Search results

This article examines whether deviations from fundamental value or closed-end country fund's discounts or premiums forecast future share price returns or net asset returns.

The main empirical (econometric) tool is a vector autoregressive (VAR) model. The authors model share price returns and net asset returns as a function of their lagged values, the discounts or premiums, and a control variable for local market returns. The authors also conduct Dickey Fuller and Granger causality tests as well as impulse response functions.

It was found that deviations from fundamental value do predict share price returns. This predictability is contrary to weak-form market efficiency. Premiums or discounts predict net asset returns but weakly.

The findings point to the idea that the closed-end fund market is somewhat predictable and inefficient (in its weak form) since the market appears to be able to anticipate a fund's future returns using information contained in the premiums (or discounts). In particular, the market has the ability to anticipate future behaviour because growing premiums forecast declining share price returns for one or two periods ahead.

The main purpose of the research is to determine if the relationship between trading volume and price changes is connected to market effectiveness and to use the volume-price relationship to compare the efficiency levels of foreign markets. The degree of the relationship is determined in this study, and the efficiency levels of different countries' capital markets are compared.

In this study, 1,024 observations are used as a data set, which includes daily closing prices and trading volume in the stock market indices of 25 countries between the dates of 01.12.2016 and 31.12.2020. In the first step of the analysis, descriptive statistics of price and volume series are examined. The stationarity of the series is then controlled using the ADF unit root test. Simple linear regression models with the dependent variable of trading volume are generated for all stock market indices after each series has reached stationarity, and the ARCH heteroscedasticity test is used to determine whether these models contain the ARCH effect. Because all models have the ARCH effect, autoregressive models are chosen, and EGARCH models are conducted for all indices to see whether there is an asymmetry in the price-volume relationship.

The study concludes that the stock market in the United States is the most effective, since it has the strongest relationship between trading volume and price changes. However, because of the financial distress caused by the COVID-19 pandemic, the relationship between price and trading volume is lower in Eurozone countries. The price-volume relationship could not be observed in some shallow markets. Furthermore, whereas the majority of countries have a negative relationship between price changes and transaction volume, China, the United Arab Emirates and Qatar have a positive relationship. When prices rise in these countries, investors buy with the sense of hope provided by the optimistic atmosphere, and when prices fall, they sell with the fear of losing money.

The study's most significant limitation is that it is difficult to ascertain a definitive conclusion about the subject under investigation. In reality, if the same research is done using data from different countries and time periods, the results are quite likely to vary.

As a result of the study, investors can decide which market to enter by comparing and analyzing the price-volume relationship of several markets. According to the study's findings, investors are advised to examine the price-volume relationship in a market before beginning to trade in that market. In this way, investors can understand the market's efficiency and whether it is overpriced.

The relationship between price movements and trade volume gives crucial information about a capital market's internal structure. Some concerns can be answered by assessing this relationship, such as whether the market has a speculative pricing problem, how information flows to the market, and whether investment decisions are rational and homogenous. Empirical studies on modeling this relationship, on the other hand, have not reached a definite outcome. The main reason for this is that the price-to-volume relationship fluctuates depending on the market structure. The purpose of this study is to fill a gap in the literature by presenting the reasons why this critical issue in the literature cannot be answered, as well as empirical findings.

The significance and originality of this research are that it examines the price-volume relationship to evaluate the efficiency levels of various markets. This relationship is being investigated in a number of multinational studies. These researches, on the other hand, were conducted to see if there is a relationship between trading volume and market volatility, and if so, how that interaction is formed. The size of the price and volume relationship is emphasized in this study, unlike previous studies in the literature.

Despite its devastating nature, the COVID-19 pandemic has also catalyzed a substantial surge in the adoption and integration of technological tools within economies, exerting a profound influence on the dissemination of information among participants in stock markets. Consequently, this present study delves into the ramifications of post-pandemic dynamics on stock market behavior. It also examines the relationship between investors' sentiments, underlying behavioral drivers and their collective impact on global stock markets.

Drawing upon data spanning from 2012 to 2023 and encompassing major world indices classified by Morgan Stanley Capital International’s (MSCI) market and regional taxonomy, this study employs a threshold regression model. This model effectively distinguishes the thresholds within these influential factors. To evaluate the statistical significance of variances across these thresholds, a Wald coefficient analysis was applied.

