Volatility Of Individual Stocks Research Articles

A generalization of the Topological Tail Dependence theory: From indices to individual stocks

This study investigates the Topological Tail Dependence (TTD) theory’s applicability to individual stock volatility and high dimensions. Utilizing a comprehensive dataset from the S&P 100, the research employs various methodologies to test the predictions and implications of the TTD theory. The theory’s main prediction of Wasserstein Distance’s predictive utility, particularly in nonlinear models during volatile periods, is confirmed. The research suggests extending the TTD theory’s application to various financial instruments and incorporating dynamic topological features to enhance understanding market dynamics. This study validates the TTD theory for individual stocks and highlights the necessity of topological considerations in financial modeling, promising advancements in financial econometrics and risk management strategies.

Identifying systemic risk drivers of FinTech and traditional financial institutions: machine learning-based prediction and interpretation

ABSTRACT We study systemic risk drivers of FinTech and traditional financial institutions under normal and extreme market conditions. We use machine learning (ML) techniques (i.e. random forest and gradient boosted regression trees) to evaluate the role of macroeconomic variables, firm characteristics, and network topologies as systemic risk drivers and perform the ML-based interpretation by Shapley individual and interaction values. We find that (i) the feature importance in driving systemic risk depends on market conditions; namely, market volatility (MVOL), individual stock volatility (IVOL), and market capitalization (MC) are positive drivers of systemic risk under extreme (downside and upside) market conditions, while under normal market conditions, institutions with high price-earnings ratio, large MC, and low IVOL play an essential role in stabilizing markets; (ii) macroeconomic variables are the most important extreme systemic risk drivers, while firm characteristics are more important under normal market conditions; and (iii) the interaction between IVOL and MC or MVOL is the significant source of extreme systemic risk, and MC is the most crucial interaction attribute under normal market conditions. The interactions between macroeconomic variables are the most prominent in systemic risk under different market conditions.

Stock price valuation analysis of the digital technology industry based on the HP-ARIMA method - with 18 listed companies of different volume sizes in its 3 segments as an example

The world securities market has formed a more complete system since its origin in the 17th century, with a considerable number of small and medium-sized investors and institutional investors, and proposing innovative new methods for securities valuation models can assist investors in making more accurate investment decisions. This paper proposes for the first time to use an interdisciplinary analysis method, ARIMA + HP filter analysis, to analyze the valuation of the securities market, by taking the opening prices of individual stocks in digital technology industry segments: blockchain, artificial intelligence, and Beidou navigation as examples for valuation analysis, firstly, the initial data of the opening prices of individual stocks from 2019-2021 are analyzed by HP filter analysis, and secondly, the data are analyzed while doing Tobit model test. By analyzing the long-term trend of the digital technology industry after noise reduction, its corresponding valuation forecast and value judgment results show that the head effect of the digital technology industry is more significant, and the opening price volatility shows a U-shaped relationship with company size and profitability, and the opening price volatility of individual stocks is less, while the shorter the company's establishment time, the greater the stock price volatility. While the past literature focused on the traditional valuation of financial conditions and historical data analysis of a single variable, this paper analyzes the data of individual stocks in the digital technology industry through an interdisciplinary analysis method, which allows for a more comprehensive use of historical data and prediction of possible stochastic fluctuations, providing a more reliable valuation method for securities investors and opening up new ideas for research related to the field of valuation analysis.

Momentum and the Cross-section of Stock Volatility

Recent literature shows that momentum strategies exhibit significant downside risks over certain periods, called “momentum crashes”. We find that high uncertainty of momentum strategy returns is sourced from the cross-sectional volatility of individual stocks. Stocks with high realised volatility over the formation period tend to lose momentum effect. We propose a new approach, generalised risk-adjusted momentum (GRJMOM), to mitigate the negative impact of high momentum-specific risks. GRJMOM is proven to be more profitable and less risky than existing momentum ranking approaches across multiple asset classes, including the UK stock, commodity, global equity index, and fixed income markets.

