Quote Data Research Articles

Budget-Aware Local Influence Iterative Algorithm for Efficient Influence Maximization in Social Networks

The budgeted influence maximization (BIM) problem aims to identify a set of seed nodes that adhere to predefined budget constraints within a specified network structure and cost model. However, it is difficult for the existing algorithms to achieve a balance between timeliness and effectiveness. To address this challenge, our study initially proposes a refined cost model through empirical scrutiny of Weibo's quote data. Subsequently, we introduce a proxy-based algorithm, i.e., the budget-aware local influence iterative (BLII) algorithm tailored for the BIM problem, aimed at expediently identifying seed nodes. The algorithm approximates the global influence by leveraging the user’s one-hop influence and circumvents influence overlap among seed nodes via iterative influence updates. Comparative experiments involving eight algorithms across four real networks demonstrate the effectiveness, efficiency, and robustness of the BLII algorithm. In terms of influence spread, the proposed algorithm outperforms other proxy-based algorithms by 20%–255% and reaches the state-of-the-art simulation-based approach by 96%. In addition, the running time of the BLII algorithm is reasonable. Generally, the proposed cost model and BLII algorithm provide novel insights and potent tools for studying BIM problems.

Retail investors, COVID and the quality of the New Zealand Stock Exchange

In this article, we investigate the impact of the influx of retail investors during the COVID-19 lockdowns on the New Zealand Stock Exchange on the efficiency of the market. On one hand, these investors bring added liquidity to the market, which generally improves market efficiency, and on the other hand, many of these investors were inexperienced retail traders who are often viewed as noise traders harming market efficiency. This natural experiment allows us to investigate the impact of retail investors. Using a sample of 99 New Zealand Exchange listed companies, we collect intraday price and quote data from 1 January 2019 to 24 March 2021. We compute a range of market quality measures across three categories: trading costs, price efficiency and speed of information incorporation. Our results clearly indicate that the influx of retail investors has reduced market quality; in particular, we observe more price predictability and information being impounded into prices more slowly. JEL Classification: C10, G14, G20

Defining and Controlling Information Leakage in US Equities Trading

We present a new framework for defining information leakage in the setting of US equities trading, and construct methods for deriving trading schedules that stay within specified information leakage bounds. Our approach treats the stock market as an interactive protocol performed in the presence of an adversary, and draws inspiration from the related disciplines of differential privacy as well as quantitative information flow. We apply a linear programming solver using examples from historical trade and quote (TAQ) data for US equities and describe how this framework can inform actual algorithmic trading strategies.

Foreign investors, private information, and price discovery

We examine the relation between intra-day price discovery and proxies for financial openness and investor accessibility using a sample of intra-day price and quote data of 1,504 stocks from 23 emerging markets. We measure price discovery by weighted price contribution across segments of the trading day. There is a reliable relation between price discovery in early trading and direct foreign ownership, controlling for other factors. We project foreign ownership onto proxies for private information and investor recognition. The residual from the projection regression captures the impact of unknown factors. We find that the role of private information is much more important than investor recognition and unknown factor in facilitating price discovery.

Liquidity and Price Impact at the 52 Week High

A stock’s 52 week high price represents an extremely salient anchor in an investor’s tradingdecision. Using trade and quote data, we document that stocks near the 52 week high priceexhibit significantly higher levels of liquidity than stocks far from the 52 week high. Thisincrease in liquidity is concentrated on the supply side, with depth on the ask side essentiallydoubling relative to a normal day. Price impact declines by as much as two-thirds on the52 week high day. We argue that the abnormal increase in liquidity is driven by dispositionand anchoring effects. These findings help explain the role of the 52 week high as a barrierto information discovery and a potential driver of long run momentum.

