1、 外文翻译 原文 What Makes Investors Trade? Material Source: The Journal Of Finance.Vol.Lvi.No.2.April 2001 Author: Mark Grinblatt and Matti Keloharju Abstract A unique data set allows us to monitor the buy sells, and holds of individuals and institutions in the Finnish stock market on a daily basis. With
2、this data set, we employ Logit regressions to identify the determinants of buying and selling activity over a two-year period. We find evidence that investors are reluctant to realize losses, they engage in tax-loss selling activity, and that past returns and historical price patterns, such as being
3、 at a monthly high or low, affect trading. There also is modest evidence that life-cycle trading plays a role in the pattern of buys and sells. I. A Unique Data Set This study employs a comprehensive data source: the central register of shareholdings for Finnish stocks in the Finnish Central Securit
4、ies Depository FCSD. Most of the details of this data set are reported in Grinblatt and Keloharju (2000). For our purposes, it is essential to understand that: A. The data aggregate holdings across brokerage accounts for the same investor, whether the shares are held in street name or not. B. Invest
5、or attributes, in substantial detail, are reported with each transaction. Among the more interesting attributes is the investor category. We primarily focus on five categories, based on a classification system that has been determined by the European Union, observed at the top of Table I. A sixth fo
6、reign investor category is added to the analysis of buys versus sells in Section III. C. When multiple stock purchases occur, we compute the basis for the holdings capital gain or loss as the share volume weighted-average basis properly adjusted for splits of the investor s inventory of stock acquir
7、ed in the sample period. Thus, an investor who purchases 100 shares of Nokia A at 600 FIM on January 6, 1995, and then 200 shares of Nokia A at 900 FIM on February 10, 1995, would in the absence of further purchases have a basis of 800 FIM in Nokia A after February 10, 1995. A sale of 150 shares of
8、Nokia A on February 11, 1995, by this same investor is thus assumed to consist of 50 shares purchased previously on January 6 and 100 shares purchased on February 10. Any existing holdings of Nokia A on December 27, 1994, plus holdings acquired since December 27, 1994, for which no purchase price is
9、 available need to have been sold before February 11, 1995, to establish this basis correctly. We would exclude the February 11 sale from our analysis if this were not the case. The data set is obviously large. There are approximately one million sell transactions and one million buy transactions th
10、at we initially screen. In addition, for comparison purposes, and consistent with Odean (1998), most of our analysis matches each sell with all stocks in the investor s portfolio that are not sold the same day. Thus, our analysis of stock sales begins with millions of events. Several factors, outlin
11、ed below and specific to the type of regression undertaken, reduce the size of the sample to that reported in our regressions. D .In Section II, which reports the results of regressions that study sell versus hold behavior, we net all same day trades in the same stock by the same investor to mitigat
12、e the effect of intraday market making and double counting due to trade splitting, and we require that the purchase price used to compute the capital gain or loss for a sale or potential sale be unambiguous. In Section III, which studies buy versus sell behavior, we net intraday buys and intraday se
13、lls separately, except for nominee-registered foreign investors for which the lack of panel data makes netting computations impossible. Finally, there is the requirement that all independent variables be available for all observations within an investor category, but this has little effect on the sa
14、mple size. II. The Sell Versus Hold Decision This section analyzes the determinants of a dummy variable representing the binary outcome: sell coded as a 1or do not sell coded as a 0. Each day that an investor sells stock, we examine all of the other stocks in his portfolio and classify them into one
15、 of these two outcomes, based on whether any of his holdings of that stock were sold. We report coefficients and t statistics from a Logit regression estimated with maximum likelihood procedures. We have verified that the results we will report shortly are neither Nokia specific nor affected by seri
16、al correlation, and that they are similar to those obtained from the less sensible OLS specification. A .Description of the regression Each of 293,034 binary data points, obtained in the manner discussed above, belongs to an investor in one of the five domestic investor classes. For each domestic in
17、vestor class, we estimate the relation between the dependent variable sell versus hold and 244 regressors, of which 18 are unique to households, 2 are unique to the finance and insurance institutions, and 1 is unique to the government sector. These regressors include a set of variables used as contr
18、ols for which coefficients are not reported3 and a set of reported variables. The latter include 122 variables related to past returns-listed in Table I, Panel A, which analyzes positive past returns over 11 horizons, and Panel B, which analyzes negative past returns over 11 horizons, two dummy vari
19、ables representing moderate and extreme capital losses Panel C, two dummy variables representing the interaction of a December dummy and the capital loss dummies Panel D4 the interaction of a dummy for a holding-period capital loss and the 22 past return variables Panels E and F, two reference price
20、 variables to assess if the sales decision is affected by the stocks price being at a one-month high or low Panel G, a pair of variables related to stock price and stock market volatility Panel H, and a set of eight miscellaneous variables that control for the investor and his portfolio Panel I. Var
21、iables related to these sets of variables have either been postulated to be related to trading, common sense suggests they should be related to trading, or they have been found in prior empirical research to be related to trading. For example, because we control for the stock traded with stock dummi
