Stock Characteristics Research Articles

Dynamical characteristics of global stock markets based on time dependent Tsallis non-extensive statistics and generalized Hurst exponents

We perform non-linear analysis on stock market indices using time-dependent extended Tsallis statistics. Specifically, we evaluate the q-triplet for particular time periods with the purpose of demonstrating the temporal dependence of the extended characteristics of the underlying market dynamics. We apply the analysis on daily close price timeseries of four major global markets (S&P 500, Tokyo-NIKKEI, Frankfurt-DAX, London-LSE). For comparison, we also compute time-dependentGeneralized Hurst Exponents (GHE) Hq using the GHE method, thus estimating the temporal evolution of the multiscaling characteristics of the index dynamics. We focus on periods before and after critical market events such as stock market bubbles (2000 dot.com bubble, Japanese 1990 bubble, 2008 US real estate crisis) and find that the temporal trends of q-triplet values significantly differ among these periods indicating that in the rising period before a bubble break, the underlying extended statistics of the market dynamics strongly deviates from purely stochastic behavior, whereas, after the breakdown, it gradually converges to the Gaussian-like behavior which is a characteristic of an efficient market. We also conclude that relative temporal variation patterns of the Tsallis q-triplet can be connected to different aspects of market dynamics and reveals useful information about market conditions especially those underlying the development of a stock market bubble. We found specific temporal patterns and trends in the relative variation of the indices in the q-triplet that distinguish periods just before and just after a stock-market bubble break. Differences between endogenous and exogenous stock market crises are also captured by the temporal changes in the Tsallis q-triplet. Finally, we introduce two new time-dependent empirical metrics (Q-metrics) that are functions of the Tsallis q-triplet. We apply them to the above stock market index price timeseries and discuss the significance of their temporal dependence on market dynamics and the possibility of using them, together with the relative temporal changes of the q-triplet, as signaling tools for future market events such as the development of a market bubble.

Uncovering the in-use metal stocks and implied recycling potential in electric vehicle batteries considering cascaded use: a case study of China.

With the rapid promotion of new energy vehicles, in-use electric vehicle batteries (EVBs) are becoming an important component of urban mining. This paper analyzed the metal stocks in EVBs in China from 2009 to 2019 using a bottom-up method, which focused on the in-use stock of seven main metals, namely, nickel, cobalt, manganese, lithium, copper, aluminum, and iron, in primary use stage and secondary use stage of three EVB types, namely, lithium nickel manganese cobalt oxide battery (NMC), lithium iron phosphate battery (LFP), and lithium manganese oxide battery (LMO). It was found that the rapid development of electric vehicles (EVs) contributed to a dramatic increase in in-use metal stocks from 0.7 kt in 2009 to 1.1 Mt in 2019. To assess the increase, three scenarios simulating metal stocks in EVBs from 2020 to 2030 were analyzed, namely, baseline, NMC-dominated, and LFP-dominated, and results indicated that metal stocks will reach 20.6 Mt, 23.2 Mt, and 17.9 Mt, respectively, by 2030. Across the scenarios there is little proportional difference in metal stocks between the two use stages. The proportion of the three EVB types correlates to the development trend of EVB technology under each corresponding scenario. Besides, the in-use metal stocks in EVBs have high implied recycling potential and environmental benefit. The recycling potential of these seven metals is 1.0 Mt in 2019, and it will reach 20.0 Mt, 22.6 Mt, and 17.4 Mt, respectively, in 2030 under the three scenarios. The results reveal the current status and evolution characteristics of metal stocks in EVBs in China, and provide data for material flow analysis and life cycle management of EVBs.

Option measures and stock characteristics

An extensive existing literature demonstrates that various option measures (e.g., option-to-stock trading volume ratio) exhibit return predictability in the cross section of stocks. Exploring the fundamental nature and robustness of these findings, we investigate whether this previously identified return predictability of option measures is highly associated with the characteristics or attributes of the underlying stock. We find that the return predictability of option measures is heavily impacted by a subset of stocks whose characteristics also predict future returns, thus providing evidence that stock characteristics are one of the main channels through which option measures capture informed trading.

