Essays on Financial Markets

<p><b>This thesis consists of five chapters. Chapter 1 is the preliminaries. Chapter 2 to chapter 4 are the three main chapters of this thesis, which covers the U.S. market, international market, and the Chinese market, respectively. Chapter 5 is the discussion.</b></p> <p>Chapter 1 is the preliminaries. It introduces the setting and motivations for the three topics covered in this thesis.</p> <p>Chapter 2 investigates how equity exchange-traded fund (ETF) ownership affects the cost of debt. I find that, by facilitating short-selling activities to execute disciplinary effects, equity ETF ownership decreases a firm's cost of debt. This negative association between equity ETF ownership and the cost of debt is more pronounced for firms with weaker information environments and lower bond ratings. The disciplinary effect works through a more active short-selling market provided by equity ETF ownership. However, I fail to establish the corporate governance channel, which is consistent with Schmidt and Fahlenbrach (2017) and Heath, Macciocchi, Michaely, and Ringgenberg (2021).. Those results are also robust to endogeneity.</p> <p>Chapter 3 studies the predictive power of the trend strategy in the international stock market. Using data from 49 markets, I find that a trend signal exploiting the short-,intermediate-, and long-term price information can predict stock returns cross-sectionally in the international market. The significance of the trend strategy is associated with market-level characteristics such as macroeconomic conditions, culture, and the information environment. The trend premium is more pronounced in markets with a more advanced macroeconomic status, a higher level of information uncertainty and individualism, and better accessibility to foreign investors. Nevertheless, the trend strategy only outperforms the momentum strategy in a relatively short horizon.</p> <p>Chapter 4 investigates whether margin-trading in the Chinese stock market reflects information or sentiment. At the aggregate level, I find no evidence of information-driven or sentiment-driven margin-trading behavior. At the individual stock level, both information-driven and sentiment-driven margin-trading exists, which are relevant to firm characteristics. I also find the likelihood of sentiment-driven margin-trading significantly declined after the regulator enforced tighter rules for margin-trading in 2015.</p> <p>Chapter 5 summarizes the main findings of the three topics, discusses the implications of the findings, and points out the future direction for research.</p>

Bimodal Characteristic Returns and Predictability Enhancement via Machine Learning

This paper documents the bimodality of momentum stocks: both high- and low-momentum stocks have nontrivial probabilities for both high and low returns. The bimodality makes the momentum strategy fundamentally risky and can cause a large loss. To alleviate the bimodality and improve return predictability, this paper develops a novel cross-sectional prediction model via machine learning. By reclassifying stocks based on their predicted financial performance, the model significantly outperforms off-the-shelf machine learning models. Tested on the U.S. market, a value-weighted long-short portfolio earns a monthly alpha of 2.4% (t-statistic = 6.63) when regressed against the Fama–French five factors plus the momentum and short-term reversal factors. This paper was accepted by Kay Giesecke, finance.

Quarterly seasonality and momentum strategy implementation

PurposeMomentum strategies exhibit quarterly seasonality, earning significantly higher average strategy returns in the third month of the quarter than the first month. The authors evaluate the magnitude of quarterly seasonality in various momentum strategies to examine the relation between quarterly seasonality and risk-adjusted monthly returns.Design/methodology/approachThe authors construct long-short portfolios for various types of momentum strategies and calculate the average returns of these portfolios in the three months of the quarter. They also calculate the average changes in institutional ownership across the different portfolios.FindingsThe authors demonstrate that quarterly seasonality is directly associated with quarterly changes in net purchases by institutional investors. Additionally, they show that near-term price momentum exhibits more seasonality than other momentum strategies, consistent with institutional investor incentives.Research limitations/implicationsResearchers studying momentum should understand that quarterly seasonality increases the standard deviation of monthly returns for different types of momentum strategies.Practical implicationsIndividual investors and investment managers should consider whether it is early or late in the calendar quarter when implementing momentum strategies.Originality/valueQuarterly seasonality explains several seemingly independent findings in the momentum literature. In cases where researchers show one momentum strategy outperforms another on a risk-adjusted basis, the authors find that the superior strategy exhibits less quarterly seasonality. This pattern holds across types of momentum strategies, strategy formation periods and asset classes.