The five-factor model, stock returns and idiosyncratic volatility: evidence from Sri Lanka

Traditionally, the success of asset pricing models is assessed in the absence of idiosyncratic volatility, as it is believed that the role of idiosyncratic volatility is irrelevant. Nevertheless, the existing literature shows that idiosyncratic volatility matters in asset pricing decisions. Hence, this study aims to test the performance of the five-factor asset pricing model of Fama and French (2015) in the presence of idiosyncratic volatility. This study utilizes a sample of 214 companies listed on the Colombo Stock Exchange (CSE) except for those listed under the banks, finance, and insurance sectors over 163 months from September 2004 to March 2018. Nelson’s (1991) Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) models are used to estimate the idiosyncratic volatility of individual stocks. The empirical findings of the study confirm that the five-factor asset pricing model of Fama and French (2015) is less successful and failed to explain the stock returns in the presence of unsystematic risk in the Sri Lankan context. This finding yields striking evidence of the success of the five-factor asset pricing model in the presence of idiosyncratic volatility while it casts doubts on the applicability of such factor models in estimating the cost of equity of firms in the real world. Although the impact of idiosyncratic volatility on stock returns is well-researched in the finance literature, there is little structured research on how idiosyncratic volatility affects the performance of asset pricing models in the Sri Lankan context. This study fills this gap by investigating the performance of the five-factor asset pricing model of Fama and French (2015) using the firms listed on the CSE. The research findings should help academia develop more pronounced asset pricing models while tackling the idiosyncratic volatility of stocks.

The impacts of investors' sentiments on stock returns using fintech approaches

This study uses fintech approaches, including web crawler technology with distributed architecture to select internet news messages largely and efficiently and a dictionary-based linguistic text mining to create sentiment variables, to explore the respective impacts of investors' optimism and pessimism on stock returns. The construction of sentiment variables in network- and dictionary-based messages is more precise and variable than that in traditional-based messages. Our results show that firms with investors' optimistic sentiments have significantly higher stock returns in the current month, whereas those with pessimistic sentiments have significantly opposite effects. The effect of both investors' optimism and pessimism on stock returns subsequently reverses. Then, the negative impacts of investors' largely pessimistic sentiments on stock returns are larger than the positive impacts of their largely optimistic sentiments within a quarter. Next, investors' optimistic sentiments significantly raise stock return volatility by approximately a quarter, but their pessimistic sentiments have the opposite effects. Furthermore, investors' high optimism more significantly and persistently raises stock return volatility than their general optimism, but the negative effects of their high pessimism on volatility become smaller and the persistence is shorter than general pessimism. In addition to the advantage of our methodology in creating sentiment variables, the simultaneous consideration of investors' optimism and pessimism to analyze the effects on the returns and the volatility of individual stocks in this study is more complete than previous related studies.

Stock volatility and trading

Both, rational and behavioral models predict that stock and market volatility affect trading by investors. Tax-induced trading hypothesis predicts that investors increase realization of capital losses short term and capital gains only long term as volatility increases. Behavioral models predict that disposition biases of holding on to losers and disposing of winners intensifies with volatility. We document that market and stock volatility influence stock trading. Evidence on trading in response to rise in market volatility supports tax-loss harvesting hypothesis – abnormal trading of losers increases and winners decreases. However, evidence on trading patterns conditional on individual stock volatility is in support of both tax-loss-harvesting and behavioral models: trading in both losing stocks (tax-loss-harvesting hypothesis) and winning stocks (disposition effect hypothesis) increases with rise in stock volatility.

Momentum and the Cross-Section of Stock Volatility

Recent literature shows that strategies exhibit significant downside risks over certain periods, or called momentum crashes. We find that the high uncertainty of strategies is sourced from the cross-sectional volatility of individual stocks. Stocks with high realised volatility over the formation period tend to lose effect, while stocks with low realised volatility show strong momentum. A new approach, generalised risk-adjusted (GRJMOM), is introduced to mitigate the negative impact of high risks. GRJMOM is proven to be more profitable and less risky than the existing ranking approaches in multiple asset classes, including the UK stock, commodity, global equity index, and fixed income markets.

