Does reviewing lead to better learning and decision making? Answers from a randomized stock market experiment

Background - The literature is not univocal about the effects of Peer Review (PR) within the context of constructivist learning. Due to the predominant focus on using PR as an assessment tool, rather than a constructivist learning activity, and because most studies implicitly assume that the benefits of PR are limited to the reviewee, little is known about the effects upon students who are required to review their peers. Much of the theoretical debate in the literature is focused on explaining how and why constructivist learning is beneficial. At the same time these discussions are marked by an underlying presupposition of a causal relationship between reviewing and deep learning. Objectives - The purpose of the study is to investigate whether the writing of PR feedback causes students to benefit in terms of: perceived utility about statistics, actual use of statistics, better understanding of statistical concepts and associated methods, changed attitudes towards market risks, and outcomes of decisions that were made. Methods - We conducted a randomized experiment, assigning students randomly to receive PR or non–PR treatments and used two cohorts with a different time span. The paper discusses the experimental design and all the software components that we used to support the learning process: Reproducible Computing technology which allows students to reproduce or re–use statistical results from peers, Collaborative PR, and an AI–enhanced Stock Market Engine. Results - The results establish that the writing of PR feedback messages causes students to experience benefits in terms of Behavior, Non–Rote Learning, and Attitudes, provided the sequence of PR activities are maintained for a period that is sufficiently long.

Financial liberalization and stock market volatility:the case of Indonesia

This article examines the relationship between financial liberalization and stock market volatility in Indonesia. By looking at the time series properties of the Jakarta Composite Index (JCI) we identify breaks in stock market volatility which coincide with the timing of major policy events. Our main findings are (i) a significant decrease in volatility after the 'official' opening of the stock market to foreign participation; (ii) a significant increase in volatility in the year before market opening following reforms that eased entry requirements and the issuance of brokerage licenses and (iii) a significant increase in volatility at the time of the Asian crisis followed by a significant decrease in the second and sixth years after the crisis.

Stock market efficiency before and after a financial liberalisation reform:do breaks in volatility dynamics matter?

This article focuses on the deviations from normality of stock returns before and after a financial liberalisation reform, and shows the extent to which inference based on statistical measures of stock market efficiency can be affected by not controlling for breaks. Drawing from recent advances in the econometrics of structural change, it compares the distribution of the returns of five East Asian emerging markets when breaks in the mean and variance are either (i) imposed using certain official liberalisation dates or (ii) detected non-parametrically using a data-driven procedure. The results suggest that measuring deviations from normality of stock returns with no provision for potentially existing breaks incorporates substantial bias. This is likely to severely affect any inference based on the corresponding descriptive or test statistics.

Price synchronicity:the closing call auction and the London Stock Market

In this paper, we consider the impact of the introduction of a closing call auction on market quality of the London Stock Exchange. We employ the market model, RDD and MEC metrics of market quality. These signify substantial improvements to market quality at both the close and open for migrating stocks.We note that these improvements are larger at the open than the close. An important contribution of our paper is that we show that changes to market quality are stronger in those securities that have the lowest liquidity in the pre-call period. In contrast, market quality changes following the introduction of a closing call auction are approximately neutral for high-liquidity securities. We conclude that the implementation of a closing call auction, for high-liquidity securities may not enhance market quality.

The effect of universal futures on opening and closing stock market price discovery

Purpose – On 29 January 2001, Euronext LIFFE introduced single security futures contracts on a range of global companies. The purpose of this paper is to examine the impact that the introduction of these futures contracts had on the behaviour of opening and closing UK equity returns. Design/methodology/approach – The paper models the price discovery process using the Amihud and Mendelson partial adjustment model which can be estimated using a Kalman filter. Findings – Empirical results show that during the pre-futures period both opening and closing returns under-react to new information. After the introduction of futures contracts opening returns over-react. A rise in the partial adjustment coefficient also takes place for closing returns but this is not large enough to cause over-reaction. Originality/value – This is the first study to examine the impact of a single security futures contract on the speed of spot market price discovery.

