Information search in times of market uncertainty: an examination of aggregate and disaggregate uncertainty

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Marshall A. Geiger ◽  
Rajib Hasan ◽  
Abdullah Kumas ◽  
Joyce van der Laan Smith
Keyword(s):  
Information Search ◽  
Institutional Investors ◽  
Volume Index ◽  
Individual Investor ◽  
Market Uncertainty ◽  
Data Set ◽  
Current Time ◽  
Content Type ◽  
Information Demand ◽  
Google Search

PurposeThis study explores the association between individual investor information demand and two measures of market uncertainty – aggregate market uncertainty and disaggregate industry-specific market uncertainty. It extends the literature by being the first to empirically examine investor information demand and disaggregate market uncertainty.Design/methodology/approachThis paper constructs a measure of information search by using the Google Search Volume Index and computes measures of aggregate and disaggregate market uncertainty using institutional investors' trading data from Ancerno Ltd. The relation between market uncertainty, as measured by trading disagreements among institutional investors, and information search is analyzed using an OLS (Ordinary Least Squares) regression model.FindingsThis paper finds that individual investor information demand is significantly and positively correlated with aggregate market uncertainty but not associated with disaggregated industry uncertainty. The findings suggest that individual investors may not fully incorporate all relevant uncertainty information and that ambiguity-related market pricing anomalies may be more associated with disaggregate market uncertainty.Research limitations/implicationsThis study presents an examination of aggregate and disaggregate measures of market uncertainty and individual investor demand for information, shedding light on the efficiency of the market in incorporating information. A limitation of our study is that our data for market uncertainty is based on investor trading disagreement from Ancerno, Ltd. which is only available till 2011. However, we believe the implications are generalizable to the current time period.Practical implicationsThis study provides the first concurrent empirical assessment of investor information search and aggregate and disaggregate market uncertainty. Prior research has separately examined information demand in these two types of market uncertainty. Thus, this study provides information to investors regarding the importance of assessing disaggregate component measures of the market.Originality/valueThis paper is the first to empirically examine investor information search and disaggregate market uncertainty. It also employs a unique data set and method to determine disaggregate, and aggregate, market uncertainty.

scholarly journals Board structure and institutional ownership at the time of IPO

2017 ◽  
Vol 43 (9) ◽  
pp. 950-965 ◽  
Author(s):  
Suman Neupane ◽  
Biwesh Neupane
Keyword(s):  
Institutional Investors ◽  
Critical Time ◽  
Institutional Ownership ◽  
Board Independence ◽  
Board Structure ◽  
Data Set ◽  
Content Type ◽  
Institutional Holdings ◽  
Foreign Institutional Investors ◽  
The Impact

Purpose The purpose of this paper is to examine the impact of mandatory regulatory provisions on board structure and the influence of such board structure on institutional holdings. Design/methodology/approach The study uses unique hand-collected data set of Indian IPOs during the 2004-2012 period after the corporate governance reforms with the introduction of clause 49 in the listing agreements in 2001. Using OLS regression, the paper empirically analyses the determinants of board size and board independence at the time of the IPOs and the influence of such a board structure on shareholdings by domestic and foreign institutional investors. Findings The authors find that complying with mandatory regulatory provisions does not impede firms from structuring their boards to reflect the firms’ advising and monitoring needs. The authors also find that complying with provisions have positive implication for the firm, as firms with greater board independence appear to attract more foreign institutional investors. Originality/value To the authors’ best knowledge, this is the first study to examine the issue in a regime where regulation mandates the composition of the board of directors. The paper also extends the literature on institutional holdings by providing evidence on the impact of board structure on institutional ownership at a critical time in a firm’s life cycle when concerns for endogeneity for empirical investigations are weaker.

