Buy-sell Imbalance Research Articles

Does data asset disclosure contribute to the market efficiency? Evidence from China

Information regarding firms' data assets and stock pricing has been more closely related when data is integrated into firms' value creation processes. However, since data asset disclosure can also be engaged in impression management by firms, its contribution to market efficiency may be impeded. Using a sample of Chinese A-share listed firms from 2014 to 2020, this study examines the impact of increased data asset information disclosure on the subsequent stock price volatility. The findings suggest that more data asset disclosure reduces stock return idiosyncratic volatility. The mechanism analysis shows that data asset disclosure reduces idiosyncratic volatility of stock prices by mitigating analyst forecast dispersion and noise trading induced by buy-sell imbalances. Moreover, this mitigating effect is more pronounced in firms with more analyst following, observable digital investments, and higher liquidity. But the effect is diminished if firms use a more positive abnormal tone in their annual reports.

Dissecting Retail, Institutional, and Foreign Buy-Sell Imbalances

Dissecting Retail, Institutional, and Foreign Buy-Sell Imbalances

Sign matters: Stock-movement-based trading decisions of individual investors

In this paper, the relation between the signs of recent returns (an up-down pattern) and the net trading of individual investors is studied. Using our comprehensive dataset from the Taiwan Stock Exchange, we find that following positive days, individual investors sell more stocks than they buy — a negative buy-sell imbalance — while following negative days, this imbalance is positive. The signs of more recent returns have stronger impacts on imbalance. The subsequent performance of this trading behavior is poor, indicating that individual investors in Taiwan make suboptimal decisions, and they are unlikely to make information-based trades. Thus, the liquidity provision explanation in the literature is not applicable to our sample.

Investor Sentiment and Stock Market Returns: Evidence from the Indian Market

Investor sentiment refers to investors' confidence in the market based on their subjective beliefs. Investor sentiment has become a topic of substantial interest among academicians as investor sentiment induces uninformed demand shocks, which can potentially drive away stock prices from their fundamental value. This study expands the literature on the effect of investor sentiment on stock market returns by constructing an Investor sentiment index for the Indian market. We use Nifty-Fifty returns and the sample period ranges from April 2012 to March 2022. The Principal Component Technique is employed to construct an investor sentiment index using seven market and firm implicit proxies: advances to decline ratio, buy-sell imbalance ratio, equity issues to total issues, volatility premium, share turnover, price-earnings ratio, and turnover volatility ratio. The regression analysis shows that the investor sentiment index has the predictive power to forecast Nifty-Fifty returns.

Option trading and returns versus the 52‐week high and low

AbstractWe show that option traders suffer from the anchoring effect induced by the stock price's 52‐week high or low. Specifically, (1) trading of all options decreases as the stock price approaches its 52‐week high or low, (2) the buy–sell imbalance for calls decreases and that for puts increases as the stock price approaches its 52‐week high, and the opposite occurs as the stock price approaches its 52‐week low, and (3) the subsequent delta‐hedged option returns for both calls and puts are higher as the stock price approaches its 52‐week extreme.

Impact of Investor Sentiment on Stock Returns*

AbstractThis study examines the relationship between investor sentiment and stock returns in two active but different Korean stock markets. Using daily KOSPI and KOSDAQ data, we construct an investor sentiment index that includes adjusted turnover rate, buy–sell imbalance, and relative strength index. We find that investor sentiment significantly affects stock returns, more so in the KOSDAQ with high individual participation. Company characteristics, including size and stock price, affect the relationship between investor sentiment and stock returns. Moreover, we introduce the relationship between mobile trading and investor sentiment, and demonstrate that mobile trading transforms irrational investors into informed, rational investors.

Who reacts to what information in securities analyst reports? Direct evidence from the investor trade imbalance

Using a comprehensive dataset that distinguishes the buy/sell volume of four investor types, we find that foreign institutions and domestic mutual funds are the primary users of analyst reports. Their buy–sell imbalances move in tandem with analysts' signals and significantly explain the size of cumulative abnormal returns across incidents of analyst report releases. Proprietary traders' buy/sell positions are less related to analyst opinions, and individual investors emerge as de facto liquidity providers to aggressive institutions. Institutional investors respond first to stock recommendations and then use target prices as supplementary information. Earnings forecasts, which contain nuanced information, do not elicit abnormal trade imbalances for any institutional investors. We further discover that trade reactions to target prices and earnings forecasts can be viewed more as a constant multiplier of the signal rather than as an increasing or decreasing function of the signal strength. The trade imbalance caused by analyst information is found to predict next-period stock returns, although the predictive ability is for the short-term period only.