The empirical results highlighted the substantive role that investors' sentiments and behavioral determinants play in shaping the predictability of returns on a global scale. However, their influence on developed economies and the continents of America appears comparatively lower compared with the Asia–Pacific markets. Similarly, the regions characterized by a more pronounced influence of behavioral factors seem to reduce their reliance on these factors in the post-pandemic landscape and vice versa. Interestingly, the post COVID-19 technological advancements also appear to exert a lesser impact on developed nations.

This study pioneers the investigation of these contextual dissimilarities, thereby charting new avenues for subsequent research studies. These insights shed valuable light on the contextualized nexus between technology, societal dynamics, behavioral biases and their collective impact on stock markets. Furthermore, the study's revelations offer a unique vantage point for addressing market inefficiencies by pinpointing the pivotal factors driving such behavioral patterns.

In a first of its kind, this paper tries to explore the long-run relationship between investors' sentiment and selected industries' returns over the period January 2010 to December 2021.

The paper uses 23 market and macroeconomic proxies to measure investor sentiment. Principal component analysis has been used to create sentiment sub-indices that represent investor sentiment. The autoregressive distributed lag (ARDL) model and other sophisticated econometric techniques such as the unit root test, the cumulative sum (CUSUM) stability test, regression, etc. have been used to achieve the objectives of the study.

The authors find that there is a significant relationship between sentiment sub-indices and industries' returns over the period of study. Market and economic variables, market ratios, advance-decline ratio, high-low index, price-to-book value ratio and liquidity in the economy are some of the significant sub-indices explaining industries' returns.

The study has relevant implications for retail investors, policy-makers and other decision-makers in the Indian stock market. Results are helpful for the investor in improving their decision-making and identifying those sentiment sub-indices and the variables therein that are relevant in explaining the return of a particular industry.

The study contributes to the existing literature by exploring the relationship between sentiment and industries' returns in the Indian stock market and by identifying relevant sentiment sub-indices. Also, the study supports the investors' irrationality, which arises due to a plethora of behavioral biases as enshrined in classical finance.

By extending Edmans et al.’s (2021) music sentiment measures to the Vietnam market, the authors aim to investigate the impacts of music sentiment on stock market returns and volatility.

The authors adopted Edmans et al.’s (2021) music-based sentiment to proxy for investor mood. The current study uses linear regression analysis.

The authors find that music sentiment is significantly and positively related to both stock returns and stock market volatility. The authors also show that music sentiment has a contagious effect: Global music sentiment and those in the United States, France and Hong Kong are significant drivers of the Vietnamese stock market. The authors also examine the effect on different industry returns and find that returns on stocks of firms in the communication services, consumer discretionary, consumer staples, energy, financials, healthcare, real-estate, information technology and utility sectors are significantly related to music sentiment. In addition to valence, the authors find that other Spotify audio features can be used to quantify music sentiment.

This study contributes to the behavioral finance literature that focuses on investor sentiment. The authors address this topic in Vietnam using high-frequency data.

The purpose of this paper is to investigate whether sentiment and mood, which are distinct theoretical concepts, can also be distinguished empirically.

Using a sample of German small-cap stocks and linear techniques, the effect of sentiment and mood on short-term abnormal stock return following earnings announcements is tested separately.

Mood tends to be a positive factor in predicting short-term abnormal stock return, as its biologically based impact uniformly affects the risk aversion of all market participants. Notably, negative mood influences stock return significantly negatively. Sentiment is no factor, however, as its cognitively based impact affects only unsophisticated investors, namely, their cash-flow expectations.

As the sample is restricted to small-cap stocks from a single stock market and only two proxies of sentiment and mood, respectively, are used, the findings should be generalized with caution. Future research might investigate other markets and employ different proxies of sentiment and mood.

Market participants should be aware of the different effect of sentiment and mood on stock return and adjust investment strategies accordingly.

As sophisticated investors are likely to profit from the irrational behavior of unsophisticated investors, who are prone to sentiment, the financial literacy of retail investors should be enhanced.

This paper is unique in distinguishing between sentiment and mood, both theoretically and empirically. Such distinction was largely ignored by related past research.

In this paper, the author examines the role of uncertainty due to pandemic on the predictability of sectoral stock returns in South Africa. This is motivated by the ongoing global pandemic, COVID-19, in predicting sector stock returns.

The study considers estimation of dynamic panel data with dynamic common correlated effects estimator and two pair-wise forecast measures, namely Campbell and Thompson (2008) and Clark and West (2007) tests in dealing with the nested predictive models.

