Trading volume in information asymmetries

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Theoretical framework

In this chapter we present the theories our problem is based on. Section 3.1 and 3.2 aim to provide back-ground information. Section 3.3 is our main framework and these theories are directly correlated with our research problems. In the end we will give some criticism to the theories.

The importance of trading volume

”It takes volume to move prices” is a common saying on the world’s stock exchanges. We wish to shed light on the fact that trading volume can explain price movements and thus have a very important role on financial markets. However, the causality in the price-volume relationships is an ambiguous matter in financial research. The price-volume phenomenon is a subject that attracts a lot of research and there are numerous studies that report a posi-tive correlation between the absolute price change and the trading volume. Table 2.1 is a synthesis of important results. All studies were made on stocks.
There exist four theoretical explanations to this relationship. They are: The sequential arri-val of information (SAI) model, the mixture of distributions (MD) model, the rational ex-pectation asset pricing (REAP) model, and the differences of opinion (DO) model (Chen, 2001). The SAI model which was originally invented by Copeland (1976) has been devel-oped by Barry and Jennings (1983) and others. In the new version, new information is spread out to traders sequentially. The traders who are not yet informed do not perfectly know if they are trading against informed counterparts. Consequently, this sequence of new information distribution will generate both trading volume and price movements; both will increase in times of information shocks.
In the MD model, developed by Epps and Epps (1976) both price movements and trading volume depend the same variable, which can be interpreted as the rate of information flow to the market. This suggests that both volume and price movements change together to new information (Chen, 2001).
In Wang’s (1994) REAP model the uninformed are trading against the informed and are facing adverse selection. Because of this a premium is demanded by the uninformed. For a given size trade the price has to adjust more than would be the case if they had the same in-formation with the result that the correlation between volume and price changes increases in information asymmetry.

Noise

To further explain the relationship between the trading volume and information asymmetry it is important to consider noise in the market. Uninformed investors are also referred to as noise traders. Black (1986) states that noise can be viewed as signals about a stock’s value or future dividend that can be mistaken for information. The noise part of the price is the difference between the price and the fundamental value. It is the part of the price that is not information, e.g. a sharp fall in the main index that affects other stocks as well. How-ever information can be just noise itself if it is already discounted in the price. It is hard to estimate fundamental value because all estimates of it are noisy themselves, so one can never know how far from fundamental value the price is. The volatility of price, in the short term, will be greater than the volatility of fundamental value but in time horizons over several years the volatility will converge because price tends to return to the funda-mental value.
Black further states that the noise affects the financial markets, making them possible, but also imperfect. Noise provides liquidity to financial markets but it is also putting noise to prices, making it hard to estimate actual value. Noise trading is trading on noise as if it were information. From an objective point of view noise traders would be better of not trading, than trading on the noise.
Noise trading creates an opportunity for informed investors to take advantage of. With many noise traders on the market it will pay off to gather information to trade on. It even pays off to gather costly information. In this situation, as a group, noise traders will lose money and informed traders will make money.
The information traders will trade with noise traders more than with other informed trad-ers, therefore cutting back in noise trading has the same effect on information trading.
The effect on noise and trading volume is shown by Wang (1994). By increasing noise in public signals, the signal becomes less informative and information asymmetry increase be-tween the two classes. This will reduce trading volume since the uninformed trader will have less information and trade less on average.

Trading volume in information asymmetries

Here we give a review of theoretical models on trading volume in information asymmetries. This is our main theoretical framework and forms the basis for our hypotheses. The first two sections discuss the behavior of trading volume in information asymmetries. Our as-sumption is, based on Chae (2005) that around scheduled corporate announcements the in-formation asymmetry much more severe than normal. The last section discusses trading volume around unscheduled announcements, when uninformed investors are unprepared.