Contemporaneous Correlation between the Return and the Change of Trading Volume

This study demonstrates the contemporaneous correlation between return and the change of trading volume (CCRV) in the Korean stock market and analyzes the effect of trading volume change on the return and its volatility of individual stocks. Also, we examine the underlying reasons for CCRV in the Korean stock market. The empirical analysis covers individual stocks listed in KOSPI and KOSDAQ and their portfolios from 1989 to 2015. The main results are as follows. First, the CCRV in the Korean stock market dominantly appears positive. Second, at the individual stock level, the daily return volatility induced by CCRV accounts for 4.22% of the total daily return volatility. Third, the return volatility induced by CCRV is largely offset by well-diversified portfolios. Lastly, the ratio of positive CCRV decreases in stocks with very high or very low liquidity. The above result suggests that the illiquidity premium hypothesis is appropriate in explaining CCRV in the Korean stock market. In addition, the above results also indicate that the changes of trading volume can act as an idiosyncratic risk factor that can additionally intensify or weaken the response of the return toward the news. Furthermore, these results suggest that a strategy with sufficient consideration for CCRV is essential for the stock price prediction, the valuation of derivatives, and portfolio management.

Forecasting crude oil futures prices using global macroeconomic news sentiment

Abstract We propose a method of incorporating macroeconomic news into a predictive model for forecasting prices of crude oil futures contracts. Since these futures contracts are more liquid than the underlying commodity itself, accurate forecasting of their prices is of great value to multiple categories of market participants. We utilize the Kalman filtering framework for forecasting arbitrage-free (futures) prices and assume that the volatility of oil (futures) price is influenced by macroeconomic news. The impact of quantified news sentiment on the price volatility is modelled through a parametrized, non-linear functional map. This approach is motivated by the successful use of a similar model structure in our earlier work, for predicting individual stock volatility using stock-specific news. We claim the proposed model structure for incorporating macroeconomic news together with historical (market) data is novel and improves the accuracy of price prediction quite significantly. We report results of extensive numerical experiments which justify our claim.

Structural Change and Dynamics of Pakistan Stock Market during Crisis: A Complex Network Perspective.

We studied the cross-correlations in the daily closing prices of 181 stocks listed on the Pakistan stock exchange (PSX) covering a time period of 2007–2017 to compute the threshold networks and minimum spanning trees. In addition to the full sample analysis, our study uses three subsamples to examine the structural change and topological evolution before, during, and after the global financial crisis of 2008. We also apply Shannon entropy on the overall sample to measure the volatility of individual stocks. Our results find substantial clustering and a crisis-like less stable overall market structure, given the external and internal events of terrorism, political, financial, and economic crisis for Pakistan. The subsample results further reveal hierarchal scale-free structures and a reconfigured metastable market structure during a postcrisis period. In addition, time varying topological measures confirm the evidence of the presence of several star-like structures, the shrinkage of tree length due to crisis-related shocks, and an expansion in the recovery phase. Finally, changes of the central node of minimum spanning trees (MSTs), the volatile stock recognition using Shannon entropy, and the topology of threshold networks will help local and international investors of Pakistan Stock Exchange limited (PSX) to manage their portfolios or regulators to monitor the important nodes to achieve stability and to predict an upcoming crisis.

Information or noise: What does algorithmic trading incorporate into the stock prices?

This study investigates the role of algorithmic trading in the price discovery process. We estimate a state space framework which decomposes stock prices into permanent price series and transient pricing errors. Algorithmic traders contribute more to the permanent price processes and less to the transient pricing errors compared to other traders overall, during periods of high individual stock volatility, and on market-wide stress days. Algorithmic order flows are more associated with future stock returns compared to non-algorithmic order flows. Our results suggest that algorithmic traders incorporate price-relevant information and mitigate transitory price pressures.

