DISCOVERING THE DRIVERS OF BID-ASK SPREAD : INTERNATIONAL EVIDENCE
DISCOVERING THE DRIVERS OF BID-ASK SPREAD : INTERNATIONAL EVIDENCE
INTRODUCTION:
The research topic of examining the variables that affect bid-ask spreads in financial markets across numerous nations is the subject of the phrase "Discovering the drivers of bid-ask spread: international evidence". The difference between the highest price that buyers will pay to buy a security (the bid price) and the lowest price that sellers would accept to sell the same security (the ask price) is known as the bid-ask spread. It acts as a gauge for market liquidity and transaction costs.
The goal of this study project is to identify and comprehend the underlying financial and economic factors that influence bid-ask spreads globally.
The research attempts to give evidence on the common characteristics or variables that regularly affect bid-ask spreads across borders by analyzing bid-ask spreads across several nations. The global market participants, regulators, and policymakers will benefit from a greater knowledge of bid-ask spread dynamics as a result of this international evidence.
OBJECTIVE OF STUDY:
The goal of this study proposal is to perform a thorough investigation into the financial and economic factors that influence bid-ask spreads of S&P 500 index. In order to gain information and understanding on the topic, the research will comprise a thorough analysis of the existing literature on bid-ask spreads. The study also aims to assemble a broad range of explanatory factors that have been suggested in the literature as potential bid-ask spread drivers.
This study's goal is to discover and examine the economic and financial factors that significantly contribute to the understanding of bid-ask spreads. The study aims to identify variables that not only offer significant explanatory information but also have predictive power in understanding bid-ask spread dynamics.
The study also aims to break down the bid-ask spread into its various components, particularly those linked to asymmetric knowledge, tarde volume etc. This breakdown will give you a better grasp of the various factors that affect bid-ask spreads. The goal of the research is to create a comprehensive framework that captures the nuances of bid-ask spread development by modelling the bid-ask spread with these decomposed components.
In general, this research project aims to investigate and examine the financial and economic factors that influence bid-ask spreads in various worldwide marketplaces. The project seeks to further knowledge in this area and offer insightful information to market participants and stakeholders through a thorough evaluation of the literature, collection of explanatory variables, deconstruction of bid-ask spread components, and modelling efforts.
Specifically, this study aims to calculate and analyse the bid-ask spread for a comprehensive set of S&P 500 stocks using high-frequency data. This analysis will provide a baseline for understanding the typical bid-ask spread behaviour and its variations within the S&P 500 index.
The research aims to develop metrics to assess the degree of informational asymmetry in the market over the past 10 years. By incorporating relevant variables such as news sentiment, trading volume, and price volatility, the study will provide a comprehensive assessment of the information environment during this period.
By utilising macroeconomic indicators, market volatility measures, and financial risk factors, the research aims to investigate the impact of economic and financial uncertainty on bid-ask spreads. This analysis will help identify periods of heightened uncertainty and their correlation with changes in bid-ask spreads.
The study aims to explore the link between informational asymmetry and bid-ask spreads by employing statistical and econometric techniques. By examining how changes in informational asymmetry affect bid-ask spreads, the research seeks to uncover the underlying mechanisms through which information availability and investor behaviour influence market liquidity.
Through the findings and analysis, the research aims to contribute valuable insights for market participants, including investors, traders, and policymakers. The study's conclusions may inform strategies for managing bid-ask spreads, enhance market efficiency, and potentially guide regulatory efforts to mitigate informational asymmetry and promote fair and transparent markets.
LITERATURE REVIEW:
THEORETICAL DEBATE SURROUNDING BID-ASK SPREAD:
5)Sahin, G., & Sahin, A. (2023) in their "An Empirical Examination of Asymmetry on Exchange Rate Spread Using the Quantile Autoregressive Distributed Lag (QARDL) Model" ran a study and the purpose of this study was to analyse the effects of macroeconomic and financial variables on the USD/TL exchange rate bid-ask spread for Turkiye using daily data spanning the period between 2 January 1990 and 2 August 2022.The quantile autoregressive distributed lag (QARDL) model was used to account for any parameter asymmetry and separate the results by location. This study used the QARDL model to investigate location and sign asymmetry in some quantile outcomes. They recommended that policymakers take into account the excessive levels and asymmetry of the bid-ask exchange rate spread while assessing its penetrating macro-financial variates since the results show that efficiency in the bid-ask exchange rate spread may be regulated.