The Promises and Pitfalls of Machine Learning for Predicting Stock Returns

Recent research suggests that machine learning models dominate traditional linear models in predicting cross-sectional stock returns. The authors confirm this finding when predicting one-month-forward-looking returns based on a set of common stock characteristics, including predictors such as short-term reversal. Despite the statistical advantage of machine learning model predictions, the authors demonstrate that the economic gains tend to be more limited and critically dependent on the ability to take risk and implement trades efficiently. Unlike traditional models, machine learning models have been somewhat more effective over the past decade at discerning valuable predictions from cross-sectional equity characteristics. <b>TOPICS:</b>Security analysis and valuation, big data/machine learning <b>Key Findings</b> ▪ The authors compare a nonlinear machine learning model called gradient boosting machine (GBM) with traditional linear models in predicting cross-sectional stock returns based on well-known equity characteristics. ▪ They demonstrate how to rationalize the mechanics and outcome of GBM to alleviate its black-box characteristics. ▪ The extent to which the statistical advantage of GBM’s performance over that of linear models can be translated into economic gains depends critically on one’s ability to take risk and implement trades efficiently.

Impacts of long-term temperature change and variability on electricity investments

Long-term temperature change and variability are expected to have significant impacts on future electric capacity and investments. This study improves upon past studies by accounting for hourly and monthly dynamics of electricity use, long-term socioeconomic drivers, and interactions of the electric sector with rest of the economy for a comprehensive analysis of temperature change impacts on cooling and heating services and their corresponding impact on electric capacity and investments. Using the United States as an example, here we show that under a scenario consistent with a socioeconomic pathway 2 (SSP2) and representative concentration pathway 8.5 (RCP 8.5), mean temperature changes drive increases in annual electricity demands by 0.5-8% across states in 2100. But more importantly, peak temperature changes drive increases in capital investments by 3-22%. Moreover, temperature-induced capital investments are highly sensitive to both long-term socioeconomic assumptions and spatial heterogeneity of fuel prices and capital stock characteristics, which underscores the importance of a comprehensive approach to inform long-term electric sector planning.

The relationship between cost behaviour and R&D efficiency: empirical evidence from Korean stock markets

ABSTRACT Strategic resource distribution and efficient use of limited resources are important in business operations. Cost behaviour is an indicator of these investment strategies. Previous studies have attributed the asymmetry of cost behaviour to managerial expectations. When managers expect future sales revenue to increase, they incur surplus expenses to avoid adjustment costs. In this research, we analyzed whether the cost behaviour stemming from such choices is related to future R&D efficiency. We divided KOSPI and KOSDAQ samples showing sales increases into ‘stickiness’ and ‘anti-stickiness’ groups and compared R&D efficiency using data from successive years. In the case of the KOSPI market, the stickiness group had higher future R&D efficiency than the anti-stickiness group. Conversely, in the case of the KOSDAQ market, the anti-stickiness group had higher future R&D efficiency than the stickiness group. The results elucidate the relationship between cost behaviour and R&D efficiency and show that investment strategy patterns depend on the specific characteristics of stock markets. This research enhances the related literature and the understanding of cost behaviours that reflect distinct investment strategies.

Genetic diversity and population structure of selected lacustrine and riverine populations of African catfish, Clarias gariepinus (Burchell, 1822), in Kenya

Determining the genetic characteristics of natural fish stocks is useful for conservation and aquaculture programs. For African catfish, Clarias gariepinus, genetic characterization could help identify populations suitable as brood stock for culture, and those in need of conservation. This study determined the genetic diversity, population structure, and demographic history of C. gariepinus from Lakes Victoria (LV), Kenyatta (LKE), Kamnarok (LKA), and Rivers Nyando (NR), Tana (TR) and Sosiani (SR) in Kenya. Using 128 DNA sequences of D-loop control region, 34 haplotypes were recovered, of which 79.4% were singletons. Only 7 haplotypes were shared between sites, implying little gene flow between sites. Number of haplotypes was highest in LKE and NR populations and lowest in SR. Haplotype diversity was highest in LV, and lowest in SR, while, nucleotide diversity was highest in LKA and lowest in LV. Phylogenetic analyses revealed five clusters: Lakes Victoria, Kamnarok and Kenyatta, and Rivers Tana and Nyando, from both maximum likelihood tree and minimum spanning network. This, together with significant FST values among the sites imply population differentiation. Mismatch distributions were multi-modal in LKA, LKE, NR and TR, signifying demographic equilibria. Neutrality tests Tajima`s D values for the sampled populations were negative and significantly different, suggesting stable populations. These results show the existence of genetically distinct populations of C. gariepinus that require spatially explicit management actions such as reducing fishing pressure, pollution, minimizing habitat destruction and fragmentation for sustainable utilisation of stocks.

