Permanent Price Research Articles

Trades, Basis, and Price Revisions in the S&P Depositary Receipts

Standard and Poor’s Depositary Receipts (SPDRs) are traded like a stock to track the performance of S&P 500 index, and there exists contemporaneous trading of S&P 500 index futures to track the same index portfolio. The basis reveals the instantaneous price difference of the S&P 500 index portfolio observed in index futures and SPDRs. This study finds that the basis conveys more information than trades for the intra-day quote price revisions in the SPDRs, and our findings are consistent with a price revision process that the basis transmits innovative information from futures prices and induces permanent price changes in the SPDRs.

Additions to and deletions from the S&P 500 index: A resolution to the asymmetric price response puzzle

We resolve the asymmetric price response puzzle for additions to and deletions from the S&P 500 documented in Chen, Noronha, and Singal (2004) by using a more appropriate methodology to measure abnormal returns and correctly adjusting for outliers. In contrast to their findings, our results show a permanent price response for both additions and deletions. We further distinguish between firms transferred into other S&P indices and true additions/deletions (firms not added from or placed into other S&P indices). We find a permanent price increase for true additions and a permanent decline in price for true deletions from the S&P 500.

Dynamic trading with Markov liquidity switching

In this paper, we solve a continuous-time portfolio choice problem of an investor under a Markov jump linear system that effectively captures stochasticity in asset returns, price impacts, and market resilience. Specifically, the investor chooses his portfolio to maximize the expected excess returns netting risks and trading costs due to the price impacts in a Markov switching market. We show that the value function and the optimal feedback trading strategy can be expressed in terms of the solution of a coupled matrix Riccati differential system. The presence of the market resilience and the Markov switching results in the coupled Riccati system being non-canonical. We manage to establish a set of transparent sufficient conditions to ensure the well-posedness of the coupled system. Our numerical results indicate that the investor trades more (less) aggressively in a liquidity regime where the temporary price impact is low (high) and permanent price impact is high (low) with low (high) market resilience.

Heterogeneously informed trading and the stock market efficiency during the COVID-19 pandemic

This study investigates the U.S. stock market efficiency from the symmetric and asymmetric perspectives during the COVID-19 pandemic. We explore that the pandemic boosts (hurts) the information role of symmetrically (asymmetrically) informed trading. Specifically, we find that the epidemic outbreak and infection scale strengthen (weaken) the stock return reaction to symmetrically (asymmetrically) informed trading. Evidence also indicates that the effect of symmetrically (asymmetrically) informed trading on stocks' permanent price shocks and price informational efficiency is enhanced (impaired) during the pandemic. Moreover, all these effects are consistently more intensive to informed buys.

Correcting for transitory effects in RCTs: Evidence from the RAND Health Insurance Experiment

AbstractTemporary randomized controlled trials are susceptible to transitory effects that would not result from a permanent treatment. We find a large and statistically significant “deadline effect”—a surge in spending in the final treatment year—in the RAND Health Insurance Experiment, identified by random allocation to three‐ or five‐year enrolment terms. Participants facing lower coinsurance rates show larger spending surges. Partialing out the price–deadline interaction reduces in magnitude estimates of the permanent price elasticity of drug spending (and in some specifications of outpatient and supplies spending). This implies higher optimal coinsurance rates and illustrates the importance of experimental design to identifying parameters of interest in randomized controlled trials.

ДИНАМІЧНЕ ЦІНОУТВОРЕННЯ – ПРОГРЕСИВНА ТЕНДЕНЦІЯ ЦИФРОВОГО МАРКЕТИНГУ

The article summarizes the theoretical foundations of dynamic pricing as a progressive trend in digital marketing. It is highlighted that the relationship between sellers and buyers is changing rapidly and fundamentally, that dynamic pricing is becoming competitively necessary, as it provides an opportunity to discretely measure personalized demand. It is noted that dynamic pricing allows you to increase profitability and the number of customers without significant investments. It has been established that digital technologies make it possible to obtain additional profit due to permanent price changes based on special software. It said that dynamic pricing allows for real-time measurement, adjustment and charging based on the value of products to each individual customer. The structure of stable and dynamic prices is considered. The fundamental advantages of dynamic pricing over classical pricing are highlighted. A SWOT analysis of the dynamic pricing method was conducted to determine the conditions and prospects for its use in digital marketing. It is indicated that the key element is an algorithm that dynamically sets prices depending on the value that each individual buyer feels in a certain place, period of time, case. It is substantiated that dynamic pricing expands the idea of price optimization and revenue management, contributes to the creation of a «win-win» situation, provides enterprises with constant market expansion and profit growth, and clients with a steadily growing value surplus. The specific features of the dynamic pricing method and the principles of implementing the dynamic pricing model into the activities of enterprises are highlighted: the presence of technological capabilities, information systems with artificial intelligence, which are capable of forming personalized price offers in real time; formation of the client base of enterprises based on the collection and protection of consumer data flows; formation of clients' beliefs that the transfer of their personal data to enterprises is beneficial based on an understanding of future personal benefits; formation of customers' beliefs that the personalized price established on the basis of a dynamic model is fair.

