Statistically significant prediction of stock price changes requires security returns' correlation with, or dependence upon, some variable(s) across time. Since a security's past return is commonly employed in forecasting, and because the lack of lower-order correlation does not guarantee higher-order independence, nonlinear testing that focuses on higher-order moments of stock return distributions may reveal exploitable stock return dependencies. This dissertation fits AR models to TAQ data sampled at ten-minute intervals for 20 small-capitalization, 20 mid-capitalization, and 20 large-capitalization NYSE securities, for the years 1993, 1995, 1997, 1999 and 2001. The Hinich Patterson Bicovariance statistic (to reveal nonlinear and linear autocorrelation) is computed for each of the 1243 trading days for each of the 60 securities. This statistic is examined to see if it is more or less likely to occur in securities with differing market capitalization, at various calendar periods, in conjunction with trading volume, or instances of changing investor sentiment, as evidenced by the put-call ratio. There is a statistically significant difference in the level and incidence of nonlinear behavior for the different-sized portfolios. Large-cap stocks exhibit the highest level and greatest incidence of nonlinear behavior, followed by mid-cap stocks, and then small-cap stocks. These differences are most pronounced at the beginning of decade and remain significant throughout the decade. For all size portfolios, nonlinear correlation increases throughout the decade, while linear correlation decreases. Statistical significance between the nonlinear or the linear test statistics and trading volume occur on a year-by-year basis only for small-cap stocks. There is sporadic seasonality significance for all portfolios over the decade, but only the small-cap portfolio consistently exhibits a notable "December effect". The average nonlinear statistic for small-cap stocks is larger in December than for other months of the year. The fourth quarter of the year for small-cap stocks also exhibits significantly higher levels of nonlinearity. An OLS regression of the put/call ratio to proxy for investor sentiment against the H and C statistic was run from October 1995 through December 2001. There are instances of sporadic correlations among the different portfolios, indicating this relationship is more dynamic than previously imagined. / Ph. D.

The dissertation consists of two essays. In the first essay, we measure herding by institutional investors in the new economy (internet) stocks during 1998-2001 by examining the changes in the quarterly institutional holdings of internet stocks relative to an average stock. More than 95% of the stocks that are examined are listed on NASDAQ. The second essay attempts to detect intra-day herding using two new measures in an average NYSE stock during 1998-2001. In the second essay, rather than asking whether institutional investors herd in a specific segment of the market, we endeavor to ask if herding occurs in an average stock across all categories of investors. The first essay analyzes herding in one of the largest bull runs in the history of U.S. equity markets. Instead of providing a corrective stabilizing force, banks, insurance firms, investment companies, investment advisors, university endowments, hedge funds, and internally managed pension funds participated in herds in the rise and to a lesser extent in the fall of new economy stocks. In contrast to previous research, we find strong evidence of herding by all categories of institutional investors across stocks of all sizes of companies, including the stocks of large companies, which are their preferred holdings. We present evidence that institutional investors herded into all performance categories of new economy stocks, and thus the documented herding cannot be explained by simple momentum-based trading. Institutional investors' buying exerted upward price pressure, and the reversal of excess returns in the subsequent quarter provides evidence that the herding was destabilizing and not based on information. The second essay attempts to detect herding in financial markets using a set of two methodologies based on runs test and dependence between interarrival trade times. Our first and the most important finding is that markets function efficiently and show no evidence of any meaningful herding in general. Second, herding seems to be confined to very small subset of small stocks. Third, dispersion of opinion among investors does not have much of impact on herding. Fourth, analysts' recommendations do not contribute to herding. Last, the limited amount of herding on price increase days seems to be destabilizing but on the price decrease days, the herding helps impound fundamental information into security prices thus making markets more efficient. Our results are consistent with Avery and Zemsky (1998) prediction that flexible financial asset prices prevent herding from arising. The seemingly contradictory results of the two essays can be reconciled based on the different sample of stocks, and the different methodologies of the two essays which are designed to detect different types of herding. In the first essay, herding is measured for NASDAQ-listed (primarily) internet stocks relative to an average stock, while the second essay documents herding for an average stock. In the first essay, we document herding in more volatile internet stocks, but we do not find any evidence of herding in more established NYSE stocks. The first essay examines herding by institutional investors, while the second essay examines herding, irrespective of the investor type. Consequently, in the first essay, we find that a subset of investors herd but in the second essay market as a whole does not exhibit any herding. Moreover, the first essay measures herding by examining the quarterly institutional holdings of internet stocks, while the second essay measures herding by examining the intra-day trading patterns for stocks. This suggests that it takes a while for investors to find out what others are doing leading to herding at quarterly interval but no herding is observed at intra-day level. The evidence presented in the two essays suggests that while institutional investors herded in the internet stocks during 1998-2001, there was very little herding by all investors in an average stock during this period. / Ph. D.

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I compared various MIDAS (mixed data sampling) regression models to predict volatility from one week to one month with different regressors based on the records of Chinese Shanghai composite index. The main regressors are in 2 types, one is the realized power (involving 5-min absolute returns), the other is the quadratic variation, computed by squared returns. And realized power performs best at all the forecast horizons. I also compare the effect of lag numbers in regression, form 1 to 200, and it doesn’t change much after 50. In 3 week and month predict horizons, the fitness result with different lag numbers has a waving type among all the regressors, that implies there exists a seasonal effect which is the same as predict horizons in the lagged variables. At last,the out-of -sample and in-sample result of RV and RAV are quite similar, but in sometimes, out-of sample performs better.

