricing of idiosyncratic volatility. This study contributes to a better understanding of the role of the conditional idiosyncratic volatility in asset pricing. As the Korean stocks provide a fresh sample, our non-U.S. investigation delivers a useful out-of-sample test on the pervasiveness of the non-news volatility effect across the emerging markets.

Statistical Inference of Spot Correlation and Spot Market Beta under Infinite Variation Jumps*

Empirical evidence has revealed that the jumps in financial markets appear to be very frequent. This study considers the statistical inference of the spot correlation and the spot market beta between two different assets using high-frequency data, in a setting where both the cojumps and the individual jumps in the underlying driving processes could be of infinite variation. Starting from the estimation of the spot covariance, we propose consistent estimators of the spot correlation and the spot market beta when the jump processes involved are general semimartingales. The second-order approximation for the estimators, namely, the central limit theorems, is established under the assumption that the jumps around zero are of stable Lévy type. Our estimation procedure is based on the empirical characteristic function of the increments of the processes and the application of the polarization identity; the bias terms stemming from the jumps are removed iteratively. The finite sample performances of the proposed estimators and other existing estimators are assessed and compared by using datasets simulated from various models. Our estimators are also applied to some real high-frequency financial datasets.