High-risk Stocks Research Articles

The Effect of Over-Confidence on Jiwasraya's 2017-2020 Payment Defaults

Overconfidence is a term that refers to the human tendency to estimate their abilities, knowledge, or results with more confidence than they should. PT. Jiwasraya Insurance (Persero), the oldest insurance company in Indonesia which is owned by BUMN, was declared to have failed to pay its customers' policies in 2020. This failure to pay was caused by errors in managing customer funds, namely by collecting funds for JS Products. Saving Plan and investing with customer funds in high risk stocks. The purpose of this research is to find out why Jiwasraya experienced payment failure. Therefore, the variable prices of high-risk shares purchased by companies from 2017 to 2020 is used as a secondary variable. Secondary data was obtained from the Indonesian Stock Exchange and the Financial Services Authority. This study uses. Vector Auto regressive (VAR) analysis technique with Stata/IC 15.0 software. The research results show that it is the Jiwasraya company's share price that influences Jiwasraya's shares in the short term. In the long term, it shows that the share price of Semen Baturaja does not have a significant effect on Jiwasraya's share price. Meanwhile, the share price of the West Java Development Bank has a significant negative effect on Jiwasraya's share price. Then, PP Properti's share price has a significant positive effect on Jiwasraya's share price. The results of this research can be used as consideration for companies and investors in making investment decisions so as not to take high-risk actions because of too high self-confidence.

Left-tail risk and UK stock return predictability: Underreaction, overreaction, and arbitrage difficulties

Recent studies challenge the standard model risk-return trade-off by showing inverse predictive power of firm-specific left-tail risk for future returns (i.e., left-tail momentum). In this work, we investigate the pricing of left-tail risk in UK stocks. Both the portfolio construction approach and Fama-MacBeth regressions reveal the underperformance of stocks with high left-tail risk. We examine alternative channels behind this pricing anomaly, namely, investor underreaction behaviour, continuous overreaction behaviour, and limits to arbitrage. Our findings suggest that the observed underperformance associated with high left-tail risk is largely a manifestation of investor underreaction to bad performance. However, the results also show that the predictable underperformance of high left-tail risk stocks is manifest in past winners. The empirical investigation reveals that, in addition to underreaction, limits to arbitrage interacts with investor high attention levels to explain part of the anomaly. The empirical findings provided here suggest several important implications for practitioners in the equity market.

A Study on the Factors Influencing Household Consumption from a Money Demand Perspective: Evidence from Chinese Urban Residents

Based on the classic Keynesian theory of money demand and city panel data, in this study, we investigate the impacts of different money demand motives on urban household consumption in China, and provide ideas for promoting sustainable growth in household consumption in China. The results of this study show the following: First, in general, the theory of money demand motivation can adequately explain household consumption in large and medium-sized cities in China. Second, the CPI time series has a significant adverse effect on the real money demand of most households. Third, residents significantly reduce food consumption to satisfy speculative money demand for financial instruments, and the lower the income level, the keener households are to invest in high-risk stocks. Fourth, even for high-income households, the precautionary money demand generated via the purchase of commercial insurance still has a significant crowding-out effect on their total consumption. Social security spending, which reduces the precautionary money demand of households, has a much more positive impact on high-income households.

Do the rich gamble in the stock market? Low risk anomalies and wealthy households

Contrary to the theoretical principle that higher risk is compensated with higher expected return, the literature shows that low-risk stocks outperform high-risk stocks. Using a large-scale household dataset, we provide an explanation for this puzzling result that the anomalous negative risk-return relation is only confined to those stocks predominantly held by rich households, whereas the anomaly disappears for stocks held by non-rich households and institutional investors. We find that social status concern of rich households and the induced lottery preference explain wealthy investors’ demand for high-risk stocks, leading to overpricing and low future returns for such stocks.

Retail investors and overpricing of left-tail risk: evidence from the Korean stock market

The authors examined whether stocks with higher left-tail risk measures earn higher or lower futures returns. Specifically, the authors estimate the cross-sectional principal component of a battery of left-tail risk measures and analyze future returns on stocks with high principal component values. In contrast to finance theories on the risk–return trade-off relationship, the study results show that high left-tail risk stocks have lower future returns. This finding is robust to various left-tail risk measures and controls for other risk factors. Moreover, the negative relationship between the left-tail risk and returns is more pronounced for stocks that are actively traded by retail investors. This empirical result is consistent with behavioral theory that when investors make decisions based on experience, they tend to underweight the likelihood of rare events.

