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In this study, we explore the empirical relationship between trading volume and volatility among KOSPI200 index stock market, futures and options markets. In particular, in explaining the volatility of each market, the trading in other markets, as well as the trading volume of other markets, also served as explanatory variables. In other words, cross-market effects of trading volume by investor types are analyzed. The empirical results show that there exist the cross-market effects of the relationship between trading volume and volatility in deeply integrated financial markets such as KOSPI200 index stock, futures and options markets. That is, the volatility of one market is explained by the trading volume of trader types in other financial markets. And, overall options trading increases the volatility of each market, while the overall futures trading volume of foreign investors reduce the volatility of each market. Trading volume of Individual investors does not reduce the volatilities of KOSPI200 index and futures markets. That is, trading volume of Individual investors in stock, futures, and options markets increase the volatilities of stock and futures. This implies that foreign investors are informed traders, whereas individual investors are liquidity traders.

Recent studies suggested the ratio of option to stock volume reflected the private information. Informed traders were drawn to the options market for its leverage effect and relatively low transaction costs. Informed traders use different intervals of option moneyness to execute their strategies. The question is which types of option moneyness were traded by informed traders and what information was reflected in the market. In this study, the authors focused on this question and constructed a method for capturing the activity of informed traders in the options and stock markets.

The authors constructed the daily measure, moneyness option trading volume to stock trading volume ratio (MOS), to capture the activity of informed traders in the market. The authors formed quintile portfolios sorted with respect to the moneyness option to stock trading volume ratio and provided the capital asset pricing model and Fama–French five-factor alphas. To determine whether MOS had predictive ability on future stock returns after controlling for company characteristic effects, the authors formed double-sorted portfolios and performed Fama–Macbeth regressions.

The authors found that the firms in the lowest moneyness option trading volume to stock trading volume ratio for put quintile outperform the highest quintile by 0.698% per week (approximately 36% per year). The firms in the highest moneyness option trading volume to stock trading volume ratio for call quintile outperform the lowest quintile by 0.575% per week (approximately 30% per year).

The authors first propose the measures, moneyness option trading volume to stock trading volume ratio, that combined with the trading volume and option moneyness. The authors provide evidence that the measures have the predictive ability to the future stock returns.

This paper aims to propose a method of forecasting the level of informed trading at merger announcements by permitting liquidity traders to adjust their trading based upon signals from informed traders. Informed traders typically take advantage of their knowledge of forthcoming mergers by trading heavily at announcement. For cash mergers, they respond to a positive signal by purchasing stock, and for stock mergers, they respond to a negative signal by selling stock. In response, exchanges (market makers) set wider spreads (charge higher transaction fees) for informed buyers. Uninformed traders are subject to such excessive fees unless they can accurately predict the period during which such fees are charged.

This paper proposes a technique by which uninformed traders may make predictions by creating a vector autoregressive framework that links informed and liquidity trading through price changes.

For cash mergers, transaction fees remained excessive for days −1 to +1. For stock mergers, fees remained high on days −1 to +1, started declining on days 2 and 3, and vanished on days 4 and 5.

Most theoretical models of informed trading have viewed informed trading and liquidity trading as tangentially linked. This study finds a direct link between these two trading activities.

Uninformed traders may wish to limit their trading until after day +1 for both types of mergers.

This paper defines the time period during which transactions costs for traders are at the maximum level. Short sellers have more information about the direction of stock movements and may sell during days of informed selling set forth by this study and repurchase stock afterwards.

Prior research posits that traders with short-lived information favor lit exchanges over dark pools due to execution certainty. This paper aims to focus on the relation between informed trading based on firm fundamentals and dark pool volume because the preferred venue for traders with longer-lived information is less certain.

The authors examine the effect of short interest, a proxy for informed traders with long-lived information, on dark pool volume using fixed effects, first difference and instrumental variable approaches. They examine the effect of dark pools on the profitability of long-lived information using market- and characteristic-adjusted returns.

The proportion of trading volume executed in dark pools is positively correlated with short interest. This result is stronger for stocks that suffer from greater uncertainty and stocks targeted by transient institutional investors. Short sellers profit substantially from their information as subsequent returns are lower for heavily shorted stocks with greater dark pool volume.

In 2014, the Financial Industry Regulatory Authority began making trading data available for dark pools. Before that, only limited information was publicly available. The authors use that data to shed more light on dark pools activity.

The evidence presented in the paper helps inform the current discussion about the role and regulation of dark pools.

This is the first study to show that informed traders with long-lived information favor dark pools due to their opacity and the possibility of price improvement.

The purpose of this paper is to assess trading strategies adopted by each large trader group and examine their effects on the volatility in the interest rate futures markets.

The Grinblatt et al.'s (1995) measure of momentum strategy is used to estimate the degree momentum and contrarian strategies. Then, regression analysis is used to determine the effects of trading strategies on volatility.

Up until 2005, the trades by non-clearing member firms in the futures market were separated from institutional traders providing us the opportunity to study trading strategies adopted by large distinct trading groups and its effects on volatility in the futures markets. It is found that individual traders use momentum strategy, whereas market makers and institutional traders use contrarian strategy. Momentum strategy adopted by individual traders increases volatility whereas contrarian strategy dampens volatility. Moreover, it is found that institutional traders engage more actively in contrarian trading when individual traders cause excessive volatility. The two distinct trading groups were separately tracked prior to 2005 giving us a unique window to determine the effect of the traders that conduct momentum trading as opposed to the ones that are contrarian traders. After the reclassification, the institutional trading group exhibited weaker contrarian strategy which can be attributed to the inclusion of non-clearing firm traders.

