Buy Volume Research Articles

Uncertainty about what is in the price

A critical question facing speculators contemplating to trade on private information is whether their signal has already been priced in by the market. In our model, speculators assess the novelty of their information based on recent price movements, and market makers are aware that speculators might be trading on stale news. An asymmetric response to past price movements ensues: after price increases, buy volume – because it may result from stale news trading – has a lower price impact than sell volume (and vice versa after price decreases). Consequently, return skewness is negatively related to lagged returns. We find strong support for these and other predictions using a comprehensive sample of US stocks.

Uncovering Retail Trading in Bitcoin: The Impact of COVID-19 Stimulus Checks

In April 2020, the U.S. government sent economic impact payments (EIPs) directly to households as part of its measures to address the COVID-19 pandemic. We characterize these stimulus checks as a wealth shock for households and examine their effect on retail trading in Bitcoin. We find a significant increase in Bitcoin buy trades of size $1,200, which is the modal EIP amount. We find similar increases in trading for other countries that paid out stimulus checks. We estimate that the EIPs have a significant impact on the U.S. dollar–Bitcoin trading pair, increasing buy volume by 3.8% and the price by 0.6%. We also find that demand for Bitcoin is highly price inelastic compared with the demand for stocks. We suggest the demographic characteristics that make people more resilient to the COVID-19 economic shock—single, computer literate, and educated—are also characteristics of people who are more interested in Bitcoin. This paper was accepted by Bruno Biais, finance. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4790 .

Technical, fundamental, and combined information for separating winners from losers

This study examines how fundamental accounting information can be used to supplement technical information to separate momentum winners from losers. We first introduce a ratio of liquidity buy volume to liquidity sell volume (BOS ratio) to proxy the level of information asymmetry for stocks and show that the BOS momentum strategy can enhance the profits of momentum strategy. We further propose a unified framework, produced by incorporating two fundamental indicators—the FSCORE (Piotroski, 2000) and the GSCORE (Mohanram, 2005)—into momentum strategy. The empirical results show that the combined investment strategy includes stocks with a larger information content that the market cannot reflect in time, and therefore, the combined investment strategy outperforms momentum strategy by generating significantly higher returns.

Technical, Fundamental, and Combined Information for Separating Winners from Losers

This study examines how fundamental accounting information can be used to supplement technical information to separate momentum winners from losers. We first introduce a ratio of liquidity buy volume to liquidity sell volume (BOS ratio) to proxy the level of information asymmetry for stocks and show that the BOS momentum strategy can enhance the profits of momentum strategy. We further propose a unified framework, produced by incorporating two fundamental indicators — the FSCORE (Piotroski, 2000) and the GSCORE (Mohanram, 2005) — into momentum strategy. Empirical results show that the combined investment strategy includes stocks with larger information content that the market cannot reflect in time, and therefore, the combined investment strategy outperforms momentum strategy by generating significantly higher returns.

Asymmetric effects of sell-side analyst optimism and broker market share by clientele

This paper provides evidence for the first time on how different broker clienteles react to earnings forecast and stock recommendations. Greater trade volume is found to be associated with optimistic earnings forecasts while stock recommendations are stronger for analysts affiliated with retail brokerage firms than those affiliated with institutional brokerage firms. We also find that the market share of buy volume (that is, volume from buy orders) substantially rises on recommendation upgrades while the market share of sell volume rises on recommendation downgrades, and that this effect is stronger for retail clientele.

Are institutions informed about news?

This paper combines daily buy and sell institutional trading volume with all news announcements from Reuters. Using institutional order flow (buy volume minus sell volume) we find a variety of evidence that institutions are informed. Institutional trading volume predicts the occurrence of news announcements. Institutional order flow predicts (i) the sentiment of the news; (ii) the stock market reaction on news announcement days; (iii) the stock market reaction on crisis news days; and (iv) earnings announcement surprises. These results suggest that significant price discovery related to news stories occurs through institutional trading prior to the news announcement date.