How to perform strategic change? A strategy as practice perspective

2020 ◽  
Vol 14 (3) ◽  
pp. 811-832
Author(s):  
Zelong Wei ◽  
Linqian Zhang
Keyword(s):  
Firm Performance ◽  
Strategic Change ◽  
Market Uncertainty ◽  
Data Set ◽  
Technological Uncertainty ◽  
Content Type ◽  
Practice Perspective ◽  
Strategic Changes ◽  
Strategy As Practice ◽  
Positive Effect

Purpose In spite of the significance of the strategic change, its high rate of failure inspires us to explore how to successfully enact new strategic change in a different environment. Based on strategy as practice perspective and effectuation theory, this study aims to extend extant literature by identifying two approaches performing strategic change (e.g. causation strategic change or effectuation strategic change) and investigating their effects on firm performance and also boundary conditions (e.g. market uncertainty or technological uncertainty). Design/methodology/approach Based on a data set from 238 firms in China, the authors empirically test the hypotheses through regression analysis. Findings The findings indicate that causation and effectuation strategic changes can promote firm performance. However, the roles of the two approaches vary with the external environment. Specifically, market uncertainty strengthens while technological uncertainty weakens the positive effect of causation strategic change. In contrast, technological uncertainty strengthens the positive effect of effectuation strategic change on firm performance. Originality/value This study extends research literature of strategic change by identifying causation and effectuation strategic changes and investigating how their roles vary with market uncertainty and technological uncertainty. The findings guide firms to adopt a fit approach to perform a strategic change in different external environments.

scholarly journals Outperforming the benchmark: online information demand and REIT market performance

2015 ◽  
Vol 33 (2) ◽  
pp. 169-195 ◽  
Author(s):  
Karim Rochdi ◽  
Marian Dietzel
Keyword(s):  
Real Estate ◽  
Online Information ◽  
Investment Strategies ◽  
Content Type ◽  
Search Volume ◽  
Information Demand ◽  
The Us ◽  
Google Search ◽  
Practical Implications ◽  
Successful Predictor

Purpose – The purpose of this paper is to investigate whether there is a relationship between asset-specific online search interest and movements in the US REIT market. Design/methodology/approach – The authors collect search volume (SV) data from “Google Trends” for a set of keywords representing the information demand of real estate (equity) investors. On this basis, the authors test hypothetical investment strategies based on changes in internet SV, to anticipate REIT market movements. Findings – The results reveal that people’s information demand can indeed serve as a successful predictor for the US REIT market. Among other findings, evidence is provided that there is a significant relationship between asset-specific keywords and the US REIT market. Specifically, investment strategies based on weekly changes in Google SV would have outperformed a buy-and-hold strategy (0.1 percent p.a.) for the Morgan Stanley Capital International US REIT Index by a remarkable 15.4 percent p.a. between 2006 and 2013. Furthermore, the authors find that real-estate-related terms are more suitable than rather general, finance-related terms for predicting REIT market movements. Practical implications – The findings should be of particular interest for REIT market investors, as the established relationships can potentially be utilized to anticipate short-term REIT market movements. Originality/value – This is the first paper which applies Google search query data to the REIT market.

Are smart beta funds really smart? Evidence from rational and quasi-rational investor sentiment data

2019 ◽  
Vol 12 (2) ◽  
pp. 97-118
Author(s):  
Rahul Verma ◽  
Gökçe Soydemir ◽  
Tzu-Man Huang
Keyword(s):  
Institutional Investors ◽  
Institutional Investor ◽  
Individual Investors ◽  
Leading Indicators ◽  
Individual Investor ◽  
Short Term ◽  
Content Type ◽  
Economic Fundamentals ◽  
Investor Sentiments ◽  
Smart Beta