6)The use of the midpoint as a benchmark for calculating transaction costs is contested in the study "Bias in the Effective Bid-Ask Spread" by Hagstrmer, B. (2021). He demonstrates that the illiquidity of the US equity markets is overestimated by the midpoint effective spread. The bias undermines assessments of trading performance and liquidity timing because it varies systematically among companies, trading platforms, and investor groups. He suggests substitute estimators that lessen the bias and can assist novice investors in lowering their execution expenses.
He demonstrates that an inaccurate estimate of the effective spread is the midway effective spread. The bias impairs assessments of trading performance and liquidity timing, and it varies systematically among equities and trading venues. The bias is statistically and economically significant, consistent across market capitalization groups, and growing over time for continuous fundamental value estimators.
Importantly, he discovers significant variations among investor groups in their capacity to determine the core value.The implication is that while knowledgeable investors are well aware of the midpoint effective spread bias, others are not. This gap can be closed by regulators or brokers by using a more precise fundamental value proxy.
7)In addition, Tremacoldi-Rossi, P., & Irwin, S. H. (2022) in their paper "The Bias of Simple Bid-Ask Spread Estimators." provide evidence of the fact that in options markets, the (percentage) implied volatility bid-ask spread rises steadily as the option's maturity date draws near. This study offers a market microstructure model for the bid-ask spread in options markets to explain this stylised reality.
The percentage implied volatility bid-ask spread increases at an increasing rate as an option's maturity date approaches, according to this paper's empirical evidence.
Utilising quotes from options on the CBOE S&P index from 2001/01/02 to 2010/04/17, this maturity effect has been verified. Both model-free and Black-Schole-Merton inferred volatilities are used to validate this effect.
8)The authors of "Volatility Uncertainty, Time Decay, and Option Bid-Ask Spreads in an Incomplete Market" by Hsieh, P., & Jarrow, R. (2019) investigate why simple bid-ask spread estimators frequently produce estimates that are negative or indeterminate, present inconsistent performance results in various markets, and present conflicting performance results overall. They also explore how to empirically evaluate and address these problems.
12)Gro-KluMann, A., & Hautsch, N. (2013) wrote Predicting bidask spreads using long-memory autoregressive conditional Poisson models This work is the first to systematically analyse projections of quoted bid-ask spreads, which was motivated by the relevance of bid-ask spreads in trading decisions and market microstructure modelling. They discover that quoted bid-ask spreads' distributional and dynamic characteristics are well captured by autoregressive conditional Poisson (ACP) models and their extended memory extension.
13)Duong, H. N., Kalev, P. S., & Tian, X. J. (2022) researched about Does the bidask spread affect trading in exchange operated dark pools? Evidence from a natural experiment. The dynamic relationship between the bid-ask spread in the lit market and dark trading activity in the exchange-operated dark pool in Japan is examined using the exogenously enforced minimum tick size change. They demonstrate that stocks affected by the minimum tick size adjustment have a reduced share of trading in the exchange-operated dark pool using a difference-in-differences methodology. Overall, their empirical results show that a sizable portion of dark trading involves liquidity seeking. Regaining market share over dark venues may be possible by lowering the minimum tick size for illuminated venues.
FOR INFORMATIONAL ASYMMETRY:
1)Andros Gregoriou, Christos Ioannidis, Len Skerratt in their.Information Asymmetry and the Bid-Ask Spread: Evidence From the UK,in their model of the bid ask spread a measure of the discrepancy in experts' profit estimates. Their metric stands in for the informational disadvantage that informed traders have over market makers. The bid-ask spread is increased by market makers in response to the increased risk. They discover that over time horizons up to and including six months, analyst disagreement is significant.
It is occasionally hypothesised that asset prices may not be entirely efficient in the sense of reflecting the sum of the information of all agents when market players have unequal access to information. In a significant study published in 1980, Grossman and Stiglitz laid out a solid theoretical foundation for this viewpoint. They contend that when information is expensive, it will not always be best for all market participants to acquire it; in an equilibrium, a certain percentage of agents will invest in information, and the pricing system will be sufficiently noisy to allow those investors to earn an additional return that makes up for the information costs they incurred.
