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.     

Dynamic and asymmetric effects between carbon emission trading,financial uncertainties,and Chinese industry stocks: Evidence from quantile-on-quantile and causality-in-quantiles analysis

This paper investigates the dynamic and asymmetric effects between carbon emission trading (CET), financial uncertainties, and Chinese stocks in different industries over the period from 19th December 2013 to 21st March 2022. We utilized a novel quantile framework including rolling window quantile regression method, quantile-on-quantile method, and causality-in-quantiles method to implement this research more comprehensively and accurately. Our contributions and findings, empirical in nature, are as follows: (i) In the early establishing stage of the carbon market, with a bullish market situation, carbon emission trading has a negative impact on most industry stocks. In the developing and improving stage of the carbon market, different industries have different impact situations. (ii) We find that the effects of financial uncertainty on stocks are stronger than CET on stocks. We also find that the dependence structures between CET, financial uncertainty, and industry stocks are asymmetric in most industries, and there are many mutation structures with significant risks in extreme situations. (iii) Carbon emissions trading, crude oil volatility, and US stock volatility all have strong causal relationships with Chinese industry stocks. (iv) We also provide policy suggestions to relevant countries to balance carbon market and stock markets and avoid risks from financial uncertainty in different industries.     

Day trading and stock price volatility

When an investor buys and sells the same stock on the same day, he is said to have made a day trade. Using the trading records of Finnish traders, this paper examines whether day trading is related to volatility of stock prices. I find a strong positive time-series relation between the number of day trades by individual investors and intraday volatility among heavily day traded stocks. This effect is robust after controlling for a previously documented volume–volatility relation. The result suggests that the joint hypothesis of price pressure and volatility induced day trading dominates the liquidity effects of day trading.     

The impact of option listing on the price volatility and trading volume of underlying OTC stocks

This paper examines the impact of an initial option listing on the price volatility and trading volume of underlying OTC stocks. The sample is divided by market value to determine whether larger firms are impacted differently by option listing than smaller firms. We find relative trading volume increases significantly, with the small and medium market value firms showing the largest gain. However, the tests show no evidence of changes in price volatility following option listing. No significant changes were found in either the firms' betas or variance following option initiation. The results provide further evidence that option listing does not destabilize the market for the underlying stock.     

Stock market dynamics with institutional trading

In this paper, the impact of institutional trading on stock market dynamics is analysed. We study the problem in an abstract context in which stocks of only one type are traded and institutions' portfolio choices are simply restricted to the partition of portfolio between stocks and bonds; in this simplified context it is shown how institutions' monitoring of individual investors' potentially destabilizing behavior (hearding and positive-feedback behavior) can be effective to obtain stock market stability.

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Sommario In questo articolo abbiamo analizzato la dinamica del mercato azionario in presenza di fondi di investimento. A tal fine, è stato proposto e studiato un modello dinamico che evidenzia alcuni nessi causali fra le scelte dei fondi e le scelte degli investitori non istituzionali.

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While remaining fully rsponsible for this paper, I thank Marcello Galeotti, Antonio Gay and Franco Gori for insightful comments and suggestions.     

Impact of leveraged ETF trading on the market quality of component stocks

We investigate the effect of leveraged ETF trading on the trading activity and market quality of their component stocks. The results show that both quoted and effective spreads of component stocks increase about 0.2–3.0 basis points after the inception of leveraged ETFs, while other liquidity measures do not show significant changes. The trading volume of component stocks is positively and significantly correlated with the trading volume of leveraged ETFs, but the volatility of component stocks is not affected by ETF trading either at the daily level or during the last hour of trading. In addition, the volatility of component stocks decreases slightly after ETF inception. These findings do not support the previous claim that the trading of leveraged ETFs increases price volatility of component stocks.     