Purpose The purpose of this paper is to examine the relative effects of rational and quasi-rational sentiments of individual and institutional investors on a set of smart beta fund returns. The magnitudes of the impacts of institutional investor sentiments are greater than those of individual investor sentiments. In addition, both rational and quasi-rational sentiments of individual and institutional investors have significant impacts on smart beta fund returns. The magnitudes of the impacts of quasi-rational sentiments are greater than those of the rational sentiments for both types of investors (quasi-rational sentiments of institutional investors have the maximum impact). These results are consistent with the arguments that professional investors consider the sentiments of individual investors as contrarian leading indicators which are mainly driven by noise while conform the sentiments of institutional investors which are driven by more rational factors. A majority of smart beta funds in the sample outperform the S&P500 returns in the short term but fail to consistently beat the market. The authors find evidence that smart beta funds with consistently high returns are relatively less (more) driven by individual (institutional) investor sentiments. Overall, the authors argue that smart beta funds appear to follow quasi-rational sentiments of both individual and institutional investors that are not rooted in economic fundamentals. Design/methodology/approach The results of the impulse functions generated from a multivariate model suggest that the smart beta fund returns are negatively (positively) impacted by individual (institutional) investor sentiments. Findings The magnitudes of the impacts of institutional investor sentiments are greater than those of individual investor sentiments. In addition, both rational and quasi-rational sentiments of individual and institutional investors have significant impacts on smart beta fund returns. The magnitudes of the impacts of quasi-rational sentiments are greater than those of the rational sentiments for both types of investors (quasi-rational sentiments of institutional investors have the maximum impact). Originality/value These results are consistent with the arguments that professional investors consider the sentiments of individual investors as contrarian leading indicators which are mainly driven by noise while conform the sentiments of institutional investors which are driven by more rational factors. A majority of smart beta funds in the sample outperform the S&P500 returns in the short term but fail to consistently beat the market. The authors find evidence that smart beta funds with consistently high returns are relatively less (more) driven by individual (institutional) investor sentiments. Overall, the authors argue that smart beta funds appear to follow quasi-rational sentiments of both individual and institutional investors that are not rooted in economic fundamentals.

Comparative study on performance of different artificial neural network methods for prediction of the Covid19

foresight ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Alireza Sedighi Fard
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Short Term Memory ◽  
Polynomial Regression ◽  
World Health ◽  
Comparative Approach ◽  
Data Set ◽  
Current Time ◽  
Content Type ◽  
Artificial Neural

Purpose This study aims to compare many artificial neural network (ANN) methods to find out which method is better for the prediction of Covid19 number of cases in N steps ahead of the current time. Therefore, the authors can be more ready for similar issues in the future. Design/methodology/approach The authors are going to use many ANNs in this study including, five different long short-term memory (LSTM) methods, polynomial regression (from degree 2 to 5) and online dynamic unsupervised feedforward neural network (ODUFFNN). The authors are going to use these networks over a data set of Covid19 number of cases gathered by World Health Organization. After 1,000 epochs for each network, the authors are going to calculate the accuracy of each network, to be able to compare these networks by their performance and choose the best method for the prediction of Covid19. Findings The authors concluded that for most of the cases LSTM could predict Covid19 cases with an accuracy of more than 85% after LSTM networks ODUFFNN had medium accuracy of 45% but this network is highly flexible and fast computing. The authors concluded that polynomial regression cant is a good method for the specific purpose. Originality/value Considering the fact that Covid19 is a new global issue, less studies have been conducted with a comparative approach toward the prediction of Covid19 using ANN methods to introduce the best model of the prediction of this virus.

Earnings management and institutional investor trading prior to earnings announcements

2019 ◽  
Vol 9 (1) ◽  
pp. 22-50 ◽  
Author(s):  
Shasha Liu
Keyword(s):  
Earnings Management ◽  
Institutional Investors ◽  
Earnings Announcements ◽  
Chinese Stock Market ◽  
Data Set ◽  
Content Type ◽  
Trading Patterns ◽  
Earnings Surprises ◽  
Trading Pattern ◽  
Investor Trading

PurposeThe purpose of this paper is to investigate if earnings management affects the trades of different investors prior to earnings announcements.Design/methodology/approachUsing a unique account-level trading data set from the Chinese stock market, the author investigates the different investor trading patterns prior to earnings announcements.FindingsThe author obtains direct evidence to show that: first, institutional investors, particularly active ones, tend to sell (buy) stocks before negative (positive) earnings surprises; second, institutional investors buy stocks intensively with the lowest earnings management and the highest earnings surprises, and the trading patterns are primarily driven by active institutions. No significant trading pattern is observed on the stocks with negative earnings surprises; and third, the author uses a natural experiment in accordance with the Chinese accounting standards reform to address endogeneity, and the causality of the results still holds.Originality/valueThe findings provide clear evidence by emphasizing the importance of earnings management in the formulation of investor decisions.