This study documents the first empirical evidence that shows off-exchange futures trader group is not composed of only pure noise makers, but there are short-term forecasters in its group. The authors also show a unique finding that noises caused by off-exchange group is from momentum strategy that they use, whereas contrarian strategy is used by institutional trader lower volatility.

We propose a novel method of forecasting the level of informed trading at merger announcements. Informed traders typically take advantage of their knowledge of the forthcoming merger by trading heavily at announcement. They trade on positive volume or informed buys for cash mergers and negative volume or informed sells for stock mergers. In response, market makers set wider spreads and raise prices for informed buys and lower prices for informed sells. As liquidity traders trade on these prices, our vector autoregressive framework establishes the link between informed trading and liquidity trading through price changes. As long as the link holds, informed trading may be detected by measuring levels of liquidity trading. We observe the link during the −1 to +1 period for cash mergers and −1 to +5 period for stock mergers.

The purpose of this paper is twofold. The first is to estimate the correlation between market activity and volatility on an exchange that does not use continuous auctions to find prices. The second is to estimate the sensitivity of that relationship to differences in opinions across traders regarding asset value.

Both objectives are accomplished by using seven years of trader‐level data from the Tokyo Grain Exchange, which uses rapid sequences of Walrasian tâtonnement auctions to discover prices. On the TGE, only one futures contract trades at any given time and all of a commodity's futures contracts are auctioned in a rapid sequence, with only seconds between a sequence's auctions. The results are interpreted under the hypothesis that this design causes traders' beliefs to become more accurate and more uniform as a sequence progresses.

Intraday volume is u‐shaped while intraday volatility is downward sloping. The volume–volatility link is positive and stays constant or strengthens as traders' beliefs about value become more precise. The link is driven by trades originating from small futures commission merchants, especially those trades entered on behalf of customers.

Evidence that accounting for cross‐correlations when estimating volatility can have an important effect on estimates is presented. Researchers are encouraged to further explore the implications of cross‐correlations.

The paper includes implications for existing theory, the measurement of volatility, and the design of central exchanges.

This paper uses the TGE as a natural laboratory to test theory. It is the first such study to use data from an exchange that does not use continuous auctions, and the first to document the simultaneous existence of u‐shape volume and downward‐sloping volatility.

In this work, the authors conduct an online survey to evaluate how Sensation Seeking and Overconfidence influences the transaction volume of day traders in Brazil.

The authors conducted a survey to gather the primary data. They applied linear regressions between the variables, and then the stepwise technique in order to eliminate the ones with the least explanatory power.

The authors found that the aggregated trace Sensation Seeking did not positively influence the trading volume of day traders, but some of its facets did, like Thrill and Adventure Seeking and Boredom Susceptibility/Impulsivity. For the Overconfidence bias, only its Overplacement form showed a positive effect on the transaction volume of day traders, while Overestimation and Miscalibration did not.

This is the first study that seeks to identify the relationship of Sensation Seeking and Overconfidence, considering their different facets and forms, in a more homogeneous sample of day traders, which have mostly speculative reasons for trading. Its results reveal the multidimensional characteristics of the Sensation Seeking and Overconfidence behavioral aspects and lighten some of the motivations for day traders to overtrade.

This paper applies a volume-price probability wave differential equation to propose a conceptual theory and has innovative behavioral interpretations of intraday dynamic market equilibrium price, in which traders' momentum, reversal and interactive behaviors play roles.

The authors select intraday cumulative trading volume distribution over price as revealed preferences. An equilibrium price is a price at which the corresponding cumulative trading volume achieves the maximum value. Based on the existence of the equilibrium in social finance, the authors propose a testable interacting traders' preference hypothesis without imposing the invariance criterion of rational choices. Interactively coherent preferences signify the choices subject to interactive invariance over price.

The authors find that interactive trading choices generate a constant frequency over price and intraday dynamic market equilibrium in a tug-of-war between momentum and reversal traders. The authors explain the market equilibrium through interactive, momentum and reversal traders. The intelligent interactive trading preferences are coherent and account for local dynamic market equilibrium, holistic dynamic market disequilibrium and the nonlinear and non-monotone V-shaped probability of selling over profit (BH curves).

The authors will understand investors' behaviors and dynamic markets through more empirical execution in the future, suggesting a unified theory available in social finance.

The authors can apply the subjects' intelligent behaviors to artificial intelligence (AI), deep learning and financial technology.

Understanding the behavior of interacting individuals or units will help social risk management beyond the frontiers of the financial market, such as governance in an organization, social violence in a country and COVID-19 pandemics worldwide.

It uncovers subjects' intelligent interactively trading behaviors.

High levels of turnover in financial markets are consistent with the notion that trading, like gambling, yields direct utility to some agents. The purpose of this paper is to show that the presence of these agents attenuates covariance risk pricing and volatility, and implies a negative relation between volume and future returns. Since psychological literature indicates that the desirability of a gamble arises from the ex ante volatility of the outcome, the authors propose that agents derive greater utility from trading more volatile stocks. These stocks earn lower average returns in equilibrium, although the risk premium on the market portfolio is positive. The authors then consider a dynamic setting where agents’ utility from trading increases when they make positive profits in earlier rounds (e.g. due to an endowment effect). This leads to “bubbles,” i.e. disproportionate jumps in asset returns as a function of past prices, higher volume in up markets relative to down markets, as well as a leverage effect, wherein down markets are followed by higher volatility than up markets.

Analytical.

The presence of gamblers attenuates covariance risk pricing and volatility, and implies a negative relation between volume and future returns. If gamblers prefer more volatile stocks, these stocks earn lower average returns in equilibrium. If agents’ utility from trading increases when they make positive profits in earlier rounds (e.g. to an endowment effect), this leads to higher volume and lower volatility in up markets relative to down markets.

No paper has previously modeled agents who derive direct utility from trading.