The results show that pandemic uncertainty has a negative and statistically significant effect on the different sector returns, implying that sector stock returns decline as the pandemic outbreak becomes more pronounced. While the single predictor model consistently outperforms the historical average model both for in-sample and out-of-sample, controlling for other macroeconomic variables effect improves the forecast accuracy of infectious diseases uncertainty. These results are consistently robust to both the in-sample and out-of-sample forecast periods, outliers and heterogeneity. These results have implications for portfolio diversification strategies, which we set aside for future research.

The empirical literature is satiated with studies on how news can predict economic and financial variables, however, the role of uncertainty due to infectious diseases in the stock return predictability especially at the sectoral level is less understudied, this is the main contribution of the study.

Stock price prediction is a hot topic and traditional prediction methods are usually based on statistical and econometric models. However, these models are difficult to deal with nonstationary time series data. With the rapid development of the internet and the increasing popularity of social media, online news and comments often reflect investors’ emotions and attitudes toward stocks, which contains a lot of important information for predicting stock price. This paper aims to develop a stock price prediction method by taking full advantage of social media data.

This study proposes a new prediction method based on deep learning technology, which integrates traditional stock financial index variables and social media text features as inputs of the prediction model. This study uses Doc2Vec to build long text feature vectors from social media and then reduce the dimensions of the text feature vectors by stacked auto-encoder to balance the dimensions between text feature variables and stock financial index variables. Meanwhile, based on wavelet transform, the time series data of stock price is decomposed to eliminate the random noise caused by stock market fluctuation. Finally, this study uses long short-term memory model to predict the stock price.

The experiment results show that the method performs better than all three benchmark models in all kinds of evaluation indicators and can effectively predict stock price.

In this paper, this study proposes a new stock price prediction model that incorporates traditional financial features and social media text features which are derived from social media based on deep learning technology.

In this paper, we analyze the trading patterns of investors around the bubble events selected for stocks traded in Korean Stock Market from 1999 to 2013, whose holding period returns exceed 200% for 250 trading days prior to the event and then drop subsequently below -50% thereafter for the next 250 trading days. We examine whether individual investors, commonly known as noise traders, drive the bubbles, and whether institutional investors and foreign investors, known as informed traders, take an arbitrage position to shrink the pricing errors or ride the bubbles to maximize their profits. We also examine whether individual investors suffer losses due to their disposition effect even after the bubble bursts.

Major findings of this paper are as follows : First, we find that individual investors are actually shown to drive the bubbles in our full sample, whereas the burst of the bubbles are largely driven by institutional investors and foreign investors. In particular, it is shown for large-cap stocks that foreign investors take the lead in raising the price at an early stage of the bubbles and then institutional investors follow them until the bubble peak point. Second, for mid-cap and large-cap stocks, institutional investors are found to ride the bubbles from about 75 days prior to the bubble peak point, when foreign investors reverse their trades and start selling to realize profits. Such bubble riding behavior of institutional investors is consistent with the synchronization risk model of Abreu and Brunnermeier (2002, 2003), where it is optimal for informed traders to ride the bubbles until all of informed traders start selling at the bubble peak point. Third, individual investors are found to suffer losses as they keep buying the bubble stocks even after the bubble bursts due to their disposition effect.

The purpose of this paper is to analyse five biases in the valuation of financial investments using a mental time travel framework involving thought investments – with no objective time passing.

An investment’s initial value, together with any periodic funding cash-flows, are mentally projected forward (at an expected rate of return) to give the value at the investment horizon; and this projected value is mentally discounted back to the present. If there is a difference between the initial and present values, then this can imply a bias in valuation.

The study identifies (and gives examples of) five real-world valuation biases: biased funding cash-flow estimates (e.g., mega infrastructure projects); biased rate of return projections (e.g., market crises, tech stock carve-outs); biased discount rate estimates (e.g., dual-listed shares, dual-class shares, short-termism, time-risk misperception, and long-termism); time-duration misestimation or perception bias when projecting (e.g., time-contracted projections which lead to short-termism); and time-duration misestimation or perception bias when discounting (e.g., time-extended discounting which also leads to short-termism). More than one bias can be operating at the same time and we give an example of low levels of retirement savings being the result of the biased discounting of biased projections. Finally, we consider the effects of the different biases of different agents operating simultaneously.

The paper examines key systematic misestimation and psychological biases underlying financial investment valuation pricing anomalies.