Web-Based Information Systems in the Stock Market Financial Information Domain

The information domain is a recognised sphere for the influence, ownership, and control of information and it's specifications, format, exploitation and explanation (Thompson, 1967). The article presents a description of the financial information domain issues related to the organisation and operation of a stock market. We review the strategic, institutional and standards dimensions of the stock market information domain in relation to the current semantic web knowledge and how and whether this could be used in modern web based stock market information systems to provide the quality of information that their stakeholders want. The analysis is based on the FINE model (Blanas, 2003). The analysis leads to a number of research questions for future research.

Unifractality and multifractality in the Italian stock market

Tests for random walk behaviour in the Italian stock market are presented, based on an investigation of the fractal properties of the log return series for the Mibtel index. The random walk hypothesis is evaluated against alternatives accommodating either unifractality or multifractality. Critical values for the test statistics are generated using Monte Carlo simulations of random Gaussian innovations. Evidence is reported of multifractality, and the departure from random walk behaviour is statistically significant on standard criteria. The observed pattern is attributed primarily to fat tails in the return probability distribution, associated with volatility clustering in returns measured over various time scales. © 2009 Elsevier Inc. All rights reserved.

Stress events in the Hungarian stock market

Central clearing and the role of central counterparties (CCP) has gained on importance in the financial sector, since counterparty risk of the trading is to be managed by them. The regulation has turned towards them lately, by defining several processes, how CCPs should measure and manage their risk. Stress situation is an important term of the regulation, however it is not specified clearly, how stress should be identified. This paper provides a possible definition of stress event based on the existing risk management methodology: the usage of risk measure oversteps, and investigates the potential stress periods of the last years on the Hungarian stock market. According to the results the definition needs further calibration based on the magnitude of the cross-sectional data. The paper examines furthermore whether stress is to be predicted from market liquidity. The connection of liquidity and market turmoil proved to be contrary to the expectations; liquidity shortage was rather a consequence, than a forecaster phenomenon in the tested period.

Using extreme value theory to value stock market returns

Extreme stock price movements are of great concern to both investors and the entire economy. For investors, a single negative return, or a combination of several smaller returns, can possible wipe out so much capital that the firm or portfolio becomes illiquid or insolvent. If enough investors experience this loss, it could shock the entire economy. An example of such a case is the stock market crash of 1987. Furthermore, there has been a lot of recent interest regarding the increasing volatility of stock prices. ^ This study presents an analysis of extreme stock price movements. The data utilized was the daily returns for the Standard and Poor's 500 index from January 3, 1978 to May 31, 2001. Research questions were analyzed using the statistical models provided by extreme value theory. One of the difficulties in examining stock price data is that there is no consensus regarding the correct shape of the distribution function generating the data. An advantage with extreme value theory is that no detailed knowledge of this distribution function is required to apply the asymptotic theory. We focus on the tail of the distribution. ^ Extreme value theory allows us to estimate a tail index, which we use to derive bounds on the returns for very low probabilities on an excess. Such information is useful in evaluating the volatility of stock prices. There are three possible limit laws for the maximum: Gumbel (thick-tailed), Fréchet (thin-tailed) or Weibull (no tail). Results indicated that extreme returns during the time period studied follow a Fréchet distribution. Thus, this study finds that extreme value analysis is a valuable tool for examining stock price movements and can be more efficient than the usual variance in measuring risk. ^