Multi-period portfolio optimization based on credibilistic lower and upper VaR ratios

Emerging markets, such as the Chinese financial market, are occasionally subject to extreme risk events that result in investor losses during the investment process. To address the challenge of investment selection amidst market fluctuations, considering the fuzzy uncertainty and tail risk compensation based on the asymmetric perspective, we propose to use the lower VaR ratio and the upper VaR ratio as investment objectives to construct a multi-period credibilistic portfolio selection model. The study reveals that the cumulative returns and terminal wealth of the constructed model surpassed those of the benchmark models, delivering greater social and economic welfare to investors. During extreme events, investors could promptly adjust their portfolio structure to achieve higher investment returns. Investors who prefer the lower VaR ratio tend to make conservative investment decisions and allocate a higher proportion to defensive assets, such as bonds and risk-free assets. Conversely, investors who favor the upper VaR ratio are inclined to adopt aggressive investment strategies and allocate a larger proportion to high-risk stocks. The findings demonstrate that the proposed model offers differentiated investment decisions, and the research conclusions serve as valuable references for investors engaged in multi-period asset allocation and risk management.

Up and down together? On the linkage of momentum and reversal

This paper presents evidence of momentum and reversal being related phenomena in the German stock market. This finding contrasts Conrad and Yavuz's (2017) results for the US stock market. In the German stock market, the linkage of both phenomena is documented to be more pronounced after the turn of the millennium. In particular, low-risk stocks produce momentum, and high-risk stocks generate return reversal in momentum portfolios. Short-run momentum is consistently generated by the long winner leg, whereas long-run return reversal is due to the short loser leg of long-short portfolios in the German stock market.

An Investigation of the Beta Anomaly in Emerging Markets: A South African Case

High-risk stocks tend to provide lower returns than low-risk stocks on a risk-adjusted basis. These results (referred to as the low-beta anomaly) run counter to theoretical expectations. This paper examines the beta anomaly in one of the largest emerging markets in Africa, the Johannesburg Stock Exchange (JSE). It employs both time-series and cross-sectional econometric techniques to analyze the risk–return relationship implied by the CAPM, using data that span over 5 years and 220 companies. To check for robustness, the analysis period was extended to 10 years, and we also applied the Fama–French three-factor model. The findings suggest the existence of the beta anomaly and a negatively sloped SML, indicating that beta is not the only determinant of risk in the South African stock market. We also found positive beta–idiosyncratic volatility (IVOL) correlations. However, after controlling for IVOL and the adverse effects of COVID-19 for an extended study period, the beta anomaly disappeared.

What drives the distress risk–return puzzle? A perspective on limits of arbitrage

AbstractEmpirical research has documented a negative relationship between distress risk and stock returns. This negative risk–return trade‐off, known as the distress puzzle, poses a challenge to asset pricing models. In this study, we provide a new explanation of the distress puzzle by considering the effect of arbitrage asymmetry. We find that the negative distress risk–return relation is stronger in stocks that have higher limits of arbitrage. The investors are virtually unable to short sell mispriced high distress risk stocks due to the low supply of lendable stocks from institutions and that arbitrage is costly. In addition, we show that the limits of arbitrage effect is distinct from liquidity effect in explaining the distress puzzle.

Low-risk investment strategy: sector bets or stock bets?