Buy-Sell Imbalances around Round Numbers and High-Frequency Trading

I show that the growth of high-frequency trading, due to its heavy reliance on computer algorithms, can be associated with a reduction of human errors and financial anomalies in the market. Trades in which a non-high-frequency trader is the liquidity demander exhibit abnormally high buy (sell) pressure when prices are immediately below (above) a round number due to psychological effects, while the pattern is completely reversed when a high-frequency trader is the liquidity demander. As a result, the overall sample does not exhibit such imbalances. Furthermore, high-frequency traders earn higher returns when trading around round number prices.

Round Number Effects in Crude Oil Futures Market

Using the entire tick data for West Texas crude oil from 1996 to 2015, we find excess buying (selling) at price points just below (above) a round number over both pre- and post-electronic periods, confirming the existence of round number effects in commodity futures market. Next, we examine whether the trading activity of hedgers or speculators influence round number effects and find that the trading activity of hedgers influence round number effects. We also examine the relation between round number effects buy-sell imbalance and 24-hour trade return. We find that the relation is negative in crude oil futures market and a trading strategy based on round number effects generates the return of negative 0.0014 percent. Our result conflicts the finding of previous literature who documents strong evidence that there is positive relation between round number effects buy-sell imbalance and 24-hour trade return and show that a strategy based on round number effects generate a positive return of 0.11 percent in US stock markets. Lastly, controlling for market liquidity and volatility, we examine whether round number effects persist and find that round number effects persist. Additionally, given that speculation activity in commodity futures market has been the subjective of intense scrutiny since the boom and bust in commodity prices in 2008, we examine the impact of trading activity of speculators on commodity market. We find that speculators provide liquidity to hedgers and find no evidence that the trading activity of speculators increase market volatility.

INDIVIDUAL MEAN-VARIANCE RELATION AND STOCK-LEVEL INVESTOR SENTIMENT

This research studies the effect of stock-level investor sentiment on individual stock returns’ mean-variance relation. Using unique buy and sell volume data of retail investors in Korean stock market, we find that a positive mean-variance relation is undermined among high-sentiment stocks, but holds among low-sentiment stocks. We adopt buy-sell imbalances of retail investors for individual stocks as a measure of stock-level investor sentiment. Further, our findings provide empirical evidence of a strong riskreturn trade-off among stocks with low retail concentration (e.g., large capitalization, high-priced, and growth stocks). Existing research only analyzes market-wide investor sentiment. However, we study the effect of stock-level investor sentiment on individual stock returns. Therefore, our findings suggest novel implications about the investment strategy that the stock-level investor sentiment is important when constructing portfolios based on variance.

Buy-sell imbalance and the mean-variance relation

Buy-sell imbalance is a crucial behavioral factor in the stock market. This paper emphasizes that buy-sell imbalance plays a systematic role in the market's mean-variance relation. Besides, the influence of buy-sell imbalance on the market's mean-variance relation is time-varying. As buy-sell imbalance is negative, the market's mean-variance relation is significantly negative; as buy-sell imbalance is positive, the market's mean-variance relation is insignificant. Furthermore, our analyses are robust across different conditional variance models and market portfolios with different values of stock capitalization.

Trading Activities Around Ex-Dividend Days: Evidence from the Taiwan Stock Market

We investigate buy–sell imbalances (BSIs) around ex-dividend days on the Taiwan Stock Exchange (TWSE) and find that trading activities are not entirely correlated with investor tax status. We find that margin traders and short sellers engage in arbitrage by selling more stocks for those with high abnormal returns cum- and ex-dividend, respectively. Tax-neutral dealers engage in overnight trading while tax-disadvantaged foreigners refrain from selling stocks cum-dividend.

Micro-Foundation of ARCH Model

The Japanese version of this paper can be found at http://ssrn.com/abstract=2710516.Many macroeconomic study argued macroeconomic models should be aggregated by micro processes models (micro-foundation) and many micro-founded macroeconomic models were built. On the other hand, there are many models for price variation of a risk asset, which is macro phenomena, however, there are few studies for micro-foundation of such models. In this study we tried micro-foundation of an ARCH model using intelligence of artificial market simulation studies. That is we tried to clarify which micro processes determine each coefficient of an ARCH model. Then, we showed that the dispersion of investors' estimated prices is larger or the orders by the buy-sell imbalance taking liquidity are more, the volatility is larger. And we showed that the ration of the normal investors taking liquidity to the noise traders providing liquidity is higher or the measure of risk aversion of the normal investors is lower, the magnitude of volatility clustering is larger.