Trading volume prior to scheduled announcements

Wang (1994) developed a model to study the relation between trading volume and return. The model gives an explanation how information affects trading volume. It is based on a simple economy with both traded assets and private investment opportunities. The differ-ent investors in the market have different private investment opportunities and different information about the stocks future dividends. The informed investors have private infor-mation whereas the uninformed investors get information from realized dividends, price and public signals to determine future dividends. Informational trading arises when the in-formed investors receive private information and trade while the noninformational trading is when they trade to optimally rebalance their portfolio. The uninformed investors trade only for noninformational reasons. Since not all trade from the informed investors are in-formation motivated, the uninformed investors are willing to trade at favorable prices and expect to earn abnormal future returns. Since the uninformed investors can not perfectly identify the incentives of their counterpart they face the risk of trading against private in-formation. Because of this, as the information asymmetry between the two groups of inves-tors increase, trading volume will decrease since the adverse selection problem worsens. Furthermore, under asymmetric information, public news about a stock’s future dividends causes abnormal trading. The different in response to the same information by the two classes of investors generates trading. The greater the information asymmetry, the larger the abnormal trading volume when public news arrives.
Foster and Wiswanathan (1990) describe a model where there is one informed investor with monopoly on information, a group of uninformed investors and a market maker. Trading is spread out on days of the week. Informed investors receive informative signals about the stock’s future payoffs, whereas the uninformed investors only get a noisy public signals. The advantage of the informed investor is reduced through time by public informa-tion and the market maker’s price responses to order flow. It is assumed that the market maker adjusts the price responses when she faces adverse selection. The more sensitive the prices are to order flow the higher are the transaction costs for all investors. They argue that, because the price is important information for uninformed investors, the longer the market is closed the greater is the advantage for informed investors with private informa-tion. Thus, after the weekend, on Mondays, is the advantage the greatest of the week. The market maker has adjusted price responsiveness to orders to its highest on Mondays because the informed investor has gathered information during the weekend which has given her an even greater advantage.
This leads to a situation where the transaction costs for all investors in this game is highest on Mondays when the adverse selection is the greatest of the week. This reduces trading volume significantly. Also, they provide a theorem that says that without an earnings report in the week, the market maker’s responsiveness of prices to order flow declines over the week, reflecting that the market maker assumes that the information asymmetry is not so great. For weeks with a report, prices are more sensitive to order flow prior to the report, making it more costly to trade. The sensitivity of prices is reduced after the report is re-leased and it gets cheaper to trade.
Kyle (1985) came to another conclusion in his model on private information and trading. As information asymmetry increase, so does trading volume. The informed investors use their information in an attempt to exploit the market. The study differs from Wang’s in the sense that quantity has not a remarkable affect on the price and uninformed investors lack timing discretion.

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Trading volume after scheduled announcements

In George et. al. (1994) a model with uninformed investors, informed investors and a spe-cialist who has monopoly on setting the bid and ask prices of a risky asset. The specialist sets his prices so as to maximize her profits. A wider bid-ask spread increases the transac-tion costs for all investors. An increase in information asymmetry increases the size of the informed investors order because then, her advantage would be greatest. It is showed that the greater the information asymmetry between the specialist and the informed investors the bigger are the specialist’s expected losses from trading the security. In response, the specialist widens the bid -ask spread during periods when she expects heavy trading by in-formed investors. When this happens, the uninformed investors will decrease their trading. The responsiveness of equilibrium trading volume to changes in information asymmetries depends on the pattern of uninformed trading. In an information asymmetry when the in-formed investors increase their trading, the specialist widens the bid-ask spread. If the net outcome is a decrease or an increase in total trading volume depends on whether unin-formed trading decreases in a decreasing or an increasing rate. If the rate is decreasing then the relatively little trading is discouraged by an increase in the spread. This makes the spe-cialist set the spread even wider in response to extreme information asymmetries, which in turn decreases total trading volume. This has the important implication that, when the in-formation asymmetry is resolved, as would be the case after an earnings announcement, the transaction costs would be significantly lower. This induces uninformed investors to trade and the effect would be an increase in total trading volume.
Foster and Wiswanathan (1990) propose that Friday is the day of the week with highest trading volume, simply put, because uninformed investors have gathered information dur-ing the week and Friday is therefore the day with the lowest information asymmetry. This reflects the fact that trading volume increases once the information asymmetry is resolved, as would be the case after scheduled announcements.

Trading volume around unscheduled announcements

The former models imply a decrease in trading volume prior to scheduled public an-nouncements. We assume, like Chae (2005), that for unscheduled announcements, it gets impossible for uninformed investors to predict informed trading and hence are unable to adjust their own trading accordingly. The informed traders are eager to exploit their advan-tage and increase their trading. When the uninformed investors trade as usual and the in-formed investors increase their trading the outcome would be an increase in total trading prior to unscheduled public announcements.

