چکیده:
پژوهش حاضر به بررسی ارتباط ویژگیهای معاملات سهام با شاخصهای متفاوت نقدشوندگی در بورس اوراق بهادار تهران میپردازد. شاخصهای نقدشوندگی بکار رفته در این پژوهش عبارتند از گردش سهام، نسبت عدم نقدشوندگی آمیهود، معیار بازده صفر، اختلاف قیمت پیشنهادی خرید و فروش نسبی سهام و معیار تعدیل تعداد روزهای بدون معامله بر اساس گردش سهام. در راستای دستیابی به اهداف پژوهش، اطلاعات 38 شرکت برای دوره زمانی 1382 لغایت 1388 به طور ماهانه مورد مطالعه قرار گرفت. برای آزمون فرضیههای پژوهش از رگرسیون چند متغیره با استفاده از دادههای ترکیبی استفاده گردیده است. نتایج پژوهش بیانگر آن است که ویژگیهای معاملات سهام، عوامل اصلی نقدشوندگی هستند. این یافته که برخی شاخصها به گونهای متفاوت با ویژگیهای معاملات سهام برخورد میکنند، نشان میدهد که نقدشوندگی یک مفهوم پیچیده چندبعدی است که هر شاخص فقط میتواند جنبهای از نقدشوندگی را منعکس کند.
Introduction: Liquidity is an important issue for securities traded in financial markets. A certain level of liquidity is necessary for securities to be traded in the quantites required in a timely fashion whithout any price discount. The goal of this paper is to examine the relationship between different liquidity proxies and stocks' trading characteristics for listed companies in Tehran Stock Exchange. In this paper، five different liquidity proxies are introduced. The proxies are stock turnover، the illiquidity ratio، zero return measure، proportional bid–ask spread and turnover adjusted number of zero daily volumes. Stocks' trading characteristics include stock price، trading volume، return volatility، absolute return، and Beedles' thin trading measure.
The efforts are significant as liquidity plays an important role in asset pricing، and the selection of liquidity proxies in a research design would have considerable influence on empirical results.
Research hypothesis: The underlying principle in the relationship between liquidity and stock characteristics is based on order execution and inventory control (Stoll، 2000). Large trading volume reduces the risk of carrying inventory for a period of time، which should increase stock liquidity. Higher return volatility increases the risk of holding inventory، and it should have a negative effect on stock liquidity. Stock price controls the effects of price discreteness and can be used as a proxy for risk، as low price stocks tend to be riskier. Absolute stock return can be treated as an alternative measure of volatility. The advantage of this measure is that it is simple to calculate، particularly in comparison to conventional volatility measures. Similar to volatility، absolute stock return should have a negative influence on liquidity. A thin trading measure proposed by Beedles، Dodd and Officer (1988) is used to create a crude proxy for the proportion of missing daily returns. Since the Beedles measure aims to capture the thin trading aspect of stock illiquidity، it should be negatively related with liquidity. Thus، out testable hypotheses are:
Hypothesis 1: Price per share is expected to be related to liquidity.
Hypothesis 2: Trading volume is expected to be related to liquidity.
Hypothesis 3: Return volatility is expected to be related to liquidity.
Hypothesis 4: Absolute return is expected to be related to liquidity.
Hypothesis 5: Beedles' thin trading measure is expected to be related to liquidity.
Methods: This research is of descriptive-correlative type. The study sample includes 38 companies listed in Tehran Stock Exchange. The analysis in this paper is carried out at the monthly level from January 2003 to September 2009. For hypothesis testing، this study uses multivariable regressiones for pooled data. The variables being considered are liquidity proxies as the dependent variable and Stocks' Trading Characteristics as independent variables.This paper employs five widely used liquidity proxies that are stock turnover (TO)، the illiquidity ratio (ILLIQUID)، proportional spread (PBA)، the zero return measure (ZERO) and turnover-adjusted number of zero daily volumes (LM). Each is discussed in turn below:
TOi،t = voli،t / sharei،t
Where voli،tis the total trading volume for stock i in month t and sharei،t، t is the number of shares outstanding for stock i in month t.
where is the return for stock i on day d in month t، and vi،d،t is the trading volume for stock i on day d in month t and D is the number of daily observations for stock i in month t.
