Beta Stocks Research Articles

Assessing Systematic Risk through Accounting Information: Evidence from the Colombo Stock Exchange

This study tests whether accounting information significantly influences systematic risk. To operationalization the relationship between the accounting information and systematic risk, a panel data analysis is conducted on stock analysis with a portfolio of higher beta stocks, moderate beta stocks, and lower beta stocks as well as overall stocks among the selected 154 listed companies in the Colombo Stock Exchange (CSE). Within this application, the present study seeks to present empirical evidence on the relationship between accounting information and systematic risk in the Colombo Stock Market. The study found that the accounting variables significantly influence systematic risk. While accruals were not significant in influencing systematic risk, moreover, the study suggests accounting variables are well explained in the systematic risk and recommends using accounting-based risk factors other than market-based risk factor models in the Sri Lankan context.

Performance Evaluation of State-Owned Company Stocks in Indonesia

This study delves into the performance of Indonesian state-owned enterprises (BUMN) stocks from February 2021 to October 2023, attracting investor attention due to their strategic position in the national economy. However, the stock performance during this period exhibited suboptimal movements, failing to surpass the returns of the Indonesia Stock Exchange Composite Index (IHSG). To address this, a prudent investment strategy is essential, encompassing portfolio construction and management. In portfolio construction, the study employs proven smart beta strategies, known for delivering superior returns compared to traditional portfolios. Portfolio management involves testing two strategies: a passive approach utilizing the buy-and-hold technique and an active approach employing portfolio recomposition. This quantitative descriptive study utilizes historical closing price data, forming portfolios based on selected beta groups. Weighting is adjusted, with higher beta stocks receiving a larger allocation in the portfolio. The research compares two beta groups (high and low) and two investment strategies (active and passive) using the Sharpe, Treynor, and Jensen indices. The results indicate that the high-beta group and active strategy outperform, aligning with the investment adage "high risk, high return." The high-beta group yields higher returns due to its increased risk profile, consistent with the notion of high-risk, high-reward investments. The active strategy provides opportunities to discard underperforming stocks and enhance the weighting of well-performing ones during recomposition. These findings are valuable for investors seeking to capitalize on BUMN stocks at lower prices. The study aids in minimizing negative returns (losses) by identifying the most suitable strategy for BUMN portfolios. Investors can leverage this research to make informed decisions and navigate the challenges associated with investing in state-owned enterprises.

Pengaruh Faktor-faktor Fundamental (Current Ratio, Return on Asset, Return on Equity, Total Asset Turn Over, Debt to Equity Ratio, dan Asset Growth) terhadap Beta Saham Telekomunikasi di Bursa Efek Indonesia Tahun 2019-2021

This study raises the issue of Fundamental Factors, namely Current Ratio, Return On Assets, Return On Equity, Total Asset Turn Over, Debt To Equity Ratio, and Asset Growth have an influence or not on Beta Stocks. The purpose of this study is to determine how much the size of the company's ability to complete each fundamental ratio and how much influence the fundamental factors have on the stock beta of the telecommunications company. The results of this study indicate that the ability of telecommunications companies to have an average Current Ratio < standard deviation which means that the distribution of Current Ratio values is not good, while Return On Assets, Return On Equity, Total Assets Turn Over, Debt To Equity Ratio, and Asset Growth are in good condition. good. For statistical t-test, stock beta has no partial effect on Current Ratio, Return On Assets, Return On Equity, Total Assets Turn Over, Debt To Equity Ratio and Asset Growth. For testing F statistics that Current Ratio, Return On Assets, Return On Equity, Total Assets Turn Over, Debt To Equity Ratio, and Asset Growth do not have a simultaneous effect on the Beta Stocks.

FACTORS AFFECTING STOCK BETA IN LQ-45 COMPANIES FOR THE 2020-2021 PERIOD

The purpose of this study is to provide empirical evidence of the effect of financial leverage, earnings per share (EPS) and asset growth on beta stocks. Samples of this study are seven companies LQ45 index listed in Indonesian stock Exchange period 20 20 -20 2 1. The model used is multiple linear regression. The results show that asset growth has a significant effect on beta stocks, but financial leverage and earnings per share have no positive effect on beta stocks. While earnings per share have no significant effect on beta stocks and have a negative effect on beta stocks. The results in the study also show that financial leverage, earnings per share and asset growth have a significant effect on beta stocks simultaneously. The results have implications for prospective investors who want to invest in the stock market, especially in buying shares, should first consider asset growth factors because these factors prove to have a significant impact on stock beta.