22、es, the stock volatility variable asks whether an investor tends to sell a stock at a time when its volatility is higher than normal. B. Past returns and the sell versurs hold decision The results are fairly consistent across the investor categories. Returns beyond a month in the past 20 trading day
23、s appear to have little impact on the decision to sell versus hold, whereas positive market-adjusted returns on any day of the last week, or during the last month, are significantly correlated with the decision to sell. Generally, the more recent the positive return, the more likely is the sell deci
24、sion. Although the results for day 0 are the strongest of all, we do not have intraday panel data that would allow us to separate out the impact of returns on trading activity from the impact of trading activity on returns. However, if there is a simultaneous equations bias, it works to bias the coe
25、fficient downwards, and because of the day 0 return with almost all of the other regressors, has little effect on the other coefficients. We know this from running our analysis without the day 0 regressors. Panel B indicates that in the prior week, the more negative are the market-adjusted return, t
26、he lower is the propensity to sell. The significance of the positive T-statistics for households and non- financial corporations for horizons going back up to one week prior to the sale appears to be weaker than the impact of the positive returns on the propensity to sell. Moreover, there are occasi
27、onal sign reversals at some of the longer horizons for some of the categories. This evidence suggests that for Finnish investors, recent large positive market-adjusted returns up to a month in the past are an important factor in triggering a sell. Strongly negative market-adjusted returns up to a we
28、ek in the past have a moderate tendency to reduce the probability of a sell. C .Evidence on the Disposition Effect Plotting the distributions of holding period realized and paper capital gains and losses without the controls in the regression is also insightful. Panel A of Figure 1 shows the distrib
29、ution of realized gains and losses for all investor categories aggregated together and Panel B shows the paper gains and losses. The left tail of Panel A, the realized capital gain returns, is much thinner than that in Panel B, the paper capital gain returns. The right tail in Panel A is much thicke
30、r. Perhaps most striking is what appears to be a discontinuity at zero for Panel As distribution of realized capital gain returns. To the left of zero in Panel A, the height of the density function immediately drops off. For the paper capital gain returns of Panel B, the distribution to the left of
31、zero appears to relatively smooth. Although these plots lack hundreds of controls found in the regressions, they are consistent with the tendency for large gains to be realized and large losses to be held onto. They also tell a story that is very hard to explain as anything but a disposition effect.
32、 For example, in the Harris and Raviv(1993) model, investors have beliefs about a companys future prospects that are not closely tied to stock prices. Hence, as stock price decline, stock in that company becomes more attractive and vice versa. However, Harrisand Ravivs (1993) model is not consistent
33、 with the discontinuity observed in Figure 1, Panel A, but rather, with a skewed yet smoother distribution than that observed.6 D .Evidence on Miscellaneous Stock and Investor Attributes as Determinants of Sales In addition to past returns, capital losses, tax-loss selling variables, reference price
34、 effects, and volatility, our regressions control for a number of other miscellaneous stock and investor attributes. These miscellaneous attributes include the number of days since a stock was purchased and the logged market value of the portfolio on the day of the sale. In addition, the regressions
35、 for two of the institutional categories break the institutions into subcategories, whereas the regression for households controls for whether the investor is male or female, and has two dummies for employment status non-employed is the default. Finally, there is also a set of unreported control var
36、iables described earlier. The coefficients and t-statistics for the reported variables are in Table I. E. Comparing the Explanatory Power of Capital Loss and Past Return Variables The capital loss variables both the disposition effect and tax-loss selling are slightly less important determinants of
37、the sell versus hold decision than past returns. For example, excluding the recent return variables and the interaction dummies between recent returns and a capital loss lowers the pseudo-R 2 of households by 0.021, whereas the exclusion of capital loss variables, tax, and there cent returncapital l
38、oss interaction dummies generates an R 2 that is 0.017 less than it previously was. The relative magnitudes of the R 2 reduction for the other two major categoriesnonfinancial corporations and finance and insurance institutionsare similar, whereas government and non-profit trading exhibit much more
39、sensitivity to the past return variables. III. Summary and Conclusion This paper presents a comprehensive analysis of the determinants of buyer and seller transactions. With a variety of tests, it shows that past returns, reference price effects, the size of the holding period capital gain or loss,
40、tax-loss selling, and, to a small extent, the smoothing of consumption over the life cycle all are determinants of trading. The regressions for the sell versus hold decisions suggest that the propensity to sell stocks one holds is positively related to recent returns. The effect of the past return n
41、on-trading activity is much more important for positive past market-adjusted returns than for negative past market-adjusted returns. Investors also tend to be reluctant to realize their losses except in December, when the urge to realize large losses for tax purposes tends to eliminate this effect.