Which stock price component drives the Amihud illiquidity premium?

We examine the components of stock prices that play a key role in the pricing of the Amihud (2002) illiquidity measure. We first decompose the stock price series into permanent and transitory components and then construct the half-Amihud illiquidity measures on the days of positive and negative permanent price returns. We find that the transitory half-Amihud measure on the days of negative permanent price returns plays an important role in pricing the Amihud illiquidity even after controlling for the turnover ratio. This finding contrasts with that of Lou and Shu (2017) in that both the trading volume component and transitory price impact, drive the Amihud illiquidity premium.

Two Approaches for Option Pricing under Illiquidity

The paper focuses on option pricing under unusual behaviour of the market, when the price may not be changed for some time what is quite a common situation on the modern financial markets. There are some patterns that can cause permanent price gaps to form and lead to illiquidity. For example, global changes that have a negative impact on financial activity, or a small number of market participants, or the market is quite young and is just in the process of developing, etc.In the paper discrete and continuous time approaches for modelling market with illiquidity and evaluation option pricing were considered.Trinomial discrete time model improves upon the binomial model by allowing a stock price not only to move up, down but stay the same with certain probabilities, what is a desirable feature for the illiquid modelling. In the paper parameters for real financial data were identified and the backward induction algorithm for building call option price trinomial tree was applied.Subdiffusive continuous time model allows successfully apply the physical models for describing the trapping events to model financial data stagnation's periods. In this paper the Inverse Gaussian process IG was proposed as a subordinator for the subdiffusive modelling of illiquidity and option pricing. The simulation of the trajectories for subordinator, inverse subordinator and subdiffusive GBM were performed. The Monte Carlo method for option evaluation was applied.Our aim was not only to compare these two models each with other, but also to show that both models adequately describe the illiquid market and can be used for option pricing on this market. For this purpose absolute relative percentage (ARPE) and root mean squared error (RMSE) for both models were computed and analysed.Thanks to the proposed approaches, the investor gets a tools, which allows him to take into account the illiquidity.

A two-player portfolio tracking game

We study the competition of two strategic agents for liquidity in the benchmark portfolio tracking setup of Bank et al. (Math Financial Economics 11(2):215–239 2017). Specifically, both agents track their own stochastic running trading targets while interacting through common aggregated temporary and permanent price impact à la Almgren and Chriss (J Risk 3:5–39 2001). The resulting stochastic linear quadratic differential game with terminal state constraints allows for a unique and explicitly available open-loop Nash equilibrium. Our results reveal how the equilibrium strategies of the two players take into account the other agent’s trading targets: either in an exploitative intent or by providing liquidity to the competitor, depending on the relation between temporary and permanent price impact. As a consequence, different behavioral patterns can emerge as optimal in equilibrium. These insights complement and extend existing studies in the literature on predatory trading models examined in the context of optimal portfolio liquidation games.

Order submission, information asymmetry, and tick size

Our study investigates traders' order submission strategies with varying market conditions under information asymmetry. It examines how a reduction in tick size affects informed traders' order choices in response to changes in market conditions and subsequently influences the information content of the limit order book on the Tokyo Stock Exchange. We quantify the permanent price impact of orders with different levels of aggressiveness to measure the information content of different kinds of orders before and after a reduction in tick size. The results show that market orders and limit orders placed within or at the best bid and offer (BBO) are more informative than limit orders placed behind the BBO. Moreover, when the quoted spread is wider (narrower) and volatility is low (high), the information content of limit orders placed within or at the BBO is larger (smaller) than market orders, providing evidence that informed traders' order choice on market orders or limit orders is a trade-off between transaction costs and adverse selection costs. We further observe that the information content increases in market orders but decreases in limit orders placed within or at the BBO when the quoted spread becomes narrower and volatility changes from low to high after tick size reduction. This suggests that informed traders tend to shift from limit orders to market orders as transaction costs decrease and adverse selection costs increase, transactions become more informative, which reduces the information content of the limit order book.