ARCHHHHHHHHHHHHHHHH (Micro-Foundation of ARCH Model)

The English version of this paper can be found at http://ssrn.com/abstract=2710457.Japanese Abstract: 経済のマクロモデルは，ミクロプロセスの積み上げによって構築する，いわいる``ミクロ的基礎付け''(micro-foundation)がなされるべきとの主張が多くなされ，ミクロ的基礎付けがなされた経済のマクロモデルが多く生まれた．一方，リスク資産の価格変動というマクロ量をモデル化したものは多く存在するが，これらのモデルをミクロ的基礎付けしようという試みは多くない．そこで本研究では人工市場研究からの知見を用いて，ARCHモデルのミクロ的基礎付け，すなわち，各係数がどのミクロプロセスから生じているのかを明らかにすることを試みた．その結果，投資家の予想価格のばらつきと需給の歪みが大きくなるとボラティリティは大きくなり，流動性を奪う投資家の存在割合が大きくなったり投資家のリスク回避度が小さくなるとボラティリティクラスタリングは大きくなることが分かった．English Abstract: Many macroeconomic study argued macroeconomic models should be aggregated by micro processes models (micro-foundation'') and many micro-founded macroeconomic models were built. On the other hand, there are many models for price variation of a risk asset, which is macro phenomena, however, there are few studies for micro-foundation of such models. In this study we tried micro-foundation of an ARCH model using intelligence of artificial market simulation studies. That is we tried to clarify which micro processes determine each coefficient of an ARCH model. Then, we showed that the dispersion of investors' estimated prices is larger or the orders by the buy-sell imbalance taking liquidity are more, the volatility is larger. And we showed that the ration of the normal investors taking liquidity to the noise traders providing liquidity is higher or the measure of risk aversion of the normal investors is lower, the magnitude of volatility clustering is larger.

Effects of dark pools on financial markets’ efficiency and price discovery function: an investigation by multi-agent simulations

In financial stock markets, dark pools, in which order books or quotes are not provided, are becoming widely used. However, increasing the use of dark pools would raise regulatory concerns as it may ultimately affect the quality of the price discovery function in the lit markets, which are normal markets in which all order books are provided to investors. This may destabilize a market and heighten financial systemic risk. In this study, we investigated effects of a dark pool on financial markets’ efficiency and the price discovery function by using an artificial market model. We found that the markets are made more efficient by raising the share of the trading value amount of the dark pool by a certain level. However, raising the share above the level makes the market significantly inefficient. This indicates that the dark pool has an optimal usage rate for market efficiency . The smart order routing (SOR) is transmitting market orders to the dark pool, and this leads the depth of limit orders to become thicker. The thicker limit orders absorb market orders, and thus a market price is still stable near a fundamental price. On the other hand, when too many waiting orders are stored in the dark pool, the orders absorb market orders in the lit market by SOR and prevent the market price converging to the fundamental price. This causes the market price to stay very different from the fundamental price and makes the lit market inefficient. We also discuss mechanisms by which a dark pool makes a market efficient or inefficient by using a simple equation model. The equations suggest that if the trading value amount is higher in dark pools than in lit markets, markets become inefficient. This suggests that when the usage rate of dark pools is low, dark pools rarely destroy the price discovery function even though a large buy–sell imbalance occurs. On the other hand, when the usage rate of dark pools is very high, dark pools very easily destroy the price discovery function by a very slight buy–sell imbalance. We also compared results of the equations with those of simulations and found similar tendencies .

Is Volatility Your Friend or Nemesis? The Links Between Transaction Costs, Market Conditions, and Trading Styles

This article summarizes results of our extensive empirical study motivated by the intuitively appealing statement that institutional clients’ average transaction costs are sensitive to market conditions. Using a comprehensive sample of client execution data covering two years of trading, we confirm that average cost of institutional trades varies considerably and systematically with volatility, volume, and trade imbalance surprises. For the overwhelming majority of buy-side institutions, implementation shortfall is higher than normal when volatility and volume exceed their historical average values. However, the deviations of trading volume in excess of the values typically observed in high volatility conditions dampen the effect of high volatility environment on execution costs of institutional orders. We document a strong dependence of transaction costs on contemporaneous trade imbalances, which is amplified by higher than normal contemporaneous volatility. We observe that cost curves are more sensitive to order size at the times of less favorable buy-sell trade imbalances, reflecting the role played by directional market pressure indicators. In summary, buy-side institutions should not neglect market conditions monitoring, as failure to adjust promptly to market conditions may result in deteriorated performance and missed cost savings opportunities.