Income-targeted marketing as a supply-side barrier to low-income solar adoption

SummaryLow- and moderate-income (LMI) households remain less likely to adopt rooftop solar photovoltaics (PV) than higher-income households. A transient period of inequitable adoption is common among emerging technologies but stakeholders are calling for an accelerated transition to equitable rooftop PV adoption. To date, researchers have focused on demand-side drivers of PV adoption inequity, but supply-side factors could also play a role. Here, we use quote data to explore whether PV installers implement income-targeted marketing and the extent to which such strategies drive adoption inequity. We find that installers submit fewer quotes to households in low-income areas and those households that receive fewer quotes are less likely to adopt. The data suggest that income-targeted marketing explains about one-quarter of the difference in PV adoption rates between LMI and higher-income households. Policymakers could explore a broader suite of interventions to address demand- and supply-side drivers of PV adoption inequity.

Income-Targeted Marketing as a Supply-Side Barrier to Low-Income Solar Adoption

Despite improving economics, low- and moderate-income (LMI) households remain less likely to adopt rooftop solar photovoltaics (PV) than higher-income households. To date, researchers and policymakers have focused on demand-side drivers of PV adoption inequity, but supply-side factors could also play a role. Here, we use quote data to explore whether PV installers implement income-targeted marketing strategies and the extent to which such strategies drive adoption inequity. The quote data show that installers submit significantly fewer quotes to households in low-income areas. Further, the data show that households that receive fewer quotes are less likely to adopt. As a first order approximation, the data suggest that income-targeted marketing explains about one-quarter of the difference in PV adoption rates between LMI and higher-income households. Policymakers could explore a broader suite of interventions to address both demand- and supply-side drivers of PV adoption inequity.

Using Brexit to identify the nature of price rigidities

Using price quote data that underpin the official U.K. consumer price index (CPI), we analyze the effects of the unexpected passing of the Brexit referendum on the dynamics of price adjustments. The sizable depreciation of the British pound that immediately followed Brexit works as a quasi-experiment, enabling us to study the transmission of a large common marginal cost shock to inflation as well as the distribution of prices within granular product categories. The bulk of the aggregate inflationary effect is attributable to the size of price adjustments, an aspect matched well by the time-dependent price-setting model. The state-dependent model fares better in capturing the endogenous selection of price changes at the lower end of the price distribution. Both models miss on the magnitude of the adjustment conditional on selection.

A Descriptive Study of High-Frequency Trade and Quote Option Data*

Abstract This paper provides a guide to high-frequency option trade and quote data disseminated by the Options Price Reporting Authority (OPRA). We present a comprehensive overview of the U.S. option market, including details on market regulation and the trading processes for all 16 constituent option exchanges. We review the existing literature that utilizes high-frequency options data, summarizes the general structure of the OPRA dataset, and presents a thorough empirical description of the observed option trades and quotes for a selected sample of underlying assets that contains more than 25 billion records. We outline several types of irregular observations and provide recommendations for data filtering and cleaning. Finally, we illustrate the usefulness of the high-frequency option data with two empirical applications: option-implied variance estimation and risk-neutral density estimation. Both applications highlight the rich information content of the high-frequency OPRA data.

Efficient Estimation of Bid-Ask Spreads from Open, High, Low, and Close Prices

We propose a novel estimation procedure of bid-ask spreads from open, high, low, and close prices. Our estimator is asymptotically unbiased and optimally combines the full set of price data to minimize the estimation variance. When quote data are not available, our estimator generally delivers the most accurate estimates of effective bid-ask spreads numerically and empirically. The estimator is derived under permissive assumptions that allow for stylized facts typically observed in real market data, is easy to implement, and can be applied to liquid and illiquid market segments, both in low and high frequency.

Machine Learning in the Corporate Bond Market and Beyond: A New Classifier

Trade signing algorithms that rely on quote data, tick data or both have been used extensively to assign a trade as either a buy or a sell. Leveraging the availability of a large panel of signed trade data in the corporate bond market, we explore machine learning methods to uncover a new trade signing model that improves upon standard trade classification methods in both the bond and equity markets. We show that both the trading and information environment at the time of the trade critically affect the accuracy of existing trade classification rules in general, and also illustrate the importance of optimizing the feature set in correctly classifying trade direction. Importantly, our approach and the Random Forest algorithm we propose can be used in markets both with and without pre-trade transparency.