2)Numerous authors have researched the best ask and bid pricing for a security dealer. Numerous authors, including Garman (1976), Amihud and Mendelson (1980), and Ho and Stoll (1981), have seen this issue as one of optimal inventory management. Many researches are based on the approach taken by (3) Copeland and Galai (1983) and Glosten and Milgrom (1985), which see the bid-ask spread as a purely informational phenomenon. The Glosten-Milgrom model assumes that the dealer is risk averse and competitive (i.e., he anticipates making little profit on any given transaction). Customers of the dealer are either "informed" or "uninformed"; in other words, they are driven to trade by liquidity concerns or because they have more information than the dealer. When an equilibrium is reached, Glosten and Milgrom can demonstrate that the dealer must typically set a non-zero gap between the ask and bid prices in order to break even.
4) Management Forecasts and Information Asymmetry: An Examination of Bid-Ask Spreads Maribeth by Coller Teri Lombardi Yohn , in the study Bid-ask spreads were regressed on forecast indicator variables and on variables that served as proxies for other factors that have been discovered to have an impact on bid-ask spreads. Indicator variables are used to capture differences in spreads between the forecasters and the non forecasters in cross-sectional analysis of forecasting and non forecasting companies prior to and after management predictions. We evaluate spreads before, during, and after the publication of a management forecast in time-series analyses of forecasting companies. Indicator variables are utilised in these analyses to capture variations in spreads between different time periods.
9) Smith & Boening in their Exogenous Uncertainty Increases the Bid-Ask Spread in the Continuous Double Auction focused on the exogenous uncertainty that increases the bidask spread in the continuous double auction. These experiments demonstrate that exogenous uncertainty can increase the bidask spread in the continuous double auction. They observed greater mean and median spreads, and a greater probability of a large spread in double auctions with randomly shifting per period supply and demand than in double auctions with constant supply and demand. Their results, and many others, demonstrate that even in the absence of transaction cost or information asymmetry, positive bidask spreads are observed and wider spreads are observed when there is greater uncertainty in the environment. A measurement problem associated with the prediction is that contracts may and often do occur without a defined bidask spread or before that spread has a chance to narrow. Thus, a bid may be entered and accepted before an ask price is established
10) Nagar, Schoenfeld, & Wellman, in their The Effect of Economic Policy Uncertainty on Investor Information Asymmetry and Management Disclosures studied how investor uncertainty about firm value drives investors information collection and trading activities, as well as managers disclosure choices. Their study examines an important source of uncertainty that likely cannot be influenced by most managers and investors: uncertainty about government economic policy. They found that this uncertainty is associated with increased bid-ask spreads and decreased stock price reactions to earnings surprises. Managers respond to this uncertainty by increasing their voluntary disclosures, but these disclosures only partly mitigate the bid-ask spread increase. They concluded that government economic policy uncertainty is an important component of firms information environments and managers voluntary disclosure decisions.
11) Gregorio wrote the paper Earnings announcements and the components of the bidask spread: Evidence from the London Stock Exchange, the purpose of this paper was to investigate the impact of the components of the bid-ask spread around earnings announcements on the London Stock Exchange using intraday data obtained from the ICV Marketeye database. The paper found that the information asymmetry cost component significantly increases around the earnings announcements, while the inventory holding and order processing cost components significantly decrease around the same period. Specifically, the economic magnitude of the increase in the asymmetric cost component implies that earnings announcements significantly increase the total bid-ask spread, even when they result in decreased inventory holding and order processing costs.
RESEARCH AND METHODOLOGY:
The following questions will be addressed by the study: Are substantial spreads on common stocks caused by asymmetric information?What impact does economic uncertainty and financial uncertainty have on the on the bid-ask spread over the S&P 500? How much of a difference does trade volume and market volatility make on the bid-ask spread?
The goal of the proposed study is to investigate an informational event in order to determine how knowledge asymmetry affects bid-ask spreads. A cross-sectional analysis comparing the actions of educated and uninformed investors and a time-series analysis concentrating on the predicting skills of informed investors can be used to do this.
The indicated informational event, investor classification, and pertinent data on bid-ask spreads will all be gathered to help with this study. The hypothesis will be based on the adverse selection theory, which states that when information asymmetry exists, bid-ask spreads grow.
The choice of variables will take into account important indicators of information asymmetry, such as order flow imbalances, to capture the divergent trading behaviours of knowledgeable and uninformed investors. Additionally, trade volume will be taken into account as a sign of market activity. To account for any confounding variables, control variables such as market liquidity, transaction costs, and volatility will also be included.