Do Individual Investors Treat Trading as a Fun and Exciting Gambling Activity? Evidence from Repeated Natural Experiments

We hypothesize that individual investors treat trading as a fun and exciting gambling activity, implying substitution between this activity and alternative gambling opportunities. To examine this hypothesis, we study the lottery jackpots and the trading of individual investors in Taiwan. When the jackpots exceed 500 million Taiwan dollars, the trading volume decreases between 5.2% and 9.1% among stocks preferred by individual investors and between 6.8% and 8.6% among lottery-like stocks. The decline in individual buy volume is statistically indistinct from the decline in sell volume. Large jackpots are associated with less trading in options with high sensitivity to volatility

Are Institutions Informed About News?

This paper combines daily non-public data on buy and sell volume by institutions from 2003 through 2005 for NYSE-listed stocks with all news announcements from Reuters. Natural language processing categorizes the sentiment associated with each news story. We use institutional order flow (buy volume minus sell volume) as a quantitative measure of net trading by institutions. We find evidence that institutional investors are informed: i) institutional trading volume predicts the occurrence of news announcements; ii) institutional order flow predicts the sentiment of the news; iii) institutional order flow predicts the stock market reaction on news announcement days; iv) institutional order flow predicts the stock market reaction on crisis news days; v) institutional order flow predicts earnings announcement surprises; and vi) institutions do not believe the hype.

The Flash Crash: Trading Aggressiveness, Liquidity Supply, and the Impact of Intermarket Sweep Orders

AbstractDuring the Flash Crash on May 6, 2010, a short period of high stock market volatility, some stock prices declined to $0.01, while others increased to $100,000. Examining Intermarket Sweep Orders (ISOs) before, on, and after May 6, we find that ISO use is substantially higher on May 6. For those stocks whose prices fell the most, over 65% of the sell volume comes from ISOs. During the price recovery period for these stocks, about 53% of the buy volume comes from ISOs. We believe that the unusual behavior of ISOs contributed to the sudden drop and recovery of the market.

A Theoretical Model of Directional Volume on Acquirer Stock in Cash Mergers

The acquisition of a target firm in a transaction financed by cash is a cash merger. Announcements of cash mergers release the positive signal that the acquirer possesses cash reserves. As stock prices rise, informed traders may obtain abnormal returns by purchasing call options, selling put options or purchasing stock. This paper constructs a theoretical model in which call buy volume forms the upper bound of the final stock price, put sell volume forms the lower bound of the final stock price and stock purchase volume reveals the final stock price.

Trading Volume Around Firm-Specific Announcements

This study investigates the impact of timing of the release of firm-specific announcements on trading volume of individual and institutional investors. We use trading data in five-minute intervals to capture the immediate impact of announcements on the trading volume. We find that individual investors exhibit positive and significant abnormal volume prior to, issued capital announcements and after earnings announcements. However, institutions exhibit significant and positive abnormal volume prior to, and after earnings, periodic and issued capital announcements. Notably, both individual and institutional investors do not exhibit significant abnormal volume prior to, and after dividend announcements. Furthermore, individual (institutional) investors’ buy (sell) volume is significantly higher than sell (buy) volume prior to, and after scheduled and unscheduled announcements. Our results suggest that timing of the release of firm-specific announcements influences investor trading volume.

Volume Reaction to Firm Specific News Announcements

This study investigates individual and institutional investors’ behaviour around firm specific news announcements. We find that individual investors and institutional investors have positive and significant abnormal volume on announcement days and significantly less abnormal volume on days without announcements. Both investor types show significant abnormal volume around progress reports, periodic reports, dividend and takeover announcements. Individual and institutional investors exhibit positive abnormal volume around scheduled and unscheduled firm specific announcements. Buy and sell abnormal volume around scheduled announcements are positive for the investor classes whereas institutional buy and individual sell abnormal volume are positive around unscheduled announcements, both at 1% significant level.

The Flash Crash: Trading Aggressiveness, Liquidity Supply, and the Impact of Intermarket Sweep Orders

During the Flash Crash on May 6, 2010, a short period of high stock market volatility, some stock prices declined to $0.01, while others increased to $100,000. Examining Intermarket Sweep Orders (ISO) before, on, and after May 6, we find that ISO use is substantially higher on May 6. For those stocks whose prices fell the most, over 65% of the sell volume comes from ISOs. During the price recovery period for these stocks, about 53% of the buy volume comes from ISOs. We believe that the unusual behavior of ISOs contributed to the sudden drop and recovery of the market.

