Digitization, Prediction, and Market Efficiency: Evidence from Book Publishing Deals

Digitization has given creators direct access to consumers as well as a plethora of new data for suppliers of new products to draw on. We study how this affects market efficiency in the context of book publishing. Using data on about 50,000 license deals over more than 10 years, we identify the effects of digitization from quasi-experimental variation across book types. Consistent with digitization generating additional information for predicting product appeal, we show that the size of license payments more accurately reflects a product’s ex post success, and more so for publishers that invest more in data analytics. These effects cannot be fully explained by changes in bargaining power or in demand. We estimate that efficiency gains are worth between 10% and 18% of publishers’ total investments in book deals. Thus, digitization can have large impacts on the allocation of resources across products of varying qualities in markets in which product appeal has traditionally been difficult to predict ex ante. This paper was accepted by Joshua Gans, business strategy.

Time Evolution of Market Efficiency and Multifractality of the Japanese Stock Market

This study investigates the time evolution of market efficiency in the Japanese stock markets, considering three indices: Tokyo Stock Price Index (TOPIX), Tokyo Stock Exchange Second Section Index, and TOPIX-Small. The Hurst exponent reveals that the Japanese markets are inefficient in their early stages and improve gradually. TOPIX and TOPIX-Small showed an anti-persistence around the year 2000, which still persists. The degree of multifractality varies over time and does not show that the Japanese markets are permanently efficient. The multifractal properties of the Japanese markets changed considerably around the year 2000; this may have been caused by the complete migration from the stock trading floor to the Tokyo Stock Exchange’s computer trading system and the financial system reform, also known as the “Japanese Big Bang”.

Institutional Herding: Causality and Persistence

We investigate the causality in herding between foreign portfolio investors (FPIs) and domestic mutual funds (MFs) in the Indian stock market. The estimated herding levels are considerably higher than those observed in other international markets, and herding is prevalent in small stocks. We find that institutional investors follow contrarian-trading strategies, unlike what was documented in most other markets. Analysis of the aggregate herding measure shows a bi-directional causality between FPIs and MFs. Further analysis using directional herding measures indicate no evidence of causality between institutional herds on the sell-side. But we find causality on the buy-side and it is running in both directions between FPIs and MFs, implying a feedback of information. Given the tendency of institutions for herding in small stocks, adopting contrarian-trading strategies, the observed sell-side causality is perhaps having a salubrious effect. As institutional investors are contrarians, their trading activity will lead to price corrections in small stocks aligning with the fundamentals, thereby contributing to market efficiency. JEL Classification: C23, C58, G23, G15, G40

A Random walk down the Dhaka Stock Exchange lane

Market efficiency has always been a matter of keen interest to the researchers of finance. Since the advancement of this concept, researchers are consistently investigating the market efficiency of different financial markets. Bangladesh, being one of the emerging economies, has also attracted the attention of many researchers. The researchers have investigated the realities regarding the market efficiency of both the stock exchanges of the country. Most of their investigations reveal that the Dhaka Stock Exchange (DSE) and the Chittagong Stock Exchange (CSE) are inefficient. This research, however, did not stop at revisiting market efficiency alone. Whether the return series follows a long-memory process, has also been tested. Besides, non-parametric tests have also been conducted to confirm the results of the parametric tests and vice versa. It generated a more reliable estimate of market efficiency for the period under study. Results of the Autoregressive Fractionally Integrated Moving Average (ARFIMA) model confirm that the return series does not follow a long memory process, and any shock in the system will eventually vanish. The findings of other tests (the run test, the Augmented Dickey-Fuller (ADF) test, the Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test, and the Kolmogorov-Smirnov (K-S) test) suggest that the return series of the DSE are time-series stationary, non-normal, and do not follow a random walk. Given these results, we must echo the prior researchers to conclude that the stock market of Bangladesh is not efficient for the period of 2015 to 2020. These findings add new knowledge to the existing knowledge pool about market efficiency and long memory of the stock market of Bangladesh.