Retail investors’ trading and stock market liquidity

The paper investigates the relation between retail investors’ participation in trading and aggregate stock market liquidity. The findings show a positive and significant relation between retail investors’ trading and stock market liquidity. Examination of the determinants of retail investors’ trading reveals that, on average, retail investors with more diversified trading activity tend to trade when liquidity is higher, the frequency of their arrival to the market is not affected by the level of liquidity, and retail investors are willing to trade at a lower liquidity level as sellers than as buyers. Moreover, retail investors’ trading does not create price noise at the aggregate market level. Overall, the evidence suggests that retail investors contribute to market quality.     

Individual investors surpass their reputation: Trading behaviour on the Polish futures market

This paper examines individual investors’ trading behaviour by testing the presence of Monday and January anomalies on the Polish futures market, where individuals are the predominant trader type. Both anomalies are well established in the literature, and they are at least partially attributed to individual investors’ trading activities. We conduct an intraday analysis of trading volume, open interest, returns, and return volatility on the futures market in Poland and find the contribution of individuals to market anomalies to be grossly overstated. Hence, individual investors’ trading on the Polish futures market surpasses the prediction by the majority of investigations for mature stock markets.     

Index futures trading and stock price volatility: Evidence from Denmark and France

This study uses an EGARCH methodology to investigate the impact of index futures trading on the price volatility of two European stock markets. The results show that index futures trading has changed the distribution of stock returns in Denmark and France, however, it has not increased stock price volatility. There is evidence that futures trading has dampened stock price fluctuations in France. The results further show that stocks in Denmark and France exhibit strong volatility persistence and asymmetry, especially during the post-futures period.     

Asymmetric positive feedback trading and stock pricing in China

Defining asymmetry of feedback trading (AFC) as the difference between buying-winners and selling-losers intensities, the paper investigates if AFC impacts stock pricing. We show that buying stocks with low AFC and selling stocks with high AFC makes significant positive returns after controlling traditional pricing factors. The return mainly comes from the long leg and cannot be simply attributed to either mispricing, liquidity, or risk premium. Further study shows that the negative impact of AFC on future stock return is reinforced with an increase in past returns, maximum daily return, relative valuation level, asset growth rate, or operating profit rate. As AFC represents retail trading intensity, the results imply that the inactiveness of retail investors may make price relative underreaction to good news and thus lead to positive expected stock return.     

An equilibrium model of insider trading in continuous time

We study a Bayesian–Nash equilibrium model of insider trading in continuous time. The supply of the risky asset is assumed to be stochastic. This supply can be interpreted as noise from nonrational traders (noise traders). A rational informed investor (the insider) has private information on the growth rate of the dividend flow rewarded by the risky asset. She is risk averse and maximizes her inter-temporal utility rate over an infinite time-horizon. The market is cleared by a risk neutral market maker who sets the price of the risky asset competitively as the conditional present value of future dividends, given the information supplied by the dividend history and the cumulative order flow. Due to the presence of noise traders, the market demand does not fully reveal the insider’s private information, which slowly becomes incorporated in prices. An interesting result of the paper is that a nonstandard linear filtering procedure gives an a priori form for the equilibrium strategy to be postulated. We show the existence of a stationary linear equilibrium where the insider acts strategically by taking advantage of the camouflage provided by the noise which affects the market maker’s estimates on private information. In this equilibrium, we find that the insider’s returns on the stock are uncorrelated over long periods of time. Finally, we show that the instantaneous variance of the price under asymmetric information lies between the instantaneous variance of the price under complete and incomplete information. The converse inequalities hold true for the unconditional variance of the price.     

Forecasting abnormal stock returns and trading volume using investor sentiment: Evidence from online search

We examine the ability of online ticker searches (e.g. XOM for Exxon Mobil) to forecast abnormal stock returns and trading volumes. Specifically, we argue that online ticker searches serve as a valid proxy for investor sentiment — a set of beliefs about cash flows and investment risks that are not necessarily justified by the facts at hand — which is generally associated with less sophisticated, retail investors. Based on prior research on investor sentiment, we expect online search intensity to forecast stock returns and trading volume, and also expect that highly volatile stocks, which are more difficult to arbitrage, will be more sensitive to search intensity than less volatile stocks. In a sample of S&P 500 firms over the period 2005-2008, we find that, over a weekly horizon, online search intensity reliably predicts abnormal stock returns and trading volumes, and that the sensitivity of returns to search intensity is positively related to the difficulty of a stock being arbitraged. More broadly, our study highlights the potential of employing online search data for other forecasting applications.     