The Accuracy of Trade Classification Systems on the Foreign Exchange Market: Evidence from the RUB/USD Market

To the best of our knowledge we are the first to test a broad set of trade classification rules on the foreign exchange interbank market. A unique data set on the Russian Rouble/US Dollar trade includes the true trade initiator. The modified EMO (Ellis, Michaely and O'Hara) rule is currently the best choice at classifying trades. When quote data is not present, the tick rule yields a considerably lower accuracy. Yearly variations in the accuracy can be attributed to the difference in the location where trades occurred. Not surprisingly, trades executed at the quotes are the most informative.

Intraday analysis of the limit order bias on the ex-dividend day of U.S. common stocks

This study reexamines Dubofsky’s (1992) limit order adjustment hypothesis via an intraday analysis of minute-by-minute trade and quote data recorded on the ex-dividend days of common stocks listed on the NYSE, AMEX, and NASDAQ. According to Dubofsky’s (1992) model, the asymmetric adjustment of open limit orders for cash dividend payments under NYSE and AMEX rules is sufficient to create abnormal returns on the ex-dividend day. Our empirical evidence shows that the limit order bias arising from the asymmetric limit order adjustment strongly influences the overnight ex-day returns, but it is significantly corrected via active trading until the close of the ex-dividend day. As a result, the significant association of the ex-day price drop discrepancy with the opening limit order bias eventually dissipates before the ex-dividend day close. We conclude that the explanatory power of this microstructure model over the price drop discrepancy at the ex-day close is much less than that suggested by earlier literature on the ex-dividend day.

Fourier instantaneous estimators and the Epps effect.

We compare the Malliavin-Mancino and Cuchiero-Teichmann Fourier instantaneous estimators to investigate the impact of the Epps effect arising from asynchrony in the instantaneous estimates. We demonstrate the instantaneous Epps effect under a simulation setting and provide a simple method to ameliorate the effect. We find that using the previous tick interpolation in the Cuchiero-Teichmann estimator results in unstable estimates when dealing with asynchrony, while the ability to bypass the time domain with the Malliavin-Mancino estimator allows it to produce stable estimates and is therefore better suited for ultra-high frequency finance. An empirical analysis using Trade and Quote data from the Johannesburg Stock Exchange illustrates the instantaneous Epps effect and how the intraday correlation dynamics can vary between days for the same equity pair.

Trade signing in fast markets

AbstractThis study assesses the accuracy of trade signing algorithms in fast trading environments using NASDAQ and NYSE trade and quote data. Using data that contain true trade signs, we show that the Lee and Ready algorithm outperforms the tick rule and classifies trades at least as well as in earlier studies from slower trading environments, even in subsamples where the market is particularly fast. We conclude that trade signing remains viable in fast markets, and that the use of quote data continues to increase trade classification accuracy.

A Descriptive Study of High-Frequency Trade and Quote Option Data

This paper provides a guide to high frequency option trade and quote data disseminated by the Options Price Reporting Authority (OPRA). We present a comprehensive overview of the U.S. option market, including details on market regulation and the trading processes for all 16 constituent option exchanges. We review the existing literature that utilizes high-frequency options data, summarize the general structure of the OPRA dataset and present a thorough empirical description of the observed option trades and quotes for a selected sample of underlying assets that contains more than 25 billion records. We outline several types of irregular observations and provide recommendations for data filtering and cleaning. Finally, we illustrate the usefulness of the high frequency option data with two empirical applications: option-implied variance estimation and risk-neutral density estimation. Both applications highlight the rich information content of the high frequency OPRA data.

Information Revelation in Decentralized Markets

ABSTRACTHow does information get revealed in decentralized markets? We test several hypotheses inspired by recent dealer‐network theory. To do so, we construct an empirical map of information revelation where two dealers are connected based on the synchronicity of their quote changes. The tests, based on the euro to Swiss franc spot rate (EUR/CHF) quote data including the 2015 crash, largely support theory: strongly connected (i.e., central) dealers are more informed. Connections are weaker when there is less to be learned. The crash serves to identify how a network forms when dealers are transitioned from no‐learning to learning, that is, from a fixed to a floating rate.