Where is the daily closing ask (bid) prices for stock i on day d in month t and D is the number of daily observations for stock i in month t.
ZEROi،t = zeroreturni،t / tradingdayi،t
Where zeroreturni،t is the number of zero daily return days for stock i in month t، and tradingdayi،t is the number of trading days for stock i in month t.
Where is the number of zero daily trading volumes for stock i in month t; is the stock turnover rate for stock i in month t. is the total number of trading days in the market in month t.
The trading characteristics include PRICE (price per share at the end of each month)، VARIANCE (return volatility of daily stock returns in each month)، VOLUME (trading volume aggregated in each month)، (ABSR) Absolute monthly stock return and Beedles that is defined as:
BEEDLES = {100 – [100/(n + 1)]}/100
Where n is the difference in time (measured in days) between the last price date and last trading date in each month.
Results: This paper examined the influences of trading characteristics on stock liquidity. Consistent with the literature، trading characteristics are important determinants of liquidity. In general the impact of the trading characteristics on PBA and LM is consistent with our hypotheses. However، their relationships with stock turnover exhibit a somewhat different pattern than the other liquidity proxies.This result suggests that the source of the stock turnover is not related to stock characteristics that are important for the other proxies. Notably، we have been silent on the question of what is the “best” liquidity proxy. This research issue is beyond the scope of the current study. However، as noted in Goyenko et al. (2009)، the selection of liquidity proxies in an empirical design depends on what exactly one wants to capture. Our results support their assertion، as liquidity is multidimensional and can be captured by differentmeasures of trading activity. The current study shows that through firm trading characteristics، we can better understand the sources of liquidity.
خلاصه ماشینی:
"فرضیه های این پژوهش بر اساس مطالـب پـیش گفتـه شکل گرفته و ارتباط میان شاخص هـای نقدشـوندگی و ویژگیهـای مبـادلات سـهام شـامل قیمت سهام، ارزش معاملات، تغییرپذیری بازده سهام، قدرمطلق بازده سهام و معیار بیدلیس مشابه پژوهش چای و همکاران (٢٠١٠) مورد بررسی قرار می گیرد.
تعدیل تعداد روزهای بدون معامله بر اساس گردش (LM) همانطور که پیش از این نیز ذکر شد برای سازگاری شاخص های نقدشوندگی ، علامت معیارهای آمیهود، اختلاف قیمت پیشنهادی خرید و فـروش نـسبی سـهام و تعـدیل تعـداد روزهای بدون معامله بر اساس گردش منفی شده و معیار بـازده صـفر بـصورت zeroi,t-1 مورد استفاده قرار می گیرد.
همانگونه که پیش از این نیز ذکـر شـد جهـت سـازگاری شـاخص هـای نقدشوندگی ، علامت معیارهای آمیهود، اختلاف قیمـت پیـشنهادی خریـد و فـروش نـسبی سهام و معیار تعدیل تعداد روزهای بدون معامله بر اساس گردش منفی شـده اسـت کـه در جدول فوق نیز این موضوع مشهود است .
همانطور که در جدول شماره ٢ نـشان داده شـده است احتمال آماره t برای متغیرهای قیمت سهام، تغییرپذیری بازده سـهام، قـدرمطلق بـازده سهام و معیار بیدلیس بزرگتر از ٥ درصد است که نشان می دهد میـان ایـن متغیرهـا و نـرخ گردش سهام به عنوان یکی از شاخص های نقدشوندگی ارتباطی معنادار وجود نـدارد.
ضریب منفی متغیرهای واریانس بـازده و قدرمطلق بازده سهام در این رگرسیون نشان می دهد که حرکت قیمـت در رابطـه بـا ارزش معاملات، برای شرکت هایی که تغییرپذیری قیمت و بـازده بـالاتری دارنـد بزرگتـر بـوده و نقدشوندگی بر اساس معیار آمیهود کمتر خواهد بود."