Dispersion in analysts’ target prices and stock returns

We propose the dispersion in analysts’ target prices as a new measure of disagreement among analysts and a proxy of ex ante stock risk. In contrast to the negative return predictability of analysts’ earnings forecast dispersion but consistent with the risk hypothesis, we document a significant positive relation between the target price dispersion and future stock returns up to 24 months. The next-month return spread between the highest and lowest deciles sorted on the target price dispersion measures can be over 2%. Our findings cannot be explained by the standard risk factors and stock characteristics including the target price revision. Further supporting the risk hypothesis, the target price dispersion is positively related to future stock risk.

Game of names: Blockchain premium in corporate names

We examine stock price response around cryptocurrency‐related corporate name change announcements using an event study framework. We find that cryptocurrency‐related name changes generate larger gains in share price and volume than other corporate name changes. The valuation gains associated with cryptocurrency‐related name changes cannot be explained by standard asset pricing factors, firm and stock characteristics, industry specific shocks, or by the presence of outliers. These gains are higher when the announcements happen during periods of high sentiment for cryptocurrencies. There is evidence that shareholders with better access to private information about such name changes are able to front‐run other shareholders.

Buy-Side Competition and Momentum Profits

Abstract We show that a new measure of buy-side competition explains momentum profits. The momentum quintile spread is 1.11% when competition is low and negligible when competition is high. Better alphas are attained with superior Sharpe and Sortino ratios, with no negative skewness, and in more investible strategies featuring value-weighted portfolios and large capitalization stocks. Stock characteristics traditionally related to momentum do not explain our results. Tests based on long-term reversals, the trading patterns of funds, their style peers, distant funds, and retail investors suggest that slow information diffusion explains the large momentum spreads and momentum reversals in low competition markets.

Practical Applications of Stock Characteristics and Stock Returns: A Skeptic’s Look at the Cross Section of Expected Returns

Practical Applications of Stock Characteristics and Stock Returns: <i>A Skeptic’s Look at the Cross Section of Expected Returns</i>

Sentiment and Covariance Characteristics

We propose a bridging model that connects risk-based factor models to sentiment models by using characteristics. Investors use stock characteristics as information to form their biased view and hence creating mispricing. Characteristics also serve as the proxy for the covariance risk to a latent factor. The α from our factor model of mispricing ranges from 0.70% to 1.38% monthly after controlling for other common factors and mispricing measures. Well-known anomalies are only represented in either underpriced or overpriced stocks but not in all the cross-section.

The gambling preference and stock price: Evidence from China's stock market

Considering the strong gambling preference of retail investors in emerging markets and using the data of Chinese A-share listed companies from 2000 to 2018, this paper constructs an index of investor's gambling preference based on the theory of explicit preference and develops a factor model to capture the risk premium of gambling by introducing gambling factors. The results show that the factor model not only fits the gambling characteristics of the Chinese stock market well but also has strong explanatory power for common market anomalies. Through the index of capital gains over-hang (Wang et al., 2016), this paper further finds that facing different degrees of losses, investors will show different gambling preferences. The factor model also shows stronger explanatory power in the sample with more losses, revealing the investment characteristics of retail investors that the more you gamble and the more you lose. This study would be meaningful for exploring behavioral pricing factors, understanding emerging stock markets and supporting investment practice.

Corporate aging and changes in the pricing of stock characteristics

This study reveals that the pricing of several prominent stock and firm characteristics changes as firms age, based on a sample of common stocks in the US. It contributes to the literature by showing that interactions between stock characteristics, particularly firm age, are pricing relevant for the cross-section of stock returns. This finding emerges from a robust multivariate panel regression approach, which helps to address the multivariate challenge in cross-sectional asset pricing and controls for firm-fixed effects.