Market potential of TikTok in the context of the communication range of soccer players on social networks

The study aims to put the social network TikTok into the context of the marketing attractiveness and potential of soccer players in terms of communication range through social networks. Soccer clubs can assess the expenses of gaining additional followers through a purchased player by an evaluation of the market efficiency of individual soccer players’ followers. The study also documents positive effects ensuing from the acquisition of further followers thanks to the purchase of such a player including image, connection with fans, global reach, additional external funds through sponsorship and the sale of television rights, loyal fans and other so-called “extra-football qualities”. The study thus brings new perspectives on TikTok, as a network which has so far not been thoroughly researched, in the field of the most popular sport in the world, soccer.

On extreme value theory in the presence of technical trend: pre and post Covid-19 analysis of cryptocurrency markets

Purpose Research on price extremes and overreactions as potential violations of market efficiency has a long tradition in investment literature. Arguably, very few studies to date have addressed this issue in cryptocurrencies trading. The purpose of this paper is to consider the extreme value modelling for forecasting COVID-19 effects on cryptocoin markets. Additionally, this paper examines the importance of technical trading indicators in predicting the extreme price behaviour of cryptocurrencies. Design/methodology/approach This paper decomposes the daily-time series returns of four cryptocurrency returns into potential maximum gains (PMGs) and potential maximum losses (PMLs) at first and then tests their lead–lag relations under an econometric framework. This paper also investigates the non-random properties of cryptocoins by computing the incremental explanatory power of PML–PMG modelling with technical trading indicators controlled. Besides, this paper executes an event study to identify significant changes caused by COVID-19-related events, which is capable of analysing the cryptocoin market overreactions. Findings The findings of this paper produce the evidence of both market overreactions and trend persistence in the potential gains and losses from coins trading. Extreme price behaviour explains volatility and price trends in crypto markets before and after the outbreak of a pandemic that substantiate the non-random walk behaviour of crypto returns. The presence of technical trading indicators as control variables in the extreme value regressions significantly improves the predictive power of models. COVID-19 crisis affects the market efficiency of cryptocurrencies that improves the usefulness of extreme value predictions with technical analysis. Research limitations/implications This paper strongly supports for the robustness of technical trading strategies in cryptocurrency markets. However, the “beast is moving quick” and uncertainty as to the new normalcy about the post-COVID-19 world puts constraint on making best predictions. Practical implications The paper contributes substantially to our understanding of the pricing efficiency of cryptocurrency markets after the COVID-19 outbreak. The findings of continuing return predictability and price volatility during COVID-19 show that profitable investment opportunities for cryptocoin traders are prevailing in pandemic times. Originality/value The paper is unique to understand extreme return reversals behaviour of cryptocurrency markets regarding events related to COVID-19 breakout.