Informativeness of trades around macroeconomic announcements in the foreign exchange market

• We study the extent of private information revealed through trades in the foreign exchange market. • We find the intraday variation in the proportion of permanent price variations driven by trade-related information has a repetitive periodic pattern, • We show the information share of private information in trades evolves across trading periods in the FX markets. • We find that, among market participants, heterogeneous interpretation of announcements and opinion disagreement after the announcement are revealed from order flow, and the efficient price is still driven by information implicit in trading activities after the announcement. We study the extent to which private information is revealed through trades in the foreign exchange market. The trade informativeness measured from the VAR framework of trades and quotes allows us to quantify the impact of asymmetric information on return variation. We find the intraday variation in the proportion of permanent price variation driven by trade-related information has a repetitive periodic pattern, and trades in the overlapping trading hours of London and New York carry most information than the other time segments. The results show the fraction of trade-related component to exchange rate variation positively relates to trading volume and reversely relates to effective spread. With a comprehensive set of 48 real-time US, European, and Japanese macroeconomic news surprises variables, and controlling for the effect of trading volume, spread, and volatility, we find news of production-related economic indicator, e.g., GDP and industrial production, has a positive relationship with the trade informativeness in a trading period, suggesting the increased private information share may be attributed to heterogeneous interpretations of the announcement among market participants. Announcement-related information is gradually resolved into the price, and the information is transited via the channel of trading activities.

Periodicity of trading activity in foreign exchange markets

AbstractUsing the high‐frequency exchange rates of 25 global currency pairs, we document a striking clock‐time periodicity in which trading activity surges at the beginning of a minute. Additional analyses indicate that clock‐time spikes are accompanied by a lower level of liquidity. Moreover, we find that time‐clustering trades yield permanent price impacts, are devoted to efficient pricing, and make a significant contribution to price discovery. Finally, we investigate three informed scenarios to ascertain how trades at spikes acquire information beforehand and reflect them in markets. Taken together, our findings reinforce the view in the literature that subminute periodicity emanates from algorithmic trading.

Social media and money: How our tweets affect the financial markets

Social media, such as Twitter, has replaced traditional media as a dominant information source (Gan et al., 2020). With social media being a fast and more efficient information dissemination channel, investors can quickly access, update and exchange stock-related information before making trading decisions. In this study, I examine the mechanism by which social media sentiment affects stock prices. I first collect a sample of 2.2 million tweets and quantify each tweet using Natural Language Processing (NLP) techniques to infer whether the tweet has a positive or negative tone. Next, I employ a modified vector autoregression (VAR) model to assess the impact of tweets on stock returns at the minute frequency. I find that Twitter sentiment affects stock prices through its impact on trades. Trades have stronger price impacts with an increase in tweet volume or investor sentiment. In addition, both bullish and bearish tweets amplify the impact of trades on stock returns. Twitter sentiment causes a permanent price movement until a new equilibrium is reached, showing Twitter sentiment contains substantial information to drive stock prices. These findings suggest that trades in the stock market become more informative as tweet activity increases, as well as when tweets contain higher sentiment. To the best of my knowledge, this is the first study looking at the exact mechanism of how social media affects the stock market and investors' response behaviour.

Estimating the effectiveness of permanent price reductions for competing products using multivariate Bayesian structural time series models

The Florence branch of an Italian supermarket chain recently implemented a strategy that permanently lowered the price of numerous store brands in several product categories. To quantify the impact of such a policy change, researchers often use synthetic control methods for estimating causal effects when a subset of units receive a single persistent treatment and the rest are unaffected by the change. In our applications, however, competitor brands not assigned to treatment are likely impacted by the intervention because of substitution effects; more broadly, this type of interference occurs whenever the treatment assignment of one unit affects the outcome of another. This paper extends the synthetic control methods to accommodate partial interference, allowing interference within predefined groups but not between them. Focusing on a class of causal estimands that capture the effect both on the treated and control units, we develop a multivariate Bayesian structural time series model for generating synthetic controls that would have occurred in the absence of an intervention, enabling us to estimate our novel effects. In a simulation study we explore our Bayesian procedures’ empirical properties and show that it achieves good frequentists coverage, even when the model is misspecified. We use our new methodology to make causal statements about the impact on sales of the affected store brands and their direct competitors. Our proposed approach is implemented in the CausalMBSTS R package.

Information contents of intraday SSE 50 ETF options trades

AbstractWe examine the intraday price discovery role of the Shanghai Stock Exchange 50 exchange‐traded fund options using transaction‐level data. Contemporaneous options trading volumes have significant and permanent price impacts on futures returns, implying that some options traders are informed. The permanent price impacts of large options trades are much greater than those of small trades. We also find that liquidity changes due to regulatory policies affect the options market's informational role. Transactions generally convey less information during special trading sessions, including post‐break and closing sessions, perhaps because informed investors prefer to hide behind high liquidity.