Do Domestic Investors with the Smallest Trading Orders Use Momentum Strategiesin The IDX?

This study aims to examine the use of the momentum strategy of domestic investors who trade small order sizes on IDX. The data used in this study is intraday trade transaction data from the LQ 45 Index for March, April and May 2017 obtained from The Indonesia Capital Market Institute. The method of data analysis used is the market-adjusted model approach. The results of this study indicate that domestic investors who trade in small order sizes tend to use the momentum strategy in government and non-government stock issuers. This finding implies that when IDX is in a good performance, investors should take advantage of this condition because buying stocks that have already been performing well provides a significant short-term abnormal return.

Momentum investing: evidence from the US tourism and hospitality

PurposeThe present research aims to examine a range of momentum trading strategies for the tourism and hospitality sector.Design/methodology/approachThe paper followed the methodology of Jegadeesh and Titman (1993) to construct the portfolios. In this methodology, all portfolios were formed and evaluated by their cumulative stock returns over the past J periods and holding the position for the next K periods. In total, nine formation and holding periods were used, represented by 3, 6 and 12. For example, strategy 3–3 (that is, strategy with J = 3 and K = 3) refers to the strategy that stocks are ranked based on their previous three months and then held for the next three months.FindingsThe findings demonstrated that none of these momentum investing strategies was profitable. Most of the results, however, show positive, but insignificant momentum returns. This finding can be interpreted as price reversal over a horizon of three to twelve months in the US hospitality and tourism sector. These results are robust to size, different formation and holding combinations, beta and turnover.Research limitations/implicationsRegarding the research limitations, this paper only considers the US tourism and hospitality sector. Therefore, the extension of results to other developed and developing markets should be taken carefully. Also, this paper relies only on the methodology of Jegadeesh and Titman (1993). Other methodologies could be suitable avenues for future research.Practical implicationsInvestors and portfolio managers who seek for earning abnormal returns by investing in the US HT stocks can attain their hopes by constructing portfolios based on existing guidelines in the literature and adopting a short-term reversal trading strategy or by buying past losers and selling past winners of the US tourism and hospitality stocks.Originality/valueThis research contributes to the hospitality finance literature by offering the investors who are interested in the US hospitality and tourism sector an uncomplicated trading rule that uses real return data and is expected to generate actual returns. Moreover, the momentum strategy of Jegadeesh and Titman (1993) is never used in the hospitality finance literature.

Assessment of Capital Market Efficiency in COVID-19

The study tries to focus on the efficiency of the capital market through investigating the randomness of return series of Dhaka Stock Exchange of Bangladesh. Due to COVID-19 pandemic the worldwide capital market faces higher volatility than before. The study finds the week form of efficiency level of Bangladesh capital market. Special focus on Run test, Auto correlation test, predictability of tock return using ARIMA model the weekend effect anomaly and momentum strategy investing. The study found that the hypothesis of randomness of the stock returns are rejected for stock price index changes by using random walk tests, normality of return distributions, runs test and at different lags using ARIMA and the momentum tests which assert Dhaka Stock Exchange is not efficient even in the weak form.

Trend Momentum II: Driving Forces of Low Volatility and Momentum

In discussions and critiques on the validity of the Efficient Market Hypothesis, there are two important research focuses: statistical analyses showing that the basic assumption of statistical independence in price series is violated and empirical findings that show that significant market anomalies exist. In this paper, we combine both viewpoints by analyzing two important mathematical factor anomalies: low volatility and momentum. By applying an explicit trend model, we show that both anomalies require trending. Additionally, we show that the trend model exhibits lognormal trend characteristics. Furthermore, the model allows us to describe how low volatility uses implicitly asymmetric trend characteristics while momentum directly exploits trends. Using Mandelbrot’s model of fractional Brownian Motions, we can finally link statistical analyses (measuring the Hurst exponent and persistence in returns) to the empirically observed momentum factor. Experimentally, the Hurst exponent in itself allows for a momentum strategy, and can be utilized to significantly improve low volatility strategies. In contrast to Mandelbrot’s approach, we offer a non-stationary view that allows us to describe both investment strategies using the trend model.