Cause of Return Reversal: Information Monopoly Hypothesis

Return reversal in the stock markets has been a puzzle for researchers. Several hypotheses have been proposed to explain this pervasive phenomenon. Such explanations provide valuable insight to the understanding of investors’ reactions but may not address the cause. In this paper, we propose the Information Monopoly Hypothesis (IMH) to fill this gap. According to the IMH, a strategic trader generates, or obtains, potentially price-lifting information. He then initiates purchases of that particular stock quietly. The information generator then disseminates the same information through a credible channel with a large investor audience; therefore, the information gets substantial publicity and credibility. Immediately after the public release of the information, numerous investors react to it by generating buying pressure in a rush, resulting in a price jump. In the meanwhile, the strategic trader begins the distribution of the shares he accumulated earlier. He closes his position at an unfair profit by trading against the induced investors. His distribution (sale) and diminishing buy volume from induced investors jointly results in a sharp return reversal or downward drifting. For potentially price-depressing information, all trade and price movement directions follow an opposite pattern. The key to return reversal in the large number of empirical studies and securities litigation cases lies in the inducement of investors, according to the potentially pricemoving information, and trading against them after the public release of the information. What is the power that induces numerous investors to trade according to the information? We propose to call that power an information monopoly. Its components are the information with price-moving potential, its publicity, and credibility. The strategic trader exercises an information monopoly power in his trading strategy. The information monopoly enables his trading strategy to be manipulative and causes a subsequent return reversal. This is a simple description of the Information Monopoly Hypothesis. We test the IMH by studying recent securities litigation cases of market manipulation as well as empirical research on sell-side analyst recommendations, and we find these to be consistent with our hypothesis.

Last Time Buy and control policies with phase-out returns: a case study in plant control systems

This research involves the combination of spare parts management and reverse logistics. At the end of the product life cycle, products in the field (so called installed base) can usually be serviced by either new parts, obtained from a Last Time Buy, or by repaired failed parts. This article, however, introduces a third source: the phase-out returns obtained from customers that replace systems. These returned parts may serve other customers that do not replace the systems yet. Phase-out return flows represent higher volumes and higher repair yields than failed parts and are cheaper to get than new ones. This new phenomenon has been ignored in the literature thus far, but due to increased product replacements rates its relevance will grow. We present a generic model, applied in a case study with real-life data from ConRepair, a third-party service provider in plant control systems (mainframes). Volumes of demand for spares, defect returns and phase-out returns are interrelated, because the same installed base is involved. In contrast with the existing literature, this article explicitly models the operational control of both failed- and phase-out returns, which proves far from trivial given the non-stationary nature of the problem. We have to consider subintervals within the total planning interval to optimise both Last Time Buy and control policies well. Given the novelty of the problem, we limit ourselves to a single customer, single-item approach. Our heuristic solution methods prove to be efficient and close to optimal when validated. The resulting control policies in the case study are also counter-intuitive. Contrary to (management) expectations, exogenous variables prove to be more important to the repair firm (which we show by sensitivity analysis) and optimising the endogenous control policy benefits the customers. Last Time Buy volume does not make the decisive difference; far more important is the disposal versus repair policy. PUSH control policy is outperformed by PULL, which exploits demand information and waits longer to decide between repair and disposal. The article concludes by mapping a number of extensions for future research, as it represents a larger class of problems.

Is Zero Return a Natural Benchmark for Investors? An Investigation with Individual Trading Records

Using a unique and large dataset of trading records, we find that individual investors view zero returns as a natural benchmark for their trading performance. They increase both buy and sell trading volume if their past investment outcomes are positive, and decrease them if the outcomes are negative, but appear to care much less for the size of the same outcomes (gains as well as losses). We also find that this trading behavior is non-rational; it results in economically significant declines in profits from current trades even without taking into account transactions costs. While our findings support the age-old knowledge that human thought processes are heavily influenced by the distinction between positive and negative numbers, our research strategy is motivated by recent research in experimental psychology that documents that individual subjects are more sensitive to the presence or absence of a stimulus than to its magnitude. Our findings provide new and empirically testable explanations for a number of extensively documented phenomena in finance and accounting, including excessive trading by overconfident investors.