Index tracking and beta arbitrage effects in comovement

This paper develops a stylised model for S&P 500 index changes with two beta-based styles: index trackers and beta arbitrageurs who trade in both high and low beta event stocks to exploit mean reversion towards one. Arbitrageurs engage in common or contrarian trading patterns relative to index funds depending on whether historical betas are below or above one. Thus, the overall comovement effect has two distinct components. After index additions, pre-event low beta stocks drive the overall beta increases due to common demand – albeit for different reasons - from indexers and arbitrageurs. By contrast, arbitrageur shorting of high beta additions diminishes or sometimes reverses the beta increases for these stocks driven by indexers. Analogous results hold for index deletions.

Betting against noisy beta

Strategies that overweight low beta stocks and underweight high beta stocks earn positive alphas. Price noise is known to affect high beta stocks, hence, noise trading can be expected to significantly affect the performance of these strategies. I study the impact of flows between bond and equity funds (net exchanges) on the Frazzini and Pedersen’s (2014) Betting Against Beta (BAB) factor in the US for a period 1984 until 2015. I find mispricing and reversal effects. In particular, when retail investors are caught up in the market euphoria, they are too optimistic, and shift their holdings from bond to equity mutual funds. My results suggest that higher-than-rational beta stocks are particularly exposed to this non-fundamental price pressure. Subsequently, the short-term reversal relation is stronger for high beta stocks and, therefore, returns are significantly lower. As a results, while the market performs poorly, the BAB factor returns are significantly positive. A dynamic trading strategy that is based on signals from past net exchanges and the BAB factor significantly outperforms the market factor by 0.71% monthly on average, during months following positive net exchanges by 1.62% and during market stress episodes by 2.14%. Accounting for transaction costs, other equity risk factors and non-standard procedures used in the BAB construction reduces the profitability of the strategy, but does not change the conclusions. My findings suggest that a major part of the success of the BAB factor is due to its exposure to flow-induced price noise.

Liquidity risk and the beta premium

AbstractAs opposed to the “low beta low risk” convention, we show that low beta stocks are illiquid and exposed to high liquidity risk. After adjusting for liquidity risk, low beta stocks no longer outperform high beta stocks. Although investors who “bet against beta” earn a significant beta premium under the Fama–French three‐ or five‐factor models, this strategy fails to generate any significant returns when liquidity risk is accounted for. Our work helps understand the beta premium from a new liquidity‐risk perspective, and draws useful implications for both fund and corporate managers.

Low liquidity beta anomaly in China

The conventional risk-based theory does not reconcile with the liquidity-beta anomaly in China: Low liquidity-beta stocks outperform high liquidity-beta stocks on a risk-adjusted basis. This striking pattern is robust to different weighting schemes, competing factor models, and other well-known return determinants in the cross section. We propose a competing behavioral-based explanation on the low liquidity beta anomaly in China. Consistent with our new perspective, liquidity beta is a negative return predictor in the cross section. Moreover, the time variation of the return differential between low and high liquidity beta stocks is led by investor sentiment after accounting for other possible economic mechanism.

Analysis Of The Impact Of Company Size On Systemic Risk Based On Capital Asset Pricing Model In Food And Beverages Companies Listed On The Indonesia Stock Exchange Year 2009-2011

Before investing, investors should consider the stock beta as a measure of systematic risk. By knowing beta stocks investors can directly determine the sensitivity of the return securities market returns. By knowing the sensitivity return, it automatically investors would be able to assess how much risk it will face when investing their funds in the company's stock. Investors can also adjust the investment that is fit to return they want to earn. This study aim is to determine the impact of company size on systematic risk based capital asset pricing models. Population of this study are all food and beverages manufacturing companies listed (listing) on ​​the Indonesian Stock Exchange from 2009 to 2011. There are 16 companies that fit in the criteria and the sample was 12 companies. Data were analyzed by multiple linear regression analysis. Results of this study showed that the size of the company significant positive effect on the systematic risk with adjusted R square value of 0.994, which means the size of the company has a strong influence in predicting systematic risk.

U.S. TRADE POLICY UNCERTAINTY AND EXPECTED STOCK RETURNS OF CHINESE LISTED COMPANIES

Our study is the first to examine the pricing effect of U.S. trade policy uncertainty (TPU) on Chinese stocks. We estimate the U.S. TPU beta, which measures Chinese stock exposure to the U.S. TPU index. Both portfolio analyses and cross-sectional regressions suggest a significantly negative relation between the U.S. TPU beta and expected returns, which cannot be explained by other pricing factors. The stocks in the lowest U.S. TPU beta quintile can generate 3.48% higher annual returns compared to stocks in the highest U.S. TPU beta quintile. Furthermore, we provide two potential mechanisms that include a real economy channel and a behavioral finance channel using vector autoregression models. Our results indicate that the negative premium can be explained by both demanding more of high TPU beta stocks in hedging against adverse effects from TPU and selling more of low TPU beta stocks due to pessimistic beliefs of noise trader.

