Increasing the Tick: Examining the Impact of the Tick Size Change on Maker‐Taker and Taker‐Maker Market Models

We investigate the effects of an increase in tick size on order and trading flow across market fee models. Using the pilot firms in the U.S. Securities and Exchange Commission's Tick Size Pilot Program, we document that trade and order volume declines on maker‐taker fee models after the tick size implementation. We find that the inverted fee models (taker‐maker) experience an increase in both trade and order volume. Additionally, we find that a tick size adjustment has a substantial influence on market participation in maker‐taker fee models. We also find that measures of both hidden and algorithmic trading decline with an increasing tick size, which is strongly moderated by the differences in the maker‐taker and taker‐maker fee models.     

Universal features of price formation in financial markets: perspectives from deep learning

Using a large-scale Deep Learning approach applied to a high-frequency database containing billions of market quotes and transactions for US equities, we uncover nonparametric evidence for the existence of a universal and stationary relation between order flow history and the direction of price moves. The universal price formation model exhibits a remarkably stable out-of-sample accuracy across a wide range of stocks and time periods. Interestingly, these results also hold for stocks which are not part of the training sample, showing that the relations captured by the model are universal and not asset-specific.

The universal model—trained on data from all stocks—outperforms asset-specific models trained on time series of any given stock. This weighs in favor of pooling together financial data from various stocks, rather than designing asset- or sector-specific models, as is currently commonly done. Standard data normalizations based on volatility, price level or average spread, or partitioning the training data into sectors or categories such as large/small tick stocks, do not improve training results. On the other hand, inclusion of price and order flow history over many past observations improves forecast accuracy, indicating that there is path-dependence in price dynamics.     

Deep neural network framework based on backward stochastic differential equations for pricing and hedging American options in high dimensions

We propose a deep neural network framework for computing prices and deltas of American options in high dimensions. The architecture of the framework is a sequence of neural networks, where each network learns the difference of the price functions between adjacent timesteps. We introduce the least squares residual of the associated backward stochastic differential equation as the loss function. Our proposed framework yields prices and deltas for the entire spacetime, not only at a given point (e.g. t?=?0). The computational cost of the proposed approach is quadratic in dimension, which addresses the curse of dimensionality issue that state-of-the-art approaches suffer. Our numerical simulations demonstrate these contributions, and show that the proposed neural network framework outperforms state-of-the-art approaches in high dimensions.     

Fair Competition and China's State-owned Enterprises Reform

MOCT-MOST: Economic Policy in Transitional Economies -     

The evolving direct-to-consumer retail model: A review and research agenda

The substantial growth of e-commerce sales, driven by digital advancements, has enticed brands to sell direct-to-consumers (DTC) online. Despite the growth of the DTC online retail model in practice, academic literature remains fragmented. This systematic review is timely as it meets the need for a comprehensive understanding of the evolution of DTC over two decades of research to 2023. A SPAR-4-SLR protocol combined with a Theory–Context–Methods (TCM) approach examines the extant literature through a marketing lens. An analysis of 81 articles reveals a need for consumer behavior theories to underpin research on the DTC retail model. We conclude the review by identifying areas for further development, such as the need for research on the technology-driven balance of power between brands, retailers, and consumers. Contextually, the authors highlight research opportunities on SME brands' internationalization and the DTC online luxury experience. Methodological directions include empirical qualitative studies and experiments on the emerging topics of technology-enabled retail services and the experiential e-commerce impact on customer experience. These future research undertakings will help us to better understand the DTC retail model.     

The Temporal Phases of Leisure Experience: Expectation,Experience and Reflection of Leisure Participation

While there have been studies whereby the methodology relied on evaluations of pre-, during, and postparticipation experiences, it is far more common that research on leisure does not consider the temporal phases of a leisure experience. Further, many of the studies that have attempted to encompass the multiple temporal phases of experiential leisure involvement have not focused on participants who had established significant leisure careers or long histories of recreation participation in a singular activity. The purpose of this study was to explore the temporal phases of a leisure experience in the context of individuals' in situ attendance at a niche music performance. These phases consisted of participants' expectations before the event, the actual experience during the event, and the memory of that experience after its completion. Because participants wanted to feel in control of their lives, personal reassessments and the prioritization of positive events sought to emphasize the value of their agency. Researchers found that the reconstruction of memory drives desire for future participation as well as the associative expectations of what is to come.     

Opportunities for higher education: the ten-year effects of conditional cash transfers on upper-secondary and tertiary enrollments

This article investigates the effect of the conditional cash transfer program Oportunidades on rural enrollment/completion in higher education, ten years after the initiation of the program in Mexico. We use data from the International Food Policy Research Institute and employ a Regression Discontinuity approach. We find that Oportunidades eligible households had higher 2007 technical school (14–18 year olds) and college (39+) enrollment/completion relative to comparable non-eligible households. However, the program benefits are heterogeneous depending on school access. Accounting for access to schools, we find that the program also had positive effects for school enrollment/completion for upper secondary (14–18) and college (19–28, 29–38). Further, we find positive tertiary education benefits for individuals in treated households who were too old to qualify for benefits directly, indicating either positive externalities or complementarities for individuals residing in eligible households. Possible mechanisms are relaxing budget constraints, and changes in educational aspirations.     

Inconsistency in Welfare Inferences from Distance Variables in Hedonic Regressions

In hedonic analysis, a common approach for eliciting information regarding the welfare significance of some landmark or (dis)amenity is to control for its distance from each observation. Unfortunately, the effects of distances to amenities on housing prices are generally not consistent indicators of the true price impact of that amenity. Instead these variables serve as proxies for the relative position of every observation in space. Whenever a household considers more than two landmarks in a housing purchase, distance variable parameter estimates are simply the best linear fitted weights for that multiple criteria location decision. Simulations illustrate extreme sensitivity in parameter estimates to the researcher’s choice of landmarks. One strategy models the location of each observation directly instead of its distances to amenities. Using the quadratic controls of longitude and latitude controls for location effects on price to assure unbiased estimates of non-distance variable regressors.     

Ability of accounting and audit quality variables to predict bank failure during the financial crisis

We examine the ability of selected accounting and audit quality variables measured in a period prior to the financial crisis (i.e., the four quarters of 2006), to predict banks that subsequently failed during the financial crisis. We employ two sets of samples from the US: a troubled banks sample that includes banks that failed in or after 2007 as well as banks classified as being troubled based on profitability, loan quality, and balance sheet position in 2007, and a full sample that includes all banks with available required data. Using the troubled banks sample, we identify six reliable predictors of bank failure: auditor type, auditor industry specialization, Tier 1 capital ratio, proportion of securitized loans, growth in loans, and loan mix. For the larger full sample of banks, we identify the following ten predictors of bank failure: auditor type, Tier 1 capital ratio, proportion of securitized loans, nonperforming loans, loan loss provisions, growth in commercial loans, growth in real estate loans, growth in overall loans, loan mix, and whether the bank is a public bank.     

Understanding Differences in Self-Reported Expenditures between Household Scanner Data and Diary Survey Data: A Comparison of Homescan and Consumer Expenditure Survey

Household scanner data contain rich information on household demographics and transactions in actual markets over a long time period. To more fully understand the characteristics of these data, we conducted an analysis to determine whether household expenditures in the Nielsen Homescan panel are similar to the Bureau of Labor Statistic's Consumer Expenditure Diary Survey. We found that many differences in reported expenditures across the two datasets can be explained by such household demographics as female head, income, and household size, for example. The largest degrees of discrepancies across datasets occur for food categories containing more random-weight foods without universal product codes.