Muddying the Waters: Who Induces Volatility in an Emerging Market?

Do all investor types contribute equally to volatility formation? Although stock volatility should ideally originate only from fundamental innovations, it is embedded into prices through the trading process. We compare the relative contributions of trading by local institutions, local individuals and foreign institutions to the volatility of individual stocks, using a proprietary dataset and a battery of robust measures. Overall, neither local nor foreign institutions are the major drivers of volatility, not even during times of financial stress. Individuals consistently appear to induce more of the volatility and liquidity, behaving as the archetypical noise traders but also as liquidity providers.

Securities Transaction Taxes and Market Quality

We study changes in market quality associated with 9 modifications to the New York State securities transaction tax (STT) between 1932 and 1981 and 3 changes to the federal STT between 1932 and 1966. We find that when there is an increase in the level of an STT, individual stock volatility increases, bid–ask spreads widen, price impacts are greater, and volume decreases. We examine the propensity of traders to switch trading locations to avoid the tax and find mixed evidence that they will change locations. Overall, our findings support the notion that the imposition of or increases in an STT harm market quality.

我国上市公司定向增发折价率影响因素的研究

本文采用主成分分析法，对选取的众多影响定向增发折价率的变量进行提取分类，以期确定这些主成分对定向增发折价率的影响程度，本文的主要研究结论如下：1) 在影响定向增发折价率的诸多因素中，信息不对称因素对其影响最大，且与其呈正相关关系；2) 市场行情影响程度次之，且与其呈正相关关系；3) 发行对象、大股东认购比例以及大股东认购比例与原持股比例的差额这三个代表利益输送因素的指标均与折价率存在正相关关系，从而佐证了利益输送假说。4) 代表流动性约束成本因素的两个指标，个股波动率和个股系统性风险均与折价率呈正相关关系，即个股波动率越大，个股系统性风险越高，从而流动性约束成本越高，折价率越高。这在一定程度上验证了流动性成本假说。5) 公司内在价值因素与定向增发折价率并不存在显著的相关关系，这一结论与理论分析及前人研究结果存在出入。 In this paper, we use principal component analysis to extract and classify many variables that af-fect the discount rate of private placement, in order to determine the extent to which these prin-cipal components affect the discount rate of private placement. The main conclusions of this paper are as follows: 1) Among the many factors affecting the rate of private placement discount, information asymmetry factors have the greatest impact on it and have a positive correlation with it. 2) Market conditions are the second largest factor and have a positive correlation with it. 3) The object of distribution, the subscription ratio of major shareholder and the difference between the subscription ratio of the major shareholder and the original shareholding ratio, all of these have positive correlations with the discount rate, and then verify the hypothesis of benefit transfer. 4) The volatility of individual stocks and the systematic risk of individual stocks which represent the cost constraint of liquidity constraints, are all positively correlated with the discount rate. In other words the larger the volatility of individual stocks, the higher the systemic risk of individual stocks, and the more liquidity constraint costs high, the higher the discount rate, and then verifies the liquidity cost hypothesis to a certain extent. 5) There is no significant correlation between the company intrinsic value and the private placement discount rate. This conclusion is different from the theoretical analysis and the predecessor research results.

Commonality In Volatility: Does It Exist In Amman Stock Exchange?