42、We also present evidence that tax-loss selling primarily arises in the last two weeks of the year and that reference prices matter. Conditional on a trade, sophisticated investor classes place less weight on past returns in deciding whether the trade is to be a buy or sell. By contrast, the less sop
43、histicated investorshouseholds, general government, and non-profit institutionsare more predisposed to sell than to buy stocks with large past returns. The buy versus sell results are largely consistent with the results of Grinblatt and Keloharju (2000), in that domestic investors particularly the l
44、ess sophisticated investor categoriestend to be contrarians and foreign investors tend to be momentum investors. They are also consistent with the sell versus hold evidence that high past market-adjusted returns generate sells. Life-cycle considerations also may account for some of the trading. Inve
45、stors tend to sell primarily inherited stock early in life, purchase stock in the prime earning years of middle age, and then sell stocks in old age. However, the results are economically unimpressive. 译文 投资者交易的原因是什么？ 资料来源：金融期刊 2001年 4月第二期 作者：马克和马蒂 一组特殊的数据使我们能够监测日常买卖过程，以及个人和机构在芬兰股市的持有股。 有了这个数据集，我们 可
46、以 采用罗吉特回归分析，以确定超过两年 的 购买和销售活动的决定因素。我们发现的证据表明，投资者都 没有意识到损失，他们从事 税损 销售活动，而过去的回报和历史价格形态，如每月 新 高或新 低， 会 影响交易的 发生 。也有 一些 证据表明，生命周期交易 对 购买和销售模式 产生一定作用 。 1 一个独特的数据集 本研究采用了全面的数据源 ， 即芬兰中央证券存管数据和芬兰股持股中央登记册。本组数据的大部分细节公布在 Grinblatt和 Keloharju(2000)。我们认为了解以下事项十分重要： （ 1）本数据收集了每位投资人 经纪帐户上的持有股 ， 无论其股份是否拥有行号代名。 （ 2）
47、在大量的细节基础上 ， 投资者属性在每笔交易中都有报告 ， 其中比较有趣的属性是投资者的类别。根据欧盟确定的分类系统 ， 我们主要分析其中五种类型。 （ 3） 当购买多个股票 ， 我们会在加权平均法的基础上计算控股公司的资本收益或损失量的份额 ， 适当调整投资者在样本期间获得的股票份额。因此 ， 投资者若以 600芬兰马克在 1995年 1月 6日购入诺基亚 A100股 ， 然后以 900芬兰马克在 1995年 2月 10日购入诺基亚 A200股 ， 他将在 1995年 2月 10日后再没有进一步购买的情况下拥有 800芬兰马克的诺基亚 A股。因此 ， 关于 95年 2月 11日出售 150诺
48、基亚同样的 A股投资者 ， 假设他购买包括 1月 6日和 2月 10日以前的 100股和 50股 ，诺基亚现有的任何控股者在普纳集团收购以来的 1994年 12月 27日 ， 需要将股份在 1995年 2月 11日前售出 ， 以保持这一数据正确性。否则我们将排除 2月 11日交易的数据。 该数据集 ， 显然十分庞大。我们最初筛选的就有大约一 百万出售交易和一百万购买交易。此外 ， 为了便于比较 ， 并与 1998年 Odean相一致 ， 我们的分析中每个投资者的投资组合都与当天所有未售出的股票匹配。因此 ， 我们首先以百万计的事件对股票销售进行分析。下文概述几个因素 ， 针对具体的回归 类型
49、， 以调整样品的大小。 （ 4） 我们都在同净股票当天由同一行业同一投资人 ， 以减轻投资者因贸易分裂而受到的盘中庄家效应和重复计算。我们要求 ， 购买价格来计算的资本收益或损失或潜在的销售数字是明确的。在研究买卖行为的第三节 ， 我们的净盘中分别计算买入和卖出盘 ， 除了被提名人注册的外国投资者 ， 而面板数据的缺乏使得净盘计算无法进行。最后 ， 还有一个规定 ， 即所有自变量的观察结论在同一投资者类别内都可用 ， 但是这对样本大小影响不大。 2 抛售决策 本节分析了一个虚拟变量代表二进制结果的决定因素：销售编码为 “ 1” 不销售则编码为 “ 0” 。投资者卖出股票的每一天 ， 我们考察他的投资组合中的其他所有股票 ， 根据股票售出情况将其划分为两种结果之一。我们以最大可能性程序报告 Logit 回归系数和 t 统计量。我们已验证 ， 我们不久将报告既不是诺基亚也不是由特定的序列相关性影响 ， 它们是类似于更合理规范的回归。 （ 1）说明回归 从上 述讨论的方式获得的 293034 点二进制数据 ， 每个都属于国内投资者的五个类别之一。每一类国内投资者 ， 我们会估计因变量之间的关系与持有和出售 244 回归量 ， 其中 18 户是家庭型 ， 2 户是金