PurposeThe portfolio of low-risk stocks outperforms the portfolio of high-risk stocks and market portfolios on a risk-adjusted basis. This phenomenon is called the low-risk effect. There are several economic and behavioral explanations for the existence and persistence of such an effect. However, it is still unclear whether specific sector orientation drives the low-risk effect. The study seeks to answer the following important questions in Indian equity markets: (a) Whether sector bets or stock bets mainly drive the low-risk effect? (b) Is it a mere proxy for the well-known value effect? (c) Does the low-risk effect prevail in long-only portfolios?Design/methodology/approachThe study is based on all the listed stocks on the National Stock Exchange (NSE) of India from December 1994 to September 2018. It classifies them into 11 Global Industry Classification Standard (GICS) sectors to construct stock-level and sector-level BAB (Betting Against Beta) and long-only low-risk portfolios. It follows the study of Asness et al. (2014) to construct various BAB portfolios. It applies Fama–French (FF) three-factor and Fama–French–Carhart (FFC) four-factor asset pricing models in addition to Capital Asset Pricing Model (CAPM) to examine the strength of BAB, sector-level BAB, stock-level BAB and long-only low-beta portfolios.FindingsBoth sector- and stock-level bets contribute to the return of the low-risk investing strategy, but the stock-level effect is dominant. Only betting on safe sectors or industries will not earn economically significant alpha. The low-risk effect is unique and not a value effect in disguise. Both long-short and long-only portfolios within sectors and industry groups deliver positive excess returns. Consumer staples, financial, materials and healthcare sectors mainly contribute to the returns of the low-risk effect in India. This study offers empirical evidence against the Samuelson (1998) micro-efficient market given the strong performance of the stock-level low-risk effect.Practical implicationsThe superior performance of the low-risk investment strategies at both stock and sector levels offers investors an opportunity to strategically invest in stocks from the right sectors and earn high risk-adjusted returns with lower drawdowns over an entire market cycle. Besides, it paves the way for stock exchanges and index manufacturers to launch sector-specific low-volatility indices for relevant sectors. Passive funds can launch index funds and exchange-traded funds by tracking these indices. Active fund managers can espouse sector-specific low-risk investment strategies based on the results of this and similar other studies.Originality/valueThe study is the first of its kind. It offers insights into the portfolio characteristics and performance of the long-short and the long-only variant of low-risk portfolios within sectors and industry groups. It decomposes the low-risk effect into sector-level and stock-level effects.

IPO underperformance and the idiosyncratic risk puzzle

We investigate the relationship between IPO long-run underperformance ( Ritter, 1991 ; Loughran and Ritter, 1995 ) and the idiosyncratic risk puzzle ( Ang, Hodrick, Xing, and Zhang, 2006 ) or the phenomenon of abnormally low returns for stocks with high idiosyncratic risk. We find that IPO long-run underperformance is a manifestation of surprisingly low returns for high idiosyncratic risk stocks. IPO underperformance disappears after we control for idiosyncratic risk. On the other hand, we find that the idiosyncratic risk puzzle is magnified by IPO underperformance. Our results are robust to various specifications or sample requirements. We evaluate a couple of potential common causes for the two puzzles and conclude that investors’ preferences for stocks with lottery features is the primary mechanism linking the two puzzles.

The Distress Anomaly is Deeper than You Think: Evidence from Stocks and Bonds

Abstract The distress anomaly reflects the abnormally low returns of high credit risk stocks during financial distress. Evidence from stocks and corporate bonds reinforces the anomaly and challenges rationales based on shareholders’ ability to extract value from bondholders, time-varying betas, lottery-type preferences, biased earnings expectations, and limits-to-arbitrage. Moreover, mispricing of distressed stocks and bonds is associated with excess investment and excess external financing. Potential real distortions are materially understated when assessed based only on equity mispricing. We emphasize the important role of corporate bonds in dissecting the distress anomaly, and show that the anomaly is an unresolved puzzle.

The Volatility Effect in China

This paper shows that low-risk stocks significantly outperform high-risk stocks in the local China A-share market. The main driver of this low-risk anomaly is volatility, and not beta. A Fama–French style VOL factor is not explained by the Fama–French–Carhart factors, and has the strongest stand-alone performance among all these factors. Our findings are robust across sectors and over time, and consistent with previous empirical evidence for the US and international markets. Moreover, the VOL premium exhibits excellent investability characteristics, as it involves a low turnover and remains strong when applied to only the largest and most liquid stocks. Our results imply that the volatility effect is a highly pervasive phenomenon, and that explanations should be able to account for its presence in highly institutionalized markets, such as the US, but also in the Chinese market where private investors dominate trading.

The Volatility Effect in China

This paper shows that low-risk stocks significantly outperform high-risk stocks in the local China A shares market. The main driver of this low-risk anomaly is volatility, and not beta. A Fama-French style VOL factor is not explained by the Fama-French-Carhart factors, and has the strongest stand-alone performance among all these factors. Our findings are robust across sectors and over time, and consistent with previous empirical evidence for the US and international markets. Moreover, the VOL premium exhibits excellent investability characteristics, as it involves a low turnover and remains strong when applied to only the largest and most liquid stocks. Our results imply that the volatility effect is a highly pervasive phenomenon, and that explanations should be able to account for its presence in highly institutionalized markets, such as the US, but also in the Chinese market where private investors dominate trading.