Bid-ask spreads will be the dependent variable in the analyses' cross-sectional regression, while investor classification and pertinent control factors will be the independent variables. This investigation will shed light on how knowledgeable and uninformed investors behave differently in terms of the bid-ask spread.
Additionally, a time-series regression will be carried out with a focus on the forecasting skills of knowledgeable investors. This investigation will determine how much forecasts from knowledgeable investors affect bid-ask spreads by taking into account lagged variables and using the right statistical methods.
To verify the robustness and validity of the results, both regression analyses will be put through a rigorous testing process that addresses any problems with heteroscedasticity and autocorrelation. The study approach will also include model comparisons and sensitivity analyses.
By employing this thorough methodology, the study seeks to illuminate the effects of information asymmetry on bid-ask spreads, adding to the body of knowledge already known about market microstructure and providing useful information for market participants, regulators, and researchers.
Methodology:
Data Collection: a. Bid-Ask Spread Data: Obtain historical bid-ask spread data for S&P 500 stocks over the past 10 years. This data can be collected from reliable financial databases or trading platforms. b. Economic and Financial Uncertainty Data: Acquire economic and financial uncertainty data from the Sydney Ludvigson website or other reputable sources. Ensure that the data covers the same time period as the bid-ask spread data. c. Market Volatility and Trade Volume Data: Gather market volatility and trade volume data for the S&P 500 index from Datastream of Eikon or similar financial databases. Ensure that the data corresponds to the same 10-year period as the other variables.
Data Preprocessing: a. Clean and preprocess the collected data to ensure consistency and accuracy. Handle missing values, outliers, and data inconsistencies appropriately. b. Align the time series of bid-ask spread data, economic uncertainty data, market volatility data, and trade volume data to ensure synchronization and consistency.
Variable Definition: a. Dependent Variable: Define the bid-ask spread as the dependent variable in the regression model. This variable represents the difference between the best bid and ask prices for S&P 500 stocks. b. Independent Variables: Define economic uncertainty, financial uncertainty, trade volume, and market volatility as the independent variables. Assign these variables based on the available data from Step 1.
Regression Model Specification: a. Specify a multiple linear regression model to assess the impact of economic and financial uncertainty, trade volume, and market volatility on the bid-ask spread. The model can be formulated as follows:Bid-Ask Spread = + * Economic Uncertainty + * Financial Uncertainty + * Trade Volume + * Market Volatility + Where:
, , , , and are the coefficients to be estimated.
Economic Uncertainty, Financial Uncertainty, Trade Volume, and Market Volatility are the independent variables.
represents the error term capturing unexplained variation in the bid-ask spread.
b. Validate the assumptions of linear regression, such as linearity, normality, and homoscedasticity, to ensure the reliability of the results.
Estimation and Analysis: a. Apply the regression model to estimate the coefficients (, , , , and ) using appropriate regression techniques, such as ordinary least squares (OLS). b. Analyze the estimated coefficients and their statistical significance to determine the impact of economic and financial uncertainty, trade volume, and market volatility on the bid-ask spread. c. Assess the overall goodness-of-fit of the regression model using relevant statistical metrics, such as R-squared, adjusted R-squared, and F-statistic.
Interpretation and Conclusion: a. Interpret the estimated coefficients to understand the direction and magnitude of the impact of each independent variable on the bid-ask spread. b. Discuss the significance of the results and provide insights into the relationship between informational asymmetry, economic/financial uncertainty, trade volume, market volatility, and bid-ask spreads in the S&P 500 stocks. c. Summarize the findings and draw conclusions regarding the research objective, highlighting any implications for market participants and policymakers.
Note: It is important to ensure the accuracy and reliability of the data sources and conduct robust statistical analysis to establish meaningful relationships between the variables. Additionally, appropriate statistical tests and control variables may need to be considered based on the specific research context and potential confounding factors.
REFERENCES:
9) Smith, V., & Boening, M. (2008). Exogenous Uncertainty Increases the Bid-Ask Spread in the Continuous Double Auction. . https://doi.org/10.1016/S1574-0722(07)00003-0.
10)Nagar, V., Schoenfeld, J., & Wellman, L. (2018). The Effect of Economic Policy Uncertainty on Investor Information Asymmetry and Management Disclosures. Financial Accounting eJournal. https://doi.org/10.2139/ssrn.2841442.
11) Gregoriou, A. (2013). Earnings announcements and the components of the bidask spread: Evidence from the London Stock Exchange. Journal of Economic Studies. https://doi.org/10.1108/01443581311283646.
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