Stock trading dynamics and pedestrian counterflows: Analogies and differences

This paper contrasts stock trading dynamics with pedestrian counterflow movements. We apply the social force model built on pedestrian movement patterns to examine micro characteristics of the Chinese stock market. Utilizing one-minute high frequency stock trading data of the Shanghai Composite Index between 2014 and 2017, we find that stock trading dynamics under loose, prudent and austerity monetary policies closely resemble pedestrian movement patterns under wide, moderate, and narrow door width, respectively. In addition, we find that stock trading patterns with unbalanced buyers and sellers correspond to pedestrian counterflows with unbalanced flows from one side of the door to the other. Our results also show that stock trading patterns under various trading volumes are similar to pedestrian counterflows with different flow rates. In general, our results indicate that stock trading patterns are influenced by investor behaviors and conflicting interests similar to those present in the social force model of pedestrian counterflows. Thus, examining the behavioral mechanism at play in these self-driven systems will generate important insights for the behavioral foundation of financial markets.     

The impact of individual investor trading on information asymmetry in the Korean stock market

This paper examines the impact of individual investor trading on information asymmetry in the market. In particular, we examine the relationship between the trading volume by individual investors and the corresponding bid-ask spread in the Korean stock market, where the majority of the trading activity is driven by individual investors and therefore information asymmetry can be evident. We find that high trading activity by individual investors increases the bid-ask spread in a short investment horizon, suggesting that individual investors, as uninformed and unsophisticated traders, amplify the degree of information asymmetry in the market through trading.     

Pair trading based on quantile forecasting of smooth transition GARCH models

Pair trading is a statistical arbitrage strategy used on similar assets with dissimilar valuations. We utilize smooth transition heteroskedastic models with a second-order logistic function to generate trading entry and exit signals and suggest two pair trading strategies: the first uses the upper and lower threshold values in the proposed model as trading entry and exit signals, while the second strategy instead takes one-step-ahead quantile forecasts obtained from the same model. We employ Bayesian Markov chain Monte Carlo sampling methods for updating the estimates and quantile forecasts. As an illustration, we conduct a simulation study and empirical analysis of the daily stock returns of 36 stocks from U.S. stock markets. We use the minimum square distance method to select ten stock pairs, choose additional five pairs consisting of two companies in the same industrial sector, and then finally consider pair trading profits for two out-of-sample periods in 2014 within a six-month time frame as well as for the entire year. The proposed strategies yield average annualized returns of at least 35.5% without a transaction cost and at least 18.4% with a transaction cost.     

Institutional investors' attention to environmental information,trading strategies,and market impacts: Evidence from China

Using a large proprietary database of intraday high‐frequency trading, we investigate the trading strategies of institutional investors in dealing with the negative environmental event disclosure of listed companies and their impact on markets, aiming to reveal the mechanism of the lack of “green efficiency” in China's capital market from the perspective of institutional investors. The results show that institutional investors react to negative environmental events prior to the announcements, indicating premature information leakage in the market; in addition, their trading behaviors mitigate the immediate effect of negative environmental event announcements on stock price. After the event is disclosed, institutional investors engage in short‐term selling and long‐term buy and hold. This trading strategy undermines the irrational selling of individual investors in the event of disclosure, short‐term decline in stock price, and long‐term reversal of market overreaction. In a China context, institutional investors generally take environmental information into consideration. However, they fail to recognize the long‐term value effect of negative environmental events and instead cater to trading strategies towards market volatility.     

Are institutional investors colluding with manipulators?