Investor attention using the Google search volume index – impact on stock returns

2019 ◽  
Vol 11 (1) ◽  
pp. 55-69 ◽  
Author(s):  
Vighneswara Swamy ◽  
Munusamy Dharani
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Volume Index ◽  
Trading Behavior ◽  
Excess Returns ◽  
Investor Attention ◽  
Content Type ◽  
Search Volume ◽  
Indian Stock Market ◽  
Google Search

Purpose The purpose of this paper is to investigate whether the investor attention using the Google search volume index (GSVI) can be used to forecast stock returns. The authors also find the answer to whether the “price pressure hypothesis” would hold true for the Indian stock market. Design/methodology/approach The authors employ a more recent fully balanced panel data for the period from July 2012 to Jun 2017 (260 weeks) of observations for companies of NIFTY 50 of the National Stock Exchange in the Indian stock market. The authors are motivated by Tetlock (2007) and Bijl et al. (2016) to employ regression approach of econometric estimation. Findings The authors find that high Google search volumes lead to positive returns. More precisely, the high Google search volumes predict positive and significant returns in the subsequent fourth and fifth weeks. The GSVI performs as an useful predictor of the direction as well as the magnitude of the excess returns. The higher quantiles of the GSVI have corresponding higher excess returns. The authors notice that the domestic investor searches are correlated with higher excess returns than the worldwide investor searches. The findings imply that the signals from the search volume data could be of help in the construction of profitable trading strategies. Originality/value To the best of the authors knowledge, no paper has examined the relationship between Google search intensity and stock-trading behavior in the Indian stock market. The authors use a more recent data for the period from 2012 to 2017 to investigate whether search query data on company names can be used to predict weekly stock returns for individual firms. This study complements the prior studies by investigating the relationship between search intensity and stock-trading behavior in the Indian stock market.

Institutional investors, monitoring and corporate finance policies

2017 ◽  
Vol 13 (2) ◽  
pp. 186-212 ◽  
Author(s):  
W. Sean Cleary ◽  
Jun Wang
Keyword(s):  
Corporate Finance ◽  
Institutional Investors ◽  
Information Quality ◽  
Cash Holdings ◽  
Specific Area ◽  
Supporting Evidence ◽  
Data Set ◽  
Corporate Policies ◽  
Content Type

Purpose The purpose of this paper is to examine the influence of institutional investors’ investment horizons (IIIH) on a wide variety of key corporate policies. Design/methodology/approach The authors perform regression analysis to a panel data set of quarterly financial statement data for US firms over the 1981-2014 using several measures of IIIH. Findings The authors argue that an increase in the presence of long-term investors contributes to more effective monitoring and information quality. This results in a reduction in agency costs and informational asymmetry problems for firms that are more heavily influenced by long-term investors, which in turn influences the corporate policies they pursue. Consistent with these arguments, the evidence suggests that firms with a greater long-term institutional investor base maintain lower investment outlays, higher dividend payments, lower levels of cash and higher levels of leverage. All results hold after controlling for potential endogeneity issues. Originality/value The authors show that a greater presence of long-term institutional investors leads to higher dividends, lower investment outlays, lower cash holdings and higher leverage. The comprehensive nature of the predictions with respect to overall corporate finance policies and the supporting evidence provided represents an important contribution, as previous studies have tended to focus on one specific area of corporate behavior (i.e. such as cash holdings).

scholarly journals Sentiment-based predictions of housing market turning points with Google trends

2016 ◽  
Vol 9 (1) ◽  
pp. 108-136 ◽  
Author(s):  
Marian Alexander Dietzel
Keyword(s):  
Real Estate ◽  
Housing Market ◽  
Turning Points ◽  
House Price ◽  
Data Set ◽  
Content Type ◽  
Forecasting Models ◽  
Search Volume ◽  
Out Of Sample ◽  
Google Search