Do Physicians Warm Up to Higher Medicare Prices? Evidence from Alaska

AbstractMedicare is a roughly $700 billion public program, with physician payments representing one of its largest expenditures. Medicare's prices are also administratively set, which leaves the structure of payment changes subject to a political process that may introduce idiosyncratic features and even perverse incentives. At the same time, physician responses to changes in Medicare reimbursements are likely to vary according to the policy's duration, scope, and size. We study a setting where broad federal laws contained specific provisions that financially benefit a narrow group: Alaskan physicians. The geographically targeted payment reforms were also unique along key dimensions. Using difference‐in‐differences strategies, we find that large, temporary price changes increase spending but elicit no detectable supply response. Conversely, generous and permanent price shocks induce greater service flows but not uniformly across specialties. Our findings suggest that Congress may engage in fiscally inefficient Medicare spending to accomplish other legislative objectives.

From upstairs to downstairs trading: Evidence from a highly segmented market

Using data on more than 18 million trades, this paper examines the information content of trades in a highly segmented market that until recently had no restrictions on trading outside the central limit order book (LOB). Consistent with theoretical predictions and earlier findings, we largely confirm that the permanent price effect is significantly lower in the upstairs market than downstairs. Furthermore, we find that a newly implemented minimum size requirement for trades in the upstairs market works as intended by the market provider. However, trades that are exempt from this requirement and small, informed institutional trades carry more information content in the upstairs market.

Measuring the Real-Time Stock Market Impact of Firm-Generated Content

Firms increasingly follow an “always on” philosophy, publishing multiple pieces of firm-generated content (FGC) every day. Current methodologies used in marketing are unfit to unbiasedly capture the impact of FGC disseminated intermittently throughout the day on stock markets characterized by ultra-high-frequency trading. They also neither distinguish between the permanent (i.e., long-term) and temporary (i.e., short-term) price impacts nor identify FGC attributes capable of generating these price impacts. In this study, the authors define price impact as the impact on the variance of stock price. Employing a market microstructure approach to exploit the variance of high-frequency changes in stock price, the authors estimate the permanent and temporary price impacts of the firm-generated Twitter content of S&P 500 information technology firms. The authors find that firm-generated tweets induce both permanent and temporary price impacts, which are linked to tweet attributes of valence and subject matter. Tweets reflecting only valence or subject matter concerning consumer or competitor orientation result in temporary price impacts, whereas those embodying both attributes generate permanent price impacts. Negative-valence tweets about competitors generate the largest permanent price impacts. Building on these findings, the authors offer suggestions to marketing managers regarding the design of intraday FGC.

DISCRETE-TIME OPTIMAL EXECUTION UNDER A GENERALIZED PRICE IMPACT MODEL WITH MARKOVIAN EXOGENOUS ORDERS

This paper examines a discrete-time optimal execution problem with generalized price impact. Our main objective is to investigate the effect of price impact caused by aggregate random trade orders posed by small traders on the optimal execution strategy when orders of the small traders have a Markovian dependence. Our problem is formulated as a Markov decision process with state variables which include the last small traders’ aggregate orders. Over a finite horizon, a large trader with Constant Absolute Risk Aversion (CARA) von Neumann–Morgenstern (vN-M) utility function maximizes the expected utility from the final wealth. By applying the backward induction method of dynamic programming, we characterize the optimal execution strategy and optimal value function and conclude that the optimal execution strategy is a time-dependent affine function of three state variables. Moreover, numerical analysis prevails that the optimal execution strategy admits a “statistical arbitrage” via a round-trip trading, although our model considers a linear permanent price impact. The result differs from the previous prevailing one that a linear permanent price impact model precludes any price manipulation or arbitrage. Thus, considering a price impact caused by small traders’ orders with a Markovian dependence is significant.

What Is in High-Frequency Price Pressure?

Trades’ transitory price impact is found to be robustly negative in high-frequency data: a market buy (sell) order on average pushes contemporaneous price pressure down (up). This pattern is explained through a model where liquidity providers do not always quote competitively. Lacking competitive quote updates, prices fail to immediately reflect new information, generating price pressure to offset the permanent price change that would have appeared in competitive quotes. Other novel yet counterintuitive model predictions also find support in data: A stock’s price pressure persistence and its return volatility are both lower when its liquidity providers are less competitive.