This paper examines the trading behavior of institutional investors in Taiwan before, during, and after a manipulation event and determines whether institutional investors benefit from their trading behavior during the period from the year 2000 to 2020. We find that stocks with a low turnover and small market capitalization are the main targets of price manipulators. In addition, the stock price of manipulated firms increases from the start date of the manipulation event, peaks at the end of the event, then falls after the event. Foreign institutions collude with manipulators to exaggerate stock prices for self-benefit. In contrast, securities dealers counter the trading behavior of manipulators and act as market stabilizers, causing them to suffer losses. Moreover, foreign institutions earn higher returns on stocks of manipulated firms with a low turnover during and soon after manipulation; however, they earn a higher return on stocks of manipulated firms with a high turnover in the long run after manipulation.     

Availability and settlement of individual stock futures and options expiration-day effects: evidence from high-frequency data

This study examines whether the expiration-day effects of stock options traded in Australian Stock Exchange on return, volatility, trading volume, and temporary price changes of individual stocks vary with the availability and the settlement method of individual stock futures contracts. Using transaction data of the stocks that have both options and futures contacts from 1993 to 1997, we find that options expiration has significant effects on return and volatility of the underlying stocks in absence of individual stock futures. After introduction of a cash-settled stock futures contract, the effects decrease notably. However, the switch of a futures contract from cash settlement to physical delivery promotes the expiration effects on return and volatility and boosts temporary price changes on expiration days. Finally, options expiration has little effect on trading volume. Trading activity tends to behave normally regardless whether stock futures contracts are available or not.     

The ‘COVID’ crash of the 2020 U.S. Stock market

We employed the log-periodic power law singularity (LPPLS) methodology to systematically investigate the 2020 stock market crash in the U.S. equities sectors with different levels of total market capitalizations through four major U.S. stock market indexes, including the Wilshire 5000 Total Market index, the S&P 500 index, the S&P MidCap 400 index, and the Russell 2000 index, representing the stocks overall, the large capitalization stocks, the middle capitalization stocks and the small capitalization stocks, respectively. During the 2020 U.S. stock market crash, all four indexes lost more than a third of their values within five weeks, while both the middle capitalization stocks and the small capitalization stocks have suffered much greater losses than the large capitalization stocks and stocks overall. Our results indicate that the price trajectories of these four stock market indexes prior to the 2020 stock market crash have clearly featured the obvious LPPLS bubble pattern and were indeed in a positive bubble regime. Contrary to the popular belief that the 2020 US stock market crash was mainly due to the COVID-19 pandemic, we have shown that COVID merely served as sparks and the 2020 U.S. stock market crash had stemmed from the increasingly systemic instability of the stock market itself. We also performed the complementary post-mortem analysis of the 2020 U.S. stock market crash. Our analyses indicate that the probability density distributions of the critical time for these four indexes are positively skewed; the 2020 U.S. stock market crash originated from a bubble that had begun to form as early as September 2018; and the bubble profiles for stocks with different levels of total market capitalizations have distinct temporal patterns. This study not only sheds new light on the makings of the 2020 U.S. stock market crash but also creates a novel pipeline for future real-time crash detection and mechanism dissection of any financial market and/or economic index.     

Trading and non-trading period realized market volatility: Does it matter for forecasting the volatility of US stocks?

We study the potential merits of using trading and non-trading period market volatilities to model and forecast the stock volatility over the next one to 22 days. We demonstrate the role of overnight volatility information by estimating heterogeneous autoregressive (HAR) model specifications with and without a trading period market risk factor using ten years of high-frequency data for the 431 constituents of the S&P 500 index. The stocks’ own overnight squared returns perform poorly across stocks and forecast horizons, as well as in the asset allocation exercise. In contrast, we find overwhelming evidence that the market-level volatility, proxied by S&P Mini futures, matters significantly for improving the model fit and volatility forecasting accuracy. The greatest model fit and forecast improvements are found for short-term forecast horizons of up to five trading days, and for the non-trading period market-level volatility. The documented increase in forecast accuracy is found to be associated with the stocks’ sensitivity to the market risk factor. Finally, we show that both the trading and non-trading period market realized volatilities are relevant in an asset allocation context, as they increase the average returns, Sharpe ratios and certainty equivalent returns of a mean–variance investor.