Demand for Idiosyncratic Lottery-like Payoffs and the Cross-section of Expected Returns

Motivated by existing evidence of a preference among investors for stocks with high maximum daily returns, we document that lottery-like payoffs measured by maximum daily returns are almost entirely idiosyncratic. Firm-level cross-sectional regressions and portfolio-sort analyses prove that there is a significantly negative relation between idiosyncratic maximum daily returns (IMAX) and future stock returns. While beta anomaly, negative (positive) risk-adjusted returns generated by high (low) market beta stocks, disappears when portfolios are constructed to be neutral to idiosyncratic maximum daily returns, the significantly negative return spread between the stocks with extreme idiosyncratic lottery-like payoffs are robust to various firm-specific controls and risk factors. Investor sentiment is the dominant factor to trigger the demand for stocks with high idiosyncratic lottery-like payoffs. Finally, the relationship between idiosyncratic maximum daily returns and subsequent returns is stronger among stocks with low institutional ownership, low market capitalization, low analyst coverage, low liquidity, and high idiosyncratic volatility.

Constructing an equally weighted stock portfolio based on systematic risk (beta)

Many people worldwide make investments in different categories, especially stocks, to save and gain more money. The purpose of this study was to show the relationship between the beta (a measure of systematic risk) and returns on selected stocks from six different industries. Besides that, our goal was to determine if, while building a long-term portfolio, picking stocks from various sectors is efficient enough to outperform an overall market. We selected stocks from six industries - technology, pharmaceutical, energy, automobile, banking, and consumer staples sectors - from 2006 through 2020 for comparison. We calculated beta for each stock and sector using the Capital Asset Pricing Model (CAPM). These results were consistent with the hypothesis and demonstrated that above-average beta stocks from most sectors showed higher returns than below-average beta stocks and the market. Sector-wise, compared to the market and low beta stocks, higher beta stocks in the technology, pharmaceuticals, and consumer staples sectors displayed higher returns. Whereas in the automobile and energy sector, above-average beta stocks did perform better than lower beta stocks; however, they underperformed compared to the market. In the banking sector, above-average beta stocks did not perform very well. Overall, these results suggest that using above-average beta stocks from different sectors could be a viable way to build a profitable stock portfolio. Our model’s uniqueness is to balance the total portfolio, reducing risk not to drop drastically even if few sectors fluctuate by calculating a systematic risk beta for each sector and each stock within the sectors.

Anxiety, Excitement, and Asset Prices

This study examines the impact of integral emotions on portfolio decisions and asset prices. Using a new dictionary of anxiety- and excitement-related keywords, we measure the emotional state of the market and compute firm-level sensitivity to changes in market-level emotions (i.e., emotion beta). We find that stocks with high emotion betas outperform low emotion beta firms and this performance differential is corrected in about four months. During the 1990-2018 sample period, a Long-Short investment strategy with high-emotion beta stocks in the Long portfolio and low-emotion beta stocks in the Short generates an alpha of 4.92%. This evidence of emotion-based predictability is distinct from the known pricing effects of mood, sentiment, economic and policy uncertainty, and tone. Collectively, our findings show that emotional connections between investors and firms are priced.

In Search of Stock Market Proxy to Calculate Systematic Risk (Beta) of Stocks in Indonesia Stock Exchange

The calculation of beta stock in Indonesia is still debatable to this day. Though many researchers who have used sophisticated methods mathematically, the assumptions applied in developing the methods are impossible to happen in the real world, such as the ability of stock market return the day after (lead) affects the market return today. This study was conducted to assess the stock price index in Indonesia Stock Exchange that can be used as a proxy of stock market in Indonesia. The results of this study showed that there was a gap between beta stocks counted with JCI return as a market proxy with beta stocks counted with index returns of LQ-45, SRI-KEHATI, PEFINDO-25, BISNIS-27, IDX-30 and KOMPAS-100. This study has also found that the beta counted by using KOMPAS-100 return produced the smallest standard error of the estimate (SEE) that it was more applicable compared to the other stock index returns.