Made available in DSpace on 2016-04-26T20:29:45Z (GMT). No. of bitstreams: 1 Marcia Eliane Fontana.pdf: 417004 bytes, checksum: 41e9f5d7090abbb66ecfe68c6859aff8 (MD5) Previous issue date: 2009-10-30 / The study object of the current work is the intervals of intra-day s work under the Brazilian labor law. An analysis on intervals is carried out with the purposes of having harmony between the force spent by worker and the results desired by employer, taking always into account the intervention by the State in order to grant the accomplishment of constitutional rights and, therefore, to protect worker s health. The approach method to be adopted herein shall be inductive in view of the fact it advances from particular to general grounds. The research to be developed shall be bibliographical. It shall be highlighted that the employee s mental and physical recovery is the purpose of the interval of the intra-day s work. Hence, it is understood that the interval of the intra-day s work is deemed to be condition of hygiene, health and safety to the employee, which is guaranteed by rules of public policy nature (article 71 of CLT Brazilian Labor Act - and article 7th, XXII, of the CF/88 1988 Brazilian Federal Constitution) and contrary to collective agreement / Este trabalho tem por objeto de estudo os intervalos intrajornadas no direito do trabalho brasileiro. Faz-se uma reflexão em torno dos intervalos para que haja harmonia entre a força despendida pelo empregado e os resultados pretendidos pelo empregador sempre tendo em monta a intervenção do Estado para cumprir os direitos elencados constitucionalmente e assim proteger a saúde do trabalhador. O método de abordagem utilizado será o indutivo porque parte do particular para o geral. A pesquisa a ser desenvolvida é a bibliográfica. Ressalte-se que o intervalo intrajornada tem a finalidade da recomposição física e mental do trabalhador. Assim, entende-se que o intervalo intrajornada constitui-se em medida de higiene, saúde e segurança do trabalho, garantido por norma de ordem pública (art. 71, da CLT e art. 7º, XXII, da CF/88), infenso à negociação coletiva

本篇論文基於市場上所存在的一些交易機制，探討漲跌停前後之股價行為。因為證券市場上存在一些交易規則，例如漲跌停限制、買賣價差、最小升降單位限制、競價制度等，這些交易規則，具有法定的效力，理所當然地會影響投資人的行為。這種以各種交易機制的存在，探討價格形成的過程，就是市場微結構理論之研究範疇。 本篇引用Hausman, Lo, and MacKinlay (1992)所建立之Ordered Probit模型來分析漲跌停前後之股價行為，以個股逐筆交易的價格變動為因變數，而建立因變數為間斷型之分析模型，並以等待撮合時間、交易量、落後期交易價格、買賣價差等經濟變數，來探討個股逐筆交易價格變動的成因。在此同時，鑑於以往研究多假定價量關係為線性，本研究引入非線性的概念，檢定價量之間是否存有非線線性之關係；最後，為使模型更具解釋力，我們引入異質性變異數。 第一章 緒論……………………………………………………………..1 第一節 研究動機……………………………………………..1 第二節 研究目的……………………………………………..7 第三節 研究範圍與限制……………………………………..7 第四節 研究架構與內容……………………………………..8 第二章 文獻回顧……………………………………………………….10 第一節 非同時交易………………………………………….10 第二節 最小升降單位……………………………………….11 第三節 買賣價差…………………………………………….14 第四節 漲跌停限制………………………………………….15 第五節 重要模型回顧…………………………………….…18 2.5.1 Chou(1996)……………………………………..18 2.5.2 Hausman, Lo, and MacKinlay(1992)…………..20 第三章 實證模型設定………………………………………………….25 第一節 資料來源…………………………………………….25 第二節 樣本選取…………………………………………….25 第三節 模型設定…………………………………………….26 3.3.1 價格的變動區間……………………………….26 3.3.2 解釋變數……………………………………….29 3.3.3 條件變異數的型式…………………………….32 3.3.4 價格與成交量之間非線性關係的檢定……….32 第四節 資料處理…………………………………………….33 第四章 實證分析……………………………………………………….36 第一節 模型基本統計分析………………………………….36 第二節 價量非線性關係的檢定…………………………….39 第三節 Ordered Probit模型實證分析……………………….40 第五章 結論與建議……………………………………………………..48 第一節 結論…………………………………………………..48 第二節 建議…………………………………………………..49 參考文獻…………………………………………………………………..50 / This thesis is an application of the market microstructure theory’. In light of some trading mechanisms in our stock market, such as price limit, bid-asked spread, tick size, and auction system, those trading rules would influence the behavior of investors. We want to study the process and outcomes of stock price under those explicit trading rules. We use the Ordered Probit model (Hausman, Lo, and MacKinlay, 1992) to investigate the stock behaviors when it hits price limits. We also use price change as the discrete dependent variable, and time elapsed, trading volume, lag price changes, bid-asked spread as explanatory variables. In order to make the model more explainable, heterogeneity is applied. Moreover, we also want to find out if there is any nonlinear relationship between price change and trading volume.