Three Essays on the U.S. Treasury Market

<p>My thesis consists of three essays on market microstructure. Focusing on the U.S. Treasury market, I investigate several interesting research questions by using twelve years of BrokerTec order books of 2-, 5-, and 10-year on-the-run U.S. Treasury notes from January 1, 2004 to December 31, 2015, and five years of BrokerTec order books of 3-, 7- and 30-year on-the-run U.S. Treasury securities from January 1, 2011 to December 31, 2015. In the U.S. Treasury market, BrokerTec is one of the two dominant electronic communication networks (ECNs). According to my calculations by using BrokerTec order books from 2011 to 2015, the average daily trading volume of BrokerTec on-the-run U.S. Treasury securities is about 134.9 billion U.S. dollars, which accounts for about 26% of that of the total U.S. Treasury primary dealer activity. To help a wider audience better understand the importance of the research questions in the following three chapters, Chapter 1 gives a brief introduction to the U.S. Treasury market. In Chapter 2, I investigate the impact of scheduled macroeconomic news announcements on the U.S. Treasury market efficiency. To control the microstructure noise, I employ a robust method to construct market inefficiency measures. I find that the U.S. Treasury market becomes less efficient starting from five minutes before news arrivals. The finding is robust for different sample periods, macroeconomic news announcements, and market inefficiency measures. Investor heterogeneity could explain the decreased market efficiency before scheduled news announcements. In Chapter 3, I investigate the impact of workup trading protocols on the U.S. Treasury market quality. Each transaction on the lit pool opens a workup window, during which the BrokerTec trading platform continues to receive order submissions and modifications, but only matches workup orders that have the same prices. Each workup transaction starts a new counting down of the workup clock. A workup window naturally closes either after the workup times out or when a limit order is submitted at a better price. I find that the workup trading activities decrease the market quality, in aspects of market efficiency and market liquidity. In Chapter 4, I empirically examine the role of heterogeneity in traders’ beliefs and public information shocks on traders’ order submission decisions around news announcements in the U.S. Treasury market. I find that during both the pre-announcement period and the post-announcement period, the traders tend to submit more market orders and aggressive limit orders when the market uncertainty is high. I also find that the belief heterogeneity influences investors’ trading behavior and order submission strategies around news announcements. The role of the belief heterogeneity on order aggressiveness depends on the type of news, and the magnitude of the information shocks. The impact of market uncertainty and belief heterogeneity influences traders’ submission of both of the market orders and aggressive limit orders. In Chapter 5, I provide a summary on the research findings in Chapter 2, Chapter 3 and Chapter 4. I also discuss the contributions of this thesis to the literature.</p>

Three Essays on the U.S. Treasury Market

<p>My thesis consists of three essays on market microstructure. Focusing on the U.S. Treasury market, I investigate several interesting research questions by using twelve years of BrokerTec order books of 2-, 5-, and 10-year on-the-run U.S. Treasury notes from January 1, 2004 to December 31, 2015, and five years of BrokerTec order books of 3-, 7- and 30-year on-the-run U.S. Treasury securities from January 1, 2011 to December 31, 2015. In the U.S. Treasury market, BrokerTec is one of the two dominant electronic communication networks (ECNs). According to my calculations by using BrokerTec order books from 2011 to 2015, the average daily trading volume of BrokerTec on-the-run U.S. Treasury securities is about 134.9 billion U.S. dollars, which accounts for about 26% of that of the total U.S. Treasury primary dealer activity. To help a wider audience better understand the importance of the research questions in the following three chapters, Chapter 1 gives a brief introduction to the U.S. Treasury market. In Chapter 2, I investigate the impact of scheduled macroeconomic news announcements on the U.S. Treasury market efficiency. To control the microstructure noise, I employ a robust method to construct market inefficiency measures. I find that the U.S. Treasury market becomes less efficient starting from five minutes before news arrivals. The finding is robust for different sample periods, macroeconomic news announcements, and market inefficiency measures. Investor heterogeneity could explain the decreased market efficiency before scheduled news announcements. In Chapter 3, I investigate the impact of workup trading protocols on the U.S. Treasury market quality. Each transaction on the lit pool opens a workup window, during which the BrokerTec trading platform continues to receive order submissions and modifications, but only matches workup orders that have the same prices. Each workup transaction starts a new counting down of the workup clock. A workup window naturally closes either after the workup times out or when a limit order is submitted at a better price. I find that the workup trading activities decrease the market quality, in aspects of market efficiency and market liquidity. In Chapter 4, I empirically examine the role of heterogeneity in traders’ beliefs and public information shocks on traders’ order submission decisions around news announcements in the U.S. Treasury market. I find that during both the pre-announcement period and the post-announcement period, the traders tend to submit more market orders and aggressive limit orders when the market uncertainty is high. I also find that the belief heterogeneity influences investors’ trading behavior and order submission strategies around news announcements. The role of the belief heterogeneity on order aggressiveness depends on the type of news, and the magnitude of the information shocks. The impact of market uncertainty and belief heterogeneity influences traders’ submission of both of the market orders and aggressive limit orders. In Chapter 5, I provide a summary on the research findings in Chapter 2, Chapter 3 and Chapter 4. I also discuss the contributions of this thesis to the literature.</p>