Beta herding through overconfidence: A behavioral explanation of the low-beta anomaly

We investigate asset returns using the concept of beta herding, which measures cross-sectional variations in betas due to changes in investors’ confidence about their market outlook. Overconfidence causes beta herding (compression of betas towards the market beta), while under-confidence leads to adverse beta herding (dispersion of betas from the market beta). We show that the low-beta anomaly can be explained by a return reversal following adverse beta herding, as high beta stocks underperform low beta stocks exclusively following periods of adverse beta herding. This result is robust to investors’ preferences for lottery-like assets, sentiment, and return reversals, and beta herding leads time variation in betas.

Individual stock sentiment beta and stock returns

Using the data in Chinese stock market, we measure the individual stock sentiment beta, which is defined as the sensitivity of individual stock returns to the individual stock sentiment changes. We demonstrate that stocks in the highest individual stock sentiment beta portfolio have significantly higher excess returns, CAPM alpha, Fama-French three-factor alpha and Fama-French five-factor alpha. Besides, we find that the high individual stock sentiment beta stocks are smaller, younger, more volatile stocks with higher price and higher market beta. After controlling for firm characteristic, the returns of High-Low individual stock sentiment beta portfolios are still significantly positive. Moreover, we show the effect of the individual stock sentiment beta on stock returns is positive and significant in different stock markets, in different sample periods, and in bull and bear market. Besides, the results of the Bayes-Stein individual stock sentiment beta are still stable.

Investor sentiment and the economic policy uncertainty premium

Motivated by recent studies documenting an equity premium associated with economic policy uncertainty (EPU), we test the hypothesis that the EPU premium is stronger (weaker) following periods of low (high) investor sentiment. We estimate stock sensitivity to an economic policy uncertainty (EPU) index and show that stocks in the Australian equities market in the highest uncertainty beta quintile underperform stocks in the lowest quintile, similar to U.S. stocks. However, we find that this negative uncertainty premium remains significant only following periods of low investor sentiment as it disappears following periods of high sentiment. Our results complement the U.S. evidence in that uncertainty averse investors are willing to pay high prices for stocks with positive uncertainty beta and require extra compensation to hold stocks with the negative beta, but only in low sentiment periods. These results are consistent with strong (weak) intertemporal hedging demand for positive EPU beta stocks in low (high) sentiment periods. It is also consistent with limited (full) participation of pessimistic investors and investors with a high aversion to uncertainty in low (high) sentiment periods. Our results suggest that betting against EPU as a trading strategy would be relatively more profitable when executed during low sentiment periods.

Bitcoin and sentiment

AbstractBaker and Wurgler identify high sentiment betas with small startup firms that have great growth potential. On the surface, cryptocurrencies share important features in common with high sentiment beta stocks. This paper investigates the degree to which, during the period July 18, 2010–February 26, 2018, the return to bitcoin displayed the characteristics of a high sentiment beta stock. Using a sentiment‐dependent factor model, the analysis indicates that in large measure, bitcoin returns resembled returns to high sentiment beta stocks. Additionally, we show that bitcoin's expected returns are low when sentiment measured by Volatility Index is high while expected returns are high when sentiment is low.

Order imbalance beta and stock returns

ABSTRACT This paper studies the impact of order imbalance beta on cross-sectional stock returns in the Chinese stock market. We measure the daily order imbalance beta, which is defined as the sensitivity of stock returns to changes in stock order imbalance, and show at the individual stock level that stocks with higher order imbalance betas contemporaneously have higher average returns. Furthermore, we find that higher order imbalance beta stocks tend to be smaller, and have lower trading volumes, lower prices and lower turnover rates. Moreover, we find that the long-horizon (1-, 3-, 6- and 12-month) effect of order imbalance beta on stock returns is consistent with the previous daily effect. Finally, we prove that the positive relationship between order imbalance beta and stock returns is robust in different stock markets in China and when using different minimum day limits (30, 45 and 60 days) to estimate order imbalance beta.

Is Personal Savings Rate Priced in the Cross-section of Stock Returns?

I investigate the importance of personal savings rate in the cross-sectional pricing of individual stocks. I estimate each stock's monthly-varying sensitivity to the personal savings rate and show that stocks in the lowest savings rate beta quintile generate 6% more annualized risk-adjusted return compared to stocks in the highest savings beta quintile. I find that the savings premium is driven by the outperformance (underperformance) of stocks with negative (positive) savings rate beta. These results are robust to controls for various firm-specific characteristics and risk factors. Moreover, the alpha spread between the highest and the lowest savings rate beta stocks increases during high economic uncertainty, low credit availability, and high income risk periods. Finally, the results are consistent with the risk correction predictions of the Consumption-CAPM literature.