A study of stock market fluctuations and their relation to business conditions

Most research on stock prices is based on the present value model or the more general consumption-based model. When applied to real economic data, both of them are found unable to account for both the stock price level and its volatility. Three essays here attempt to both build a more realistic model, and to check whether there is still room for bubbles in explaining fluctuations in stock prices. In the second chapter, several innovations are simultaneously incorporated into the traditional present value model in order to produce more accurate model-based fundamental prices. These innovations comprise replacing with broad dividends the more narrow traditional dividends that are more commonly used, a nonlinear artificial neural network (ANN) forecasting procedure for these broad dividends instead of the more common linear forecasting models for narrow traditional dividends, and a stochastic discount rate in place of the constant discount rate. Empirical results show that the model described above predicts fundamental prices better, compared with alternative models using linear forecasting process, narrow dividends, or a constant discount factor. Nonetheless, actual prices are still largely detached from fundamental prices. The bubblelike deviations are found to coincide with business cycles. The third chapter examines possible cointegration of stock prices with fundamentals and non-fundamentals. The output gap is introduced to form the nonfundamental part of stock prices. I use a trivariate Vector Autoregression (TVAR) model and a single equation model to run cointegration tests between these three variables. Neither of the cointegration tests shows strong evidence of explosive behavior in the DJIA and S&P 500 data. Then, I applied a sup augmented Dickey-Fuller test to check for the existence of periodically collapsing bubbles in stock prices. Such bubbles are found in S&P data during the late 1990s. Employing econometric tests from the third chapter, I continue in the fourth chapter to examine whether bubbles exist in stock prices of conventional economic sectors on the New York Stock Exchange. The ‘old economy’ as a whole is not found to have bubbles. But, periodically collapsing bubbles are found in Material and Telecommunication Services sectors, and the Real Estate industry group.

A study of stock market linkages between the US and frontier countries

My dissertation investigates the financial linkages and transmission of economic shocks between the US and the smallest emerging markets (frontier markets). The first chapter sets up an empirical model that examines the impact of US market returns and conditional volatility on the returns and conditional volatilities of twenty-one frontier markets. The model is estimated via maximum likelihood; utilizes the GARCH model of errors, and is applied to daily country data from the MSCI Barra. We find limited, but statistically significant exposure of Frontier markets to shocks from the US. Our results suggest that it is not the lagged US market returns that have impact; rather it is the expected US market returns that influence frontier market returns The second chapter sets up an empirical time-varying parameter (TVP) model to explore the time-variation in the impact of mean US returns on mean Frontier market returns. The model utilizes the Kalman filter algorithm as well as the GARCH model of errors and is applied to daily country data from the MSCI Barra. The TVP model detects statistically significant time-variation in the impact of US returns and low, but statistically and quantitatively important impact of US market conditional volatility. The third chapter studies the risk-return relationship in twenty Frontier country stock markets by setting up an international version of the intertemporal capital asset pricing model. The systematic risk in this model comes from covariance of Frontier market stock index returns with world returns. Both the systematic risk and risk premium are time-varying in our model. We also incorporate own country variances as additional determinants of Frontier country returns. Our results suggest statistically significant impact of both world and own country risk in explaining Frontier country returns. Time-variation in the world risk premium is also found to be statistically significant for most Frontier market returns. However, own country risk is found to be quantitatively more important.

An Analysis of Stock Market Performance:The Dow Jones Industrial Average and the Three Top Performing Lodging Firma: 1982-1988