The prospect of a perfect ending: Loss aversion and the round-number bias

Studies across a range of domains have shown that individuals tend to focus on round numbers as cognitive reference points; a so-called left-digit effect. We explain this effect by combining analog numerical heuristics with prospect theory in order to develop an analog value function that predicts the key characteristics of the left-digit effect. Most importantly, this value function predicts an unreported phenomenon, namely; that the left-digit effect will be more pronounced in situations involving losses (cf. gains). We confirm this prediction in both a laboratory experiment regarding hypothetical investments and analysis of buy–sell imbalances in over 15million trades by investors in a financial market. We conclude that our analog value function is a promising explanation for the left-digit effect. Furthermore, we suggest that interventions aimed at reducing costly buy–sell imbalances in financial markets should focus on the decisions made by investors when they are facing loss.

The 99% Market Sentiment Index

Dans cet article, nous construisons un indice de sentiment de marché, uniquement à partir des variations dans le temps du nombre de titres différents détenus par des investisseurs individuels. Ni les prix ou les rentabilités d’actions, ni les volumes globaux de transactions sur lesdites actions n’interviennent dans la construction de l’indice. En revanche, les transactions des « petits » investisseurs sont surpondérées dans le calcul de notre indice de sentiment. À partir des transactions réalisées par 87373 investisseurs individuels sur une période de huit années (1999-2006), nous montrons que notre indice surperforme les autres indices usuels (sur données d’enquêtes, données macro-économiques ou sur les déséquilibres achat/vente) dans la prévision des rentabilités mensuelles de portefeuilles long-short fondés sur la taille des firmes ou le ratio book-to-market. Un accroissement d’un écart-type de notre indice de sentiment sur un mois donné entraîne une baisse de rentabilité de 1,05 % du portefeuille « taille » le mois suivant. Une stratégie dynamique élémentaire guidée par notre indice conduit à un ratio de Sharpe supérieur à celui de stratégies dynamiques aléatoires dans 99,6 % des cas et à un ratio de Sharpe très supérieur à celui d’une stratégie buy-and-hold .

Hot money effect or foreign exchange exposure? Investigation of the exchange rate exposures of Taiwanese industries

In this study, we use stock index data of various industries in Taiwan from 2001 to 2010 to estimate the exchange rate exposures of these industries under various data frequencies (daily, monthly, and quarterly). We add the effect of hot money on exchange rate exposures and find that significant exchange rate exposures exist for most industries. After including the buy–sell imbalance of foreign investors as a factor, we find that the positive effect of currency value changes on stock returns probably contributes to the concurrent increase in the stock and foreign exchange markets caused by the hot money flowing into Taiwan from foreign investors. We likewise observe a more significant exposure in the lower frequency data. This result indicates that avoiding economic exposure in exchange rate exposures is comparatively more difficult.

Excess Comovement and Individual Investors: Direct Evidence from China's Bank Stocks

This paper investigates how individual investor trading can cause stock prices to move for reasons unrelated to fundamentals. We use a sample of dual-listed stocks to identify the sources of their price movements. The results show that excess price comovement driven by the market-specific shock is persistent over the monthly horizon for some A-shares traded in China, but not for their H-shares traded in Hong Kong. Further trade-based analysis indicates that the market-specific shock to the A-shares' prices may be initiated by individual sentiment changes, as proxied by the buy-sell imbalance (BSI) of individual investors (except the wealthiest individuals), but the mechanism for its persistence must be an indirect one. We then hypothesize and confirm a feedback view that the sentiment-related feedback of sophisticated individuals can strengthen the persistence of sentiment-induced excess volatility. We find that the monthly correlation in daily BSI between two investor groups, the wealthiest individuals and all individuals, is positively related to the shock at the monthly frequency. Meanwhile, the BSI of the wealthiest individuals is negatively related to the shock’s magnitude, suggesting that their trading behavior cannot be explained by an unawareness of the documented excess comovement but can be explained as the risk averse behavior of sophisticated investors. Our evidence and interpretation suggest that individual investors are more than a simple carrier of noise trading, and they can contribute to market inefficiency not only through individual irrationality, but also through individual rationality. We use the feedback view to shed light on several cross-country empirical anomalies.