Fat and Fatter: Monthly Crash Risk and Investor Trading

I estimate ex-ante crash probabilities for individual stocks via novel machine learning methodologies. In particular, I introduce imbalanced learning techniques to facilitate rare events prediction. I show that stocks with high crash probabilities tend to have lower returns. Further results indicate that at least a subset of retail investors, as proxied by Robinhood traders, tend to chase high crash risk stocks, which may bid up their prices and result in lower returns subsequently. Using Robinhood's introduction of commission-free option trading at the end of 2017 as a quasi-natural experiment, together with textual information from Reddit, I document causal evidence that retail participation significantly increases stock crash risk. This effect is stronger for small firms.

The Contrarian Put

We develop an asset-pricing model to quantify how a typical behavior of retail investors affects stock prices in equilibrium. Retail investors like distressed stocks. Hence, in a distress scenario, their increased demand, coupled with high short-selling costs, allows agents to exit at higher prices. Rational investors anticipate this and agree to overpay in normal times. This effect is economically large for popular high-risk stocks. The model parameters are estimated using detailed trading data on OGX, a failed Brazilian oil giant. We estimate an overpricing of 6% well before distress, and an average overpricing of US$ 1.7 billion over two years.

The Real Side of Financial Distress

The distress anomaly reflects the abnormally low returns of high credit risk stocks during financial distress. Evidence from stocks and corporate bonds reinforces the anomaly and challenges rationales based on shareholders' ability to extract value from bondholders, time-varying betas, lottery-type preferences, biased earnings expectations, and limits-to-arbitrage. Moreover, mispricing of distressed stocks and bonds is associated with excess investment and excess external financing. Potential real distortions are materially understated when assessed based only on equity mispricing. We emphasize the important role of corporate bonds in dissecting the distress anomaly, and show that the anomaly is an unresolved puzzle.

Deep Learning Model for Financial Time Series Prediction

Stock market is considered complex, fickle, and dynamic. Undoubtedly, prediction of its price is one of the most challenging tasks in time series forecasting. Traditionally, there are several techniques to effectively predict the next t lag of time series data such as Logistic Regression and Random Forest. With the recent progression in sophisticated machine learning approaches such as deep learning, new algorithms are developed to analyze and forecast time series data. This paper employs Long-Short Term Memory (LSTM) deep learning approach to predict future prices for low, medium, and high risk stocks. To the best of our knowledge, we are proposing an innovating technique to evaluate deep learning and other prediction techniques w.r.t. the stocks’ risk factor. The proposed approach is compared with other traditional algorithms over different periods of training data. The results show that our LSTM approach outperforms other traditional approaches for all stock categories over different time periods. Experimental results illustrate that, for low and medium risk stocks, it is better to use LSTM with long time period of training data. However, for high risk stocks, short time period of training data provides more accurate predictions.

Deep Learning Model for Financial Time Series Prediction

Stock market is considered complex, fickle, and dynamic. Undoubtedly, prediction of its price is one of the most challenging tasks in time series forecasting. Traditionally, there are several techniques to effectively predict the next t lag of time series data such as Logistic Regression and Random Forest. With the recent progression in sophisticated machine learning approaches such as deep learning, new algorithms are developed to analyze and forecast time series data. This paper employs Long-Short Term Memory (LSTM) deep learning approach to predict future prices for low, medium, and high risk stocks. To the best of our knowledge, we are proposing an innovating technique to evaluate deep learning and other prediction techniques w.r.t. the stocks’ risk factor. The proposed approach is compared with other traditional algorithms over different periods of training data. The results show that our LSTM approach outperforms other traditional approaches for all stock categories over different time periods. Experimental results illustrate that, for low and medium risk stocks, it is better to use LSTM with long time period of training data. However, for high risk stocks, short time period of training data provides more accurate predictions.

Do the Rich Gamble in the Stock Market? Low Risk Anomalies and Wealthy Households

Contrary to the theoretical principle that higher risk is compensated with higher expected return, the literature shows that low-risk stocks outperform high-risk stocks. Using a large-scale household dataset, we provide an explanation for this puzzling result that the anomalous negative risk-return relation is only confined to those stocks held by rich households, whereas the anomaly disappears for stocks held by non-rich households and institutional investors. We find that skewness preference and optimism of rich households as well as their attention/attraction to lottery stocks explain wealthy investors' demand for high-risk stocks, leading to overpricing and low future returns for such stocks.