Purpose – Recent research has found significant relationships between internet search volume and real estate markets. This paper aims to examine whether Google search volume data can serve as a leading sentiment indicator and are able to predict turning points in the US housing market. One of the main objectives is to find a model based on internet search interest that generates reliable real-time forecasts. Design/methodology/approach – Starting from seven individual real-estate-related Google search volume indices, a multivariate probit model is derived by following a selection procedure. The best model is then tested for its in- and out-of-sample forecasting ability. Findings – The results show that the model predicts the direction of monthly price changes correctly, with over 89 per cent in-sample and just above 88 per cent in one to four-month out-of-sample forecasts. The out-of-sample tests demonstrate that although the Google model is not always accurate in terms of timing, the signals are always correct when it comes to foreseeing an upcoming turning point. Thus, as signals are generated up to six months early, it functions as a satisfactory and timely indicator of future house price changes. Practical implications – The results suggest that Google data can serve as an early market indicator and that the application of this data set in binary forecasting models can produce useful predictions of changes in upward and downward movements of US house prices, as measured by the Case–Shiller 20-City House Price Index. This implies that real estate forecasters, economists and policymakers should consider incorporating this free and very current data set into their market forecasts or when performing plausibility checks for future investment decisions. Originality/value – This is the first paper to apply Google search query data as a sentiment indicator in binary forecasting models to predict turning points in the housing market.

Institutional investor management of Australian healthcare property assets as a pandemic-proof asset class

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Muhammad Jufri Marzuki ◽  
Graeme Newell
Keyword(s):  
Institutional Investors ◽  
Global Financial Crisis ◽  
Institutional Investor ◽  
Investment Strategy ◽  
Portfolio Diversification ◽  
Portfolio Optimisation ◽  
Empirical Examination ◽  
Data Set ◽  
Content Type ◽  
Asset Class

PurposeAs the prolonged effect of the COVID-19 pandemic has materially impacted investment returns significantly, it is more crucial than ever for institutional investors to redefine their property portfolios using assets with better investment management potential and meaningful diversification benefits. The “alternative asset revolution” is gaining traction in the property investment space internationally among institutional investors due to the shifting investment attitudes towards the alternative property sectors. Australia's $205bn healthcare property sector is at the forefront of this revolution due to its societal significance, as well as its attractive investment qualities. This paper investigates the institutional investor management of the Australian healthcare property sector via both the direct and listed channels and empirically analyses its investment attributes.Design/methodology/approachUsing the unique Morgan Stanley Capital International/Property Council of Australia quarterly data set for Australian direct healthcare property over 2006–2020, the risk-adjusted performance and portfolio diversification potential direct healthcare property and listed healthcare were assessed. A constrained mean-variance portfolio optimisation framework was used to develop a six-asset portfolio scenario to analyse the portfolio added-value benefits of both direct healthcare property and listed healthcare in a mixed-asset investment strategy. A similar set of analysis was performed using the post-global financial crisis (GFC) quarterly time series of 2009–2020 to investigate the healthcare asset class' performance dynamics in the post-GFC investment timeframe.FindingsThe results indicate that direct healthcare property and listed healthcare offer two key advantages for institutional investors in managing their property portfolios: (1) a stable yet superior risk-adjusted performance and (2) significant portfolio diversification potential in managing their property portfolios. Importantly, both direct healthcare property and listed healthcare provided valuable contributions in strengthening an investment portfolio's performance. The post-GFC sub-period analysis revealed a consistent conclusion regarding the healthcare asset class's performance attributes.Originality/valueThis is the first research that provides an independent empirical examination of the strategic importance of Australian healthcare property as a maturing alternative property sector that can serve both investment and environmental, social and governance goals of investors. This research presents a positive investment prognosis for the Australian healthcare property sector to achieve its institutionalised status as a mainstream asset class of the future.

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