Sharma et al. (2014) uncovered significant evidence of co-movement between individual stock volatility and market volatility in NYSE. Their study provides the first evidence of commonality in volatility in the developed markets. Commonality seems to be more vital in emerging markets, which are characterized by higher average sample returns and higher levels of stock price volatility. This study investigates whether individual stock volatility co-moves with the overall market volatility in emerging markets. Our data set consisted of the daily observations of 105 companies listed in Amman Stock Exchange (ASE) over the period 2006-2015. Time series GARCH (1, 1) regressions were estimated for each stock individually. The results indicated a strong evidence of commonality in volatility in ASE. Thus, 76%- 89% of the firms investigated showed a statistically significant positive co-movement of their volatilities with the overall market volatility. However, when sectoral volatility was added to the models, the evidence of co-movement with market volatility became less important in the financial and industrial sectors. The firms in these two sectors showed a stronger co-movement of their volatilities with sectoral volatility if compared to their co-movement with market volatility. Moreover, our results exhibited no size effect in the commonality in volatility. No significant differences were found between the portfolios of different sized stocks. Our findings are vital to investors, portfolio managers, researchers and all parties who are interested in stock exchanges. To the best of authors’ knowledge, this study is the first in Jordan that tackles commonality in volatility.

Implied volatility in the individual stocks call options market: evidence from Malaysia

Among options traders, implied volatility is regarded as one of the most important variables for determining profitability in options trading. Implied volatility implies the future underlying stock volatility, and whilst it cannot predict market direction, it can forecast the stock's potential for large fluctuations in the future. Once the implied volatility has been calculated, the traders can estimate how high or low the stock might swing by the option's expiration, and this estimation helps traders to make informed trading decisions. In this paper, we examine the information content of the implied volatility of individual stocks call options in the Malaysian stock market. We use a daily dataset for 100 trading days for a period between November 2013 and February 2014. Our findings suggest that, for the Malaysian market, although implied volatility does contain some relevant information about future volatility, it is a less accurate predictor than historical volatility.

Impact of Short Selling on Volatility of Individual Stocks and Aggregate Market: Empirical Evidence from China.

This paper explored the linkage between short selling and market stabilization. While financial and economic theory advocates the positive role played by short sellers for market stabilization, short sellers are frequently blamed for causing market decline and increasing the panic in falling markets. China's regulators allowed short selling in 2010 and this reform provided us an interesting data set to check whether short sellers destabilize the market or play a positive role. By using asymmetric TGARCH model on daily return data of Shanghai Stock Exchange 50 index from January 5, 2007 to June 11, 2012, and taking into account Day-of-the-Week effect, financial crisis and regional and international spillovers, we found robust evidence that short sale trading reduces the volatility and stabilizes the market. Our findings have implications for China's regulators in policy making as well investors in the Chinese stock market in portfolio development.

What are returns outside trading hours capturing for volatility of individual stocks?

ABSTRACTThis study is the first to harness the negative returns and squared returns outside trading hours, trading volume and leverage effects in an augmented heterogeneous autoregressive model for forecasting volatility of individual stocks. Besides significant leverage effects and trading volume impact, we find that an increase in the negative returns is associated with a decline in volatility, but an increase in the squared returns is associated with a rise in volatility. This new finding suggests that the negative returns and squared returns outside trading hours are capturing additional leverage effects and additional volatilities, respectively. Moreover, the relations display differences amongst various firm categories which arise from firm heterogeneity.

Statistical properties of short-selling and margin-trading activities and their impacts on returns in the Chinese stock markets

We investigate the collective behaviors of short-selling and margin-trading between Chinese stocks and their impacts on the co-movements of stock returns by cross-correlation and partial correlation analyses. We find that the collective behaviors of margin-trading are largely attributed to the index cohesive force, while those of short-selling are mainly due to some direct interactions between stocks. Interestingly, the dominant role the finance industry plays in the collective behaviors of short-selling could make it more important in affecting the co-movement structure of stock returns by strengthening its relationship with the market index. By detecting the volume–return and volume–volatility relationships, we find that the investors of the two leverage activities are positively triggered by individual stock volatility first, and next, at the return level, margin-buyers show trend-following properties, while short-sellers are probably informative traders who trade on the information impulse of specific firms. However, the return predictability of the two leverage trading activities and their impacts on stock volatility are not significant. Moreover, both tails of the cumulative distributions of the two leverage trading activities are found following the stretched exponential law better than the power-law.