In their dialogue - An Analysis of Stock Market Performance: The Dow Jones Industrial Average and the Three Top Performing Lodging Firms 1982 – 1988 - by N. H. Ringstrom, Professor and Elisa S. Moncarz, Associate Professor, School of Hospitality Management at Florida International University, Professors Ringstrom and Moncarz state at the outset: “An interesting comparison can be made between the Dow Jones lndustrial Average and the three top performing, publicly held lodging firms which had $100 million or more in annual lodging revenues. The authors provide that analytical comparison with Prime Motor Inns Inc., the Marriott Corporation, and Hilton Hotels Corporation.” “Based on a criterion of size, only those with $100 million in annual lodging revenues or more resulted in the inclusion of the following six major hotel firms: Prime Motor Inns, Inc., Marriott Corporation, Hilton Hotels Corporation, Ramada Inc., Holiday Corporation and La Quinta Motor Inns, Inc.,” say Professors Ringstrom and Moncarz in framing this discussion with its underpinnings in the years 1982 to 1988. The article looks at each company’s fiscal and Dow Jones performance for the years in question, and presents a detailed analysis of said performance. Graphic analysis is included. It helps to have a fairly vigorous knowledge of stock market and fiscal examination criteria to digest this material. The Ringstrom and Moncarz analysis of Prime Motor Inns Incorporated occupies the first 7 pages of this article in and of itself. Marriot Corporation also occupies a prominent position in this discussion. “Marriott, a giant in the hospitality industry, is huge and continuing to grow. Its 1987 sales were more than $6.5 billion, and its employees numbered over 200,000 individuals, which place Marriott among the 10 largest private employers in the country,” Ringstrom and Moncarz parse Marriott’s influence as a significant financial player. “The firm has a fantastic history of growth over the past 60 years, starting in May 1927 with a nine-seat A & W Root Beer stand in Washington, D.C.,” offer the authors in initialing Marriot’s portion of the discussion with a brief history lesson. The Marriot firm was officially incorporated as Hot Shoppes Inc. in 1929. As the thesis statement for the discussion suggests the performance of these huge, hospitality giants is compared and contrasted directly to the Dow Jones Industrial Average performance. Reasons and empirical data are offered by the authors to explain the distinctions. It would be difficult to explain those distinctions without delving deeply into corporate financial history and the authors willingly do so in an effort to help you understand the growth, as well as some of the setbacks of these hospitality based juggernauts. Ringstrom and Moncarz conclude the article with an extensive overview and analysis of the Hilton Hotels Corporation performance for the period outlined. It may well be the most fiscally dynamic of the firms presented for your perusal. “It is interesting to note that Hilton Hotels Corporation maintained a very strong financial position with relatively little debt during the years 1982-1988…the highest among all companies in the study,” the authors paint.

A Study of Stock Market Fluctuations and their Relations to Business Conditions

Most research on stock prices is based on the present value model or the more general consumption-based model. When applied to real economic data, both of them are found unable to account for both the stock price level and its volatility. Three essays here attempt to both build a more realistic model, and to check whether there is still room for bubbles in explaining fluctuations in stock prices. In the second chapter, several innovations are simultaneously incorporated into the traditional present value model in order to produce more accurate model-based fundamental prices. These innovations comprise replacing with broad dividends the more narrow traditional dividends that are more commonly used, a nonlinear artificial neural network (ANN) forecasting procedure for these broad dividends instead of the more common linear forecasting models for narrow traditional dividends, and a stochastic discount rate in place of the constant discount rate. Empirical results show that the model described above predicts fundamental prices better, compared with alternative models using linear forecasting process, narrow dividends, or a constant discount factor. Nonetheless, actual prices are still largely detached from fundamental prices. The bubble-like deviations are found to coincide with business cycles. The third chapter examines possible cointegration of stock prices with fundamentals and non-fundamentals. The output gap is introduced to form the non-fundamental part of stock prices. I use a trivariate Vector Autoregression (TVAR) model and a single equation model to run cointegration tests between these three variables. Neither of the cointegration tests shows strong evidence of explosive behavior in the DJIA and S&P 500 data. Then, I applied a sup augmented Dickey-Fuller test to check for the existence of periodically collapsing bubbles in stock prices. Such bubbles are found in S&P data during the late 1990s. Employing econometric tests from the third chapter, I continue in the fourth chapter to examine whether bubbles exist in stock prices of conventional economic sectors on the New York Stock Exchange. The ‘old economy’ as a whole is not found to have bubbles. But, periodically collapsing bubbles are found in Material and Telecommunication Services sectors, and the Real Estate industry group.

What Moved Share Prices in the Nineteenth-Century London Stock Market?

Using a new weekly blue-chip index, this paper investigates the causes of stock price movements on the London market between 1823 and 1870. We find that economic fundamentals explain about 15 per cent of weekly and 34 per cent of monthly variation in share prices. Contemporary press reporting from the London Stock Exchange is used to ascertain what market participants thought were causing the largest movements on the market. The vast majority of large movements were attributed by the press to geopolitical, monetary, railway-sector, and financial-crisis news. Investigating the stock price changes on an independent list of events reaffirms these findings, suggesting that the most important specific events which moved markets were wars involving European powers.