All are Smart: Do Short Sellers Convey More Information than Option Investors?

We provide a comprehensive comparison of equity short-selling activity and option market activity in predicting future returns. Three variables are constructed, namely the equity short interest ratio (SIR), put-call open interest ratio (PCOIR), and put-call buy volume ratio (PCBVR). The difference between abnormal returns (based on 4-factor risk adjustment model) to the lowest SIR and highest SIR decile portfolios is 1.27% in the first month, while the corresponding differences are 0.91% and 1.78% for PCOIR and PCBVR portfolios, respectively. Based on double-sort portfolio approach and cross-sectional regressions of returns on individual stocks, we find the predictive ability of PCOIR diminishes after controlling for SIR, while the predictive ability of PCBVR persists and even subsumes that of SIR. We find that a high level of institutional ownership will dampen the predictive ability of SIR, and PCOIR and PCBVR as well. While the SIR can predict future returns up to 9 to 12 months, the predictive power might not be all related to information, but simply reflects some omitted firm characteristics.

What kind of trading drives return autocorrelation

This paper proposes new tests for the prediction of Llorente, Michaely, Saar, and Wang (2002) that information trading drives positive autocorrelation. Daily data from the Taiwan Stock Exchange is used to exploit the differences in the trading motivations of three groups of institutional investors. Consistent with the predictions, we find that heavy trading by foreigners and mutual funds will increase the autocorrelation particularly for large firms, and that heavy trading by dealers will not. We also find that the sell volume of mutual funds - short sales are disallowed by regulation - has significantly smaller effect on the autocorrelation of returns than buy volume. A portfolio strategy that exploits the observed autocorrelation pattern can generate a significantly positive daily return. Key words: Institutional trading, information trading, return autocorrelation, short-sale constraints

Buy and sell dynamics following high market returns: Evidence from China

We provide a closer look at the trading dynamics which may give rise to the positive relationship between market trading volume and its lagged returns. Chinese market turnover increases sharply with past day returns. A comprehensive dataset which facilitates the tracing of trading activities among different groups of investors reveals that when previous market returns are high, investors with larger (smaller) average trade size increase their buy (sell) volume. Our findings indicate an important role of differing responses to market information among different classes of investors (e.g. different priors) in explaining this recently documented phenomenon.

Last Time Buy and Control Policies with Phase-Out Returns: A Case Study in Plant Control Systems

This research involves the combination of spare parts management and reverse logistics. At the end of the product life cycle, products in the field (so called installed base) can usually be serviced by either new parts, obtained from a Last Time Buy, or by repaired failed parts. This paper, however, introduces a third source: the phase-out returns obtained from customers that replace systems. These returned parts may serve other customers that do not replace the systems yet. Phase-out return flows represent higher volumes and higher repair yields than failed parts and are cheaper to get than new ones. This new phenomenon has been ignored in the literature thus far, but due to increased product replacements rates its relevance will grow. We present a generic model, applied in a case study with real-life data from ConRepair, a third-party service provider in plant control systems (mainframes). Volumes of demand for spares, defects returns and phase-out returns are interrelated, because the same installed base is involved. In contrast with the existing literature, this paper explicitly models the operational control of both failed- and phase-out returns, which proves far from trivial given the non-stationary nature of the problem. We have to consider subintervals within the total planning interval to optimize both Last Time Buy and control policies well. Given the novelty of the problem, we limit ourselves to a single customer, single-item approach. Our heuristic solution methods prove efficient and close to optimal when validated. The resulting control policies in the case-study are also counter-intuitive. Contrary to (management) expectations, exogenous variables prove to be more important to the repair firm (which we show by sensitivity analysis) and optimizing the endogenous control policy benefits the customers. Last Time Buy volume does not make the decisive difference; far more important is the disposal versus repair policy. PUSH control policy is outperformed by PULL, which exploits demand information and waits longer to decide between repair and disposal. The paper concludes by mapping a number of extensions for future research, as it represents a larger class of problems.