How to Outlast a Pandemic: Corporate Payout Policy and Capital Structure Decisions During COVID-19

The outbreak of the Covid-19 pandemic massively increased uncertainty about firms’ cash flows and access to financial markets. We examine its effect on firms’ strategies for preserving cash by suspending dividends and share repurchase programs and raising new funds through bond and equity issues. Our estimates suggest that between March and December 2020 US firms saved a combined $86bn by suspending or reducing dividend payments and another $140bn from suspending buybacks. We identify a short list of firm and stock characteristics that explain most of the cross-sectional variation in firms’ payout and financing decisions. We show that the expansive monetary policies pursued by the Federal Reserve in the early phase of the pandemic crucially affected the timing and sequencing of firms’ decisions. Announcement effects on stock returns were highly unusual during the pandemic as dividend and buyback suspensions were associated with a more rapid recovery in firms’ stock prices, consistent with investors interpreting them as prudentactions that helped reduce risks.

The Strategic Use of 13F Restatement by Hedge Funds

Hedge funds can amend their originally reported 13F holdings using restatements subsequently. We conduct the first systematic analysis of such filings, which are as common as the confidential filings (used by funds to delay holdings disclosure), but affect three times more stocks. We find the restated holdings are associated with significant abnormal returns, suggesting that restatements are often used strategically to hide funds' trading intention (for both buys and sells). We examine fund and stock characteristics that are related to this strategic choice. We also construct a return gap measure to gauge the value added from such restatements and find it to predict future fund performance. Finally, we show that commonly used databases such as Thomson Reuters do not fully adjust for restatements. While the resulting discrepancy is small in aggregate, it can be large for many funds.

Is sector-neutrality in factor investing a mistake?

Stock characteristics have two sources of predictive power. First, a characteristic might be valuable in identifying high or low expected returns across industries. Second, a characteristic might be useful in identifying individual stock expected returns within an industry. Past studies generally find that the firm-specific component is the strongest predictor, leading many to sector neutralize their factor exposures. We show that this problem is equivalent to the classic two risky-asset problem and derive the condition that decides when the sector component of a characteristic should be omitted. We show both analytically and empirically that the long–short investor is more likely to benefit from hedging out sector bets, whereas the long-only investor is more likely to benefit from investing in the factor as it stands.

Who Owns What? A Factor Model for Direct Stockholding

We build a cross-sectional factor model for investors' direct stockholdings, by analogy with standard time-series factor models for stock returns. We estimate the model using data from almost 10 million retail accounts in the Indian stock market. We find that stock characteristics such as firm age and share price have strong investor clienteles associated with them. Similarly, account attributes such as account age, account size, and extreme underdiversification (holding a single stock) are associated with particular characteristic preferences. Coheld stocks tend to have higher return covariance, suggestive of the importance of clientele effects in the stock market.

Option Return Predictability with Machine Learning and Big Data

Drawing upon more than 12 million observations over the period from 1996 to 2020, we find that allowing for nonlinearities significantly increases the out-of-sample performance of option and stock characteristics in predicting future option returns. Besides statistical significance, the nonlinear machine learning models generate economically sizeable profits in the long-short portfolios of equity options even after accounting for transaction costs. Although option-based characteristics are the most important standalone predictors, stock-based measures offer substantial incremental predictive power when considered alongside option-based characteristics. Finally, we provide compelling evidence that option return predictability is driven by informational frictions and option mispricing.

The Cross-Section of Stock Returns before 1926 (And Beyond)

We study the cross-section of stock returns using a novel constructed database of U.S. stocks covering 61 years of additional and independent data. Our database contains data on stock prices, dividends and hand-collected market capitalizations for 1,488 major stocks between 1866-1926. Results over this ‘pre-CRSP’ era reveal a flat relation between market beta and returns, an insignificant size premium, and significant momentum, value and low-risk premiums that are of similar size as over the post-1926 period. Overall, stock characteristics can explain over 25% of variation in stock returns. Further, recent machine learning methods are successful in predicting cross-sectional returns out-of-sample. These results show strong out-of-sample robustness of traditional factor models and novel machine learning methods.