Critique of the theoretical framework

Kyle’s (1985) model has a fundamental flaw; it assumes that uninformed investors have no discretion of when to place their trades which is a bit harsh, it must be that uninformed traders can select strategically when to place their trades.
A clear enhancement of this model was developed by Foster and Wiswanathan (1990) where they introduced continuous trading days and allowed new information to enter the market each day. The model also assumes that there is only one informed investor which gives the model a bit nondynamic features. Also it is argued that the implications of the model mean that the decrease in volume should be greater for bigger, more actively traded stocks, opposing the theory stating that information asymmetries are greater for “riskier” stocks (firm size often function as a proxy for risk). This model is also non-competitive, it only assumes that there is an investor with superior knowledge that she is trying to exploit strategically.
Both Kyle and Foster and Wiswanathan and Geroge et. al. (1994) assume that there exists a market maker. The role of the market maker is to warrant trading in a security by providing bid-ask spreads. In Sweden, only the smallest stock indices like “Nya marknaden” and “NGM” practice market making so in practice a model that assumes a market maker is not the most relevant on a larger stock index, however the main aspects of it are still applica-ble.
Wang’s (1994) model does include the role of the market maker. It also includes competi-tive trading where everyone is trying to maximize their profits. It also models both infor-mational and noninformational trading as investors’ optimizing behavior, whereas the first three models model noninformational trading as liquidity trading without determining its economic origin. The main drawback of Wang’s model is that there are no transaction costs involved, every investor is free to do as many trades as she wants which encourages an abnormal trading activity.

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Previous Studies

Previous studies relevant to our own thesis is presented in the following chapter. We will use our own hy-potheses to present the findings in earlier research.

Trading Volume, Information Asymmetry, and Timing In-formation

Joon Chae (2005), Journal Article
There have been quite a few theoretical models developed in the field of information asymmetry and abnormal trading volume. The ones that are relevant to our study are men-tioned in the previous chapter. Yet, there has only been one article written that study trad-ing volume around scheduled and unscheduled announcements. Below follows a presenta-tion of the thesis our study is based on.
Chae’s study was carried out using earnings- , acquisition- and Moody’s announcements, all from New York Stock Exchange (NYSE) and American Stock and Option Exchange (AMEX) companies.. The time period studied was 1986 to 2000. Chae’s results are pre-sented in table 4.1. Each value is in abnormal log turnover the same measurement as we use. Respective t-value is presented in parenthesis.
Hypothesis 1: The trading volume decreases before a scheduled announcement.
A scheduled announcement refers to an announcement for which the public know the spe-cific date it will be released. Chae used earnings announcements as scheduled announce-ments. The result of the study was that cumulative trading volume decrease by more than 15 percent before scheduled announcements. He found that there was a relationship be-tween trading volume and information asymmetry. The findings were consistent with the theories of asymmetric information. As uninformed traders are facing high adverse selec-tion costs, they decrease their trading.
Hypothesis 2: There is a corresponding increase in trading volume after the scheduled announcement is released.
Consistent with the first hypothesis, Chae found that trading volume increased after a scheduled announcement. Abnormal turnover in trading volume was significant each of the days following a scheduled announcement. This is evidences that uninformed investors time their trades and the decrease in trading volume before the announcement is the amount of delayed trading. Uninformed investors make up for their delayed trading once the information is released.
Hypothesis 3: The trading volume increases before an unscheduled announce-ment.
Unscheduled announcements refers to an announcement for which the public do not know the date it will be released. Chae used acquisition, target and Moody’s bond rating an-nouncements for unscheduled announcements. The findings were that trading volume be-fore unscheduled announcements increased. By comparing this result to the result before a scheduled announcement, Chae showed that the uniformed investors have no chance to time their trade around an unscheduled announcement. The increase in trading volume be-fore unscheduled announcements indicates that the informed investors exploit their advan-tage in information.
Hypothesis 4: All the assumed effects are greater for the firms on O-listan.
When doing regressions Chae found that there was a relation between firm size and the behavior of trading volume for scheduled announcements. On the other hand, size was not positively related to trading volume before either of the unscheduled announcements. Chae argues that, if the argument that information asymmetry is greater for smaller companies is accepted, then these results imply that information asymmetry affects trading behavior only before scheduled announcements.

1 Introduction
1.1 Background
1.2 Research problems
1.3 Purpose
1.4 Definitions
1.5 Disposition
2 Method
2.1 Theoretical approach
2.2 Research approach
2.3 Deduction
2.4 Hypotheses
2.5 Summary of technical method
2.6 Data Collection
2.7 Analyzing data
2.8 Reliability and validity
3 Theoretical framework
3.1 The importance of trading volume
3.2 Noise
3.3 Trading volume in information asymmetries
3.4 Critique of the theoretical framework
4 Previous Studies
4.1 Trading Volume, Information Asymmetry, and Timing Information
5 Empirical findings
5.1 Preliminary study
5.2 Main study
5.3 Comparison A-listan and O-listan
5.4 Robustness check
6 Analysis
6.1 Comparison of studies
6.2 The result’s relation to theory
7 Conclusion and Final Discussion
7.1 Conclusion
7.2 Final discussion
7.3 Suggestions for further studies
References
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