How does information disclosure affect liquidity? Evidence from an emerging market

Cross-sectional models positively relate firm information disclosure with stock liquidity, but dynamic models on days of news releases show the opposite relation. We investigate this relationship by studying the effects of information disclosure on liquidity and its determinants. We use trade and quote data from Colombia for 2015 and 2016, along with a complete database of news releases as reported by firms to the regulator. The results of panel data and panel vector autoregressive (PVAR) models suggest that news releases increase both informed and uninformed trading. Overall, the negative effect of news releases on liquidity is temporary and fully explained by increasing asymmetric information.

Using Brexit to Identify the Nature of Price Rigidities

Using price quote data that underpin the official U.K. consumer price index (CPI), we analyze the effects of the unexpected passing of the Brexit referendum to the dynamics of price adjustments. The sizable depreciation of the British pound that immediately followed Brexit works as a quasi-experiment, enabling us to study the transmission of a large common marginal cost shock to inflation as well as the distribution of prices within granular product categories. A large portion of the inflationary effect is attributable to the size of price adjustments, implying that a time-dependent price-setting model can match the response of aggregate inflation reasonably well. The state-dependent model fares better in capturing the endogenous selection of price changes at the lower end of the price distribution, however, it misses on the magnitude of the adjustment conditional on selection.

The VIX Volatility Index - A Very Thorough Look at It

The CBOE Volatility Index, known by its ticker symbol VIX, is a popular measure of the stock market’s expectation of volatility implied by S&P 500 index options, calculated and published by the Chicago Board Options Exchange (CBOE). It is colloquially referred to as the fear index or the fear gauge. The current VIX index value quotes the expected annualized change in the S&P 500 index over the following 30 days, as computed from options-based theory and current options-market data. Despite its theoretical foundation in option price theory, CBOE’s Volatility Index is prone to inadvertent and deliberate errors. We shed light on many claims that have been brought up against the VIX in recent years. Replicating the VIX index by using empirical options data from CBOE we show which one of those claims are justified and lead to meaningful alterations of the VIX. Three main areas can be identified: Eight different alteration possibilities of the current VIX formula, three deficiencies in the theoretical derivation of the VIX and two major issues with the market microstructure of the VIX and its derivatives. First, eight theoretically possible ways of altering the current VIX formula are identified. We will thoroughly investigate those ways both from a theoretical point of view and using empirical data. The two major alteration possibilities are identified and their potential impact on the VIX calculation is shown using actual option quote data from the CBOE. Second, the theoretical derivation behind the VIX formula has three major flaws. We show their importance based on an empirical analysis of the VIX and the underlying options on the S&P 500. Our analysis helps to understand and put into context previous claims that were brought up against the VIX. Third, the market microstructure behind the pricing of VIX derivatives appears to be flawed. Very liquid derivatives are priced off an illiquid auction mechanism of out-of-the-money put and call options. To make matters worse, those derivatives are cash-settled and the settlement value of the VIX, as used for the pricing of its derivatives, is based on a different calculation than the regular VIX index. Finally, we also investigate claims that the settlement values can substantially deviate from the previous-day close. We show that those claims are based on a wide-spread misunderstanding of how the settlement values and the VIX index are calculated and that they are not justified in the way that they are brought up at the moment. Our empirical methodology is based on replicating the VIX based on the underlying quote data. Our conclusion is that the VIX needs a thorough review of both its theoretical foundations, its calculations and the market mechanism behind computing the value of its derivatives. Furthermore, some of the claims brought up against the VIX are more of a theoretical nature and not important, but some might have a dramatic impact on the value of the VIX as well as the pricing of its derivatives. To our knowledge, that is the first comprehensive study of the VIX index and its shortcomings that is both based on theoretical and empirical observations. We are confident that our analysis will lead to new ways of how to make the VIX index safer for investors and market participants as well as less prone to manipulation and errors.